Proceedings of the Second All-USGS Modeling Conference, 

February 11–14, 2008: Painting the Big Picture

Scientific Investigations Report 2009–5013

U.S. Department of the Interior

U.S. Geological Survey
Cover: Graphic depicting the four U.S. Geological Survey (USGS) science disciplines (Water, Geology, Geography, and Biology) 

was taken from the Second All-USGS Modeling Conference logo.
Proceedings of the Second All-USGS Modeling 

Conference, February 11–14, 2008: Painting the 

Big Picture

Edited by Shailaja R. Brady

Scientific Investigations Report 2008–5013

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

Brady, S.R., ed.., 2009, Proceedings of the Second All-USGS Modeling Conference, February 11–14, 2008—Painting 

the Big Picture: U.S. Geological Survey Scientific Investigations Report 2009–5013, 70 p., available only online at 

http://pubs.usgs.gov/sir/2009/5013.
iii

Preface

The Second USGS Modeling Conference was held February 11–14, 2008, in Orange 

Beach, Ala. Participants at the conference came from all U.S. Geological Survey (USGS) 

regions and represented all four science disciplines—Biology, Geography, Geology, and Water. 

Representatives from other Department of the Interior (DOI) agencies and partners from the 

academic community also participated. The conference, which was focused on “painting the 

big picture,” emphasized the following themes: Integrated Landscape Monitoring, Global 

Climate Change, Ecosystem Modeling, and Hazards and Risks. The conference centered on 

providing a forum for modelers to meet, exchange information on current approaches, identify 

specific opportunities to share existing models and develop more linked and integrated models 

to address complex science questions, and increase collaboration across disciplines and with 

other organizations. Abstracts for the 31 oral presentations and more than 60 posters presented 

at the conference are included here.

The conference also featured a field trip to review scientific modeling issues along the 

Gulf of Mexico. The field trip included visits to Mississippi Sandhill Crane National Wildlife 

Refuge, Grand Bay National Estuarine Research Reserve, the 5 Rivers Delta Resource Center, and Bon Secour National Wildlife Refuge. On behalf of all the participants of the Second 

All-USGS Modeling Conference, the conference organizing committee expresses our sincere 

appreciation for the support of field trip organizers and leaders, including the managers from 

the various Reserves and Refuges. 

The organizing committee for the conference included Jenifer Bracewell, Sally Brady, 

Jacoby Carter, Thomas Casadevall, Linda Gundersen, Tom Gunther, Heather Henkel, 

Lauren Hay, Pat Jellison, K. Bruce Jones, Kenneth Odom, and Mark Wildhaber. 
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v

Contents

Preface ...........................................................................................................................................................iii

Monitoring Snow Water Status With a Distributed Snowmelt Model (Poster)...................................1

Three-Dimensional Circulation and Transport Modeling in Devils Lake, North Dakota 

(Oral Presentation)...........................................................................................................................1

Addressing the Vulnerability of Western Mountain Ecosystems and Water Resources to 

Climate Change With RHESSys, the Regional Hydro-Ecological Simulation System 

(Oral Presentation) ..........................................................................................................................2

The Puget Sound Integrated Landscape Monitoring Project (Poster).................................................2

A New Method of Multiscale Topographic Analysis: Extracting Landforms (Poster)........................3

Taking SPARROW Predictions to the Web: A Tool For Research and Resource Management 

(Poster) ..............................................................................................................................................3

Strategic Habitat Conservation (SHC) and the Importance of Ecosystem Models 

(Oral Presentation)...........................................................................................................................4

Numerical Simulation of Ground-Water Flow Within the Atlantic Coastal Plain Aquifers 

of North Carolina and South Carolina (Poster) ...........................................................................4

MOAB: Modeling Individual Movement and Behavior Using an Expert System Approach 

(Poster) ..............................................................................................................................................5

Modeling Nutria (Myocastor coypus) Invasions Across Regional and National Landscapes 

(Poster)...............................................................................................................................................5

Communicating Remote Sensing Requirements and Working Together to Satisfy 

Project Needs (Poster)....................................................................................................................6

Data Management and Interoperability in the USGS Volcano Hazards Program 

(Oral Presentation)...........................................................................................................................6

Cost-Benefit Analysis of Computer Resources for Machine Learning (Poster) .................................7

EdGCM: An Interactive Climate Modeling Framework for Educators and Scientists 

(Oral Presentation) ..........................................................................................................................7

Mutually Interactive State-Parameter Estimation of a Carbon Cycle Model Using a 

Smoothed Ensemble Kalman Filter (Poster) ...............................................................................7

Hydrodynamic and Water Quality Modeling of the A.R.M. Loxahatchee National Wildlife 

Refuge, North Everglades, Florida (Poster) .................................................................................8

Toward a National Land-Change Community Model (Oral Presentation)............................................8

Application of a Multimodeling Framework to Linking Ecosystem Pattern and Process 

Across Scales: A Tale of Two Ecosystem Models (Oral Presentation)...................................9

Modeling Natural Resource Management Decision Scenarios Using Decision Analysis: 

The Flow of Selenium in Appalachian Watersheds from Rocks to Ducks 

(Oral Presentation) ..........................................................................................................................9

A Proposed Ecosystem Simulation Model to Predict the Effects of Common Carp and Zebra 

Mussels in Shallow Lake Ecosystems (Poster) ........................................................................10

Integrating 3-D Hydrodynamic Transport and Ecological Plant Models of the Savannah River 

Estuary Using Artificial Neural Network Models (Poster) .....................................................10

Using Data Mining Techniques and Artificial Neural Network Models to Integrate 

Hydrology, Ecology, and Biology—Two Case Studies (Oral Presentation)..........................10

Animation and Publication of Three-Dimensional and Four-Dimensional Physical Models 

(Poster) ............................................................................................................................................11
vi

Modeling an Aquatic Trophic Chain in a Two-Dimensional Topography in a Seasonally 

Varying Wetland (Poster)..............................................................................................................12

The Land Use Portfolio Model: A Tool for Natural-Hazards Risk Analysis (Oral Presentation)......12

Forecasting the 21st Century World-Wide Status of Polar Bears Using a Bayesian Network 

Modeling Approach (Poster)........................................................................................................13

Modeling Past, Present, and Future Polar Bear Habitats (Oral Presentation) .................................13

HURASIM: Hurricane Simulation Model for Reconstructing Wind Fields and Landfall 

Frequencies of Historic Storms (Poster) ....................................................................................14

Landscape Simulation Model for Invasive Species Spread and Detection (Poster).......................14

SELVA-MANGRO: Landscape Model for South Florida Mangrove Ecosystems (Poster) ..............14

SLRRP: Sea-Level Rise Rectification Program and Seawater Inundation Model Under 

Climate Change (Poster) ..............................................................................................................15

Reference Cited...................................................................................................................................15

CONGFLO: Floodplain Inundation Model of Congaree National Park (Poster)..................................15

Conceptual and Rapid Prototype Models for Evaluating the Effects of Land-Use 

and Land-Cover Changes on Ecosystem Services in the Lower Mississippi Valley (LMV) 

(Oral Presentation) ........................................................................................................................16

Implementation of a Bayesian Geostatistical Parameter Estimation Module into PEST 

(Poster).............................................................................................................................................17

Joint Inverse Modeling to Estimate Flood-Flow Depths and Their Uncertainty in Ungaged 

Coastal Basins, El Salvador, Central America (Poster)............................................................17

Joint Inverse Modeling to Estimate Extreme Rainfall Events and Their Uncertainty in Ungaged 

Coastal Basins, El Salvador, Central America (Poster) ...........................................................18

Application of Stochastic Modeling to Forecast Likely Urbanization Effects on Ecological 

Integrity in the Upper Illinois River Basin (Poster) ..................................................................18

Stochastic Modeling to Forecast Likely Effects of a Proposed Reservoir on Colorado River 

Quantity and Quality Near Grand Junction, Colorado (Poster) .............................................19

CASCaDE: Computational Assessments of Scenarios of Change for the San Francisco 

Bay-Delta Ecosystem (Oral Presentation) ................................................................................20

Modeling the Hydrodynamic and Morphologic Response of an Estuary Restoration 

(Oral Presentation).........................................................................................................................20

Addressing Among-Group Variation in Covariate Effects Using Multilevel 

Modeling Approaches With Emphasis on Multilevel Count Models (Poster) .....................21

Spatial and Age-Structured Population Model of the American Crocodile for 

Comparisons of CERP Restoration Alternatives (Poster) .......................................................21

The Future: Data-Driven Lake and Reservoir Monitoring Using Real-Time 3-D 

Hydrodynamic and Water-Quality Simulations (Oral Presentation) .....................................22

Using Such Web Services as StreamStats to Increase Your Modeling Productivity (Poster).......22

Using a Coupled Ecosystem-Geochemical Model to Assess Ecological, 

Biogeochemical, and Hydrological Processes and Their Responses to Atmospheric 

Deposition and Climate Change for Diverse Ecosystems Within the United States 

(Oral Presentation).........................................................................................................................23

Using GIS/Remote Sensing Models to Quantify and Monitor Southwestern 

Willow Flycatcher Breeding Habitat (Poster) ...........................................................................23

Hydrologic Effects of Climate Change in the Yukon River Basin (Oral Presentation) ......................24

Evaluation of Environmental Flow Components Produced Using a Distributed Watershed 

Model of the Flint River Basin, Georgia (Poster) ......................................................................24

Hydrologic Climate Change From a Deterministic View: Using GSFLOW to Simulate Climate 

Change in a Northern Temperate Climate (Oral Presentation) ..............................................24
vii

Advanced Species Modeling (Poster) ....................................................................................................25

Spatial and Temporal Modeling of Metal Transport Through Ground Water and Surface 

Water in Watersheds With Abandoned Mine Lands (Oral Presentation) ...........................25

Modeling Species Responses to Historical Climate Change: From Molecules to 

Continental Landscapes (Oral Presentation).............................................................................26

Limitations to Modeling Nonnative Plant Species Distributions for Early Detection 

Monitoring in Arid Environments (Poster) .................................................................................27

The South Florida Ecosystem Portfolio Model: Integrating Ecological, Community, 

and Economic Values for Decision Support (Oral Presentation) ...........................................27

Developing an Infrastructure for Knowledge-Based, Spatially Explicit Population 

Modeling of Multiple Species (Poster) ......................................................................................28

Regional Modeling of Carbon Dynamics Change in the Green River Basin, Wyoming, 

Based on Remotely Sensed Data (Poster) ................................................................................28

Modeling Terrestrial Carbon Dynamics in the Eastern United States (Poster).................................29

Modeling With Stakeholders: When the Process is as Valuable as the Product (Poster)..............29

Use of Operational and Research Models to Assess Volcanic Tephra Hazards (Oral 

Presentation) ..................................................................................................................................30

Extirpations and Elevational-Range Changes of the American Pika Linked to Climate 

Warming Through Climate-Envelope Modeling (Poster) ........................................................30

Delineation of Vegetation Communities Using Airborne Lidar Data at Jean Lafitte 

National Park, Louisiana (Poster) ...............................................................................................31

Spatially Continuous Interpolation of Water Stage and Water Depths Using the Everglades 

Depth Estimation Network (EDEN) (Poster) ..............................................................................32

Modeling Nonlinear Time Sequences in Earth Change and Human Disease Pandemics 

(Poster) ............................................................................................................................................32

Elevation Derivatives for Modeling Postwildfire Debris Flows (Poster) ............................................33

GSFLOW—Coupled Ground-Water and Surface-Water Flow Model (Poster) ................................33

De-Trending for Climatic Variations to Reveal Stressed Ecosystems (Poster) .................................33

An Integrated Modeling Approach for Analyzing fhe Effects of an Expanding 

Biomass-for-Biofuel Economy in the Northern Great Plains (Oral Presentation) ..............34

Modeling Standardized, Meso-Scale Ecosystem Distributions for the United States and the 

Planet (Oral Presentation) ............................................................................................................34

Utilizing Structural Equation Modeling Techniques to Model Watershed Structure 

and Land Use Effects on Stream Geochemistry and Ecological Responses 

(Oral Presentation).........................................................................................................................35

Landscape Evapotranspiration Estimation Using a Simplified Surface Energy Balance 

Approach for Watershed Water-Balance Modeling (Poster).................................................35

Community Sediment-Transport Modeling Project (Poster) ................................................................36

A Spatially Explicit Stage-Based Population Model of American Alligators in Support of 

Everglades Restoration (Poster) .................................................................................................36

The Delta Research and Global Observation Network (Oral Presentation) ......................................36

Integrating Hydrology and Individual-Based Models: Issues and General Approaches to 

Simulating Animal Movement Using Artificial Intelligence and Navigation Theory 

(Poster) ............................................................................................................................................37

Modeling the Historical Influence of Soil and Water Management on Sediment and 

Carbon Budgets in the United States (Oral Presentation).......................................................37

Applications of the FTLOADDS Coupled Code and Linkages to Hydrologic and 

Ecological Models (Oral Presentation) ......................................................................................38
viii

 Ecosystem Carbon Budgets and Crop Yields in a Tropical Savanna Ecosystem as 

Related to Changes in Climate and Management (Poster) .....................................................39

Seeing the Forest Fire Through the Trees: Integrating Multiple Data Streams to 

Model the Presettlement Fire Regime in the Longleaf Pine Ecosystem (Poster)................39

Predictive Models of Suitable Habitat for the Mojave Desert Tortoise: “Truthiness” in 

Pseudo-Absence (Poster) ............................................................................................................40

A Knowledge Management Approach for Complex Regional Ecosystem Modeling (Poster) .......40

Linking Plots to Landscapes: A Synthetic Framework for Monitoring Change in the 

Great Basin Ecosystem (Oral Presentation) .............................................................................40

Credibility and Acceptability of Land Use Planning Approaches: A Case Study in the 

Coastal Area of the Mekong Delta, Vietnam (Poster) .............................................................41

The FRAME (Framing Research in Support of the Adaptive Management of Ecosystems) 

Project: A Collaborative Modeling Approach to Natural Resource Management 

(Oral Presentation).........................................................................................................................41

Elements of a Global Model: An Example of Sea Level Rise and Human Populations at Risk 

(Oral Presentation).........................................................................................................................42

Modeling and Generalizing Population Pressure at the Regional Scale by Interpolating a 

Kriging Surface (Poster) ...............................................................................................................43

Formalizing Model-Specific GIS Methodology With the GIS Weasel (Poster) ................................43

The Integration of Environmental Simulation Models and GIS Using Metadata (Poster)...............43

Processing Time-Series Data to Calibrate the GSFLOW Coupled Ground-Water/

Surface-Water Model in the Trout Lake Watershed, Northern Wisconsin (Poster) ..........44

Modeling Surface-Water Sulfate Dynamics in a Northern Everglades Wetland (Poster)..............44

Variations in Pesticide Leaching Related to Land Use, Pesticide Properties, and 

Unsaturated Zone Thickness in an Agricultural Watershed (Poster) ..................................45

The USA National Phenology Network: Towards an Integrative Assessment of Global 

Change Impacts at the National Scale (Poster)........................................................................45

Application of Bayesian Hierarchical and Information Theoretic Approaches in ModelFacilitated Recovery of Endangered Fish Populations Based on Behavior and 

Physiology as Part of an Individual-Based Spatially Explicit Model (Poster) .....................46

Designing Sustainable Landscapes for Bird Populations in the Eastern United States: A 

Collaborative Approach (Poster) ................................................................................................46

Geospatial Modeling to Assess the Risk of American Ginseng Poaching in Shenandoah 

National Park (Poster)....................................................................................................................47

Empirically Modeling Carbon Fluxes in the Northern Great Plains Grasslands (Poster) ...............48

Appendix 1. Conference Agenda .............................................................................................................49

Appendix II. List of Conference Participants ........................................................................................56

Appendix III. Acronyms .............................................................................................................................65
ix

Conversion Factors

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.4047 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

acre-foot (acre-ft) 1,233 cubic meter (m3

)

Flow rate

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

mile per hour (mi/h) 1.609 kilometer per hour (km/h) 

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square kilometer (km2

) 247.1 acre 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)
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Proceedings of the Second All-USGS Modeling 

Conference, February 11–14, 2008: Painting the Big Picture

Edited by Shailaja R. Brady

Monitoring Snow Water Status With a 

Distributed Snowmelt Model (Poster)

Verdin2

1

Science Applications International Corporation, Sioux Falls, S. Dak. 

2

U.S. Geological Survey, Sioux Falls, S. Dak.

A large amount of the Afghanistan water supply comes 

from reservoirs fed by snowmelt; therefore, monitoring 

the status of snow water in key areas during the winter and 

spring is an important function of the water resources and 

disaster management entities of the country. In this study, 

we illustrate the utility of monitoring the status of the snow 

across Afghanistan by employing a spatially distributed snow 

accumulation and ablation model forced solely with remotely 

sensed data, weather model assimilation fields, and globally 

available meteorological data in near-real time. The snowmelt 

model we used was a spatially distributed version of the Utah 

Energy Balance model. The model calculates the melt and 

accumulation of snow using physically based calculations of 

advective, latent, sensible, and radiative heat exchange. The 

Utah Energy Balance is forced by air temperature, average horizontal wind speed, humidity, precipitation, and total 

incoming solar radiation recorded at a standard height. The 

model time step can vary from 1 to 6 hours. The model state 

variables are liquid water equivalent content, snow energy 

content, and snow surface age. We also tested the model 

uncertainty decrease accrued by using a dynamic Moderate 

Resolution Imaging Spectroradiometer (MODIS)-based albedo 

product instead of a simple parameterization. The MODISbased snow albedo we used was an integrated 8-day runningaverage value calculated for areas that were established to be 

cloud-free by the MODIS cloud mask and snow-covered by 

the MODIS snow algorithm. Model input meteorological data 

were estimated from satellite or weather-model output. We 

downscaled the grid resolution of Mesoscale Model, version 

5 (MM5) model fields of air temperature, air relative humidity, and solar radiation variables to 10 kilometers (km) from 

45 km. The downscaling algorithms rely on the topographic 

data to downscale the coarse weather-forecasting model’s 

By Guleid A. Artan1, Jodie L. Smith1, and James P. 

output fields to the higher resolution. The meteorological data 

that we used, except for the rainfall, have a 6-hour time step, 

which is sufficient to resolve the diurnal cycle. The spatial 

distribution of the simulated snow-water equivalent compared 

well with MODIS-observed snow cover. Furthermore, modeled snow-water equivalent provided a good early indication 

of the relative magnitude of the water available for irrigation 

in the upcoming summer. The modeled snow-water maps were 

also useful in mapping basins that were likely to experience a 

higher risk for floods after anomalously high snowmelt. 

Three-Dimensional Circulation and 

Transport Modeling in Devils Lake, 

North Dakota (Oral Presentation)

By Jerad Bales1 and Rochelle A. Nustad1

1

U.S. Geological Survey, Raleigh, N.C. 

The Devils Lake basin is a 3,810-square-mile (mi2) 

subbasin of the Red River of the North. The basin is closed at 

Devils Lake levels of less than 1,446.5 feet (ft) above sea level 

(asl), but spills into Stump Lake at higher levels, and subsequently spills into a tributary of the Red River of the North 

at elevations greater than 1,450 ft asl. As a result of generally 

greater than normal precipitation, the Devils Lake water level 

has exceeded 1,446 ft asl more or less continuously, and the 

surface area of the lake has essentially doubled to the current 

area of 128,600 acres. In the same time, the volume of the lake 

has increased by 107 percent to 2,512,000 acre-ft. Hundreds 

of homes and businesses and thousands of acres of productive 

farmland have been lost as a result of the lake-level rise. More 

than $450 million has been spent relocating roads and bridges 

and building levees, and additional work is planned. 

Most of the inflow to the lake occurs in the late spring 

during snowmelt. The inflow may take as long as 1 month to 

move through the lake. Following the inflow period, circulation in Devils Lake is driven primarily by wind, which sets 

up fairly large water-level gradients across the lake. The lake 

has a complex morphometry, with large bays separated by 
2 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

narrow passes, and bridges cross the lake at several locations. 

The water at the downstream (eastern) end of the lake is poor 

in quality and has high solute concentrations, and there is 

concern that the solutes are moving west. 

In this study, the magnitude of the differential lake levels 

and their effects on flooding, water circulation, and solute 

transport have been simulated by using the three-dimensional 

model UnTRIM. An extensive campaign was conducted 

to collect synchronous data throughout the lake, including streamflow at all the major inflows and at the outflow 

to Stump Lake. Weather data were measured at three sites 

around the lake; the lake level was recorded at eight sites; and 

an Acoustic Doppler Current Profiler (ADCP) was deployed 

at one site in the lake. The model grid was developed up to 

a shoreline elevation of 1,453 ft asl to account for possible 

future flooding. The unstructured grid contains five vertical 

layers and more than 10,000 computational cells. The model 

was calibrated for existing conditions and will be applied 

using stochastically generated inflow to investigate the effects 

of various inflow and wind conditions on flooding and solute 

transport. 

Addressing the Vulnerability of 

Western Mountain Ecosystems 

and Water Resources to Climate 

Change With RHESSys, the Regional 

Hydro-Ecological Simulation System 

(Oral Presentation) 

By Jill S. Baron1, Christina Tague2, 

Lindsey Christensen3

, Nate Stephenson4, Craig Allen5, 

Dan Fagre6

, David Peterson7, and Donald McKenzie7

1

U.S. Geological Survey, Fort Collins, Colo.

2

University of California, Santa Barbara, Calif.

3

Colorado State University, Fort Collins, Colo.

4

U.S. Geological Survey, Three Rivers, Calif.

5

U.S. Geological Survey, Los Alamos, N. Mex.

6

U.S. Geological Survey, West Glacier, Mont.

7

U.S. Forest Service, Seattle, Wash.

Western mountain ecosystems are changing rapidly 

in response to climate change. Earlier onset of snowmelt, 

larger forest areas affected by wildfire and insect outbreaks, 

and increasing rates of evapotranspiration are beginning 

to strain the ability of mountain environments to provide 

water, wood products, carbon sequestration, biodiversity, 

and recreational opportunities. The Western Mountain Initiative, a U.S. Geological Survey (USGS) global change 

program, uses the Regional Hydro-Ecological Simulation 

System (RHESSys) model in conjunction with long-term 

monitoring, regional assessments, paleoclimate and paleoecological reconstructions, and synthesis workshops to understand the sensitivities, thresholds, resistance, and resilience 

of Western mountain ecosystems to climatic variability 

and change. RHESSys is a geographic information system 

(GIS)-based hydro-ecological modeling framework designed 

to simulate carbon, nutrient, and water fluxes. By combining 

a set of physically based process models and a methodology 

for partitioning and parameterizing the landscape, RHESSys 

is capable of modeling the spatial distribution and temporal 

interactions between different processes at the watershed 

scale. 

We applied RHESSys to catchments in Glacier, North 

Cascades, Rocky Mountain, and Yosemite National Parks to 

examine how ecosystem properties, including ecosystem water 

use (evapotranspiration, or ET), productivity or net primary 

production (NPP), and stream runoff, are affected by climate 

variability, and where these effects are most pronounced on 

the landscape. The sites were selected because of the extensive 

databases associated with them. Annual precipitation was 

positively correlated with ET, NPP, and runoff at all the sites. 

Temperature, however, was negatively correlated with ET, 

NPP, and runoff at the Mount Rainier catchment, and had 

no relation to ET, NPP, and runoff at the Glacier and Rocky 

Mountain National Park catchments. 

There was a positive correlation with ET and NPP but a 

negative correlation with runoff at the Yosemite catchment, 

suggesting a tradeoff between ecosystem and stream processes. The middle elevations at Yosemite were most responsive to changes in precipitation and temperature, similar to 

many empirical studies. Topographic complexity in the West 

reduces our ability to generalize from the four sites to other 

Western catchments at different elevations and aspects, but 

we are beginning to see what may be regional and elevational 

differences in ecosystem response. Future work will place the 

National Park watersheds in a West-wide vulnerability context 

by coupling model output that identifies the processes most 

responsive to climate change, including forecasts, with climate 

and geographic filters. 

The Puget Sound Integrated Landscape 

Monitoring Project (Poster)

By Susan Benjamin1

1

U.S. Geological Survey, Menlo Park, Calif.

The deteriorating environmental quality of the waters 

of Puget Sound has raised alarms at private, local, State, 

and Federal levels. The U.S. Geological Survey (USGS) is 

working with many partners in the Sound to understand the 

effects that a changing landscape has had on the second largest estuary in the United States. Determining the impacts of 

natural processes and human actions, predicting their effects, 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 3

and developing models and tools to evaluate different actions 

as expressed through changes in the landscape are critical 

to ensuring a sustainable future for all, both ecologically 

and economically. In the Puget Sound Integrated Landscape 

Monitoring Project (PS-ILM), a conceptual model of the 

landscape is being developed to describe the components of 

the landscape (structure) and the interactions among those 

components (function). This conceptual model will also be 

used to develop an interdisciplinary approach to measuring 

and evaluating potential indicators of landscape condition and 

change at scales necessary to understand management issues 

and develop successful management practices. Providing this 

information to partners with regulatory and advisory responsibility may assist efforts to restore the environmental health of 

Puget Sound. 

A New Method of Multiscale 

Topographic Analysis: Extracting 

Landforms (Poster)

By Norman B. Bliss1

1

Science Applications International Corporation, Sioux Falls, S. Dak.

Most existing algorithms for topographic analysis make 

use of moving windows (for example, for slope or aspect) 

or accumulate statistics on upstream areas (for example, 

flow accumulation). This poster introduces a new method of 

analysis that uses the standard flow direction and streamline 

calculations to create a vector dataset of flowpaths. Using 

the nodes of the streamline vector dataset, a new data structure of pointers relates each node to the nodes upstream and 

downstream. The current implementation creates pointers to 

nodes at distances away on the network for each power of 2 

(for example 1, 2, 4, 8, 16 nodes away). It is then possible 

to calculate slopes and curvatures on actual flowpaths and to 

identify areas of concavity and convexity on the landscape at 

multiple scales. 

I anticipate applications of this method for delineating 

landforms with great precision. Such features as floodplains, 

low hills, and steep mountains can be identified using criteria 

at generalized scales (for example, over distances of hundreds 

of meters), and yet boundaries for the features can be delineated near the resolution of the digital elevation model (for 

example, 30 meters). 

The customized landform delineations will have applications in many areas of integrated landscape monitoring (for 

example, vegetation productivity and phenology), climate 

change (for example, the influence of soil wetness on soil 

carbon storage or release to the atmosphere), and hazards and 

risks (for example, locating the probability of debris flows following wildfires and heavy rains). 

Taking SPARROW Predictions to 

the Web: A Tool For Research and 

Resource Management (Poster) 

By Nate Booth1 and Eric Everman1

1

U.S. Geological Survey, Middleton, Wis.

The U.S. Geological Survey (USGS) National Water 

Quality Assessment Program (NAWQA) is developing waterquality predictive models for eight major river basins of the 

conterminous United States. In addition to extrapolating measured water-quality conditions to unmonitored areas to help 

assess water-quality status, a calibrated Spatially Referenced 

Regressions On Watershed Attributes (SPARROW) model can 

be used to produce statistically defensible estimates of load, 

flow-weighted concentration, or yield of any modeled constituent under different land use and management scenarios. 

A decision support infrastructure is being developed to 

offer sophisticated prediction capabilities for research and 

resource management. The intention is to provide users of 

the system with these capabilities through a standard web 

browser without the need for proprietary software licenses or 

significant training. Development and operations costs of the 

system will be mitigated by defining a common data storage 

format for large geographic information system (GIS) datasets 

(for example, river networks) and by centralizing the data 

processing function in a set of industry-standard database and 

web-application servers. 

Based on previous SPARROW modeling products and 

current work by the USGS Geography and Water in the 

Chesapeake Bay Program, the following four general areas 

of predictions are planned: (1) Estimate load or concentration for river reaches of interest for specified source input or 

land use changes. Users can adjust source terms in upstream 

basins individually or across source categories (for example, 

waste-water treatment facilities) and view maps of source 

allocations, model prediction metrics, and changes from 

model baseline; (2) Investigate reaches in nonattainment 

(305b/303d reporting) to allow users to rank and map nonattainment reaches and produce summary reports for river miles 

impacted; (3) Rank incremental delivered yield by catchment 

to show reaches that contribute the most significant load to a 

targeted downstream reach of interest; and (4) Develop cost 

optimization procedures for either (a) targeted load reduction 

or (b) defined amount of available treatment resources to allow 

users to choose reaches on which to focus treatment dollars 

based on incremental delivered yield and treatment cost estimates by input source type. 
4 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Strategic Habitat Conservation (SHC) 

and the Importance of Ecosystem 

Models (Oral Presentation)

By Laura A. Brandt1

1

U.S. Fish and Wildlife Service, Davie, Fla.

The U.S. Fish and Wildlife Service (FWS) bears 

the Federal Government’s final legislative accountability for 

realizing the public trust interests in sustaining migratory 

birds, threatened and endangered species, interjurisdictional 

fisheries, and some marine mammals. That responsibility is shared legislatively by State governments as well as 

other Federal agencies and voluntarily by nongovernmental organizations. Strategic Habitat Conservation (SHC) 

is an approach to conservation that focuses on the Federal Government’s responsibility to assess, monitor, and 

predict the ability of landscapes to sustain populations of 

public trust species and work in partnership with organizations to implement necessary actions to achieve desired 

outcomes. The achievement of sustainable populations of 

trust species is how the FWS will measure progress toward 

its mission of working with others to conserve, protect, 

and enhance fish, wildlife, and plants and their habitats for 

the continuing benefit of the American people. The SHC 

framework is a science-based, iterative, adaptive framework that includes biological planning, conservation design, 

conservation delivery, monitoring, and research. Key 

elements of SHC are (1) setting explicit biological objectives, 

(2) identifying factors that limit the population or ecosystem function, and (3) developing models and decision support tools. Modeling at population and ecosystem scales and 

making that information available to managers is a component 

in developing the information needed to make decisions on 

where and how much habitat is necessary to ensure population and landscape sustainability and to forecast effects of 

alternative land use scenarios. Ecosystem models will be 

particularly important in projecting the effects of climate 

change on species and habitats. Having such information 

will put the FWS in a better position to respond proactively. 

The FWS has limited capacity to develop ecosystem models 

and will look to expertise within the U.S. Geological Survey 

(USGS) to assist in meeting those needs. This effort will 

require a stronger and expanded relationship between the 

management functions of the FWS and the science expertise 

of the USGS. One step in expanding the relationship between 

the FWS and the USGS is to go beyond the traditional communications “need-response model” toward a more functionbased relationship that is built around the elements of SHC. 

This presentation will highlight how the science anticipated 

by an SHC approach to conservation calls for a closer working relationship between science (USGS) and management 

(FWS) if the Public Trust Doctrine is to be realized for future 

generations of Americans. 

Numerical Simulation of Ground-Water 

Flow Within the Atlantic Coastal Plain 

Aquifers of North Carolina and South 

Carolina (Poster) 

By Bruce Campbell1

1

U.S. Geological Survey, Columbia, S.C.

The Atlantic Coastal Plain aquifer system of North 

Carolina and South Carolina is an important source of potable 

water for a growing population. The 2000 Census reported that 

the combined populations of Coastal Plain counties in North 

Carolina and South Carolina totaled nearly 6 million people, 

with 3.2 million located in North Carolina and 2.5 million 

located in South Carolina. In North Carolina, the population 

grew by 21 percent from 1990 to 2000. The population is 

projected to grow by another 13 percent by the year 2015. The 

numbers are similar in South Carolina, which had a population 

increase of 15 percent from 1990 to 2000 and whose population is projected to grow by 13 percent by 2015. The Atlantic 

Coastal Plain aquifer system has a large aerial extent, covering approximately 25,000 square miles in North Carolina and 

approximately 20,000 square miles in South Carolina. Most 

of the Coastal Plain is underlain by at least one, and in some 

areas, several highly productive aquifers. Although groundwater resources have been intensively developed in some areas 

of the Atlantic Coastal Plain, large areas remain relatively 

undeveloped. Some municipalities, such as Myrtle Beach 

and Charleston, S.C., which developed the Atlantic Coastal 

Plain aquifers in previous years and created large cones of 

depression in aquifer potentiometric surfaces, have switched 

to surface-water sources, thus allowing ground-water levels to 

recover partially. 

Saltwater encroachment is occurring in several places 

within the Atlantic Coastal Plain aquifers in North Carolina and South Carolina in response to the lowering of these 

potentiometric surfaces. Most of the onshore parts of the North 

Carolina and South Carolina Atlantic Coastal Plain aquifers 

contain freshwater (total dissolved solids are less than 1,000 

milligrams per liter); however, brackish water and saltwater 

are present in the aquifer system, especially near the coast. 

Even these brackish ground-water resources, however, are 

being used by some communities because the cost and efficiency of desalination technology have improved. 

The complicated history of water use, along with 

substantial areal variations in aquifer properties, pose challenges for the State agencies who are charged with managing these important resources. As more users demand more 

water, the need for scientifically based management tools has 

increased. A numerical ground-water-flow model is such a 

tool; it can be used to integrate and evaluate large volumes 

of ground-water data to assist in the management of these 

important ground-water resources. To help State agencies 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 5

address issues concerning the management and development 

of the Atlantic Coastal Plain aquifers in North Carolina and 

South Carolina, steady-state and transient ground-water flow 

models are being developed by the U.S. Geological Survey 

(USGS) using the USGS finite-difference ground-water flow 

model, MODFLOW-2000. 

The steady-state model consists of 16 layers and contains 

130 rows by 275 columns of 2-square-mile grid cells. Net 

recharge, upper Coastal Plain rivers, and numerous wells are 

simulated as fluxes within the model. Specified-head model 

boundaries are set laterally at the James River in Virginia and 

the Oconee River in Georgia. No-flow model boundaries are 

set at the Fall Zone and at the freshwater/saltwater interface. 

The upper boundary of the model is simulated mostly as a 

specified-head boundary to represent the water table, except 

in the higher elevations of the upper Coastal Plain, which are 

actively modeled and where direct net recharge is applied. 

Model calibration criteria include predevelopment water levels 

and synoptic water levels for the study area collected in 1980, 

1982, 1989, and 2004. Analysis of the baseflow of selected 

upper Coastal Plain streams using hydrograph separation techniques also are being used to constrain the model solutions. 

MOAB: Modeling Individual Movement 

and Behavior Using an Expert System 

Approach (Poster) 

By Jacoby Carter1

1

U.S. Geological Survey, Lafayette, La.

There are three general approaches to modeling individual behavior. One is to use a stochastic model whereby individuals move across the landscape in a random walk. These 

walks can be modified by using different probabilities for 

different states. A second approach is to use a rigid rule-based 

system, such as a decision tree or a decision matrix. A third 

approach is to use techniques from artificial intelligence, such 

as neural nets or heuristic approaches. The neural net approach 

works by comparing the behavior of the model with measured 

behavior of the observed system and adjusting the weights of 

the neurons until the model approximates the observed result. 

A rule-based approach uses “rules of thumb” to determine 

behavior. This approach differs from a decision tree or a 

decision matrix approach in that the rules can be unstructured 

and state-dependent, rather than fixed. The Model of Animal 

Behavior (MOAB) model allows the user to simulate movement and foraging behavior across a landscape using either 

a random walk model that can be modified to approach that 

of a structured rules system, or an expert system mode that 

uses rules of thumb provided by the user. By allowing both 

approaches in MOAB, a rule-based approach can be compared 

to a “null” model. Individual movement is determined by a 

suite of variables, including habitat type; the presence, type, 

and quantity of food; the internal state of the animal with 

respect to energy, sex, and anxiety levels; the recent history of 

surrounding cells; the recent history of the animal; and the animal’s ability to detect its surrounding environment. Because 

the model is spatially explicit and updates the landscape in 

response to animal behavior, it can be used to predict individual responses to landscape change. 

Modeling Nutria (Myocastor coypus) 

Invasions Across Regional and 

National Landscapes (Poster)

By Jacoby Carter1

1

U.S. Geological Survey, Lafayette, La. 

Nutrias (Myocastor coypus) are aquatic rodents native 

to the southern half of the South American continent. They 

are highly valued for their fur. Originally introduced to North 

America by fur farmers, they have escaped or were released 

in more than 30 states. Their populations flourished in some 

regions; in other areas, they became locally extinct. The most 

common reasons given for local extinction are weather related. 

Nutria can be an important pest species for several reasons. 

First, through their feeding activities, they can destroy a marsh 

when their population densities get too high. Second, they can 

cause damage to water control and retention structures with 

their burrowing activities. They have also been implicated as 

a disease vector and seem to out-compete the native muskrat 

(Ondatra zibethicus). A general prediction of climate change 

models is that winters will generally become milder and there 

will be lesser differences between nighttime and daytime temperatures. If these predictions are correct, we should expect 

nutrias to expand their range to areas where they were formerly weather limited. Prevention of this range expansion will 

require both monitoring of current distributions and modeling 

to predict where they are likely to move. Predictive models 

will assist in early detection and control. This talk will review 

how the U.S. Geological Survey nutria research program is 

developing and integrating multiple approaches to understanding this problem. Lines of attack include geographic information systems modeling and development of habitat suitability 

models; development of movement models; and understanding 

how other species (for example, beaver, alligators, muskrats, 

and domestic dogs) may also play a role in the ability of nutria 

to become established in new areas. 
6 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Communicating Remote Sensing 

Requirements and Working Together to 

Satisfy Project Needs (Poster)

By Thomas H. Cecere1

1

U.S. Geological Survey, Reston, Va. 

Historically, Government users of remotely sensed data 

have not been effective in communicating their requirements 

and data holdings to others within the Government and often 

have had difficulty finding suitable data to meet their needs. 

Through the efforts of the President’s 2003 Commercial 

Remote Sensing Space Policy’s (CRSSP’s) Requirements and 

Shared Execution teams, Federal agencies are partnering to 

make their requirements known to Government and industry, 

to leverage established infrastructures for data archiving and 

distribution, and to utilize existing contract mechanisms and 

consortiums to help get data into the hands of those who need 

them. 

The first goal of this exhibit is to introduce the CRSSP 

Imagery-Derived Requirements (CIDR) tool that was developed by the CRSSP Requirements team. The purpose of the 

CIDR tool is to capture specifics regarding agency remote 

sensing needs so these needs can be analyzed and shared easily throughout the appropriate communities of interest. The 

second goal of this exhibit is to show the analysis process, 

which incorporates knowledge of multiple agency holdings 

and vendor archives. The third goal is to show that our efforts 

are resulting in Government users having greater access to 

high-resolution commercial remote sensing data and other 

sources of remotely sensed data and that the key lies in the 

communication process. 

The process of sharing and gaining improved understanding of all Federal agency remote-sensing requirements 

[including electro-optical, light detection and ranging (lidar), 

and synthetic aperture radar (SAR)], can ultimately stimulate 

ideas as to how Government agencies can work together to use 

existing and future technologies to help save lives, improve 

the environment, and increase the efficiency and accuracy of 

day-to-day tasks. 

Data Management and Interoperability 

in the USGS Volcano Hazards Program 

(Oral Presentation)

By Peter F. Cervelli1

1

U.S. Geological Survey, Anchorage, Alaska

During the past 5 years, the U.S. Geological Survey (USGS) 

Volcano Hazards Program (VHP) has made a substantial 

investment in data management. The key results of this 

investment are the widespread adoption within the VHP of 

Structured Query Language (SQL) databases for storing and 

managing geophysical data streams, and the development 

of web services that enable both the distribution of data 

to outside partners and seamless interoperability among the 

volcano observatories. We have also developed and deployed 

specialized client applications for the visualization and 

analysis of data available via web services. Our development 

strategy has been to adopt and extend existing protocols 

and schema where available, so as to permit backwards 

compatibility with legacy applications. The web services 

also answer queries for metadata that describe the geospatial 

context and instrumentation details associated with the 

primary data. 

The rationale for our investment in data management 

is threefold. First, the quantity of data collected by the 

VHP currently exceeds 10 gigabytes per day; we require a 

modern system for storing and serving this large and growing 

data stream. Second, the VHP collects data from a variety of 

different sources, including ground-based instrumentation, 

field observations and measurements, and satellite imagery. 

We require a fast and simple means for interpreting these 

disparate data. This leads to the requirement of a system that 

can quickly depict many different data types on a common 

map-base or timescale. Finally, we require the capability for 

offsite analysis—that is, we wish to leverage all the intellectual resources of the VHP, whether those resources are local or 

remote. 

Though incomplete, the investment has resulted in 

several production systems, which are in use throughout the 

VHP and at other volcano observatories worldwide. The 

Volcano Analysis Visualization Environment (VALVE) system 

stores, generic data streams and geographical information 

in an SQL database and implements a web service for their 

distribution. The WINSTON system follows an identical 

storage and presentation model but is specially tailored for 

seismic data, which is characterized by high data rates (100 

Hz or greater). The Seismic Wave Analysis and Real-time 

Monitor (SWARM) application connects to a WINSTON (or 

other legacy) server and enables rapid interactive visualization 

and interpretation of seismic data. All these systems have 

been ”battle-tested“ in that they have been repeatedly used 

for the short-term forecast of volcanic hazards. Each of these 

systems and applications are written in the Java programming 

language, are open source, and are freely available to all. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 7

Cost-Benefit Analysis of Computer 

Resources for Machine Learning 

(Poster)

By Richard A. Champion1

1

U.S. Geological Survey, Menlo Park, Calif. 

Machine learning describes pattern-recognition algorithms—in this case, probabilistic neural networks (PNNs). 

These can be computationally intensive, in part because of 

the nonlinear optimizer, a numerical process that calibrates 

the PNN by minimizing a sum of squared errors. This poster 

suggests efficiencies that are expressed as cost and benefit. 

The cost is computer time needed to calibrate the PNN, and 

the benefit is “goodness-of-fit,” or how well the PNN learns 

the pattern in the data. There may be a point of diminishing 

returns where a further expenditure of computer resources 

does not produce additional benefits. Sampling is suggested 

as a cost-reduction strategy. One consideration is how many 

points to select for calibration and another is the geometric 

distribution of the points. The data points may be nonuniformly distributed across space, so that sampling at some 

locations provides additional benefit while sampling at other 

locations does not. A stratified sampling strategy can be 

designed to select more points in regions where they reduce 

the calibration error and fewer points in regions where they 

do not. Goodness-of-fit tests ensure that the sampling does 

not introduce bias. This approach is illustrated by statistical 

experiments for computing correlations between measures of 

roadless area and population density for the San Francisco Bay 

area. The alternative to training efficiencies is to rely on highperformance computer systems. These may require specialized 

programming and algorithms that are optimized for parallel 

performance. 

EdGCM: An Interactive Climate 

Modeling Framework for Educators 

and Scientists (Oral Presentation)

By Mark A. Chandler1

1

Columbia University, NASA/GISS, Madison, Wis.

Most climate researchers believe that climate change 

will profoundly impact both our planet’s environment and the 

world’s economic and geopolitical landscape in the coming 

decades. Because these impacts have the potential to affect 

everyone, a basic knowledge of Earth’s climate system is 

critical to making informed judgments about climate-related 

issues. To achieve that goal, educators and scientists who are 

not necessarily climate specialists need accessibility to scientific and technological tools used to forecast climate change. 

Computer-driven general circulation models (GCMs) are the 

primary tool used today in climate research, but until now 

they have been little more than black boxes to most people. 

As a practical matter, few educators or researchers have had 

access to GCMs because they typically require supercomputing facilities and skilled programmers to operate. The resulting 

lack of familiarity with climate modeling techniques has often 

engendered public distrust of important scientific findings 

based on such methodologies and has kept many researchers 

from using these important simulation capabilities. Education 

Global Climate Modeling (EdGCM) addresses this problem 

by providing a research-grade GCM within a user-friendly 

interactive framework that can be run on desktop computers. 

Our primary goal is to improve the quality of teaching and 

learning of climate-change science through broader access to 

GCMs, and to provide appropriate technology and materials to 

help educators use these models effectively. However, secondarily, we hope to increase greatly the number of scientists who 

have access to the tools that form the backbone of the global 

climate forecasting system. With research-quality resources in 

place, linking educational institutions to actual research and 

development labs is not only possible, but will be beneficial to 

both the education and research communities alike. 

Mutually Interactive State-Parameter 

Estimation of a Carbon Cycle Model 

Using a Smoothed Ensemble Kalman 

Filter (Poster) 

By Mingshi Chen1, Shuguang Liu1, and 

Larry L. Tieszen2

1

Science Applications International Corporation, Sioux Falls, S. Dak.

2

U.S. Geological Survey, Sioux Falls, S. Dak.

The General Ensemble Biogeochemical Modeling 

System (GEMS) is designed to simulate terrestrial carbon 

dynamic processes and predict carbon trends from plot to 

regional scale. Accurate forecasts by GEMS rely on a fairly 

accurate estimation of the state variables and parameters and 

proper treatment of various sources of uncertainties stemming 

from inputs, outputs, and parameters. In this study, we used a 

Smoothed Ensemble Kalman Filter (SEnKF) to sequentially 

assimilate measurements from eddy covariance tower sites 

and from a Moderate Resolution Imaging Spectroradiometer (MODIS) into GEMS over a mixed forest area of 

10 kilometers (km) × 10 km surrounding the Howland, Maine, 

site to estimate state variables and parameters simultaneously. 

Measurements, field estimates, and satellite observations 

included half-hourly net ecosystem exchange (NEE), daily 

gross primary production (GPP), and ecosystem respiration 

(Re) at the Howland site and monthly net primary production (NPP) from MODIS. All measurements were scaled up 
8 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

to monthly data to match GEMS. Our analyses demonstrate 

that important parameters, such as the maximum ecosystemspecific net production rate, varied with time, although it was 

traditionally treated as a static parameter in GEMS. Results 

also show that model predictions with parameters modified 

by the SEnKF were remarkably improved compared with 

those without progressive data assimilation. Furthermore, the 

SEnKF dramatically reduced the variances of state variables 

stemming from uncertainties of parameters and driving 

variables. 

Hydrodynamic and Water Quality 

Modeling of the A.R.M. Loxahatchee 

National Wildlife Refuge, North 

Everglades, Florida (Poster)

By Chunfang Chen1, Michael G. Waldon1, 

Hongqing Wang2

, Matthew C. Harwell3, 

Alonso G. Griborio3

, Ehab A. Meselhe2

1

U.S. Fish and Wildlife Service, Lafayette, La.

2

University of Louisiana at Lafayette, Lafayette, La.

3

U.S. Fish and Wildlife Service, Vero Beach, La.

4

Hazen and Sawyer, Hollywood, Calif.

The Arthur R. Marshall Loxahatchee National Wildlife Refuge (Refuge) is a 58,000 hectare (ha) remnant of 

the Northern Everglades in Palm Beach County, Fla. The 

U.S. Fish and Wildlife Service has determined that changes 

in water quality, quantity, and timing are introducing negative 

impacts to the Refuge’s ecosystem. A priority of the Refuge is 

to ensure appropriate water quality and quantity that maximize 

benefits for fish and wildlife, flood control, and water supply. 

To this end, a better understanding of the impacts of contaminants and excessive nutrient loading is needed. 

Hydrodynamic and hydrologic models coupled with 

water-quality models are valuable tools to provide projections 

of water movement and water-quality constituent concentrations. To study the water budget and water quality of the 

Refuge, two modeling approaches were employed. The first 

approach, named the Completely Mixed Flow (CMF) model, 

uses a compartment-based model to study water budget and 

water constituent transport. The model was developed using 

Microsoft Excel and the U.S. Environmental Protection 

Agency’s Water Quality Analysis Simulation Program 

(WASP). 

A complex spatially explicit set of linked models has 

also been developed using the MIKE_FLOOD and ECO Lab 

modeling frameworks (DHI, formerly the Danish Hydraulic Insitute) to provide fully dynamic hydrodynamic and 

water-quality models. These models link a one-dimensional 

channel model with a 400 meter regular grid marsh model. In 

addition to water stage and flow, constituent transport through 

advection and dispersion, and transformation through reactive processes are modeled. The models simulate chloride, 

sulfate, and total phosphorus. These models were calibrated 

for a 5-year period and validated for two other periods. Results 

show good agreement between observed and predicted stages 

and concentrations at specific locations. Statistical analyses 

demonstrate the applicability of these models for temporal and 

spatial prediction of water levels and water quality concentrations. This project illustrates the linkage and use of models at 

different levels of spatial complexity. 

Toward a National Land-Change 

Community Model (Oral Presentation)

By Peter R. Claggett1, Michael E. Colvin2, 

and David I. Donato3

1

U.S. Geological Survey, Annapolis, Md.

2

Iowa State University Coop Unit, Ames, Iowa

3

U.S. Geological Survey, Reston, Va.

In the U.S. Geologica Survey (USGS), as in the Nation 

at large, the impact of land-change modeling on public policy 

has—so far—fallen short of its promise and potential. Among 

the many reasons, two stand out: first, the research has been 

fragmented, leading to specialized models that cannot interoperate; and second, the disproportionate share of research 

has been directed to local extents and too little to regional 

and national extents. The USGS Geographic Analysis and 

Monitoring program, among others, recognizes the need for 

improving the effectiveness of ecological and environmental 

modeling by addressing the neglected regional scale, and 

for improving the generality and robustness of land-change 

models by fostering a vibrant, interactive national community 

of modelers. 

The National Land-Change Community Model, or 

NLCCM, will be eclectic and inclusive. By developing 

software toolkits along with standards for integrating landchange models within and across spatial and temporal scales, 

the national land-change modeling community will make 

it possible for modelers to select the best features from 

various existing models; to build progressively refined and 

increasingly credible and widely accepted models; and thus 

to inject believable alternative futures into the formation of 

public policy at all levels of government. Perhaps operating 

much like open-source software development projects, the 

NLCCM, as envisioned, will be collaboratively developed 

by geographically and organizationally dispersed researchers 

and modeling-software developers drawn from all levels of 

government, the academic community, and nongovernmental 

organizations. Because of the variety of land-change modeling 

applications, drivers, and consequences, and the complexity 

of land-change processes, the NLCCM can also be viewed as 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 9

a national-extent model of national and global drivers and as 

a mosaic of regional models that serve to integrate and bridge 

the output from global and national process models with local 

micro-simulation and agent-based models. 

The authors are developing the Chesapeake Bay LandChange Model (CBLCM), a model that covers the entire 

Chesapeake Bay Watershed, which crosses six States. This 

model will serve as a prototype of a regional-scale model 

developed by integrating and adapting two models (the 

New Jersey Growth Allocation Model and the Slope, Land 

cover, Exclusions, Urban areas, Transportation, Hydrologic 

(SLEUTH) cellular-automaton urban-growth model), which 

were originally addressed to only local extent. To promote 

the kind of standards-based integration needed to fulfill the 

promise of land-change modeling, the authors are developing 

a USGS-hosted NLCCM web site and sponsoring a competitive land-change modeling intercomparison to catalyze 

national interest and collaboration. 

Application of a Multimodeling 

Framework to Linking Ecosystem 

Pattern and Process Across Scales: A 

Tale of Two Ecosystem Models (Oral 

Presentation)

By Jon C. Cline1 and Joseph Koonce1

1

Department of Biology, Case Western Reserve University, Cleveland, Ohio

This poster reports on recent advances in the development of a generic multilevel modeling framework for ecological modeling. The model framework includes an Extensible 

Markup Language (XML)-based metadata format, support 

for a model repository that allows dynamic loading of model 

components specified by metadata, and a simulation server 

that provides a Discrete Event System Specifications (DEVS) 

environment for assembling and running hierarchical modular 

models. An object model that includes support for open geospatial data standards for grid coverages and simple features 

is used to exchange model state information between model 

components and between the simulation server and a userfriendly geographic information system (GIS) client. 

To demonstrate the generality of the modeling approach, 

we present two applications of the framework to modeling 

ecosystem process across scales. The first is an implementation of a regional-scale stressor-response model for the 

management of the Lake Erie ecosystem. The second 

is implementation of the ALFISHES model, which is one of 

a suite of Across Trophic Levels System Simulation (ATLSS) 

models designed to assess the impact of changes in hydrology 

on a suite of higher trophic-level species of the southern 

Florida ecosystem. The ALFISHES model combines field data 

with hydrologic data from the Southern Inland and Coastal 

System (SICS) model to assess the impact of salinity on fish 

biomass. The model output may be used to assess the impact 

of changes in hydrology on fish biomass and its availability to 

the Roseate Spoonbill (Ajaia ajaja), which is a key indicator 

species, and other wading birds. 

Modeling Natural Resource 

Management Decision Scenarios 

Using Decision Analysis: The 

Flow of Selenium in Appalachian 

Watersheds from Rocks to Ducks (Oral 

Presentation) 

By James L. Coleman1, Karen Jenni2, Tim Nieman3

and Ione L. Taylor1

1

U.S. Geological Survey, Reston, Va.

2

Insight Decisions LLC, Reston, Va.

3

Decision Applications, Inc., Reston, Va.

Through the use of decision analysis principles, techniques, and tools, the U.S. Geological Survey (USGS) Eastern 

Energy Resources Team, in collaboration with other USGS, 

Federal, and State partners, developed a computer model to 

examine the potential generation of bioavailable selenium in 

Appalachian watersheds as a result of large-scale earth-moving and construction projects. Selenium is present naturally 

in the Earth’s crust and is frequently concentrated in coal and 

organic-rich shales, such as those found in the Appalachian 

coal fields of the eastern United States. Without special handling at the project site, selenium may be concentrated through 

physical and (or) biological processes in the watersheds below 

the project areas, where it may reach toxic levels in fish before 

it is sufficiently diluted downstream where it is no longer an 

environmental concern. 

Our model was created to characterize the flow of 

selenium from the host rocks into the initial drainage basin, 

through a holding or settling pond, and finally into the natural 

watershed. Biological, engineering, geologic, and hydrologic 

constants, factors, and transformation equations were used 

where known, or approximated where unknown. Uncertainty 

in those factors was modeled explicitly, and various scenarios 

representing alternative decisions and site conditions were 

included. The end result is a model that projects selenium 

concentrations (with uncertainty) at various locations downstream from a disturbance. The use of alternative scenarios 

in the model leads to postulated outcomes within a specific 

context that can be used to help decisionmakers understand 

and use USGS data and interpretations to make more informed 

natural resource and environmental management decisions. 

Because the model architecture is modular, the overall 

model could be extended further to include components to 
10 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

estimate social or economic tradeoffs that might also be of 

interest to decisionmakers. Because of its modular approach, it 

can also be scaled up to address all the acreage under potential 

effect from large-scale projects. 

As a final outgrowth of this effort, it became clear that 

scientists from various disciplines enjoy working together on 

an issue of mutual interest and concern. This particular modeling effort was successful because we had access to professional decision analysts to help frame, guide, and motivate 

the effort; highly knowledgeable scientists who accepted the 

decision analysis process and shared their data, uncertainties, 

and concerns in a free and open environment; and the strong 

support and encouragement of USGS Eastern Region management to undertake this experiment. 

A Proposed Ecosystem Simulation 

Model to Predict the Effects of 

Common Carp and Zebra Mussels in 

Shallow Lake Ecosystems (Poster) 

By Michael E. Colvin1

1

Iowa State University Coop Unit, Ames, Iowa

The management of water quality in shallow lakes is a 

tradeoff between biological productivity and water clarity. 

Invasive species can have both negative and positive effects 

on the ecosystem. Common carp are known to have a negative effect on water quality by uprooting aquatic vegetation 

and suspending sediment and nutrients sequestered in the lake 

benthos, possibly resulting in a hypereutrophic state. Zebra 

mussels have been shown to have a positive effect on water 

quality in eutrophic aquatic ecosystems over the short term 

by filtering and sequestering nutrients from the water column. 

As zebra mussel populations grow over time, however, the 

amount of nutrients removed and sequested from the water 

column increases, potentially limiting the biological productivity of the ecosystem. In the short term, zebra mussels have 

the potential to mediate the negative effects of common carp 

on water quality in shallow lake ecosystems. Whether zebra 

mussels in the long term will have a negative or positive effect 

on the water quality of shallow lake ecosystems is uncertain. 

In light of the recent introduction of zebra mussels and the 

possible mediation of the effect of common carp on water 

quality in Clear Lake, Iowa, an ecosystem simulation model 

has been proposed to evaluate the effect of invasive species 

on future water quality and the aquatic community. Our poster 

will address the mechanics of the proposed ecosystem simulation model that will evaluate the effect of invasive aquatic 

species in shallow lake ecosystems. 

Integrating 3-D Hydrodynamic 

Transport and Ecological Plant Models 

of the Savannah River Estuary Using 

Artificial Neural Network Models 

(Poster) 

By Paul A. Conrads1 and Wiley Kitchens1

1

U.S. Geological Survey, Columbia, S.C.

To assess the environmental impacts of a potential 

deepening of the Savannah Harbor and to evaluate various 

mitigation options, a three-dimensional (3-D) hydrodynamic 

model and a marsh succession model (MSM) were developed. 

The 3-D model predicts changes in water levels and salinity in 

the system in response to potential harbor geometry changes. 

The MSM predicts plant distribution in the tidal marshes in 

response to changes in the salinity conditions in the marsh. 

To link the 3-D hydrodynamic and marsh-succession modeling studies, data-mining techniques, including artificial neural 

network (ANN) models, were applied to integrate the riverine 

predictions from the 3-D model to the MSM. 

The riverine and marsh databases, the riverine and marsh 

ANN models, model control, 3-D hydrodynamic model input, 

and simulation output were integrated into a spreadsheet 

application. The application allows users to use the predicted 

water level and salinity values from the 3-D model to predict 

changes in the tidal marshes. Output from the application is a 

10- or 100-meter grid of the marsh system to be used as input 

to the MSM. The salinity values and grid parameters can be 

exported as an American Standard Code for Information Interchange (ASCII) file for input into a geographic information 

system (GIS). 

Using Data Mining Techniques and 

Artificial Neural Network Models 

to Integrate Hydrology, Ecology, and 

Biology—Two Case Studies (Oral 

Presentation)

By Paul A. Conrads1 and Wiley Kitchens1

1

U.S. Geological Survey, Columbia, S.C.

U.S. Geological Survey (USGS) hydrologists and ecologists have been working on integrating a long-term hydrologic 

data network and a short-term ecological database to support 

ecological models for the Savannah Harbor tidal marshes and 

the snail kite habitat in the Everglades. The two applications 

demonstrate how very accurate empirical models can be built 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 11

directly from the data to fully utilize historical databases in 

support of current interdisciplinary studies. 

Ecologists are studying tidal wetlands of the Savannah 

River watershed in response to the potential deepening of the 

Savannah Harbor. The linkage between freshwater inflows, 

tidal water levels, and specific conductance is critical to 

enhancing the predictive capabilities of a successful marsh 

succession model. The USGS has maintained a riverine waterlevel and specific conductance data-collection network since 

the 1980s and a network of continuous marsh water-level and 

pore-water salinity gages in the tidal marsh since 1999. Datamining techniques, including artificial neural network (ANN) 

models, were applied to address various needs of the ecology 

study. ANN models were developed to simulate riverine water 

levels and specific conductance in the vicinity of the tidal 

marshes for the full range of historical conditions. ANN models also were developed to simulate the marsh water levels and 

pore-water salinities using inputs from the long-term USGS 

data network. Using the marsh ANN models, the continuous marsh network was hindcasted to be concurrent with the 

long-term riverine network. The databases, ANN river and 

marsh models, simulation controls for the models, streaming 

graphics, and model output were integrated into a Microsoft 

Excel application. The application is easy to disseminate and 

allows ecologists and engineers to evaluate river and marsh 

response to user-defined streamflow conditions easily. 

A similar approach was applied to evaluating the hydrology of the snail kite habitat in Water Conservation Area 3 of 

the Everglades. Hydroperiods have a substantial effect on the 

nesting and foraging of the threatened and endangered snail 

kite. Seventeen water-depth recorders are colocated at transects where extensive plant samples are collected. These continuous recorders, maintained by the USGS, were established 

in 2003. The USGS has maintained a network of three waterlevel recorders since 1991. ANN models were developed using 

the three long-term gages as input to simulate the water levels 

at the 17 short-term sites and to hindcast water levels to 1991. 

The hindcasted water-level record will increase the predictive 

capabilities for evaluating the snail kite habitat to changing 

hydrology. The databases, ANN models, simulation controls 

for the models, streaming graphics, and model output were 

integrated into a Microsoft Excel application. As part of the 

Everglades restoration, Interim Operating Plan (IOP) water 

levels are generated that characterize modified flow regulation. 

The application will allow ecologists to evaluate IOP water 

levels for the long-term gages and the hydrology critical to the 

snail kite habitat. 

Animation and Publication of ThreeDimensional and Four-Dimensional 

Physical Models (Poster) 

By Christopher Polloni1, Michael Pantea2, 

Michael Kelly3

, and Brian N. Davis4

1

U.S. Geological Survey, Woods Hole, Mass.

2

U.S. Geological Survey, Denver, Colo.

3

University of Northern Arizona 

4

Science Applications International Corporation, Sioux Falls, S. Dak.

Scientists developing earth science models must also 

convey the scientific information and interpretations to audiences of colleagues, decisionmakers, policymakers, nonexperts, students, and the general public. Because most human 

understanding is attained through visual interpretation of 

our surroundings, visualization of scientific information can 

help portray models more effectively in the context of larger 

societal implications. Development of three-dimensional (3-D) 

and four-dimensional (4-D) visualizations of earth science 

models can help integrate geoscience information from multiple sources, providing insight into complex interdisciplinary 

studies. These visual communication tools are essential to the 

understanding of complex scientific issues by nonexperts. 

Common software applications can now be used to 

visually interpret results of earth science models and aid their 

integration with other earth science data. Use of interactive 

3-D and 4-D visual display tools can facilitate the common 

understanding of the scientific information contained within 

models and can be incorporated into all phases of earth science 

research. Visualization of results is often treated as an afterthought instead of as an interpretive tool. The development of 

self-contained, digital interactive visual publications would 

incorporate necessary data and visualization software tools 

into the publication of results. 

We will show model results in 3-D and 4-D interactive 

displays using commercial geographic information system 

(GIS) software and commodity hardware, which contrast with 

the expensive virtual reality (VR) systems of the past and 

enable broader use. We will show scientific models in 3-D 

and 4-D animations and present tutorial information on how 

scientists can present model results using these tools. In many 

cases, model results are produced in other software packages, 

such as Matlab. Such robust visualization tools are necessary 

to integrate multiple models into a common geospatial view. 

Portable display systems of commodity components are 

becoming commonplace, making data visualization more 

affordable. A portable GeoWall display system will be used 

to highlight some of the capabilities described. Also, new 

laptop displays are now capable of stereo display and may be 

available for demonstration. Software demonstrated will be 

ArcGIS, EarthVision, Fledermaus, Google Earth, and ROMA. 

Other packages, such as GeoModeller, are being reviewed and 

can be discussed.
12 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Modeling an Aquatic Trophic Chain in 

a Two-Dimensional Topography in a 

Seasonally Varying Wetland (Poster)

By Donald L. DeAngelis1, Joel C. Trexler2, and 

Douglas D. Donalson3

1

U.S. Geological Survey, Coral Gables, Fla.

2

Florida International University, Miami, Fla.

3

U.S. National Park Service, Homestead, Fla.

Real food webs occur in a spatially and temporally 

varying environment. An extreme example of a food web 

subject to changes in space and time is the aquatic food web of 

the Everglades. Seasonal variation in rainfall leads to continual changes in the extent of flooded area. As areas become 

wet, aquatic species invade and, conversely, as areas dry, 

aquatic species emigrate or become stranded and die. Because 

species invade (or dormant species recover) at different rates, 

the structure of the aquatic food web is continually changing, leading to continual changes in the direction of control 

(resource limitation or predator limitation) within the food 

web. Understanding these changes and how they affect the 

abundance of fish species, particularly the small fishes, is critically important because the small fishes are the most abundant vertebrates in the Everglades and Big Cypress wetland 

ecosystems of southern Florida. Small-bodied species, made 

up primarily of about 10 species of livebearers (poeciliids) and 

killifishes (cyprinodontids and rivulids), having similar life 

cycles and resource use, dominate the community by numbers. 

They constitute an ecosystem component whose biomass is 

the major energy resource for higher trophic levels, especially 

wintering and breeding wading birds. Systematic humaninduced changes in hydrology over the past several decades 

have altered hydroperiods in most wetland areas, thereby 

diminishing this fish forage-base or changing the pattern of its 

availability. Lack of sufficient biomass and availability of prey 

are hypothesized to have been major factors in the decline 

of wading bird nesting at traditional Everglades’ rookeries. 

Recovery of historic fish patterns is a primary goal for restoration of southern Florida wetlands. 

To help understand the dynamics of the aquatic food 

web, and especially the small fishes, the model GEFISH 

(Greater Everglades Fish) was created under the Across 

Trophic Levels System Simulation (ATLSS) program 

of the U.S. Geological Survey (USGS). Its purpose is to 

describe fish functional groups in freshwater marshes of the 

greater Everglades area of southern Florida. In particular, 

it is intended to assess the spatial pattern of fish densities 

through time across freshwater marshes and to help interpret and extrapolate monitoring data. This model has the 

capability of providing a dynamic measure of the spatially 

explicit food resources available to wading birds. The current version of GEFISH (GEFISH 1.1) simulates three 

functional groups of small fishes, as well as crayfish, the 

trophic base of the fish, and a generalist piscivorous fish that 

can feed on the smaller ones. The marsh landscape is modeled as 100-meter (m) × 100-m spatial cells on a grid across 

the southern Everglades. The model is being used, in particular, to indicate the impact of canals (which provide a permanent habitat for the large piscivorous fish) on the small fish 

community. 

The Land Use Portfolio Model: A Tool 

for Natural-Hazards Risk Analysis 

(Oral Presentation)

By Laura Dinitz1 and Peter Ng1

1

U.S. Geological Survey, Menlo Park, Calif.

We will describe and demonstrate the Land Use Portfolio 

Model (LUPM), a tool that U.S. Geological Survey (USGS) 

researchers developed to guide funding decisions for effective reduction of natural-disaster losses. The model, based 

on financial portfolio theory, offers an approach to compare 

loss-reduction measures in terms of expected return on the 

investment and financial risk. Required model inputs are 

hazard-event probabilities, planning-time horizons, community assets at risk, dollar values and (or) vulnerability of 

assets, spatial-damage probabilities, and costs and effectiveness of risk-reduction measures. To run the LUPM, the user 

defines a hazard scenario and selects a portfolio of locations or 

mitigation measures, or both, in which to invest. This selection 

may be guided by such policies as mitigating losses in schools, 

high-hazard locations, or other criteria. The LUPM estimates 

total cost, number of locations selected, expected loss, and 

the mean and standard deviation of return-on-investment and 

community wealth retained. Users can apply the LUPM to 

any natural hazard and location for which they have available the required input data. Two case studies that have been 

done include earthquake-triggered liquefaction in Watsonville, 

Calif., and flooding in Squamish, British Columbia, Canada. 

The software we are developing to calculate the LUPM equations is designed to work within ArcGIS desktop applications. 

Two versions will be available: (1) an ArcGIS tool extension, 

and (2) an ArcGIS Model Builder tool accessible through 

ArcGIS Toolbox. They will be packaged together and released 

in 2008 to internal USGS researchers and partners. Ongoing 

research includes developing better measures of uncertainty, 

expanding to multiple hazards, analyzing spatial autocorrelation, and combining the LUPM software with other tools, such 

as the Federal Emergency Management Agency’s Hazards 

US Multi-Hazard (HAZUS-MH) loss estimation software. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 13

Forecasting the 21st Century WorldWide Status of Polar Bears Using a 

Bayesian Network Modeling Approach 

(Poster)

By Dave Douglas1, Steve Amstrup2, and 

Karen Oakley2

1

U.S. Geological Survey, Juneau, Alaska

2

U.S. Geological Survey, Anchorage, Alaska

To inform the U.S. Fish and Wildlife Service decision 

on whether or not to list polar bears as threatened under the 

Endangered Species Act (ESA), we forecast the status of the 

world’s polar bear (Ursus maritimus) populations 45, 75, and 

100 years into the future. We combined the world’s 19 polar 

bear subpopulations into four ecological regions on the basis 

of current and projected sea ice conditions: (1) the Seasonal 

Ice Ecoregion, which occurs mainly at the southern extreme 

of the polar bear range; (2) the Archipelago Ecoregion of the 

Canadian Arctic; (3) the Polar Basin Divergent Ecoregion, 

where ice is formed and then advected away from near-shore 

areas; and (4) the Polar Basin Convergent Ecoregion, where 

sea ice formed elsewhere tends to collect against the shore. We 

incorporated projections of future sea ice in each ecoregion 

from 10 Intergovernmental Panel on Climate Change (IPCC) 

general circulation models into a Bayesian network (BN) 

model structured around population stressors that could affect 

the factors considered in ESA decisions. The BN model combined empirical data, interpretations of data, and professional 

judgment into a probabilistic framework. The BN model 

forecasted extinction of polar bear populations in the seasonal 

ice and polar basin divergent ecoregions by 45 years from the 

present. These two areas currently are home to approximately 

two-thirds of the world’s polar bears. The BN model projected 

high nonzero probabilities that Archipelago polar bears could 

occur in smaller numbers through the end of the century, and 

in the Polar Basin Convergent Ecoregion through midcentury. 

Decline in ice habitat was the overriding factor driving the 

model outcomes. Sensitivity analyses of the BN model indicated that habitat loss was the overarching stressor responsible 

for the population declines. That is, the BN model outcomes 

were not qualitatively different when the effects of stressors 

other than sea ice decline (such as hunting pressure or industrialization) were controlled. The outcomes were also not qualitatively different when we applied only the general circulation 

models that projected the smallest decline in sea ice. 

Modeling Past, Present, and Future 

Polar Bear Habitats (Oral Presentation) 

By Dave Douglas1, Steve Amstrup2, 

George Michael Durner2

, and Karen Oakley2

1

U.S. Geological Survey, Juneau, Alaska

2

U.S. Geological Survey, Anchorage, Alaska

Diminishing sea ice is one of the strongest and most 

visible signals of contemporary change in the Earth’s climate 

system. The dependence of polar bears (Ursus maritimus) on 

hunting at the sea ice surface raises concern about the implications of habitat loss on present and future polar bear populations worldwide. In this study, we used resource selection 

functions (RSFs) to document changes in polar bear habitat 

over the past two decades and to forecast future habitat conditions through the end of the century. RSFs are quantitative 

expressions of the habitats animals choose to use, relative to 

the habitats that are available to them. We constructed RSFs 

with location data for 1985 to1995 from 333 satellite radiotagged female polar bears, monthly passive-microwave sea 

ice concentration maps, and digital bathymetry and coastline 

maps. Satellite imagery documented the available habitat, and 

satellite telemetry revealed the choices bears made. We used 

discrete-choice modeling to distinguish between the available and chosen habitats based on six environmental covariates: ocean depth, distance to land, ice concentration, and 

distances to the 15%, 50% and 75% ice edges. We used the 

1985 to 1995 period to establish a baseline of preferred polar 

bear habitat selection criteria because during this early period 

of our study, year-round polar bear movements were less 

restricted and hence more likely to represent preferences than 

during the more recent years of reduced sea ice extent. The 

RSF models showed that polar bears have a strong preference 

for sea ice habitats that were near the periphery of the ice pack 

and over the shallow waters of the continental shelf. When 

extrapolated to the ice conditions of 1996 to 2006, the models 

revealed that preferred habitats had already started to decline, 

especially in spring and summer, and with greatest losses in 

the Barents, Chukchi, Greenland, and Southern Beaufort seas. 

To assess 21st century habitat changes, we extrapolated the 

RSF models to sea ice projections from 10 Intergovernmental 

Panel on Climate Change (IPCC) general circulation models 

(GCMs) forced with the “business as usual” Special Report on 

Emissions Scenarios (SRES-A1B) greenhouse gas emissions 

scenario. Results from the 21st century-based projections 

exacerbated the observed trends in habitat losses and added 

losses throughout all regions bordering Russia. Average annual 

habitat loss for the full Arctic basin was projected upwards of 

35% by the end of the century, with summer losses of nearly 

80% for the Alaska-Eurasia portions of the basin. In contrast, 

polar bear habitats were projected to be relatively stable 

during the 21st century in the high-latitude regions along the 

northwestern Canadian Archipelago and northern Greenland. 
14 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

These results are consistent with the general observation that 

most GCMs project modest ice declines in winter but very 

strong declines in summer, resulting in either ice-free summers 

or remnant summer ice at the northernmost latitudes of North 

America. 

HURASIM: Hurricane Simulation 

Model for Reconstructing Wind Fields 

and Landfall Frequencies of Historic 

Storms (Poster)

By Thomas W Doyle1 and Randy Westbrooks2

1

U.S. Geological Survey, Lafayette, La.

2

U.S. Geological Survey, Whiteville, N.C.

The Hurricane Simulation Model (HURASIM) is a 

spatial simulation model of hurricane structure and circulation 

for reconstructing chronologies of estimated windforce and 

vectors of past hurricanes. The model uses historical tracking and meteorological data of dated North Atlantic tropical 

storms from 1851 to the present. The model generates a matrix 

of storm characteristics (that is, direction, quadrant, and wind 

speed) within discrete spatial units and time intervals specified 

by the user for any specific storm or set of storms. HURASIM 

recreates the spatial structure of past hurricanes based on a 

tangential wind function, inflow angle offset, forward speed, 

and radius of maximum winds. Data input for the model 

includes tracking information of the storm’s position (latitude 

and longitude) every 6 hours or less and the maximum sustained wind speed. HURASIM model output from Hurricane 

Andrew (1992) was correlated with field data to construct data 

tables of damage probabilities by site and species and to determine critical wind speeds and vectors of tree mortality and 

injury. HURASIM has also been applied to reconstruct probable wind fields of past hurricanes for remote field locations 

and correlated with tree ring growth patterns and direction of 

leaning trees and downed logs. HURASIM is also used to construct landscape templates of past hurricane activity that are 

linked with landscape simulation models of coastal habitat for 

predicting the effects of climate past and future on the growth 

and succession of important wetland communities. 

Landscape Simulation Model for 

Invasive Species Spread and Detection 

(Poster)

By Thomas W. Doyle1, Randy Westbrooks2, and 

Ken Krauss1

1

U.S. Geological Survey, Lafayette, La.

2

U.S. Geological Survey, Whiteville, N.C.

A landscape simulation model of Federal lands across 

the southeastern United States has been developed to foster 

early detection of and rapid response to the translocation and 

movement of invasive species. The model documents known 

population loci of invasive plants and insects and predicts their 

potential spread by generational patterns and rates combined 

with probabilities for rapid relocation of viable progeny by 

tropical storms making landfall. The counterclockwise circulation of tropical storms and hurricanes in the northern hemisphere may hasten the western and northern spread of spores, 

seeds, and reproducing adults across the Gulf coastal plain. 

The landscape model works in concert with another physical 

model, the Hurricane Simulation Model (HURASIM), which 

tracks hurricane paths, historical and hypothetical, to investigate the role tropical storms may have on invasive spread. The 

ultimate goal is to monitor the paths of tropical storms across 

coastal areas where particular invaders are known to occur, 

and to simulate translocation across impacted inland areas. 

In this project, we modeled the potential spread of the South 

American Cactus Moth (Cactoblastis cactorum) by Hurricane Dennis in July 2005 from infested areas along the Gulf 

Coast of Alabama to inland areas where prickly pear cacti 

(Opuntia spp.—the only host of the Cactus Moth) are known 

to occur. With such a system, conservation land managers in 

impacted areas could be alerted soon after a storm event and 

thus be able to implement appropriate actions to prevent the 

establishment and spread of target species. In the future, the 

system will be expanded by adding distribution information 

on high profile invasive plants of concern, such as cogongrass 

(Imperata cylindrica) and old world climbing fern (Lygodium 

microphyllum). 

SELVA-MANGRO: Landscape Model for 

South Florida Mangrove Ecosystems 

(Poster) 

By Thomas W. Doyle1 and Ken Krauss1 

1

U.S. Geological Survey, Lafayette, La.

A landscape simulation model, SELVA-MANGRO, was 

developed for mangrove forests of south Florida to investigate the potential impacts of climate change on the quality 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 15

and distribution of mangrove habitat. The SELVA-MANGRO 

model represents a hierarchically integrated landscape model 

that manages the exchange of system parameters up, down, 

and across scale between linked simulation models SELVA 

and MANGRO. The Spatially Explicit Landscape Vegetation 

Analysis (SELVA) model tracks predicted changes in the 

biotic and abiotic conditions of each land unit (1 square 

hectare) on an annual basis for the entire simulated landscape. 

The SELVA model administers the spatial articulation of 

landscape units composed of habitat classifications (forest, 

marsh, and aquatic) and any forcing functions that predict 

changes in hurricane activity, sea-level rise, and freshwater runoff. Intertidal forest units are then simulated with the 

MANGRO model based on unique sets of environmental 

factors and forest history. MANGRO is an individual (agent)-

based, spatially explicit stand-simulation model constructed 

for mangrove forests of the neotropics. It is composed of 

a set of species-based functions for predicting the growth, 

establishment, and death of individual trees. MANGRO 

predicts the tree and gap replacement process of natural forest 

succession as influenced by stand structure and environmental 

conditions. SELVA-MANGRO has been used to evaluate the 

role of hurricane history and frequency on forest structure and 

composition and to predict mangrove migration upslope and 

displacement of freshwater habitats under rising sea levels 

projections from climate change. 

SLRRP: Sea-Level Rise Rectification 

Program and Seawater Inundation 

Model Under Climate Change (Poster) 

By Thomas W. Doyle1

1

U.S. Geological Survey, Lafayette, La.

The Sea-Level Rise Rectification Program (SLRRP) is 

a software program designed with a user-friendly interface to 

generate a suite of future sea-level projections from various global circulation models (GCM) and emission scenario 

options obtained from the Intergovernmental Panel on Climate

Change (IPCC) (2001). The SLRRP model allows the user to 

select a region-based tide station, GCM model, and emission 

scenario to generate a graph and output file of future sea-level 

change. Sea-level rise was modeled as a function of historic 

sea-level conditions at long-term tide stations based on mean 

monthly tide records projected into the 21st century with the 

addition of curvilinear rates of eustatic sea-level rise expected 

from climate change. The historical record was retained 

to mimic the natural cycle of high and low tidal variation 

attributed to astronomical and meteorological causes. The data

record was extended into the next 100 years with the addition of eustatic rates of sea-level rise based on IPCC (2001) 

low, mid, and high projections obtained from various global 

climate change models. Model simulations were achieved for 

each of seven climate change models and six emission scenarios included in the IPCC (2001) dataset. SLRRP rectifies 

the historic tide record and future eustatic sea-level rise into a 

common datum [default = North American Vertical Datum of 

1988 (NAVD88)] to facilitate comparison with landbase features and elevations. The SLRRP model generates a sea-level 

prediction by wrapping the historic mean monthly records 

for the period of record for all future years up to year 2100. A 

series of sequential popup windows is used to facilitate user 

selection of GCM models, scenarios, and manual entries for 

projecting future sea levels. The SLRRP model allows the user 

to manually enter a local subsidence rate and a eustatic rise 

by the year 2100 in lieu of model defaults. After selecting a 

GCM model and emission scenario, the user can specify the 

actual effects and components of the GCM results that include 

degree of glaciation and thermal expansion. The program 

gives the user options for saving graphical and digital formats 

of SLRRP predictions and generating a supplemental graph to 

visualize the timing and extent of yearly flooding potential for 

a given elevation (NAVD88). In effect, the model shows the 

prospective data and time period for which sea level will overtop a given landscape feature under a future changing climate. 

Flooding potential is the percentage of months within a year 

when there is inundation by seawater at a select land elevation 

determined by the user. 

Reference Cited

Intergovernmental Panel on Climate Change, 2001, 

Climate change 2001—The scientific basis—Contribution of 

Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change: Geneva, Switzerland, 

Intergovernmental Panel on Climate Change, 563 p. 

CONGFLO: Floodplain Inundation 

Model of Congaree National Park 

(Poster)

By Thomas W. Doyle1

1

U.S. Geological Survey, Lafayette, La.

A floodplain hydrology study of Congaree National Park 

was conducted to determine the flood relations of backswamp forests and park trails as influenced by river stage and 

floodplain elevation using an integrated field and modeling 

approach. Water-level gages were spread across the length and 

width of a 5-kilometer (km) × 16-km floodplain to capture the 

lag and peak flood relations to river stage and were monitored over a period of years. Historical water-level stage and 

discharge data from the Congaree River have been digitized 

from published sources and U.S. Geological Survey (USGS) 

archives to obtain long-term daily averages for an upstream 
16 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

gage at Columbia, S.C. Elevation surveys were completed for 

all park trails and for additional circuits of roads and boundaries. All elevation surveys were then processed and rectified 

into a geographic information system (GIS) and predictive 

flood inundation model. Regression models have been applied 

to establish time lags and stage relations between gages at 

Columbia, S.C., and in the upper, middle, and lower reaches 

of the river and backswamp within the park. Flood relations 

among backswamp gages show the retention and recession 

behavior between floodplain reaches with greater hydroperiod 

in the lower reaches than in the upper and middle reaches of 

the Congaree Swamp. A “flood alert” program has been developed to predict critical river stages and potential inundation of 

hiking trails. Development of the Congaree landscape simulation model has been upgraded with a three-dimensional (3-D) 

interactive user interface that is web accessible on a dedicated 

USGS data server. A flood inundation model has been developed to predict critical river stages and potential inundation of 

hiking trails on a real-time basis and 24-hour forecast. 

Conceptual and Rapid Prototype 

Models for Evaluating the Effects of 

Land-Use and Land-Cover Changes 

on Ecosystem Services in the Lower 

Mississippi Valley (LMV) (Oral 

Presentation) 

By Bogdan Chivoiu1, Stephen P. Faulkner2, 

Wylie Barrow2

, and Susan Walls3

1

ASci Corporation, Lafayette, La.

2

U.S. Geological Survey, Lafayette, La.

3

U.S. Geological Survey, Gainesville, Fla.

The landscape-scale alteration and resulting loss of 

hydrologic, biotic, and biogeochemical functions in the Lower 

Mississippi Valley (LMV) require an integrated, landscapescale restoration and monitoring approach to replace those 

lost functions. Large-scale efforts are under way to restore 

former riparian forest habitats on both public and private lands 

in the LMV with explicit goals to restore wildlife habitat, 

improve water quality, and increase flood storage and retention. However, the success of these restoration efforts in 

achieving these goals is not clear. In addition to field research, 

the use of conceptual and rapid prototype models (RPMs) 

can help identify the underlying natural processes that control 

ecosystem structure and function and the response to stressors 

and disturbance. These models will help provide a framework 

for developing appropriate performance standards, monitoring approaches, and decision support tools to guide resource 

management decisions. 

We are combining patch- and landscape-scale field 

research with modeling to quantify the ecological services of 

restored wetlands in the LMV. The interdisciplinary research 

team collected a variety of edaphic, vegetative, and morphologic characteristics on restored wetlands, active cropland, and 

native forested wetland sites in two LMV watersheds located 

in Arkansas and Louisiana. The species richness of migratory 

and breeding birds and amphibians was measured at each site. 

Various landscape variables were calculated at different scales 

from geographic information system layers. 

A frame-based RPM was selected to explore the relationships between site and landscape variables and the response 

variables (species richness and abundance). The RPM parameters were chosen based on expert-designed conceptual 

models, analysis of field data and existing literature. The RPM 

was designed using a spreadsheet application to simulate a 

10-kilometer (km) × 10-km land use map (landscape) surrounding a restored wetland. The land use classes are represented by different cell values and several landscapes are 

depicted on the same spreadsheet to illustrate the impact of 

different landscape patterns on the response variables. The 

response variable value is automatically updated as the input 

variables are changed to reflect spatial and temporal effects. 

The temporal change at the site level could be a change in 

forest structure representing a mature native-forested wetland. 

Changes in the size or number of restored wetland patches, or 

the connecting of restored wetland patches, reflect the spatial 

changes in the landscape at discrete steps in time. 

The RPM approach has several advantages. The simple 

structure of the RPM leads to fast model development and 

allows for testing of various scenarios. Sensitivity analysis 

helps identify the variables that should be targeted by monitoring. New variables from monitoring can be added later to 

refine the model or to develop it into a more complex type of 

model. The design transparency and simplicity also make the 

RPM an effective way of presenting the modeled processes 

and their outcomes to various stakeholders. We will discuss 

the results of the model runs under various management and 

land use alternatives and the applicability of this approach to 

integrated landscape science and monitoring. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 17

Implementation of a Bayesian 

Geostatistical Parameter Estimation 

Module into PEST (Poster)

By Michael Fienen1, Randy Hunt1, John Doherty2, 

Tom Clemo3

, and Wolfgang Nowak4

1

U.S. Geological Survey, Middleton, Wis.

2 Watermark Numerical Computing, Brisbane, Queensland, Australia

3

Center for Geophysical Investigation of the Shallow Subsurface, Boise 

State University

4

 University of California Berkeley, Calif.

For this poster, researchers have approached parameter 

estimation for environmental models from several perspectives. Reducing problem complexity such that the problem 

is overdetermined—more data than parameters—allows for 

traditional regression methods, such as computer code for 

universal inverse modeling (UCODE) and Parameter Estimation code (PEST), to perform the estimation. In many cases, it 

is beneficial to allow a problem to remain underdetermined—

more parameters than data—and regularization techniques, 

such as Tikhonov and pilot points are applied to make the 

inverse parameter estimation problem tractable. Regularization 

allows more freedom for the parameter field to be explored 

and is less tied to initial decisions on simplification that are 

required up front by the user. PEST is the most commonly 

used tool available for general regularized inversion. 

What if one could look at the most probable parameter set 

in addition to the “best fit” parameter set? The Bayesian geostatistical parameter estimation method is one such method of 

regularized inversion that allows this type of analysis through 

probability theory. When properly applied, this technique provides the most likely and minimum variance set of parameters 

given the data and conceptual model. Posterior covariance—a 

measure of uncertainty in the estimated parameter field—can 

also be calculated. A major roadblock to the widespread application of Bayesian parameter estimation has been the lack of a 

practical tool that is readily available to the modeling community. The focus of this work is to incorporate the Bayesian 

approach into PEST in the form of a module. In this way, users 

may learn the general framework of PEST and perform Bayesian inversion as an extension of the currently available tools 

in PEST rather than needing to learn a completely separate 

software package. 

While PEST and the Bayesian extension are modelindependent, a coupled ground-water flow and transport model 

[U.S. Geological Survey Modular Ground-Water Flow Model 

(MODFLOW) and Modular Three-Dimensional Transport 

Model (MT3D)] for the Trout Lake Water, Energy and Biogeochemical Budgets (WEBB) project in northern Wisconsin 

served as both motivation and proving ground for development of this module. The cross-sectional model simulates flow 

and transport across an isthmus consisting of sandy glacial 

sediments enveloping a laterally continuous thin layer of silt (a 

pattern typical of ice-block lake formation in continental glaciated terrain). Head and head-difference data were augmented 

with 18O/16O isotope transport data to distinguish provenance 

between lake water and terrestrial recharge. Tritium isotope 

data provide time-of-travel and residence-time information. 

Underdetermined problems are most always characterized by a very large number of parameters; for example, this 

problem estimates about 2,400 parameters based on about 120 

observations. In the specific case of MODFLOW and MT3D, 

adjoint state sensitivity matrix calculations are critical for 

reducing the massive computational time burdens for calculation of sensitivity that is needed to perform the parameter 

estimation. Other computational challenges related to prior 

covariance and algorithmic stability are also important to consider, and their implementation is discussed. 

Joint Inverse Modeling to Estimate 

Flood-Flow Depths and Their 

Uncertainty in Ungaged Coastal 

Basins, El Salvador, Central America 

(Poster)

By Michael J. Friedel1

1

U.S. Geological Survey, Lakewood, Colo.

This poster presents a regionalization procedure that 

was used to predict probable flooding in four ungaged coastal 

river basins of El Salvador, including Goascoran, Grande de 

San Miguel, Jiboa, and Paz. The flood-prediction problem 

is sequentially solved for two regions: upstream mountains 

and downstream alluvial plains. In the upstream mountains, 

a set of rainfall-runoff parameter values and recurrent peakflow discharge hydrographs are simultaneously estimated 

for 20 tributary-basin models. Application of dissimilarity 

equations among tributary basins (soft prior information) 

permitted development of a parsimonious parameter structure constrained by measurement information in the recurrent 

peak-flow discharge values derived using regression equations based on measurements recorded outside the ungaged 

study basins. The estimated joint set of parameter values gave 

estimates of probable minimum and maximum peak-flow 

discharge; limits were then estimated, revealing that prediction uncertainty increases with basin size. In the downstream 

alluvial plain, model application of the estimated minimum 

and maximum peak-flow hydrographs facilitated simulation of 

probable 100-year flood-flow depths in confined canyons and 

across unconfined coastal alluvial plains. The regionalization 

procedure provides a tool for hydrologic risk assessment and 

flood protection planning that can be applied elsewhere. 
18 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Joint Inverse Modeling to Estimate 

Extreme Rainfall Events and Their 

Uncertainty in Ungaged Coastal 

Basins, El Salvador, Central America 

(Poster) 

By Michael J. Friedel1

1

U.S. Geological Survey, Lakewood, Colo.

This poster presents a regularized joint inverse procedure that was used to estimate the magnitude of extreme 

rainfall events in ungaged coastal river basins of El Salvador, 

including, Goascoran, Grande de San Miguel, Jiboa, and Paz. 

Because streamflow measurements reflect temporal and spatial

rainfall information, peak-flow discharge is hypothesized to 

represent a similarity measure suitable for regionalization. To 

test this hypothesis, peak-flow discharge values determined 

from streamflow recurrence information (10-year, 25-year, 

and 100-year) collected outside the study basins were used to 

develop country-wide regression equations. Peak-flow discharge derived from these equations, together with preferred 

spatial parameter relations as soft prior information, were 

used to constrain the simultaneous calibration of 20 tributary 

basin models. The nonlinear range of uncertainty in estimated 

parameter values (1 curve number and 3 recurrent rainfall 

amounts for each model) was determined using an inverse 

calibration-constrained Monte Carlo approach. Cumulative 

probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase 

in rainfall magnitude and range among basins with increasing 

return interval. The estimated median rainfall amounts for 

all return periods were reasonable but larger (by 3.2 percent 

to 26 percent) than rainfall estimates computed using the 

frequency-duration (traditional) approach and individual rain 

gage data. The observed 25-year recurrence rainfall amount at 

La Hachadura in the Paz River basin during Hurricane Mitch 

(1998) is close to the estimated rainfall confidence limits. The 

similarity between joint inverse and traditionally computed 

rainfall events, however, suggests that the rainfall observation 

may likely be the result of under-catch and not model bias. 

Application of Stochastic Modeling to 

Forecast Likely Urbanization Effects 

on Ecological Integrity in the Upper 

Illinois River Basin (Poster) 

By Michael J. Friedel1

1

U.S. Geological Survey, Lakewood, Colo.

 This poster presents the results of a study that was undertaken to predict the probable effects of future urbanization 

on the ecological integrity of the Upper Illinois River Basin 

(Chicago area). Biotic indices and sediment trace-element 

concentrations for 43 streams, determined by Illinois State 

agencies and as part of the U.S. Geological Survey’s National 

Water Quality Assessment program, were examined along an 

agricultural-to-urban land use gradient. Relations among biotic 

integrity, sediment chemistry, and urbanization were derived 

from annual samples collected from 1982 through 1993. 

Because these annual samples were from different tributary 

basins with different urban percentages and geologic settings, 

the trends along the gradient suggest the absence of bias. Analytical equations were fit to bivariate relations, and probability 

density functions were fit to residuals for use with the Monte 

Carlo technique so that stochastic modeling could be performed. Stability of stochastic modeling required 1,500 Monte 

Carlo trials; reliability of stochastic modeling was evaluated 

by comparing statistical summaries of measured to simulated 

biotic indices, and future predictions were validated against an 

independent Alternate Index of Biotic Integrity (AIBI) score 

for Long Run Creek. Stochastic modeling of future urbanization-induced changes in ecological integrity for basins (Big 

Rock Creek, Des Plaines River, Mill Creek, and Flag Creek) 

along an urban gradient (1990 percent urban land use of 1, 5, 

10, and 87 percent, respectively) resulted in a broad range of 

probable biotic resource quality (excellent to very poor). Predictors used to simulate changes in basin ecological integrity 

from 1990 to 2000 and from 2000 to 2010 included fish and 

invertebrate biotic indices and streambed sediment nickel 

concentration. These predictors suggest that the degradation of 

ecological integrity in tributary basins occurred at differential 

rates and with a probable distribution of likely outcomes. For 

example, the AIBI median predictions of ecological integrity 

from 1990 and 2010 was two quality classes (good to poor) 

in the Big Rock Creek and Des Plaines River tributary basins, 

and one quality class (poor to very poor) in the Flag Creek and 

Mill Creek tributary basins. A scale was devised for converting Marcoinvertebrate Index scores to biotic resource quality 

classes for interchanging results with AIBI scores. This calibrated scale is likely to be useful in more urbanized streams 

where it is not always possible to compute AIBI scores, and 

for comparison between biotic indices in other studies. Bed 

sediment nickel concentration was a useful predictor of ecological integrity and percent urban land use (and population 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 19

density) in the basin. Because the time and costs for determining nickel concentrations are much less than for determining 

biotic integrity scores, future studies could use this scale or 

other correlated variables as predictors. 

Stochastic Modeling to Forecast Likely 

Effects of a Proposed Reservoir on 

Colorado River Quantity and Quality 

Near Grand Junction, Colorado (Poster)

By Michael J. Friedel1

1

U.S. Geological Survey, Lakewood, Colo.

A proposed 16,000 acre-foot reservoir would be located 

about 25 miles east of Grand Junction, Colo., on a tributary 

of the Colorado River that drains into the Sulphur Gulch 

watershed between De Beque and Cameo, Colorado. The 

proposed Sulphur Gulch Reservoir, which would be filled 

by pumping water from the Colorado River, is intended to 

provide the Colorado River with at least 5,412.5 acre-feet of 

water during low-flow conditions to meet the East Slope’s 

portion of the 10,825 acre-feet of water required under the 

December 20, 1999, Final Programmatic Biological Opinion 

for the Upper Colorado River. The reservoir also may provide 

additional water in the low-flow period and as much as 10,000 

acre-feet of water to supplement peak flows when flows in the 

Colorado River are between 12,900 and 26,600 cubic feet per 

second. For this study, an annual stochastic mixing model with 

a daily time step and 1,500 Monte Carlo trials was used to 

evaluate the probable effect that reservoir operations may have 

on water quality in the Colorado River at the Government 

Highline Canal and the Grand Valley Irrigation Canal. 

Simulations of the divertible flow (ambient background 

streamflow), which takes into account demands of downstream water rights, indicate that divertible flow will range 

from 621,860 acre-feet of water in the driest year to 4,822,732 

acre-feet of water in the wettest year. Because of pumping 

limitations, pumpable flow (the amount of streamflow available after considering divertible flow and subsequent pumping constraints) will be less than divertible flow. Assuming a 

pumping capacity of 150 cubic feet per second and year-round 

pumping, except during reservoir release periods, the simulations indicate that there is sufficient streamflow to fill a 16,000 

acre-feet reservoir 100 percent of the time. Simulated pumpable flows in the driest year are 91,669 acre-feet and in the 

wettest year are 109,500 acre-feet. Simulations of carryover 

storage together with year-round pumping indicate that there 

will be sufficient pumpable flow available to refill the reservoir to capacity each year following peak-flow releases of as 

much as 10,000 acre-feet and low-flow releases of 5,412.5 

acre-feet of water. 

Simulations also indicate that peak-flow release conditions (flows of between 12,900 and 26,600 cubic feet per 

second) would occur in about 50 percent of the years to allow 

release of 10,000 acre-feet of stored water in the spring. Under 

typical (5 of 10 years) to moderately dry (3 of 10 years) hydrologic conditions, the duration of the peak-flow conditions will 

not allow the full 10,000 acre-feet to be released from storage 

to supplement peak flows. During moderate to extremely dry 

(2 of 10 years) hydrologic conditions, the peak-flow release 

conditions will not exist, and there will be no opportunity to 

release water from storage to supplement peak flows. 

In general, the simulated daily background dissolved-solids concentrations (salinity) at the Government Highline Canal 

increase because of the reservoir releases as annual hydrologic 

conditions go from wet to dry. The simulated median concentrations during the low-flow period range from 417 milligrams 

per liter (wet year) to 723 milligrams per liter (dry year), 

whereas the simulated median concentrations observed during the peak-flow period range from 114 milligrams per liter 

(wet year) to 698 milligrams per liter (dry year). Background 

concentration values at the Grand Valley Irrigation Canal are 

generally only a few percent less than those at the Government 

Highline Canal, except during dry years.

Low-flow reservoir releases of 5,412.5 acre-feet and 

10,825 acre-feet were simulated for a 30-day period in September, and low-flow releases of 5,412.5 acre-feet were simulated for a 78-day period in August through October. In general, these low-flow reservoir releases resulted in changes to 

salinity concentrations ranging from slight decreases to slight 

increases in dissolved-solids concentrations over the range of 

hydrologic conditions simulated. Low-flow releases of 5,412.5 

acre-feet of water over the 78-day period resulted in increases 

in salinity greater than the salinity measurement error for 

fewer than 10 percent of the driest years simulated. Low-flow 

releases of 5,412.5 acre-feet of water over the 30-day period 

coupled with peak-flow releases of as much as 10,000 acrefeet of water also resulted in increases in salinity greater than 

the salinity measurement error in fewer than 10 percent of the 

driest years simulated. Observed trends in stream dissolvedsolids concentrations at the Grand Valley Irrigation Canal are 

similar to those observed at the Government Highline Canal; 

however, the magnitude of the percent change and the absolute 

change is less, except under very dry hydrologic conditions. 

Understanding instream changes in selenium concentration following reservoir releases also are of concern because 

selenium can be toxic to fish and other biota. Instream 

selenium concentrations are an order of magnitude greater in 

tributary creeks like Sulphur Gulch (1 to 25 micrograms per 

liter) than in the Colorado River (0.3 to 0.7 microgram per 

liter). Simulations indicate that random sampling may result 

in a 1 percent and 35 percent chance, respectively, of exceeding Colorado instream acute (18.4 micrograms per liter) and 

chronic (4.6 micrograms per liter) water quality standards in 

Sulphur Gulch runoff. The lack of selenium in water pumped 

from the Colorado River to storage likely will result in diluting reservoir concentrations to a range of 0.37 micrograms 
20 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

per liter (wet conditions) to 1.48 micrograms per liter (dry 

conditions). Therefore, based on the simulations and inherent assumptions, selenium concentrations in the proposed 

reservoir are expected to be less than the acute and chronic 

standards. 

CASCaDE: Computational Assessments 

of Scenarios of Change for the San 

Francisco Bay-Delta Ecosystem (Oral 

Presentation) 

By Neil K. Ganju1, Lisa V. Lucas2, James E. Cloern2, 

Noah Knowles2

, Michael Dettinger3, Dan Cayan3, and 

David Schoellhamer1

1

U.S. Geological Survey, Sacramento, Calif.

2

U.S. Geological Survey, Menlo Park, Calif.

3

U.S. Geological Survey, La Jolla, Calif.

Design of this modeling study is built on the following 

hypotheses: (1) California’s hydrology will change during the 

21st century in response to global warming; (2) water management and ecosystem structure and function will respond to 

changes in California’s water supply, land use, sea level, and 

efforts to restore many ecosystem services; (3) information 

is available to project plausible examples of change in each 

of these forcings; (4) climatic, hydrologic, hydrodynamic, 

water quality, geomorphic, and ecosystem processes are linked 

in the San Francisco Bay-Delta River Watershed (BDRW) 

system, and thus models to project future conditions must also 

be linked; and (5) strategic planning by ecosystem managers 

will benefit from mechanistic, ecosystem-scale projections of 

future forcings and responses, posed as plausible scenarios of 

system change. We are in the process of linking output from 

climate, streamflow, estuarine, geomorphic, and ecosystem 

models to generate scenarios of ecological change for the 

BDRW. Global climate models are run under selected scenarios of future greenhouse gas emissions, and resulting precipitation and temperature projections are downscaled for use in 

the hydrologic model, which provides input for the estuarine 

models. The estuarine models simulate transport of water, 

salt, and suspended sediment for use in projecting changes in 

geomorphology and water quality within the estuarine regions 

of the BDRW. Throughout this interaction of physical models, 

ecological models of phytoplankton, benthic organisms, and 

fish utilize physical model output, such as water temperature, 

salinity, light attenuation, and depth. The final outcome is a 

range of scenarios of ecosystem function under specified scenarios of future climate. Each model functions independently; 

output from a “producing” model is translated into an appropriate input that can be used by the “receiving” model in the 

necessary format. We have completed a preliminary example 

of model interaction, demonstrating a “cascade” from climate 

change to estuarine geomorphology to primary productivity. 

One scenario of precipitation and warming is passed through 

the hydrologic model to generate altered conditions for the 

year 2030 (in terms of streamflow timing and magnitude), 

which then cascades to an estuarine geomorphic model. This 

model is also provided with an estimate of sea-level rise and 

watershed sediment supply in the year 2030. The model calculates changes in sediment transport and bathymetry, yielding 

future estimates of turbidity and depth. Overall, the estuary 

becomes deeper and less turbid as sea-level rises and sediment 

supply continue to decrease. The effects of these changes on 

phytoplankton biomass and productivity are estimated by a 

model of phytoplankton growth and loss under the prescribed 

scenario. The structure of this modeling endeavor, in terms of 

linking models and output, is suited for interdisciplinary studies, where spatial and temporal scales may differ substantially. 

Modeling the Hydrodynamic and 

Morphologic Response of an Estuary 

Restoration (Oral Presentation)

By Guy Gelfenbaum1, Curtis Tanner2, and 

Doug George1

1

U.S. Geological Survey, Menlo Park, Calif.

2

Washington Department of Fish and Wildlife, Olympia, Wash.

Fifty years of sediment infilling of Capitol Lake, 

Olympia, Wash., which is an artificial lake created by a tide 

gate across an estuary mouth, prompted researchers for the 

Deschutes Estuary Feasibility Study (DEFS) to evaluate 

the possibility of a restored estuary as an alternative to lake 

management. The researchers are is investigating removing 

the dam and allowing tidal processes to return to the historic 

predam basin. Numerical modeling of the hydrodynamics and 

morphological evolution of four restored estuary scenarios 

provides predictions of physical habitat, scour potential, and 

sediment accumulation rates to the other components of this 

multidisciplinary study, which also encompasses predictions 

of biological colonization, assessment of threats to existing 

infrastructure, and appraisal of how restoration would affect 

the surrounding communities. A morphodynamic model, 

Delft3D, is used to predict the flow, wetting and drying, salinity regime, sediment transport, and morphological change that 

would occur under several restoration scenarios. Hydrodynamics were modeled in three dimensions to explore the extent 

of estuarine circulation processes, and sediment transport and 

morphological change were modeled in two dimensions. The 

development of long-term (10-year) morphological simulations of the restored estuary required simplifying several data 

inputs and implementing a time-scale acceleration technique. 

A river sediment discharge climatology was developed to 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 21

characterize the Deschutes River sediment discharge to 

the estuary and a representative tide was selected from the 

4-meter (m) semidiurnal tidal range. To reduce the computational time of the simulations, a variable morphological factor 

was employed to scale hydrodynamic time up to the longer 

morphological time scales. Four sediment classes (2μm, 

31μm, 200μm and 2,000μm) were modeled to represent the 

observed mixed bed and fluvial sediment. A range of erosion 

parameters obtained from the literature was utilized for the 

mud fractions because no field data were collected to parameterize the erodibility. 

Model simulations reveal that the changing estuary morphology would have a strong impact on the physical 

habitat (bathymetry, substrate, inundation frequency, mean 

salinity, salinity range, and circulation) causing it to evolve 

over time. According to the model, the estuary would transition through multiple phases and approach a dynamic equilibrium within several years of restored tidal processes. The 

evolved estuary showed many bathymetric similarities with 

the predam estuary, including a deep central channel and 

broad mud flats. The rate of erosion of the channel and amount 

of sediment deposited near the port and marina, which are 

located seaward of the current tide gate, depend on the values 

used for the mud erodibility parameters. The uncertainty in 

erodibility of mud introduced more variability in the results 

for the restored estuary than any of the alternate restoration 

scenarios. 

Addressing Among-Group Variation 

in Covariate Effects Using Multilevel 

Modeling Approaches With Emphasis 

on Multilevel Count Models (Poster) 

By Brian R. Gray1, Jim Rogala1, and Roger Haro2

1

U.S. Geological Survey, La Crosse, Wis.

2

University of Wisconsin, La Crosse, Wis.

Multilevel or hierarchical models are used to analyze data 

that may be hierarchically structured in space, time, or both. 

Such structure may arise because of natural processes or by 

human design. For example, multiple observations from each 

of multiple lakes are nested within groups (lakes)—whether 

by design or otherwise. An advantage of using multilevel 

models with hierarchically structured data is that they offer 

insights into whether covariate effects vary by group (for 

example, by lake), by scale (for example, whether effects 

arise from sampling- or lake-level processes), or both. In 

this poster, we illustrate inferences on variation in covariate effects using counts of mayfly larvae with a continuous 

covariate water depth. Counts were modeled under negative 

binomial (NB) and zero-modified NB distributional assumptions. The estimated number of covariate associations varied 

substantially across groups under both NB and zero-modified 

NB models. The effects of acknowledging this among-group 

variation were (1) wider confidence interval estimates on the 

mean covariate association (intervals increased in width by 

35% to 55%) and (2) the implication that covariate associations varied substantially across groups (a 95% credible 

interval on depth associations from the zero-modified NB 

model included decreases of up to 27% and increases of up to 

224%). The latter inference has important general implications 

for study design: When the study includes inferences that span 

groups and where covariate effects may vary among those 

groups, the study design should address numbers of groups 

and numbers of samples within groups. Regarding questions 

of variation by scale, the mayfly-depth covariate association 

would not have been inferred had the covariate been modeled as its mean (that is, by the annual sample means of the 

covariate). This finding illustrates that the common practice of 

substituting group means for measured covariate values may 

yield incorrect inferences in the presence of nonlinearities, 

unmodeled interactions, or contextual effects (that is, effects 

that arise at the group scale). Study inferences were derived by 

collaboration among a physical scientist and a statistician from 

the U.S Geological Survey, and a macroinvertebrate ecologist 

from the University of Wisconsin-La Crosse. 

Spatial and Age-Structured Population 

Model of the American Crocodile for 

Comparisons of CERP Restoration 

Alternatives (Poster) 

By Timothy Green1

1

U.S. Geological Survey, Gainesville, Fla. 

 The Comprehensive Everglades Restoration Plan 

(CERP) has been described as the world’s largest ecosystem 

restoration effort. Interim goals designed to measure progress 

toward ecological benefits have been set by the Restoration 

Coordination and Verification (RECOVER) component of 

CERP. A list of indicators was created by the RECOVER team 

to help determine the success of interim goals in achieving 

restoration of the natural system. One of these indicators is the 

American crocodile (Crocodylus acutus).

 The American crocodile has been identified as having 

the potential to provide a quantifiable measure of restoration 

success. The crocodile is considered to be a useful indicator of the ecological condition of mangrove estuaries under 

the CERP owing to its reliance upon estuarine environments 

characterized by appropriate salinity regimes and freshwater 

inflow. Several laboratory and field studies have reported a 

negative relationship between salinity and the growth rate 

in hatchling and juvenile crocodiles in Florida. Increased 

growth rates are hypothesized to result in increased survival 
22 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

rates of hatchling crocodiles by reducing their vulnerability to 

predators. Therefore, CERP plans to take measures to reduce 

the salinity in habitats occupied by crocodiles in Everglades 

National Park and Florida Bay. Reduced salinity is expected to

enhance hatchling and juvenile survival and growth, stimulate 

food production, and reduce dispersal distance.

 To more fully understand the long-term impacts of various CERP restoration alternatives, a spatial and age-structured 

population model for the American crocodile is being created. 

This model will be based on code from the recently developed 

landscape-level Alligator Population Model. The crocodile 

model will couple local age-structured models into a spatial 

dispersal model that incorporates crocodile movement behavior using two-dimensional (2-D) spatial convolution. Spatial 

parameter maps from the Florida Everglades that reflect salinity and water levels and the crocodile habitat suitability index 

will be the driving functions of the spatially structured model. 

State variables and drivers will include hatchling growth and 

survival, forage production, forage availability (at times of 

both high and low water levels), and proximity to known nesting locations.

 The model will be integrated with the U.S. Geological 

Survey Southern Inland and Coastal System (SICS) salinity 

and hydrology models. Daily salinity and hydrology model 

output will be integrated into the crocodile population model 

to produce monthly crocodile maps that show changes in population densities across the landscape in response to various 

restoration alternatives. As an ecosystem-level hydrologically 

based tool, this model will also be useful for predicting crocodile population shifts in response to possible increases in water

levels and changing salinity as a result of climate change. 

The Future: Data-Driven Lake and 

Reservoir Monitoring Using Real-Time 

3-D Hydrodynamic and Water-Quality 

Simulations (Oral Presentation) 

By William R. Green1

1

U.S. Geological Survey, Little Rock, Ark.

Recent advances in sensor technology and data telemetry 

allow a range of surface meteorological and vertical watercolumn data to be collected simultaneously, in real time, for 

lakes and reservoirs. With recent advancements in computer 

technology, three-dimensional lake and reservoir models can 

be run in much shorter timeframes, allowing for real-time 

simulations of hydrodynamics and water quality. Together, 

these advances allow for the development of quasi-real-time 

decision-support systems for water quality management 

of individual lakes and reservoir systems. Using real-time 

instrumentation, the models can “learn” from the data and 

continuously check their predictive capabilities. Real-time 

model simulations will provide necessary information for 

data-driven monitoring schemes designed to examine current 

biological, chemical, and physical conditions that impair the 

water quality of a lake or reservoir, such as algal blooms. For 

example, real-time model simulations and the resultant animations of algal patch development (to include functional groups, 

such as nitrogen-fixing cyanobacteria or even such species as 

Microcystis aeruginosa)—which may be responsible for taste 

and odor or toxin problems in drinking water—will provide 

up-to-date information that can be used by monitoring teams 

to cost-effectively target data collection to specific locations in 

the lake or reservoir and to collect data throughout the growth 

phase and subsequent crash of the algal population. Until 

recently, recognition of an algal bloom in a lake or reservoir 

did not happen until after the bloom peaked or crashed, and 

then it is too late to collect information about the conditions 

that propagated the bloom. Understanding the processes that 

lead to an algal bloom and water quality impairment will aid 

in the design of in-lake or landscape engineering or management solutions to reduce or eliminate future impairments. 

Examples of applications of the recent technology (using 

idealized conditions) include two reservoir systems: Beaver 

Lake, which is an impounded mountain valley reservoir in the 

Ozarks of northwestern Arkansas, and Lake Houston, which 

is an impounded flood plain reservoir located near the Gulf 

Coast of Texas. 

Using Such Web Services as 

StreamStats to Increase Your Modeling 

Productivity (Poster)

By John D. Guthrie1

1

U.S. Geological Survey, Denver, Colo. 

StreamStats is a web-based tool (available at 

http://streamstats.usgs.gov) that provides streamflow 

statistics, drainage-basin characteristics, and other information for U.S. Geological Survey streamgaging stations and 

for user-selected ungaged sites on streams. When users select 

the location of a streamgaging station, StreamStats provides 

previously published information from a database. When users 

select a site on an ungaged stream, StreamStats determines the 

drainage-basin boundary for the site, computes a variety of 

drainage-basin characteristics, and solves regression equations 

to estimate streamflow statistics for the site. The information provided by StreamStats is used by engineers, land and 

water-resource managers, biologists, and many others to help 

guide decisions in their everyday work. 

Some functions accessible through the StreamStats user 

interface are also available as web services, meaning that other 

remote computer applications can initiate a request for a particular function over the Internet, and StreamStats can perform 

that function and deliver the result back to the remote user. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 23

The use of such web services promotes collaboration among 

modelers and allows them to take advantage of work that has 

been previously done. 

Using a Coupled EcosystemGeochemical Model to Assess 

Ecological, Biogeochemical, and 

Hydrological Processes and Their 

Responses to Atmospheric Deposition 

and Climate Change for Diverse 

Ecosystems Within the United States 

(Oral Presentation)

By Melannie D. Hartman1, and Jill S. Baron2

1

Colorado State University, Fort Collins, Colo.

2

U.S. Geological Survey, Fort Collins, Colo.

Ecological and hydrochemical responses to atmospheric 

deposition and climate change are of interest to regulatory and 

land management agencies. Models can be excellent heuristic 

tools that can reveal what is understood and not understood 

about how ecosystems function. As investigative tools, models 

are also useful for solving the inverse problem of describing 

the processes and parameters that account for empirical evidence. This may be particularly useful where there are limited 

field measurements, experimental studies, or spatial coverage. Additionally, because such long-term events as gradual 

warming and changes in atmospheric deposition occur over 

timescales not measurable within the duration of most field 

experiments, models can be used to create myraid scenarios to 

examine the potential range of dynamic ecosystem responses. 

DayCent-Chem is a nonspatial, daily time step, ecosystem, and geochemical model for investigating and predicting 

how ecosystems respond to atmospheric deposition, climate 

change, and extreme events. The model links two widely used 

and tested models, one of daily biogeochemistry, DayCent, 

and the other of soil and water geochemical equilibrium, 

PHREEQC. DayCent-Chem predicts plant growth, soil 

organic matter and nutrient cycling, hydrologic fluxes, stream 

and soil water chemistry, cation exchange, and sulfate adsorption. The model can simulate short-term events, such as episodic acidification, as well as long-term ecosystem dynamics. 

With collaborators among government agencies and 

other research institutions, we modeled seven small watersheds—three at national parks and four at long-term ecological research (LTER) sites. The sites were selected to represent 

a range of ecosystem types, climates, and nitrogen (N) and 

sulphur (S) deposition rates. The sites included a temperate 

deciduous forest (Coweeta), two maritime coniferous forests 

(Acadia and HJ Andrews), a northern hardwood mixed forest 

(Hubbard Brook), a temperate coniferous forest (Great Smoky 

Mountains), a subalpine forest (Mount Rainier), and an alpine 

tundra (Niwot Ridge). 

We explored how these ecosystems process both nutrients 

and pollutants under current and potential future climates. 

Model performance was evaluated by comparing model results 

to multiple types of observations at each site. We describe 

what we learned about the sites, including the relative importance of abiotic and biologic controls at each location, and we 

describe what we learned about the model by contrasting its 

performance between sites. The gaps in empirical data that we 

determined would be helpful to understanding processes that 

regulate stream chemistry at each site include mineral denudation rates; time series exchangeable cation measurements; and 

dry and fog deposition, particularly for base cations. 

Our modeling exercises lay the foundation for future 

work. With the model calibrated for this diverse set of ecosystems, we plan to use it to predict ecosystem response, including carbon storage, trace gas emissions, and stream chemistry, 

to scenarios of future potential climate and atmospheric 

deposition. 

Using GIS/Remote Sensing Models to 

Quantify and Monitor Southwestern 

Willow Flycatcher Breeding Habitat 

(Poster) 

By James R. Hatten1, Eben H. Paxton2, and 

Mark Sogge2

1

U.S. Geological Survey, Cook, Wash.

2

U.S. Geological Survey, Flagstaff, Ariz.

The Southwestern Willow Flycatcher (SWFL) (Empidonax traillii extimus) is an endangered migratory passerine 

that breeds exclusively in riparian habitats scattered throughout portions of the southwestern United States. The riparian 

habitat that SWFLs depend on is constantly shifting across 

the landscape, making the determination and protection of 

such habitats difficult. To construct a set of predictive models 

of the SWFL’s breeding habitat, a team of interdisciplinary 

scientists compared vegetation and floodplain characteristics 

surrounding breeding and nonuse locations at multiple spatial 

and temporal scales. Our study site, Roosevelt Lake, Ariz., 

contains one of the largest known SWFL breeding populations. Extensive survey efforts have been conducted throughout the breeding season to detect all territorial flycatchers at 

Roosevelt Lake between 1995 and 2004. The breeding habitat 

at Roosevelt Lake consists of a heterogeneous mosaic of 

discrete riparian forest patches composed of native, exotic, or 

mixed tree species. The spatial variables we examined were 

extracted from Landsat Thematic Mapper (TM) imagery and 

digital elevation models, plus some geographic information 
24 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

system (GIS)-derived proximity variables. In addition to 

spatial variables that were created from a single year of 

vegetation data, we created a temporal class of variables that 

characterized the variability, stability, and changes in predicted 

flycatcher breeding habitat over a range of years. We accomplished this by running the spatial models in successive years 

and populating them with TM imagery data specific to each 

year modeled. The temporal variables allowed us to examine 

the influence of habitat stability and quality on breeding site 

selection over a range of years. We examined more than one 

dozen spatial and spatial-temporal models. Our best model 

was spatial-temporal, containing two spatial variables that 

quantified the density of vegetation at the site (30-meter cell) 

and the variability in vegetation density within a 120-meter 

radius, plus two temporal variables that characterized the 

stability and variability of habitat over a 5-year period. Our 

models explained up to 68% of the variability in territory 

occurrence in a given year and up to 82% of the fluctuation in 

territory numbers between 1995 and 2004. Dense, vigorous 

riparian vegetation is the most critical feature of SWFL breeding habitat, and land use practices need to be carefully monitored to maximize this valuable resource. Our models indicate 

that SWFL habitat can form in less than 5 years and that 

reservoir management at Roosevelt Lake appears beneficial to 

SWFL habitat formation during prolonged drawdown periods. 

We expect our GIS remote sensing models to be applicable to 

a broad range of riparian obligate species and to be an effective monitoring tool for the health of riparian ecosystems. 

Hydrologic Effects of Climate Change 

in the Yukon River Basin (Oral 

Presentation)

By Lauren E. Hay1 and Gregory J. McCabe1

1

U.S. Geological Survey, Denver, Colo.

A monthly water balance (WB) model was developed 

for the Yukon River basin (Pilot Station in Alaska, with a 

drainage area of 831,390 square kilometers). The WB model 

was calibrated using monthly precipitation and temperature 

climatology inputs derived from the Parameter-Elevation 

Regressions on Independent Slopes Model (PRISM) model 

of the Yukon. Potential hydrologic effects of climate change 

were assessed for the basin by imposing changes in precipitation and temperature derived from Intergovernmental Panel 

on Climate Change (IPCC) climate simulations. Two scenarios from the IPCC were chosen that represent a range of 

future possible emissions of greenhouse gases. The range in 

outcomes shows a large amount of uncertainty in projected 

future hydroclimate in the Yukon basin. 

Evaluation of Environmental Flow 

Components Produced Using a 

Distributed Watershed Model of the 

Flint River Basin, Georgia (Poster) 

By Lauren E. Hay1

1

U.S. Geological Survey, Denver, Colo. 

Environmental flow components (EFCs) are components 

of daily flow that have been identified as important to river 

ecosystem health in a large range of hydro-climatic regions. 

The EFCs are classified as extreme low flows, low flows, 

high-flow pulses, small floods, and large floods. This poster 

presents a multiple-objective, step-wise, automated procedure for hydrologic model calibration using the EFCs. This 

procedure includes the sequential calibration of a model’s 

simulation of solar radiation and potential evapotranspiration 

followed by the annual water balance and daily runoff, evaluated by EFCs. The Shuffled Complex Evolution global search 

algorithm is used to calibrate the U.S. Geological Survey’s 

(USGS) Precipitation Runoff Modeling System (PRMS) at the 

outlet of the Upper Flint River basin of Georgia and selected 

subbasins. This process assures that intermediate states of the 

model, as well as the annual water balance, daily runoff, and 

EFCs are properly simulated for flow at the outlet and within 

the basin. As part of the USGS Water Availability Thrust in the 

Upper Flint River project, the calibrated model can be used 

to study the effects of urbanization and climate change on 

the EFCs. Finally, the results of this evaluation will provide a 

transferable procedure to support multidisciplinary modeling 

efforts for evaluating water availability and ecological flows in 

other watersheds of the United States and abroad. 

Hydrologic Climate Change From a 

Deterministic View: Using GSFLOW to 

Simulate Climate Change in a Northern 

Temperate Climate (Oral Presentation) 

By Randy Hunt1, John F. Walker1, 

Steve Westenbroek1

, and John Doherty2

1

U.S. Geological Survey, Middleton, Wis.

2

Watermark Numerical Computing, Brisbane, Queensland, Australia

Simulations of climate change effects on hydrologic 

systems have often been coarse, utilizing an approach such 

as “X percent reduction in recharge.” More encompassing 

simulations of the hydrologic system historically have been 

difficult because of differences in temporal and spatial scales 

and system boundary conditions. The recently developed 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 25

Ground-Water and Surface-Water Flow model (GSFLOW) 

(Markstrom et al., in press) that couples two widely used 

U.S. Geological Survey models: the Precipitation Runoff 

Modeling System (PRMS) and the Modular Ground-Water 

Flow Model (MODFLOW). The advantage of this coupled 

approach is that common outputs of climate change models 

[for example, changes to temperature and (or) precipitation 

intensity] can be directly input into the model, which in turn 

can be applied to the hydrologic system in a physically based, 

consistent, and transparent manner. 

In this presentation, two examples of GSFLOW are 

contrasted: a lake-dominated system in northern Wisconsin 

simulated as part of the Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) program and a trout stream in 

southcentral Wisconsin simulated as part of the reimbursable 

Water Cooperative Program. Two relatively simple climate 

scenarios were examined. The first evaluated a uniform 4.4°C 

increase in air temperature that represented a possible year 

2100 average air temperature. The second evaluated the same 

uniform increase in air temperature but added the effects of 

additional extreme precipitation events by binning weekly 

precipitation into a single day in that week, which results in 

changes to precipitation timing, but not in the total annual 

rates. 

The results of the heuristic simulations of increased 

average air temperature showed expected decreases in lake 

stage and stream flow. More interesting, results suggested 

that climate change can result in changes in dominant sources 

of water to ecosystems, as illustrated by a rain-dominated 

soft-water lake changing to a ground-water-dominated 

flow-through lake in the Trout Lake WEBB watershed with 

only an increase in temperature. In addition, when extreme 

events were added to a temperature increase, the changes to 

the southcentral Wisconsin trout stream system dynamics 

(decreased baseflow and increased flood peaks) suggest that 

the system may not be able to continue to support trout if such 

a climate change came to pass. Even though the simulations 

could be improved by including more climate sophistication, 

the results demonstrate a potential utility for GSFLOW modeling for today’s resource managers, especially given their need 

to prioritize activities according to the funding available. 

Reference Cited

Markstrom, S.L., Niswonger, R.G., Regan, R.S., Prudic, 

D.E., and Barlow, P.M., in press, GSFLOW- Coupled 

Ground-Water and Surface-Water Flow Model based on the 

integration of the Precipitation-Runoff Modeling System 

(PRMS) and the Modular Ground-Water Flow Model 

(MODFLOW-2005): U.S. Geological Survey Techniques 

and Methods 6-D1. 

Advanced Species Modeling (Poster) 

By Catherine S. Jarnevich1, Thomas J. Stohlgren1, 

and Tracy R. Davern1

1

U.S. Geological Survey, Fort Collins, Colo.

Numerous modeling techniques are available to predict 

the potential distribution and abundance of invasive species 

in their invaded range and the distribution and abundance of 

any species under altered climate scenarios. These techniques 

may perform differently as spatial and temporal scales change, 

based on the taxa being examined and the data available for 

independent and dependent variables. We are examining 

differences between modeling techniques applied to plants, 

animals, and pathogens using datasets from various researchers within the U.S. Geological Survey (USGS) for organisms 

as diverse as diatoms, Asian carp, and pythons. These techniques include the latest presence-only, presence-absence, and 

abundance modeling approaches reported in the literature, 

along with models derived for a particular species. Different 

predictor variables have been obtained from USGS researchers and other sources and processed to be used to predict both 

current habitat suitability and habitat suitability under altered 

climate scenarios. Each different technique has very strict 

requirements for the format of input layers for both dependent 

and independent variables, making it difficult to learn the 

different techniques and prepare data for multiple techniques. 

The lessons we are learning about what works for a particular situation can assist other researchers in determining the 

best approach(es) from the plethora of available ones. We are 

making this knowledge available through the development of 

an advanced species modeling center at Fort Collins Science 

Center. This center will provide capabilities for long-distance 

learning of the latest evaluated modeling techniques for species distributions, along with access to data layers for both 

present and future climate scenarios that are already processed 

to meet the requirements of each modeling technique. 

Spatial and Temporal Modeling of 

Metal Transport Through Ground Water 

and Surface Water in Watersheds 

With Abandoned Mine Lands (Oral 

Presentation) 

By Raymond H. Johnson1 and Michael J. Friedel1

1

U.S. Geological Survey, Denver, Colo.

In the upper Animas River watershed, a 

410-square-kilometer (km2) area in southwestern Colorado, 

metal loads from mining effects (acid mine drainage) degrade 

the surrounding ecosystem beyond the naturally occurring 
26 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

metal loads (acid-rock drainage). Research in this watershed 

has been ongoing for 10 years as part of the U.S. Geological 

Survey Abandoned Mine Lands Initiative (AMLI) and the 

Process Studies of Contaminants Associated with Mineral 

Deposits (Contaminants Project) of the Mineral Resources 

Program. This research has been multidisciplinary from the 

beginning in an effort to integrate biologic, geochemical, 

geologic, and geophysical, information. The main objective 

of this research is to understand how mining has influenced 

ecosystem quality and in particular the fisheries in the upper 

Animas River. Understanding the health of the water is especially important because the local area has recently converted 

from a mining-based to a tourism-based economy. This 

presentation examines the procedures and data requirements 

for modeling metal transport through the environment in a 

series of steps: (1) mining disturbance of the natural system, 

with initial metal release; (2) metal transport through colluvial 

material and fractured bedrock (flow and geochemistry); (3) 

locations of metal discharge from ground water and its effects 

on stream water quality; and (4) final metal concentrations in 

the stream water, spatially and temporally. Results from the 

Contaminants Project and the AMLI provide detailed information on stream-water quality (which is easier to collect than 

data on ground-water quality), and indicate the importance of 

ground-water discharge to streams as a significant pathway for 

surface-water metal loading from both acid-mine drainage and 

acid-rock drainage. As part of the U.S. Geological Survey’s 

Mendenhall Postdoctoral program, Prospect Gulch, a small 

(4.4-km2

) alpine watershed within the upper Animas River 

watershed, was selected for further study of ground-water 

and surface-water quality. Data on the hydrogeology of the 

Prospect Gulch system include geochemistry of ground water 

and surface water, geologic mapping, geophysics, groundwater modeling, multilevel ground-water monitoring wells, 

rock-core analyses, and stream tracer-dilution studies. An 

integrated-science approach was taken to assemble these multiple datasets and provide a detailed conceptual model of the 

controls on metal transport in ground water and surface water 

in and around Prospect Gulch. Data on the ground water and 

surface water collected in Prospect Gulch and the whole upper 

Animas River watershed will be presented and compared in 

terms of (1) data collection challenges, (2) identifying a conceptual model, (3) usefulness in understanding metal transport, 

and (4) scale issues. All these data assist in providing a model 

of metal transport through the environment. At this point, a 

holistic numerical model that describes metal transport from 

mine wastes through ground water through surface water 

through uptake in the ecosystem has not been developed. A 

current substitute is to join individual models, such as ground 

water models (MODFLOW), transport models (MT3DMS), 

surface water models (OTIS and OTEC), geochemical models 

(PHREEQC), and ecosystem uptake models (biotic ligand 

model). This “combined model” approach will be discussed 

as it applies to modeling metal transport at the small watershed scale (Prospect Gulch) versus the larger watershed 

scale (upper Animas River). These methodologies must be 

“calibrated” to existing abandoned mine land areas before they 

can be used in a predictive mode, such as evaluating remedial 

scenarios or evaluating impacts from future mining (which 

is a goal of the current Mineral Environment Assessment 

Project of the USGS Mineral Resources Program). Calibration 

difficulties and possible applications of the “combined model 

approach” to future land-use management will be highlighted. 

Modeling Species Responses to 

Historical Climate Change: From 

Molecules to Continental Landscapes 

(Oral Presentation)

By K. Bruce Jones1, Timothy G. Wade2, and 

Brett R. Riddle3

1

U.S. Geological Survey, Reston, Va.

2

Environmental Protection Agency, Research Triangle Park, N.C.

3

University of Las Vegas, Las Vegas, Nev.

Considerable concern has been raised over potential 

responses of biota to climate change and global warming. 

Of particular concern is how species might adapt to climate 

change, including shifting their ranges in response to geographic shifts in suitable habitat. We compared and integrated 

approaches from phylogeography, historical biology, species habitat modeling, and landscape ecology to assess the 

responses of two widespread lizards, the short-horned lizard 

(Phrynosoma douglasi) and the desert horned lizard (Phrynosoma platyrhinos), to historical shifts in habitats associated 

with modeled climate change. We modeled historical habitat 

change using well-established relationships between temperature and elevation, and between temperature and species 

distribution. Habitat changes during glacial-interglacial cycles 

were modeled by changing habitat suitability along elevation 

gradients from digital elevation model data. Molecular markers and phylogeographic analyses were used to independently 

establish relationships and diversification patterns within and 

among populations from across each species’ ranges throughout western North America. Phylogenetic patterns were then 

compared to modeled habitat change. We found remarkable 

concordance between each species’ pattern of diversification 

and connectivity and modeled habitat change. Additionally, 

phylogenetic signals of the short-horned lizard (P. douglasi) 

suggest a deep-history response to mountain building in the 

western United States. Patterns of dispersal and contraction in 

response to climate change reflected these lizards’ preference 

for open space and moderately disturbed sites (for example, 

along river basins rather than across neighboring terrestrial 

habitats), and contraction and expansion of habitat associated with different scenarios of temperature change. The 

results of this paper demonstrate how multiple approaches 

and disciplines, ranging from molecular phylogenetics to 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 27

continental-scale landscape ecology, can be used to evaluate potential species responses to climate change. We discuss 

integration of these approaches and how they can be applied to 

other types of investigations. 

Limitations to Modeling Nonnative 

Plant Species Distributions for 

Early Detection Monitoring in Arid 

Environments (Poster) 

By Robert Klinger1, Leslie Smith2, and 

Matthew Brooks3

1

U.S. Geological Survey, Bishop, Calif.

2

University of California at Davis

3

U.S. Geological Survey, Henderson, Nev.

The probability of eradicating populations of nonnative 

plant species decreases dramatically when infestations exceed 

several hectares; thus, early detection of incipient populations 

is considered to be a particularly useful strategy for preventing 

or managing impacts. We evaluated the use of species distribution models (SDMs) to help in the development of early 

detection programs for nonnative plants at three National Park 

Service (NPS) units in the Colorado Plateau biogeographic 

province. SDMs have been used increasingly to predict the 

likelihood of nonnative plants colonizing continental and biogeographical regions, but their use at more local scales, such 

as in parks and reserves, has been limited. A potential drawback of using SDMs at local scales is that the cost associated 

with collecting data for modeling individual species can be 

quite high. Therefore, our goal was to develop models based 

on pre-existing data from vegetation classification studies. 

Two groups of models were developed; those based on the 

incidence of nonnative plants at a plot, regardless of species 

identity (pooled models), and models developed for individual 

species (individual models). Our strategy was to conduct a 

comprehensive statistical analysis of distribution and abundance patterns of nonnative plants within each of the three 

NPS units, then to calibrate spatially explicit models using 

available geograpahic information system (GIS) data that 

best matched the variables with the greatest predictive ability 

from the statistical analysis. The SDMs were developed with 

maximum entropy methods (MAXENT) for three types of 

data; presence only, presence with site-specific data included, 

and presence-absence with site-specific data included. Overall, 

the pooled models were not helpful for use in an early detection program. There were two primary reasons for the lack of 

usefulness of the models; problems with available GIS data 

and ecological distribution and abundance patterns. GIS data 

layers were not available for some important predictor variables (for example, vegetation structure and soils), the types 

of data available among the units (for example, disturbance 

types) were inconsistent, and the data were often incomplete 

or at an inappropriate scale for modeling local distribution 

patterns (for example, soils). Even without the data problems, 

the ecological distribution and abundance patterns would 

have limited the usefulness of the models. Nonnative plants 

were present in a large proportion of the plots at two of the 

units, so prediction surfaces of the pooled models were very 

broad. Models for individual species were more useful but still 

limited to a relatively small subset of those in the total species 

pool. Individual species tended to have either very broad or 

very narrow ranges, which resulted in either wide prediction 

surfaces or such restricted prediction surfaces that modeling 

was unnecessary. Models based on presence only or presence with site-specific data underestimated the potential range 

compared with models based on presence-absence data. SDMs 

of nonnative species in the Colorado Plateau region would 

probably be most helpful when developed for high-priority 

species. Modeling at larger scales could be based on pre-existing climate data, but finer-scale physiographic data would be 

required at the level of individual units. 

The South Florida Ecosystem Portfolio 

Model: Integrating Ecological, 

Community, and Economic Values for 

Decision Support (Oral Presentation)

By William B. Labiosa1

1

U.S. Geological Survey, Menlo Park, Calif. 

Researchers from the U.S Geological Survey’s Western and Eastern Geographic Science Centers are refining 

the prototype for a web-enabled geospatial information tool 

[the South Florida Ecosystem Portfolio Model (EPM)] that 

uses an ecological-value model, quality-of-life indicators, 

and a market-land value model to screen possible future 

land use/land cover patterns in South Florida. The focus for 

this presentation is on the ecological-value model implemented for Miami-Dade County, an area that is facing intense 

development pressures on its remaining agricultural lands. 

The ecological-value model was created in close consultation with Biscayne and Everglades National Parks and is 

designed to support their resource management responsibilities within the context of local land use decisionmaking. 

The ecological-value model scores land-use and land-cover 

patterns in terms of criteria specific to habitat protection, 

landscape fragmentation and patterns, water-quality buffering, and ecological restoration potential. The scores for the 

individual criteria are based on appropriate models, including habitat models used in the Florida Gap Analysis Program 

(GAP) analysis, the Florida Natural Areas Inventory, and the 

U.S. Fish and Wildlife Service Multi-Species Recovery Plan, 

as well as land attribute-based contaminant loading models 
28 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

and ecological-restoration-potential models. Criteria scores are

integrated in a multiattribute utility (MAU) model developed 

in consultation with Biscayne and Everglades National Park 

users. The approach combines natural science models, decision science theory, collaborative model design, and information technology to yield a robust land-use and land-cover 

pattern evaluation tool. When integrated with community 

quality-of-life indicators and a market land-value model, the 

final web tool will be used to explore land use scenarios using 

criteria that are important to diverse stakeholders in the land 

use planning process in Southeastern Florida. 

Developing an Infrastructure for 

Knowledge-Based, Spatially Explicit 

Population Modeling of Multiple 

Species (Poster) 

By Edward J. Laurent1, Christina A. Drew1, and 

Jaime A. Collazo2

1

North Carolina State University, Raleigh, N.C.

2

U.S. Geological Survey, Raleigh, N.C.

Anthropogenic changes to vegetation communities, 

climate, and landscape patterns are known to affect wildlife 

species’ distributions, abundances, and population viability. 

Hence, there is a need to understand these relationships better and to predict their consequences given various change 

scenarios. The development and application of data-driven 

predictive modeling approaches are limited, however, by the 

complexity of relationships between wildlife populations and 

their environments and by the resources necessary to strategically sample and monitor this complexity over large areas 

for multiple species. Knowledge-driven modeling, whereby 

model structure and parameter values are based on a thorough 

review of the literature, offers a prudent alternative strategy 

for predicting and mapping population rates. These models 

can be tested with data when available and be extended to predict changes under various scenarios. This knowledge-based 

approach may be optimized through an infrastructure that promotes collaboration and innovation by way of online access to 

information and tools. We present an infrastructure framework 

that includes three primary environments: (1) knowledge 

(collaboration and dissemination); (2) information (data); and 

(3) modeling (analysis). The knowledge environment enables 

collaboration and the dissemination of information, tools, and 

knowledge by way of Internet services with the goal of minimizing entropy (that is, disorder, inefficiency, and information 

loss) across partner actions. The information environment 

provides a structure for partners to describe species’ habitat 

associations, densities, and vital population rates under spatially explicit conditions. The modeling environment enables 

partners to design and share components of hierarchically 

structured, spatially and temporally explicit models of species’ 

distributions and productivity. 

As an example of synthesis and synergy among the three 

infrastructure environments, text descriptions of a species’ 

productivity in a specified spatial context may be atomized 

online using forms that allow partners to summarize them as 

records in a tabular format. The resulting records within the 

tables can be quantitatively summarized, along with records 

added by other partners, to derive parameter estimates of vital 

population rates and their statistical distributions under spatially explicit conditions. These parameter estimates and others 

describing the same rates given different spatial contexts can 

be used as informative priors in hierarchical empirical Bayes 

models that estimate the productivity of a specified area. Such 

models may be developed by using functions pulled from 

packages contributed by other partners or by writing new compatible functions that are later shared. Data can be collected 

or acquired from partners to evaluate the models, and multimodel inferences can be employed for confirmatory analysis 

of competing model structures. Finally, the process and results 

of these analyses can be documented and used to inform new 

model structures, code new models, create multiauthor species 

wikis, provide guidance to other partners through web forums, 

and be published in the peer-reviewed literature. Combined, 

this infrastructure framework enables partners to increase 

their individual output and the efficiency of the collaboration 

through selfish actions. Entropy is reduced across partners by 

structuring individual actions within a broader, hierarchical 

context that removes redundancies, facilitates innovation, and 

enables the sharing of knowledge. 

Regional Modeling of Carbon 

Dynamics Change in the Green River 

Basin, Wyoming, Based on Remotely 

Sensed Data (Poster) 

By Zhengpeng Li1 and Zhengxi Tan1

1

Science Applications International Corporation, Sioux Falls, S. Dak.

A regional model based on satellite observations was 

used to estimate long-term carbon dynamics in the Green 

River Basin, Wyo. Gross primary production (GPP) was predicted using a eddy covariance-light use efficiency (EC-LUE) 

model derived from eddy flux measurements and remotely 

sensed data. The impacts of GPP on the dynamics of soil 

organic carbon (SOC) were quantified and evaluated using the 

Erosion Deposition Carbon Model (EDCM). The spatial and 

temporal changes of biophysical settings and land management practices were incorporated in the regional simulations 

of ecosystem changes using the General Ensemble Biogeochemical Modeling System (GEMS). Responses of ecosystem carbon stocks (including SOC) and fluxes to various 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 29

management scenarios were simulated. Results indicate that 

the sagebrush-dominated shrubland accumulated less soil carbon than did the grass-dominated area. Grazing could lead to 

a greater decrease in net primary production (NPP) and SOC 

in shrubland than in grassland under the same grazing intensity. A grazing intensity of 0.03 animal units per hectare could 

reduce SOC at a rate of 3.1 gC m-2 yr-1 in shrubland, but SOC 

changed little in grassland during 30 years of simulation (1971 

to 2000). Our model simulations also show that conversion of 

shrubland to grassland can enhance soil carbon sequestration 

in the Green River Basin. 

Modeling Terrestrial Carbon Dynamics 

in the Eastern United States (Poster)

By Shuguang Liu1, Zhengpeng Li1, Thomas Loveland2, 

and Larry L. Tieszen2

1

Science Applications International Corporation, Sioux Falls, S. Dak.

2

U.S. Geological Survey, Sioux Falls, S. Dak.

Estimating the dynamic evolution of the magnitude, spatial patterns, mechanisms, and uncertainty of carbon sources 

and sinks at the regional scale is challenging because of the 

spatial and temporal covariance of driving variables and the 

uncertainties in both the model and the input data. Although 

various modeling approaches have been developed to facilitate 

the upscaling process, few deal with error transfer from model 

input to output and error propagation in time and space. We 

developed the General Ensemble Biogeochemical Modeling 

System (GEMS) for upscaling carbon stocks and fluxes from 

sites to regions with measures of uncertainty. GEMS relies on 

well-tested site-scale biogeochemical models [for example, the 

Erosion Deposition Carbon Model (EDCM) and CENTURY)] 

to simulate the carbon dynamics at the site scale. The spatial 

deployment of the site-scale model in GEMS is based on the 

spatial and temporal joint frequency distribution of major driving variables (for example, land cover and land use change, 

climate, soils, disturbances, and management). At the site 

scale, GEMS uses stochastic ensemble simulations to incorporate input uncertainty and to quantify uncertainty transfer 

from input to output. Using data assimilation techniques, 

GEMS simulations can be constrained by field and satellite 

observations or census data, including estimates of net primary 

productivity from the Moderate Resolution Imaging Spectroradiometer (MODIS), grain yield and cropping practices, and 

forest inventories. 

We applied GEMS-EDCM to quantify the spatial and 

temporal distributions of the terrestrial carbon sources and 

sinks in the eastern United States. Results indicated that 

frequent land use change strongly affected the magnitude 

of carbon sources and sinks at the regional scale. Ignoring 

detailed land cover transitions and changes led to significant overestimation of carbon sink strength. The interannual 

variability of regional carbon sources and sinks was strongly 

affected by climate variability and, to a lesser degree (at the 

local scale), by land use change. However, land use change 

often induced large instant changes in carbon stocks and fluxes 

at the local scale, followed by prolonged lagged impacts that 

affected the magnitude of carbon sources and sinks. We also 

found that the spatial and temporal changes of carbon stocks 

and fluxes at the local to regional scales must be quantified 

and analyzed. Equally important, the carbon sources and sinks 

must be correctly attributed to different sectors and processes 

(for example, croplands, forests, urbanization, and nitrogen 

deposition). 

Modeling With Stakeholders: When 

the Process is as Valuable as the 

Product (Poster)

By Ken Lubinski1, Todd BenDor2, Bruce Hannon2, 

Sara Metcalf2

, and Emily Wheeler2

1

U.S. Geological Survey, La Crosse, Wis.

2

University of Illinois at Urbana-Champaign, Urbana, Ill.

We organized a workshop to explore the concept that 

collaborative learning among diverse stakeholders, through 

modeling, could nurture the common understanding required 

to promote mutually acceptable management decisions on the 

Upper Mississippi River. The exercise provided glimpses of 

often hidden communication barriers and the different value 

systems that exist between stakeholders and between scientists and managers. The three-day workshop was planned and 

executed in steps intended to attract, enlighten, and motivate nonscientists. We invited experienced and influential 

individuals; helped the participants select the workshop theme 

(seeking floodplain functionality in concert with development); secured onsite support from discipline and modeling 

experts and a professional facilitator; developed an agenda 

that captured individual participant knowledge in the model 

structures while allowing for regular feedback between the 

participants and the modelers; selected simple but real-time 

modeling tools; and distributed pre- and post-workshop 

participant surveys. With guidance, the participants prioritized 

questions that were vital to understanding the ecological and 

economic conditions, relationships, and values of the floodplain system and for constructing focused models that were 

important as well as doable within the workshop’s timeframe. 

Interest in the causal factors driving floodplain land allocation 

led to two independent models, developed in parallel by participant subgroups, which simulated future changes in floodplain composition. The major criticism of the process was that 

there was not enough time to develop models to the stage of 

verification and testing, and participants suggested that future 

efforts be structured as a series of workshops to first design, 

then test, then apply the models. While participants agreed 
30 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

that the exchange of ideas was valuable, several expressed 

doubt that this academic exercise would impact their future 

behavior. These responses left open questions about the value 

of additional workshops on different themes, who would be 

responsible for the exercises, and if collaborative modeling 

needs to be instituted as a mandatory step in operational river 

management programs. 

Use of Operational and Research 

Models to Assess Volcanic Tephra 

Hazards (Oral Presentation) 

By Larry G.. Mastin1, John Ewert1, Robert Wardwell1, 

Evan Thoms2

, and Roger Denlinger1

1

U.S. Geological Survey, Vancouver, Wash.

2

U.S. Geological Survey, Anchorage, Alaska

Among the most widespread hazards posed by volcanic 

eruptions is the dispersal of tephra. Tephra that falls out of 

the atmosphere may reduce visibility to ground-based traffic, 

clog air filters, short electrical circuits, and cause breathing 

and respiratory problems. Fine ash that remains in the atmosphere poses a hazard to aircraft, abrading exterior surfaces 

and, in extreme cases, causing engines to stall. These two 

hazards (tephra fall and ash clouds) are both assessed through 

numerical models that have been verified by comparison with 

mapped deposits and satellite images. For tephra fall, the 

U.S. Geological Survey (USGS) forecasts hazards at volcanoes that are in a state of unrest (for example, Mount St. Helens or, during the summer of 2007, Pavlof Volcano in Alaska) 

by generating daily maps showing areas that may receive 

tephra fall if an eruption were to occur in the upcoming 24 

hours. Such maps are generated by a numerical model (ASHFALL) developed by the Institute of Geological and Nuclear 

Sciences in New Zealand that uses as input a geologically 

reasonable erupted volume, plume height, vertical distribution of tephra within the plume, and atmospheric wind vectors 

obtained from meteorological models for several elevations 

above the volcano. ASHFALL is a simple advection-diffusion 

model in which the rate of widening (in map view) of the 

tephra deposit with downwind distance is determined by an 

empirical diffusion coefficient that assumes outward spreading

of the ash cloud at a rate proportional to the tephra concentration gradient. Comparison with mapped deposits of small- to 

medium-sized eruptions gives reasonable agreement; however, the model does not consider spatial variation in wind, 

or effects of moisture or particle aggregation that could affect 

fallout patterns in larger eruptions or under moist conditions. 

Hazards to aviation from ash clouds are modeled in real 

time, during an eruption, not by the USGS, but by Volcano 

Ash Advisory Centers (VAACs), which are operated by the 

International Civil Aviation Organization. The VAACs use 

one of several Lagrangian particle- or cloud-tracking models 

[Hybrid-Single Particle Lagrangian Integrated Trajectory 

(HYSPLIT), Puff Dispersion Model (PUFF), Canadian Emergency Response Model (CANERM), and the NAME dispersion model], whose input includes global three-dimensional 

(3-D) wind data and volcanic “source parameters,” such as 

eruption rate, plume height, duration, and grain-size distribution, estimated by the USGS or corresponding agencies in 

other countries. Estimates of plume height or duration can be 

obtained directly from observations, whereas eruption-rate 

estimates must be constrained from plume height through 

relationships based on theory, numerical models, or empirical 

observations. The USGS Volcano Hazards Program is working 

on several fronts to improve tephra hazard models and input 

parameters. Near-term improvements involve automating the 

process of generating tephra-fall maps and posting them on 

the web, and incorporating meteorological 3-D wind field 

forecasts to consider spatial and temporal variations in wind. 

Longer term goals include development of a 3-D tephra fall 

model that directly calculates ash diffusion from atmospheric 

turbulence and modeling projects to better constrain relationships between plume height and eruption rate under conditions 

that include atmospheric moisture and variable winds. 

Extirpations and Elevational-Range 

Changes of the American Pika Linked 

to Climate Warming Through ClimateEnvelope Modeling (Poster) 

By Erik A. Beever1 and Karen Oakley1

1

U.S. Geological Survey, Anchorage, Alaska

The American pika (Ochotona princeps) is a coldadapted small mammal that resides in rocky montane habitats 

of western North America. In October 2007, The U.S. Fish and 

Wildlife Service was petitioned to list the species as threatened under the Endangered Species Act because of population 

declines thought to be related to climate warming. We used 

climate-envelope modeling to explain local extirpations and 

range changes of this species in the Great Basin region (for 

example, Nevada and Oregon). Surveys of pikas in montane 

taluses of the Great Basin during the 1990s and subsequent 

information-theoretic analyses suggested that population 

extirpations during the 20th century were best explained by 

a combination of climatic, biogeographic, and anthropogenic 

factors. Surveys during 2003 to 2007 documented additional 

extirpations and upslope migration of the minimum elevation 

of pikas at remaining sites. For both extirpations and upslope 

migrations, losses during the past decade occurred 3.8 times 

faster than during the 20th century. 

To explore alternative hypotheses of thermal stress on 

pikas, we placed 236 temperature sensors (iButtons) within 

pika habitats for 30 months and performed vegetation surveys 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 31

in the vicinity of 25 sites with historical records of pikas in 

the Basin. We correlated our sensor data with data from the 

nearest weather stations and used this relationship, combined 

with long-term data from the same weather stations, to backestimate temperatures within pika habitats at hundreds of 

locations each year between 1945 and 2006. We posited three 

alternative mechanisms of direct thermal stress: (1) wintercold stress (indexed by number of days below 0°C and number 

of days below -5°C, which we predicted would be a novel 

stress, because most areas had previously been covered by an 

insulating layer of snow); (2) acute-heat stress (indexed by 

number of days above 28°C, an ambient temperature found to 

be lethal for pikas in previous studies); and (3) chronic-heat 

stress (indexed by average temperature during 1 June through 

31 August). Magnitude of change was defined as change in 

our thermal metrics between 1945 to1975 and 1976 to 2006, 

to avoid climatic anomalies; recent stress was indexed by field 

conditions during 2005 to 2006. We compared the strength of 

evidence in support of competing multiple-logistic-regression 

models with different combinations of thermal predictors and 

temporal periods in an information-theoretic framework, using 

the corrected Akaike Information Criterion (AICc). Predictive 

accuracy of climate-envelope models, our most powerful tool 

for forecasting species’ responses to various climate scenarios, 

hinges critically on three elements that our work embodies: 

broad geographic domain of field-data collection; mechanistic understanding of climatic effects on focal organisms; and 

incorporation of biogeographic, climatic, and anthropogenic 

variables into models. 

We expect future modeling efforts will increasingly 

mirror our approach. We molded the talents of a climatologist 

with extensive modeling skills, a vertebrate-population modeler, a quantitative ecologist, and three decades of field and 

literature-based experience. Although some disciplinary and 

logistical hurdles (for example, jargon, file transfer, or being 

located in different states) arose, these were minor and are 

now largely overcome. Conference calls, group e-mails, and 

file transfer using File Transfer Protocol (FTP) sites have been 

the primary modes of communication. 

We will move now from site-based analyses to patchbased understanding of pika distributions relative to climate 

and other drivers within a hierarchical framework of analysis. 

In addition to the iButtons, we gained insights or fed into models data from National Weather Service Cooperative Observer 

Program (COOP) stations, the Historical Climate Network, 

Parameter-Elevation Regressions on Independent Slopes 

Model (PRISM) data, and Hadley Center Climate Research 

Unit (CRU) records, essentially merging the strengths of each 

data source. Although precipitation may be important for 

this and other montane species, we did not model any aspect 

of it retrospectively, given that variability in those estimates 

would be comparatively much larger. We also found that our 

cold-season metrics were not estimable backward in time during periods of snow cover at iButton location, because these 

(stabilized) conditions had no correlation with data at weather 

stations, which are explicitly constructed to measure ambient 

temperature above snow on the ground. Thus, our historical 

modeled data for days below threshold temperatures had to 

rely on trends during the ‘shoulder seasons’ of fall and spring, 

which exhibited results counter to our hypothesized coldseason mechanism and to the iButton results. 

Biotic responses to climate change will vary among taxa 

and across latitudes, elevational gradients, and degrees of 

insularity. Ecological niche forecasting uses biotic and abiotic 

variables characterizing the existing geographical range of 

a species to predict how the species will respond to altered 

conditions, such as those resulting from global climate change. 

However, it remains poorly understood whether species losses 

would be greatest in populations experiencing the greatest 

climatic change or in populations living closest to the ‘edge 

of the bioclimatic envelope’. Our investigations provide 

an example of how to employ climate data to model biotic 

responses to climate change. 

Delineation of Vegetation Communities 

Using Airborne Lidar Data at Jean 

Lafitte National Park, Louisiana 

(Poster) 

By Monica Palaseanu-Lovejoy1, Amar 

Nayegandhi2

, John C.Brock3, Wayne C. Wright4, and 

Robert Woodman5

1

U.S. Geological Survey/Jacobs, St. Petersburg, Fla.

2

U.S. Geological Survey/ETI, St. Petersburg, Fla.

3

U.S. Geological Survey, St. Petersburg, Fla.

4

National Aeronautics and Space Administration, Wallops Island, Va.

5

U.S. National Park Service, Lafayette, La.

 This study evaluates the capabilities of the National 

Aeronautics and Space Administration (NASA) Experimental 

Advanced Airborne Research Lidar (EAARL) to delineate 

vegetation communities in Jean Lafitte National Park, La. 

(JELA.), using a hierarchical approach. Five-meter-resolution 

grids of bare earth (BE), canopy height (CH), canopy-reflection ratio (CRR), and height of the median energy (HOME) 

were derived from spatially dense EAARL data acquired in 

September 2005. The dataset comprises 35 tiles of 2 square 

kilometers for each metric. The unsupervised classification 

was carried out within the statistical open-source software R.

 The first step of our hierarchical classification used a 

statistical-based approach to divide the CH into five distinct 

classes. Within each height class, a principal component analysis (PCA) and an independent component analysis (ICA) of all 

four different metrics were carried out. Lastly, for each height 

class, the principal components (PC), the independent components (IC), and the original metrics were classified into four 

groups either by the k-means or the neural-gas algorithms.
32 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

 Unsupervised original metrics classifications and PCAand ICA-based classifications were compared with JELA color 

infrared (CIR) aerial photography. Ground-truth data are being 

acquired at two test sites along specific transects to assess species composition and canopy cover and to identify biologically 

consistent vegetation patches. Our study reveals that neuralgas PCA- and ICA-based classifications performed better in 

categorizing different vegetation communities than direct 

unsupervised classification of the four metrics or k-means 

based classifications of PC and IC. 

Spatially Continuous Interpolation of 

Water Stage and Water Depths Using 

the Everglades Depth Estimation 

Network (EDEN) (Poster) 

By Leonard Pearlstine1, Monica Palaseanu-Lovejoy2, 

Pamela Telis3

, Heather S. Henkel3, and Aaron Higer4

1

U.S. National Park Service, Fort Lauderdale, Fla.

2

U.S. Geological Survey/Jacobs, St. Petersburg, Fla.

3

U.S. Geological Survey, Fort Lauderdale, Fla.

4

University of Florida, Davie, Fla.

The Everglades Depth Estimation Network (EDEN) is 

an information network that integrates real-time water-level 

monitoring with ground-elevation and water-surface modeling. 

EDEN provides scientists and managers with current (2000 to 

present), online water-stage and water-depth information for 

the entire freshwater portion of the Greater Everglades. Continuous daily spatial distributions of the EDEN network stage 

data are interpolated on a 400-square-meter (m2) grid spacing. 

EDEN offers a consistent and documented dataset that can be 

used by scientists and managers to (1) guide large-scale field 

operations; (2) integrate hydrologic and ecological responses; 

and (3) support biological and ecological assessments that 

measure ecosystem responses to the implementation of the 

Comprehensive Everglades Restoration Plan (CERP). The 

target users are biologists and ecologists examining trophiclevel responses to hydrologic changes in the Everglades. 

Water-stage interpolation uses daily median values from 240 

water-level gages operated and maintained by four separate 

agencies: U.S. Geological Survey, Everglades National Park 

(ENP), South Florida Water Management District (SFWMD), 

and Big Cypress National Preserve (BCNP). All transmitted data are entered and stored in the USGS National Water 

Information System (NWIS) database. Hourly water-level data 

for the historical period 2000 to 2007 were compiled for all 

existing water-level gages in the study area and converted to 

the North American Vertical Datum of 1988 (NAVD88) using 

correction values obtained by global positioning system (GPS) 

differential, optical survey, or VERTCON. 

The EDEN area is divided by canals and levees into eight 

distinct regions. Abrupt water-level discontinuities across the 

regions, owing to surface-water barriers, complicate interpolation across the entire area, and boundary conditions were 

simulated by linearly interpolating along both sides of levees 

using head- and tail-stage data. Radial-basis function interpolation with the multiquadric method was used to create a continuous mathematical representation of the water surface that 

was sampled further on a 400 x 400 meter (m) grid spacing 

to record the interpolated values. These results are combined 

with a digital elevation model (DEM) to obtain daily water 

depth distribution. Only deterministic methods of interpolation were considered because the abrupt spatial discontinuities across the landscape cause statistical nonstationarity and 

because of the sparse number of gages that would be available 

if interpolation was subdivided into individual compartments. 

Multiquadric and kriging methods produce comparable results 

when semivariograms are developed using data from specific 

events. However, kriging requires data stationarity. Kriging 

also is reported to have inferior results when the semivariogram parameters were developed from averaging data over a 

period of time. 

Modeling Nonlinear Time Sequences 

in Earth Change and Human Disease 

Pandemics (Poster) 

By Bruce H. Pugesek1

1

U.S. Geological Survey, Bozeman, Mont. 

The golden mean 1.6183, derived from the asymptotic 

solution of the division of progressive Fibonacci or Lucas 

numbers (Fibn+1 / Fibn or Lucn+1 / Luc1), is ubiquitous in 

nature. The ratio occurs in the complex mathematics of the 

shape of the universe, the spiral of galaxies, the shape and 

spiral of deoxyribonucleic acid (DNA), and the dimensions of 

plants and animals, to name a few. I have explored the golden 

mean and the Fibonacci and Lucas sequences as they relate to 

the element of time and have found significant connections to 

Earth change, namely volcanism, drought, weather-induced 

famines, and human disease pandemics. These changes may 

be associated with the 11-year solar cycle, the 18-year Saros 

cycle, and the 200-year solar cycle. Specific dates defined by 

these relationships are also highly correlated with warfare and 

commodity price rises that precede them. Both of these factors may be associated with human disease resistance as well 

as demographic, emigration, and immigration shifts that are 

conducive to disease outbreak. Results indicate that the years 

2008 and 2014 may be danger periods for disease pandemic 

outbreaks. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 33

Elevation Derivatives for Modeling 

Postwildfire Debris Flows (Poster) 

By Vivian R. Queija1, Bruce Worstell2, and 

Susan Greenlee2

1

U.S. Geological Survey, Seattle, Wash.

2

U.S. Geological Survey, Sioux Falls, S. Dak.

Debris flows can be initiated by rainfall in areas of recent 

wildfires to create severe hazards for people and their property. 

Models to predict postwildfire debris flows are dependent on 

the ability to quantify independent basin parameters (such 

as total area, degree of slope, soil attributes, burn severity, 

and total rainfall). By applying the Elevation Derivatives for 

National Applications (EDNA) database, independent variables are calculated continuously so that upstream variables 

are computed on a cell-by-cell basis. This process facilitates 

obtaining parameters at multiple resolutions for additional 

sample sites and, potentially, for applying models at unsampled locations. In this poster, we show an application of the 

approach in the Southern California Multi-Hazards Demonstration Project. 

GSFLOW—Coupled Ground-Water and 

Surface-Water Flow Model (Poster) 

By Steve Markstrom1, Robert S. Regan1, 

Richard G. Niswonger2

, David E. Prudic2, and 

Paul M. Barlow3

1

U.S. Geological Survey, Denver, Colo.

2

U.S. Geological Survey, Carson City, Nev.

3

U.S. Geological Survey, Northborough, Mass.

An interdisciplinary approach is needed to assess 

the effects of variability in climate, biota, geology, and 

human activities on water availability and flow. The new 

Ground-Water and Surface-Water Flow model (GSFLOW) 

integrates the U.S. Geological Survey Precipitation Runoff 

Modeling System (PRMS) and the U.S. Geological Survey 

Modular Ground-Water Flow Model (MODFLOW-2005). 

GSFLOW builds on existing science and technology; is easily 

extensible; provides mechanisms for modeling over different spatial and temporal scales; and provides for integration 

between science and management objectives. Future versions 

of GSFLOW can include additional models to simulate other 

environmental and anthropogenic processes, such as climate, water quality, ecology, geochemistry, and management 

strategies. 

De-Trending for Climatic Variations to 

Reveal Stressed Ecosystems (Poster)

By Shuguang Liu1, Jennifer A. Rover1, Li Zhang1, 

Bruce K. Wylie1

, Lei Ji1, and Norman B. Bliss1

1

Science Applications International Corporation, Sioux Falls, S. Dak.

Short-term variation in climatic conditions results in 

dramatic fluctuations in ecosystem performance, particularly 

in water-limited ecosystems. These climatic-driven variations confound the effects of management, insect infestations, 

and changing soil conditions, such as a thickening active soil 

layer in high latitudes. In this study, we presented a method 

to account for the influence of climatic variations on ecosystem performance, so that changes in the underlying ecological condition are emphasized. We adopted a growing season 

integration of coarse resolution, remotely sensed Normalized 

Difference Vegetation Index (NDVI) as a surrogate for actual 

ecosystem performance. Piecewise regression models were 

used to predict expected growing season performance. The 

models were developed using large samples of random pixels 

over multiple years from areas with the same land cover and 

within an ecoregion class. Independent variables in the models 

include seasonal (winter, spring, and summer) climate data 

(precipitation and temperature) and site potential or longterm historical performance. Model results were then used to 

construct annual maps representing the deviations, or performance anomalies, between expected ecosystem performance 

and actual ecosystem performance. Regression confidence 

limits were used to identify significant anomalies on the maps. 

Performance outside the confidence limits over multiple years 

indicates areas with consistent management, insect, fire, or 

ecosystem stress impacts. The trend in ecosystem anomalies 

over several years identifies areas where ecosystem stress is 

becoming more severe or less severe. 

This approach has been applied to boreal forests and 

sagebrush lands in the western United States using both the 

250-meter-resolution Moderate Resolution Imaging Spectroradiometer and 1-kilometer-resolution Advanced Very High 

Resolution Radiometer Normalized Difference Vegetation 

Index. Piecewise regression R2 values have varied from 0.84 

in the Yukon River Basin to 0.96 in Wyoming sagebrush 

regions, and from 0.86 to 0.95 for sagebrush and grasslands in 

a portion of southern Idaho. This approach holds promise for 

identifying stressed ecosystems that are vulnerable to changing to a new ecological state. 
34 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

An Integrated Modeling Approach for 

Analyzing fhe Effects of an Expanding 

Biomass-for-Biofuel Economy in 

the Northern Great Plains (Oral 

Presentation) 

By Terry L. Sohl1 and Kristi L. Sayler2

1

Science Applications International Corporation, Sioux Falls, S. Dak.

2

U.S. Geological Survey, Sioux Falls, S. Dak.

An understanding of potential future scenarios of landuse and land-cover change is essential to anticipate impacts on 

regional carbon dynamics, climate change, hydrology, and biodiversity. The Forecasting Scenarios of change (FORE-SCE) 

model was developed to create high-resolution projections 

of land cover under multiple scenarios for large geographic 

regions. FORE-SCE applies a combination of theoretical, 

statistical, and deterministic modeling techniques to forecast 

plausible landscapes. Historical information on land-cover 

change is used to parameterize key components of the model. 

This information is obtained from ecoregional data on past 

rates, thematic categories, and spatial characteristics of change 

provided by the U.S. Geological Survey Land Cover Trends 

project. 

Results from initial applications of FORE-SCE support 

analyses led by cooperator Roger Pielke, Sr., University of 

Colorado, to assess the effects of change on regional weather 

and climate variability. We have provided Dr. Pielke with 

maps for three scenarios of land-cover change for the western 

Plains (modeling period 1992 to 2020), two scenarios for the 

southeastern United States (1992 to 2050), and one scenario 

for the northeastern United States (1992 to 2050). 

Research in FY2008 will focus on higher resolution 

applications covering four ecoregions in the northern Great 

Plains. This work will address the effects of an expanded agricultural base for biofuels and concurrent changes in climate on 

ecosystem sustainability. We will project alternative landscapes at annual time steps through 2050, analyzing the results 

to estimate effects on ecosystem processes and services. We 

will incorporate socioeconomic drivers, such as national 

policy and programs, commodity prices, and biofuel demand, 

to develop multiple scenarios that variously emphasize the 

production of corn, soybeans, switchgrass, and mixed prairie 

species for biofuel conversion. FORE-SCE will be integrated 

with the General Ensemble Biogeochemical Modeling System 

(GEMS) model and the Better Assessment Science Integrating 

Point and Nonpoint Sources (BASINS) model to incorporate 

environmental feedbacks into the forecasting process. For 

each change scenario, we will assess environmental quality 

and sustainability based on levels of estimated carbon stocks, 

greenhouse gas emissions, sediment and nutrient loadings to 

water bodies, wildlife habitat availability, agricultural profitability, and energy costs and benefits. 

Modeling Standardized, Meso-Scale 

Ecosystem Distributions for the 

United States and the Planet (Oral 

Presentation)

By Roger G. Sayre1

1

U.S. Geological Survey, Reston, Va.

A variety of applications ranging from biodiversity 

conservation to economic valuation of ecosystem goods and 

services require spatially explicit maps of the distribution of 

ecosystem occurrences. Ecosystems exist at multiple scales 

and can be described and geospatially delineated at sizes ranging from a grain of soil to the entire Earth. While macro-scale 

(thousands to tens of thousands of hectares) ecosystems have 

been mapped for the Nation and the planet in a number of 

different ecoregional classifications, standardized ecosystem 

maps at finer scales are not yet available for the entire United 

States (Bailey, 1998; Olson et al., 2001). A new geospatial 

model has been developed to map standardized meso-scale 

(tens to thousands of hectares) ecosystems as unique abiotic 

environments and their associated land cover. The model 

combines national datalayers for land surface form, lithology, 

isobioclimate regions, and land cover in a geospatial union 

that produces unique ecosystem occurrences, or footprints, at 

management-appropriate scales. 

This geospatial model has been prototyped for 

South America and is currently being implemented for the 

United States (Sayre et al., in press). The model is also being 

used for a U.S. Geological Survey-led standardized global 

ecosystem mapping under the Global Earth Observation 

System of Systems (GEOSS) intergovernmental protocol. 

The South America ecosystem modeling effort produced new 

continent-wide datalayers for elevation, landforms, lithology, 

climate regions, and land cover as inputs to the model, as well 

as a new meso-scale ecosystems map for the continent. The 

U.S. ecosystems modeling effort has produced new, contiguous U.S. datalayers for landforms, surficial materials lithology, 

and isobioclimates, which are now being combined with the 

recently released National Land Cover Database to produce 

standardized, robust, meso-scale ecosystem footprints for the 

Nation. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 35

Utilizing Structural Equation Modeling 

Techniques to Model Watershed 

Structure and Land Use Effects on 

Stream Geochemistry and Ecological 

Responses (Oral Presentation)

By Travis S. Schmidt1, Richard B. Wanty1, 

Stanley E. Church1

, Carma San Juan1, 

William H. Clements2

, David Fey1, Philip L. Verplank1, 

James G. Crock1

, Monique Adams1, and 

Michael W. Anthony1

1

U.S. Geological Survey, Denver, Colo.

2

Colorado State University, Fort Collins, Colo.

Watersheds are complex systems composed of terrestrial 

and aquatic components that function together to determine 

local stream geochemistry and aquatic communities. Variance 

in geochemistry and aquatic community composition are influenced by both terrestrial landscape and longitudinal aquatic 

gradients. Most studies that investigate the linkages between 

aquatic and terrestrial interfaces do so by means of correlation. 

However, this approach does not fit the prevailing paradigm to 

which most watershed scientists subscribe: that watersheds are 

complex hierarchical systems. One of the primary objectives 

of the Central Colorado Assessment Project (U.S. Geological 

Survey Mineral Resources Program) is to assess the effects of 

catchment lithology on aquatic resources. To better describe 

the phenomena observed in this watershed assessment, we 

employed structural equation modeling (SEM) techniques. 

SEM relies upon a hypothesis developed from theory and 

expert knowledge expressed in the form of a conceptual 

diagram. This hypothesis is evaluated by determining to what 

degree it is supported by the empirical data. The advantage 

is that much thought is required before statistical analysis is 

done, which minimizes the risk of obtaining spurious results. 

SEM realistically models watershed interactions by including 

both direct and indirect interactions among model variables 

(for example, rock-type, aquatic communities). Correlation 

and covariances are explicitly accounted for, which minimizes 

the problem of colinearity among descriptors. SEM also 

allows for the inclusion of multiple descriptors of rock-type, 

which were necessary to describe terrestrial and aquatic community responses. Results of this statistical evaluation were 

depicted as conceptual diagrams that could be easily understood by nontechnical resource managers. These advantages 

over traditional statistical models make SEM an alternative for 

ecosystem scientists who conduct observational field studies. 

Landscape Evapotranspiration 

Estimation Using a Simplified Surface 

Energy Balance Approach for 

Watershed Water-Balance Modeling 

(Poster)

By Gabriel B. Senay1, Michael E. Budde1, and 

James P. Verdin1

1

U.S. Geological Survey, Sioux Falls, S. Dak.

Large-scale regional water balance modeling is important 

for various hydrologic and environmental studies. Evapotranspiration (ET) is a key component of the water balance 

equation. Regional-scale ET estimates can be made at various 

levels of complexity and accuracy, involving point-based field 

measurements and spatial modeling. We present the application of a simplified surface-energy balance (SSEB) approach 

for estimating landscape ET. These estimates are then used to 

compute watershed discharge using a water balance modeling 

approach that operates on a monthly timescale. Landscape ET 

was calculated at 1-kilometer (km) resolution using the SSEB 

model that integrates 8-day ET fractions and reference ET. The 

daily reference ET is computed from the Global Data Assimilation System (GDAS) dataset using the standardized PenmanMonteith equation at the U.S. Geological Survey (USGS) 

Center for Earth Resources Observation and Science (EROS). 

Eight-day ET fractions are derived at 1-km resolution from the 

Moderate Resolution Imaging Spectroradiometer (MODIS) 

land surface temperature (LST) dataset using a relative surface 

temperature difference with respect to the “hot” and “cold” 

pixels in each 8-day composite image. Watershed boundaries 

generated from the HYDRO1k dataset were used to determine 

watershed area. The study used gridded (5-km) daily rainfall 

data from the National Weather Service (NWS). The data is 

a blend of Next-Generation Radar (NEXRAD) and station 

rain gage data. Stream discharge data from the USGS will be 

used to evaluate and validate the discharge estimate from the 

watershed water balance model. The study is conducted over 

the Missouri River basin using 2006 data for the time period 

between April and October. The contribution of ground water 

toward the total watershed ET was estimated using irrigation 

maps and forest cover classes. ET estimates from the SSEB 

model captured both the spatial and temporal variability of 

ET within the watershed. Future efforts will include evaluating discharge estimates from the water balance model against 

observed measurements. The model will also be evaluated by 

using multiple years of data at different spatial and temporal 

scales. The approach has a potential application for integrating remotely derived estimates of ET for operational water 

balance modeling. Such modeling would provide the ability to 

monitor the availability of water for different uses as well as 

the onset and progress of drought at a watershed scale. 
36 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Community Sediment-Transport 

Modeling Project (Poster) 

By Christopher R. Sherwood1, W. Rockwell Geyer2, 

John C. Warner1

, Richard P. Signell1, and 

Bradford Butman1

1

U.S. Geological Survey, Woods Hole, Mass.

2

Woods Hole Oceanographic Institution, Woods Hole, Mass.

The U.S. Geological Survey, Woods Hole Oceanographic 

Institution, and more than one dozen partner institutions 

have been funded by the National Oceanographic Partnership 

Program to develop a coastal ocean sediment-transport model. 

The Community Sediment-Transport Modeling (CSTM) 

project is entering its second year and has released an opensource model based on the Regional Oceanographic Modeling System (ROMS). The model is interdisciplinary and is 

coupled with models for atmospheric forcing, waves, sea ice, 

and plankton ecology. The CSTM is intended for research and 

to help solve practical problems. Researchers are currently 

using the model to study the fate of sediment in coastal and 

estuarine environments, and we anticipate that the model will 

become a tool for addressing practical problems involving 

coastal geologic hazards (flooding and coastal erosion associated with storms and sea-level rise) and hazards to the natural 

resources and ecosystems, including coastal pollution and 

habitat change. The model was developed using a community 

approach: open-source tools for source-code version control and web-based issue tracking, and documentation. This 

poster provides an overview of the CSTM project, an update 

on the model capabilities, and information on the community 

development tools used to build the model. More information 

is available at http://www.cstms.org/. 

A Spatially Explicit Stage-Based 

Population Model of American 

Alligators in Support of Everglades 

Restoration (Poster) 

By Daniel H. Slone1 and Kenneth G. Rice1

1

U.S. Geological Survey, Gainesville, Fla.

The Comprehensive Everglades Restoration Plan 

(CERP), which is available at http://www.evergladesplan.org, 

requires ecological models to assist in the evaluation of restoration project alternatives, formation of restoration targets, and

evaluation of restoration success. The American alligator is a 

keystone predator in the Florida Everglades that is dependent 

on the success of water flow restoration efforts and is indicative of the restoration’s effect upon other species. Historically, 

alligators were more abundant in prairie habitats of the eastern 

floodplain than in deep-water central sloughs. They are now 

concentrated in the deep central sloughs and canals, with 

relatively few individuals residing in the edge habitats because 

of the extremely short hydroperiod under managed conditions. 

The alligator model described in this poster supports the CERP 

by predicting alligator population densities and dynamics over 

time from hydrologic model output of historical, current, or 

projected flows from restoration alternatives. The alligator 

model is especially useful for identifying spatial differences 

among several alternatives. Using historical hydrologic data 

and baseline hydrology models, we compared the modeled 

population density to historical counts of alligators conducted 

along airboat trails at night with a spotlight. We also compared 

simulation results of alligator nesting to nesting site surveys 

and a body condition index in the model to body condition 

data collected from the region. Validation results from diverse 

locations throughout their range and implications for restoration and management decisions are discussed. 

The Delta Research and Global 

Observation Network (Oral 

Presentation)

By Gregory J. Smith1

1

U.S. Geological Survey, Lafayette, La. 

The U.S. Geological Survey National Wetlands Research 

Center (NWRC) has initiated an effort to understand biological, geologic, and hydrologic processes and management 

outcomes for massive deltaic coastal systems like that of 

the Mississippi River Delta. The Delta Research and Global 

Observation Network (DRAGON) is a science framework 

initiated to establish an international community of practice, to 

develop new visualization tools and applications, and to compare and predict outcomes of various management scenarios. 

Hurricanes Katrina and Rita have prompted the assessment, design, and new construction of flood and hurricane 

protection systems in the Mississippi River Delta. Now, more 

than ever, there is a critical need to share information and 

data from deltas around the world and to develop models that 

guide and inform decisions, management, and policy related to 

deltas and large river systems. 

Massive public works engineering projects have been 

constructed in many large deltas around the world for flood 

protection, storm protection, navigation, and energy extraction. These projects can change physical and biological 

processes that ultimately affect aquatic habitats, migratory bird 

habitats, wetlands, coastal ecosystems, and nutrient dynamics. 

Comparative studies with other large deltas can be a useful 

tool for understanding and predicting the effects of major 

engineering projects, land use change, hydrological change, 

and other anthropogenic impacts in these sensitive ecosystems. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 37

The goal of the web-based DRAGON is to facilitate data 

and information sharing and predictive model development. 

The keystone of the network is the community of practice 

concept, which is envisioned as a framework that will build 

an association of researchers focusing on deltaic systems. The 

network will benefit the international community by providing 

tools for data integration and modeling, such as an extensive 

digital library focusing on 12 globally important deltas, a map 

viewer, a data repository, and a database of scientists involved 

in delta-related research to stimulate collaboration and information sharing. It will also provide a watershed comparison 

tool to identify deltas facing similar environmental challenges 

and to understand the intended and unintended results of management decisions implemented in deltaic systems. 

The community of practice concept is also applicable to 

scientists working along the Mississippi River. With monitoring and research activities occurring along the length of the 

Mississippi, there is a need to unify and integrate data analysis 

and dissemination across such systems as the Coastwide Reference Monitoring System (CRMS) in southern Louisiana and 

the Long Term Resource Monitoring Program (LTRMP) in the 

upper Mississippi River. These long-term monitoring programs have enormous amounts of data that, given the proper 

tools and approaches, scientists could use to model complex 

systems, such as the entire Mississippi River. 

Integrating Hydrology and IndividualBased Models: Issues and General 

Approaches to Simulating Animal 

Movement Using Artificial Intelligence 

and Navigation Theory (Poster) 

By Brad Stith 1

1

U.S. Geological Survey/ASci, Gainesville, Fl. 

The integration of regional-scale hydrologic and individual-based species models provides a powerful framework for 

examining the response of organisms to complex temporal and 

spatial processes, including restoration activities, hurricanes, 

and climate change. Simulating animal movement in response 

to landscape level processes is a general problem faced by 

many spatially explicit models. Such diverse fields as artificial 

intelligence and navigation theory are transferable to ecosystem modeling and are very helpful for simulating animal 

behavior and movement. 

We present a general approach to modeling species with 

varying capabilities for learning, navigating, and responding to 

a changing environment. Many vertebrate species show similar movement patterns that lead to the establishment of home 

ranges, which shift in response to changing environmental 

conditions. To simulate such movement behavior in a general 

modeling framework, we developed “intelligent agents” that 

have long-term memories, learning abilities, and navigational 

skills. These intelligent agents perceive the simulated environment, learn, and respond to changing conditions. Different 

types of organisms can be simulated by modifying the learning 

or navigating capabilities of the intelligent agents. We illustrate some of these basic principles of animal movement and 

behavior using difficult-to-acquire telemetry data from tagged 

manatees. These and other data suggest that manatee behavior 

shows a high level of intelligence and a predictable response 

to water temperature and salinity. 

We used the intelligent agent paradigm to develop an 

individual-based manatee model that is integrated with the 

Tides and Inflows in the Mangroves of the Everglades (TIME) 

model, a U.S. Geology Survey (USGS) hydrology model. This 

integration of models allows us to investigate how manatees in 

the Florida Everglades and Ten Thousand Islands may respond 

to ecosystem-level changes associated with hydrologic restoration. Collaboration between USGS biologists and hydrologists 

resulted in the identification of key output parameters from the 

hydrology model needed to adequately represent hydrologic 

factors important to such estuarine species as the manatee. 

An identified need to simulate water temperature led to the 

development of a new heat-transport module for the hydrology 

model. Actual linkage between the hydrology and individualbased model is made via a database of output hydrologic values input to the individual-based model at specific time intervals and locations. To validate the individual-based model, 

we are comparing model output against field data from aerial 

survey “snapshots.” The data requirements for this integrated 

modeling study are enormous, but the hydrology model is 

transferable to other areas and is already being used for other 

species in South Florida. The individual-based model is likely 

to be adaptable to other species or localities as well. 

Modeling the Historical Influence 

of Soil and Water Management on 

Sediment and Carbon Budgets in the 

United States (Oral Presentation)

By Eric T. Sundquist1, Kate Ackerman1, 

Robert F. Stallard2

, and Norman B. Bliss3

1

U.S. Geological Survey, Woods Hole, Mass.

2

U.S. Geological Survey, Boulder, Colo.

3

Science Applications International Corporation, Sioux Falls, S. Dak.

 The documented history of U.S. soil and water management provides a unique opportunity to examine soil and sediment carbon storage under conditions of changing management practices. Historical acceleration of erosion owing to 

cultivation has been moderated by improved soil management. 

Increased construction of dams and locks has expanded areas 

of aquatic sedimentation in reservoirs and ponds. Enhanced 
38 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

historical sediment deposition rates have been documented in 

lakes and estuaries. All these changes have had an impact on 

terrestrial carbon storage and turnover. The present-day carbon 

budget associated with erosion and burial cannot be determined without quantifying the time-dependent changes caused 

by past and present soil and water management.

 We use existing datasets with geographic information 

system and modeling techniques to estimate sediment and 

carbon budget trends since the year 1700 in the conterminous 

United States. We begin by calculating historical sediment 

budget scenarios representing effects of soil- and watermanagement practices. We derive soil erosion estimates for 

recent decades from the U.S. National Resources Inventory 

(NRI). Using estimates of historical cropland erosion yields 

(erosion rates per unit area) and cropland areas and distributions (derived from U.S. census data), we calculate simplistic 

“hindcast” erosion scenarios for the years prior to the NRI 

estimates. We use sedimentation rates compiled in the U.S. 

Reservoir Sedimentation Survey Information System. Systematic relationships among these rates are applied to estimate 

historical sedimentation for the more-extensive U.S. National 

Inventory of Dams, and for lakes and ponds. Because our analysis uses a combination of diverse existing datasets, we devote 

particular attention to methods of data integration that are 

consistent with the statistical character of the source data. Our 

analysis indicates that historical export of sediments to coastal 

areas is relatively insignificant, whereas substantial sediment 

deposition in upland areas is necessary to balance the historical sediment budget. Relatively recent rates of sedimentation 

in lakes and impoundments appear to match or exceed rates 

of upland erosion, suggesting that only a fraction of recent 

sediment transport is derived from channel and bank erosion, 

including remobilization of historically deposited alluvium 

and colluvium.

 For each historical sediment budget scenario, we apply 

models of carbon dynamics to time-dependent accounting of 

carbon in erosional and depositional environments. Our carbon 

calculations begin with estimates of the carbon content of soils 

at erosional and depositional sites based on the NRI soil database. Rates of soil carbon production, erosion, degradation, 

transport, and burial are constrained by both sediment and 

carbon mass-balance coupled to representations of landscape 

soil-carbon dynamics. Carbon burial in aquatic environments 

is calculated from estimates of composition and deposition 

rates of autochthonous and allochthonous sediment. We find 

that cumulative amounts of carbon translocated by historical 

erosion and deposition are comparable to amounts of cumulative soil carbon depletion estimated in previous studies that 

have not considered erosion and deposition. 

 Our historical sediment budget scenarios imply a large 

historical transient of eroded and redeposited terrestrial 

sediments. An improved understanding of non-steady-state 

carbon dynamics in these sediments and in eroding croplands 

is needed to estimate the net effect of erosion and deposition 

on the historical and present-day exchange of carbon between 

the land and the atmosphere. The transient sediment pulse and 

accompanying biogeochemical and ecological responses have 

broader implications for management of water and ecosystems 

(for example, to address effects of sedimentation and nitrogen 

loading on riparian zones and wetlands). Continuing investigation of these important topics will require more robust 

collaboration across disciplines and institutions. 

Applications of the FTLOADDS Coupled 

Code and Linkages to Hydrologic and 

Ecological Models (Oral Presentation) 

By Eric D. Swain1 and Melinda Lohmann1

1

U.S. Geological Survey, Ft. Lauderdale, Fla. 

To represent the complex hydrology of South Florida 

with its highly connected surface water and ground water, very 

low gradients, and dynamic coastal interfaces, the Flow and 

Transport in a Linked Overland/Aquifer Density-Dependent 

System (FTLOADDS) code was created. FTLOADDS 

combines the Surface Water Integrated Flow and Transport 

in 2 Dimensions (SWIFT2D) model and computer program for three-dimensional ground-water flow (SEAWAT) 

with multiple improvements in both the surface-water and 

ground-water representations. Numerous field studies were 

implemented to support hydrologic model development. 

Subsequent FTLOADDS applications use improved methods of representing evapotranspiration, frictional resistance, 

barriers to flow, and heat transport. The code has been recently 

updated to make it applicable to most coastal estuarine areas. 

The following discussion illustrates a number of completed, 

ongoing, and planned linkages of FTLOADDS to other 

hydrologic and ecological models.

A comprehensive model is planned that utilizes two localscale models previously developed with the FTLOADDS 

code—the established Tides and Inflows in the Mangroves 

of the Everglades (TIME) model application of the southern 

Everglades, and the Biscayne Bay model, which encompasses 

Biscayne National Park and most of Miami-Dade County. 

Combining these into a large sub-regional model will give 

an integrated comprehensive assessment of how different 

Comprehensive Everglades Restoration Program (CERP) 

scenarios will affect surface-water coastal discharge and stage, 

ground-water flow, and salinity in Everglades National Park 

and Biscayne National Park. Once the sub-regional model 

is calibrated, additional simulations will be performed to 

estimate predevelopment hydrologic conditions and predict 

hydrologic conditions under proposed restoration alternatives. 

These simulations include linkage with the South Florida 

Water Management District’s Natural Systems Model (NSM) 

and the South Florida Water Management Model (SFWMM). 

Because FTLOADDS simulates water levels and salinities, it 

can also be used to run simulations that can predict the potential impacts of sea-level rise. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 39

One purpose of developing such a complex model 

application is to provide necessary hydrologic information 

to develop predictive ecological models. The challenge is 

to pass the relevant information to ecological models in a 

configuration that yields a realistic response. This involves 

the complex issues of spatial and temporal averaging, correlating topographic features between the hydrologic and 

ecological models, and determining output uncertainty. The 

hydrologic model outputs a number of variables that are 

significant to the ecological models. Water depth and flow are 

the primary output of the FTLOADDS surface-water simulation, but transport components, such as salinity, temperature, 

and nutrients, may be required to link to ecological models. 

The TIME/Biscayne application simulates salinity transport 

needed for Across Trophic Levels System Simulation (ATLSS) 

fish, wading bird, crocodile, and alligator models. One ATLSS 

model, ALFISHES, uses gridded daily depth and salinity 

data from FTLOADDS to simulate fish population dynamics. 

FTLOADDS was modified to simulate heat transport and thus 

supplies temperature and salinity data along known manatee 

migration pathways to an individual-based manatee model for 

the Ten Thousand Islands area. Along with the TIME/Biscayne 

regional model, the FTLOADDS application to the Ten Thousand Islands area allows the entire southern coast of Florida to 

be covered. 

Future development of FTLOADDS and its applications 

to linked hydrologic and ecological models will involve incorporating nutrient-transport capabilities for phosphorous and 

nitrogen modeling. Hydrologic effects on invasive species, 

oyster beds, sharks, and other biota can be determined and 

the effects of regional restoration scenarios can be simulated. 

Linking hydrologic and ecological models provides an essential set of tools that can be used to assess ecological restoration 

efforts in South Florida and elsewhere. 

Ecosystem Carbon Budgets and 

Crop Yields in a Tropical Savanna 

Ecosystem as Related to Changes in 

Climate and Management (Poster)

By Zhengxi Tan1, Larry L. Tieszen2, and 

Emmanuel Tachie-Obeng3

1

Science Applications International Corporation, Sioux Falls, S. Dak.

2

U.S. Geological Survey, Sioux Falls, S. Dak. 

3

Environmental Protection Agency, Accra, Ghana

The tropical savanna in West Africa is a critical ecosystem that provides products for people and livestock. It is, however, vulnerable to land disturbances and sensitive to climate 

change. We selected the Bawku savanna ecoregion in northern 

Ghana to simulate the responses of both natural and managed 

ecosystems to climate change and management scenarios 

using General Ensemble Biogeochemical Modeling System 

(GEMS), a biogeochemical model. Our results from this study 

show that the conversion of forest to agricultural use resulted 

in a substantial reduction in ecosystem carbon stocks across 

this ecoregion during the past century. Climate changes would 

lead to a continuous decline in soil carbon stock of the entire 

ecoregion through the 21st century; however, this result could 

be offset by increasing nitrogen fertilization of the cultivated 

lands. An increase in nitrogen fertilization could also significantly increase grain yields of all crops, except groundnut. The 

biomass production and soil carbon stocks within woodlands 

would depend heavily on cutting options and little on climate 

change. The model simulation results are fairly comparable 

with the field observations. 

Seeing the Forest Fire Through the 

Trees: Integrating Multiple Data 

Streams to Model the Presettlement 

Fire Regime in the Longleaf Pine 

Ecosystem (Poster)

By Adam Terando1 and Alexa McKerrow1

1

Southeast Gap Analysis Program, Raleigh, N.C.

Fire regimes are dependent on the local abiotic and 

biotic factors that regulate the probability and frequency of 

fire ignition and spread. Anthropogenic influences on these 

fire regimes, such as fire suppression or global warming, can 

be large and variable through time. Anthropogenic influences 

may even mask or decouple drivers of an area’s historical fire 

regime that developed through unique ecological, geologic, 

and climatic circumstances. For example, in the southeastern 

United States, prior to large-scale fire suppression efforts, 

the longleaf pine (Pinus palustris) ecosystem was characterized by high-frequency, low-intensity fires that impeded 

woody vegetation encroachment, replenished soil nutrients, 

and facilitated seed germination and growth. Understanding 

how abiotic and biotic (absent anthropogenic) factors influence the intra- and inter-annual variability of the fire season 

is thus a critical step in gaging how current fire regimes differ 

from presuppression conditions or what could be expected 

given some scenario in the future (for example, anthropogenic 

climate change). To better understand the influences that 

affect fire in longleaf pine systems, we present a hierarchical 

spatio-temporal model depicting the pre-European settlement 

fire season across the entire known range of longleaf pine. 

Our model predicts presettlement fire ignition probabilities by 

modifying the U.S. Forest Service’s Fire Potential Index (FPI). 

Modified FPI values were calculated as a function of presettlement Normalized Difference Vegetation Index (NDVI) values 

and daily meteorological conditions to estimate live and 

dead fuel moistures. The pre-settlement NDVI values were 
40 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

estimated by using point sample data from known healthy 

longleaf pine stands and meteorological data at those sites. 

Multimodel inference was used to evaluate the contribution of 

the local climate to the seasonal NDVI values. The coupling 

of climate and fire season within our model will allow for a 

better estimation of the impacts of past, present, and future 

anthropogenic effects on the amplitude, duration, and variability of the fire season in this ecosystem. The study is an 

example of a successful integration of data at multiple scales 

from multiple agencies and research institutions into a model 

that is highly transferable and robust to alternative conditions 

and environments. 

Predictive Models of Suitable Habitat 

for the Mojave Desert Tortoise: 

“Truthiness” in Pseudo-Absence 

(Poster)

By Kathryn A. Thomas1, Leila Gass1, Todd Esque2, and 

Ken Nussear2

1

U.S. Geological Survey, Tucson, Ariz.

2

U.S. Geological Survey, Henderson, Nev.

Our team developed and assessed predictive models of 

suitable habitat for the Federally threatened Mojave population of the desert tortoise. In the development of these models, 

we made decisions regarding which modeling algorithms to 

use, how to parameterize the algorithms, which input data to 

select, and what methods to use to assess the resulting model 

output. While we had a robust sampling for tortoise presence 

data, we lacked input data of locations of tortoise absence and 

struggled with methods for creating data to represent absence 

locations. There are four modeling stages for which absence 

data are a consideration: (1) model building, (2) assessment of 

continuous model output, (3) determination of a threshold to 

convert continuous output to binary, and (4) accuracy assessment of the binary output. A general method to deal with the 

lack of absence data in modeling is to develop an artificial set 

of absence locations or “pseudo-absence” data. We developed 

pseudo-absence data using two methods: the random background method and the Ecological Niche Factor Analysis 

(ENFA)-constrained method. While only one of the three 

modeling algorithms we applied required the use of pseudoabsence data in model building, the output of all three models 

required pseudo-absence data to assess the continuous output, 

develop a binary representation of the output, and assess the 

binary output. We found that by comparing the assessment 

metrics derived with the random background and with the 

ENFA pseudo-absence data, we improved our understanding 

of the types of potential errors, both of commission and omission, in model output. Our approach and results illustrate that 

choices regarding structure and use of pseudo-absence data are

nontrivial. In addition, our methods allowed us to represent 

degrees of uncertainty in the prediction of suitable habitat 

graphically. This approach to model assessment also facilitates 

interpretation of model output by such users as the Desert 

Tortoise Science Advisory Committee. 

A Knowledge Management Approach 

for Complex Regional Ecosystem 

Modeling (Poster) 

By Alicia Torregrosa1, Danielle Aiello1, and 

Andrea Woodward2

1

U.S. Geological Survey, Menlo Park, Calif.

2

U.S. Geological Survey, Seattle, Wash.

The Puget Sound Integrated Landscape Monitoring Pilot 

is approaching the complex task of integrating the human 

dimension as a component of the landscape-level ecosystem 

dynamics by using an innovative knowledge management system to conceptualize the interactions between various parts of 

the greater Puget Sound ecosystem. The knowledge management system software “Personal Brain” has been implemented 

to develop conceptual models of both human social systems 

and ecosystem processes. The framework that has been developed is providing insights into the linkages and relationships 

between ecosystem processes at different scales and humaninitiated programs to address and repair ecosystem damage. 

This session will provide a hands-on opportunity to investigate 

the computer-based system. 

Linking Plots to Landscapes: A 

Synthetic Framework for Monitoring 

Change in the Great Basin Ecosystem 

(Oral Presentation) 

By Alicia Torregrosa1, Andrea Woodward2, 

David Miller1

, Marie Denn3, David Bedford1, 

Pierre D. Glynn4

, and Sean Finn5

1

U.S. Geological Survey, Menlo Park, Calif.

2

U.S. Geological Survey, Seattle, Wash.

3

U.S. National Park Service

4

U.S. Geological Survey, Reston, Va.

5

U.S. Geological Survey, Boise, Idaho

Questions asked by decisionmakers and policymakers are rarely answered at the same scale as the observations 

required to answer them. There is a growing recognition that 

ecosystem stressors and drivers accumulate across landscapes, 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 41

yet monitoring data collected by land management agencies 

are often limited to site-specific local plots. There is an urgent 

need to scale between plots and landscapes. The model we 

have developed integrates concepts from landscape ecology, 

ecosystem ecology, geostatistics, and geographic information science. The methodology shows the spatio-temporal 

linkages between subsystems, such as riparian, sagebrush 

steppe, pinyon-juniper, ground water, and springs, by using 

a hierarchical cross-scale approach. Ongoing work includes 

implementing the model as a spatially explicit framework to 

quantify the cumulative effects that are important to natural 

resource managers in the Great Basin. 

Credibility and Acceptability of Land 

Use Planning Approaches: A Case 

Study in the Coastal Area of the 

Mekong Delta, Vietnam (Poster) 

By Nguyen Hieu Trung1, Le Quang Tri1, 

M. E.F. van Mensvoort2

, and A. K. Bregt2

1

Can Tho University, Cantho City, Vietnam 

2

Wageningen University, The Netherlands

This poster compares three land use planning (LUP) 

approaches in the coastal areas of Bac Lieu Province, Mekong 

Delta, Vietnam. The selected LUP approaches represent three 

levels of complexity in land resources study, ranging from 

empirical and qualitative to mechanistic and quantitative. 

The three approaches are: (1) participatory LUP (PLUP), 

which takes into account the local people’s perception of land 

utilization; (2) guidelines for land use planning by the Food 

and Agriculture Organization of the United Nations (FAO) 

enhanced with multicriteria evaluation (FAO-MCE), and (3) 

the land use planning and analysis system (LUPAS), which 

uses interactive multiple-goal linear programming. By using 

the same planning goal and applying the three approaches in 

the same study area and at the same planning period, we were 

able to compare the pros and cons of these LUP approaches in 

terms of time, money, equipment, and human resource requirements and genetic applicability and whether the approaches 

were acceptable in terms of their credibility, accuracy, and 

benefit to stakeholders. 

MapInfo was used for spatial analysis, multicriteria 

evaluation, and mapping of the model’s results. GAMS (which 

was developed by GAMS Development Corporation) was 

used to build the interactive multiple-goal linear programming 

model. The approaches were applied at the village level (about 

10,000 hectares). The land use in the study area is diverse, 

quickly shifting, and strongly contrasting. The contrast is not 

only in terms of resource use (for example, shrimp, mangrove 

forest or salt that demand brackish water, as opposed to rice 

or vegetables that require fresh water), but is also in terms of 

social economic sustainability and profitability. Shrimp cultivation, for instance, brings a very high income compared with 

rice production but it is not a very stable industry because of 

risks, such as shrimp diseases and water pollution. 

The FRAME (Framing Research in 

Support of the Adaptive Management 

of Ecosystems) Project: A Collaborative 

Modeling Approach to Natural 

Resource Management (Oral 

Presentation)

By Christine E. Turner1, Jim Chew2, 

George Leavesley1

, and George San Miguel3

1

U.S. Geological Survey, Denver, Colo.

2

U.S. Forest Service, Missoula, Mt.

3

U.S. National Park Service, Mesa Verde, Colo.

Federal land managers are currently under siege. They are 

challenged to make science-based decisions and to optimize 

the management of multiple resources while minimizing negative impacts of any given decision—all while under increased 

public scrutiny. Thus, there is considerable need for an adaptive management framework that will accommodate changing 

conditions and meet multiple objectives. Through the interdisciplinary USGS Framing Research in Support of the Adaptive Management of Ecosystems (FRAME) project, which 

includes partners from other agencies (including the National 

Park Service, Bureau of Land Management, Bureau of Indian 

Affairs , and U.S. Forest Service) and universities, our strategy 

is to couple the adaptive capabilities of an integrated modeling approach with accepted principles of collaboration. Initial 

efforts within the project focused on natural resource and firemanagement issues at Mesa Verde National Park (MVNP) in 

Colorado, where prolonged drought in the Western Interior is 

dramatically affecting vegetation patterns and the fire regime. 

The knowledge to model a complex natural system is distributed among resource managers, diverse scientific experts, 

and physical-process modelers. In our study, the scientists 

involved included hydrologists, ecologists, and experts in 

pinyon-juniper woodlands and cheatgrass. The distributed 

knowledge was elicited through the collective learning that is 

characteristic of collaborative problem-solving environments 

when all participants are continuously and actively engaged. 

The scientists brought their diverse expertise to the discussion, 

and modelers provided the means to capture this expertise in 

numerical models. The resource manager contributed knowledge of the ecosystem and also helped maintain the focus on 

the decision context so that pertinent science, not just “sound” 

science, was brought to bear on the resource management 

issues. Framing the science issues collaboratively not only 
42 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

ensured that the pertinent science was brought to bear, but also 

promoted the interdisciplinary science approach necessary to 

address resource management needs. 

The USGS’s Modular Modeling System (MMS), which 

can be accessed online at http://www.brr.cr.usgs.gov/mms, 

offers an ideal framework to facilitate the integration and linking of process models and the execution of them in a coupled 

manner. The framework also facilitates adaptive management 

approaches where alternative scenarios and model combinations can be applied and refined iteratively with new scientific 

understanding and observations from monitoring results. In 

our efforts, the principle models initially used are the Precipitation Runoff Modeling System (PRMS), and the Simulating 

Patterns and Processes at Landscape Scales (SIMPPLLE) 

landscape model. We also incorporated results from an empirical sedimentation model related to post-fire runoff and erosion. Efforts are currently underway to downscale climate data 

for the modeling effort. Populating a model’s structure with 

both numerical values and logic relationships and then validating its performance involved a process of iterative interaction 

with modelers working collaboratively with the scientists and 

resource managers. 

The adaptability of the collaborative modeling approach 

is key. For example, during the course of the project, ongoing field studies by FRAME biologists revealed that the rapid 

spread of cheatgrass (Bromus tectorum) in the park posed an 

increasing threat with respect to fire frequency and spread. 

In collaboration with all project participants, a group consensus emerged: We decided that the spread of cheatgrass 

had become a priority because it significantly altered the fire 

regime and thus posed an increased threat to the pinyon-juniper ecosystem. 

A conceptual model developed by one of the project 

scientists served as the basis for incorporating the interactions 

of cheatgrass and its effect on the ecosystem into SIMPPLLE. 

The decision of the group to focus modeling efforts on the 

potential threat of cheatgrass to the ecosystem dynamics at 

MVNP illustrates the adaptive nature of the collaborative 

modeling approach. In resource management, it is typical 

for priorities to shift with time. The collaborative modeling 

approach is designed to accommodate these shifts. 

The FRAME case study at MVNP is providing an opportunity to implement and refine the principles and components 

of a collaborative modeling approach. By coupling the principles of collaboration with integrated modeling approaches, 

we are developing a transportable collaborative modeling 

framework to facilitate adaptive, multiobjective resource 

management that is applicable across a wide range of ecosystems. Recent trends in natural resource management—toward 

integrated science approaches, comanagement of public 

lands, adaptive management in the face of uncertainty, and 

public engagement in land use decisionmaking—are trends 

that developed in response to a greater appreciation of the 

inherent complexity, feedback mechanisms, and uncertainty 

in natural systems, plus increased public scrutiny of decisions on public lands. The FRAME collaborative modeling 

approach was developed to address the challenges faced by 

natural resource management and provides a way to effectively link integrated science to natural resource management 

needs. Our initial effort in MVNP focused on the resource 

manager–modeler–scientist interactions, but the FRAME 

approach can also be used readily to engage the public in 

participatory natural resource management efforts. The future 

of resource management depends on the success of such collaborative efforts. 

Elements of a Global Model: An 

Example of Sea Level Rise and Human 

Populations at Risk (Oral Presentation)

By E. Lynn Usery1, Jinmu Choi2, and Michael P. Finn1

1

U.S. Geological Survey, Rolla, Mo.

2

Mississippi State University, Miss.

Global and regional environmental models often rely on 

raster datasets of varying geometric and thematic properties to 

simulate geospatial processes and events. It usually is necessary to project the raster datasets to a common coordinate 

framework to support tabulation of statistical properties and 

visualizations, such as simulation and animation. Because 

projecting global raster datasets is subject to significant distortion, including pixel value gain and loss, and thus creates 

corresponding errors in the process or event model, effective 

methods of raster map projection and resampling are critical to the accuracy of the models. In our research, we have 

developed effective raster transformation and resampling in a 

publicly available software package, MapImage or mapimg. 

We illustrate the methods using Global 30 Arc-second 

Elevation Data Set (GTOPO-30), land cover data (from the 

U.S. Geological Survey Global Land Cover Dataset), and 

population data (from LandScan Dataset) with 1-kilometer 

(km) resolution in a simulation and animation of worldwide 

sea-level rise. The simulation provides locations of high 

population densities in low-lying coastal areas that are subject 

to risk from sea-level rise and particularly from surges of sea 

water, such as from hurricanes and tsunamis. We also provide 

projection and resampling examples with regional simulations 

and animations of Maylasia and the United States using higher 

resolution [90-meter (m) elevation from the Shuttle Radar 

Topography Mission] geospatial data. 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 43

Modeling and Generalizing Population 

Pressure at the Regional Scale by 

Interpolating a Kriging Surface (Poster) 

By Dalia E. Varanka1

1

U.S. Geological Survey, Rolla, Mo.

 Urbanization presently is generalized most commonly by 

census numbers or by impervious surface area. The purpose of 

this research is to expand the representation of the geographical extent of urbanized areas to the degree that urbanization 

encompasses the environmental resources of the surrounding 

region that sustain cities, sometimes called the urban environmental “footprint.” The regional scale of urbanization is 

relevant to the integration of human population pressure on 

the landscape and affects change at other scales from local to 

global. A spatial model of generalized population pressure was 

interpolated using kriging of a variable combining population and wealth based on the U.S. Census Bureau data in a 

geographic information system. The outcome was adjusted by 

visualizing the interaction of urbanized activity between urban 

block groups and specific urban sites. The findings from the 

research will be organized into a conceptual model of urbanization scale transformation. The extension of urbanization to 

the regional scale potentially may form the basis for integrating generalized geospatial data of urbanized areas with other 

natural resource models and data. 

Formalizing Model-Specific GIS 

Methodology With the GIS Weasel 

(Poster) 

By Roland Viger1

1

U.S. Geological Survey, Denver, Colo. 

The U.S. Geological Survey geographic information 

system (GIS) Weasel is an extensive, stable interface to a GIS 

that facilitates execution of many of the common tasks needed 

to derive the geographic information used in an environmental 

simulation model (ESM). Despite bundling basic GIS functionality into methodological chunks that are more meaningful to modelers, the GIS Weasel still does not help new users 

lower the learning curve for using the GIS Weasel in their 

particular ESM. To allow the managers of an ESM to define 

and communicate their ideas in a formal way, the GIS Weasel 

has been designed to integrate software “plug-ins” that can 

be developed, published, and distributed independently of the 

GIS Weasel software package. Plug-ins may include written documents, data, or new software. Most plug-ins contain 

a configuration file that specifies the subset of GIS Weasel 

functionality that should be used. Regardless of the plug-in 

content, this concept provides a way to formally specify how 

the geographic information consumed by an ESM should be 

prepared. This specification not only helps new users learn 

about an ESM and avoid inappropriate model usage but also 

can serve as documentation of an ESM that is much more 

specific than most forms of metadata. 

The Integration of Environmental 

Simulation Models and GIS Using 

Metadata (Poster)

By Roland Viger1

1

U.S. Geological Survey, Denver, Colo. 

Geographic information systems (GIS) are powerful tools 

for the improved management of natural resources, notably 

by producing information used in environmental simulation 

models (ESM). The effectiveness of natural resources management can be further enhanced by the improved integration 

of ESMs with GIS. Generic methods for carrying out such 

integration have been difficult to develop because ESMs fail 

to document or describe geographic information in a way that 

can be readily used in computational contexts external to the 

ESM. This paper will present research on the generic methods 

for integration of GIS services using formalized definitions 

of the geographic information used in ESMs. The definitions are specified using the geoprocessing methodologies 

used to derive the information. Geoprocessing methodology 

refers to the GIS operations performed to generate a piece of 

geographic information that will be used in an ESM. Definitions are encoded in a metadata scheme designed to improve 

the interoperability of an ESM with different GIS software, as 

well as that of the geographic information with other ESMs. 

This paper also presents a middleware infrastructure for 

handling communication between a GIS and an environmental 

model using this metadata. The direct result of this research is 

a more formalized specification of knowledge about methodologies for the creation of geographic information used in an 

ESM. Further, these specifications allow for the systematic 

evaluation, comparison, and evolution of geographic information concepts used in modeling and, ultimately, the improvement of ESMs and natural resource management. 
44 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Processing Time-Series Data to 

Calibrate the GSFLOW Coupled 

Ground-Water/Surface-Water Model 

in the Trout Lake Watershed, Northern 

Wisconsin (Poster) 

By John F. Walker1, Randy Hunt1, and John Doherty2

1

U.S. Geological Survey, Middleton, Wis.

2

Watermark Numerical Computing, Brisbane, Queensland, Australia

A major focus of the U.S. Geological Survey’s Trout 

Lake Water, Energy and Biogeochemical Budgets (WEBB) 

project is the development of a watershed model to allow predictions of hydrologic response to future conditions, including 

land use and climate change. Because of the highly conductive 

nature of the outwash sand aquifer and the topography of the 

watershed, stream flow is dominated by ground-water contributions; however, runoff does occur during intense rainfall 

periods and spring snowmelt. The coupled Ground-Water and 

Surface-Water Flow model (GSFLOW) was chosen because it 

could easily incorporate an existing ground-water flow model 

and it provides for simulation of surface-water processes. 

Data collected from 1992 to 2006 in the study area 

include lake levels, ground-water levels, and streamflow. The 

frequency of data collection varies from monthly to daily; in 

general, the more frequent data was collected over a shorter 

period and during the latter portion of the period monitored. 

The time-series processing software (TSPROC) (Doherty, 

2003) was used to distill the large time-series dataset to a 

smaller set of observations and summary statistics that captured the salient hydrologic information. The TSPROC software was also used to process model output, thus providing 

equivalent comparisons of modeled and observed variables. 

Calibration targets for lake and ground-water levels 

included the mean and range for the entire simulation period, 

as well as incremental differences in monthly measurements. 

For wells with daily water levels, the time series was first 

smoothed with a digital filter and then resampled at the middle 

of each month. Targets for streamflow included monthly 

volumes, comparison of points on the flow-duration curve, 

and total streamflow smoothed with a digital filter and then 

resampled at specific dates to capture the inherent variability 

of the observed time series. Baseflow separation was also carried out using a recursive digital filter to separate the quickflow component from total streamflow; the smoothed baseflow 

values were resampled at specific dates to capture the seasonal 

variation in baseflow. The time-series processing reduced hundreds of thousands of observations to less than 5,000. Parameter estimation was performed on the complete time-series 

dataset, as well as on the reduced set of calibration targets. 

The distilled set of calibration targets resulted in a more-robust 

calibration that effectively represented the components of the 

system response that are important for model prediction. 

Reference Cited 

Doherty, J., 2003, TSPROC— A time-series processor utility: 

Brisbane, Australia, Watermark Numerical Computing.

Modeling Surface-Water Sulfate 

Dynamics in a Northern Everglades 

Wetland (Poster)

By Hongqing Wang1, Michael G. Waldon2, 

Ehab A. Meselhe1

, Jeanne C. Arceneaux1, 

Chunfang Chen2

, and Matthew C. Harwell3

1

University of Louisiana at Lafayette, Lafayette, La.

2

U.S. Fish and Wildlife Service, Lafayette, La.

3

U.S. Fish and Wildlife Service, Vero Beach, Fla.

Sulfate contamination has been identified as a serious environmental issue in the Everglades. However, it has 

received less attention compared with phosphorus enrichment. Sulfate enters the Arthur R. Marshall Loxahatchee 

National Wildlife Refuge (Refuge), a remnant of the historic 

Everglades, in pumped stormwater discharges with a mean 

concentration of approximately 50 mg L-1; marsh interior concentrations at times fall below a detection limit of 0.1 mg L-1. 

There have been few studies of how patterns of flow and canal 

sulfate concentration affect sulfate concentrations and dynamics in the Refuge marsh. In this research, we developed a 

sulfate mass balance model, the Refuge Water Quality Model 

(RWQM), to examine the response of surface water sulfate in 

the Refuge to changes in sulfate loading and hydrologic processes. The Refuge was divided into four compartments along 

a gradient from rim canal to marsh interior: canal, perimeter 

marsh, transitional marsh, and interior marsh. The hydrologic, meteorological, and water-quality data were obtained 

primarily from the South Florida Water Management District 

hydrologic online database (DBHYDRO). The RWQM is 

driven by a water budget model using historic inflow, outflow, 

and meteorological data. The RWQM was implemented using 

the U.S. Environmental Protection Agency’s Water Quality 

Analysis Simulation Program (WASP) model. Sulfate loss is 

modeled using a first-order apparent settling model. Apparent 

settling of sulfate from the marsh water column is not intended 

to imply an actual particulate settling process but rather is 

assumed to represent primarily loss by sulfate reduction. The 

RWQM has been calibrated and validated using long-term 

monitoring data (1995 to 2006). Model results show that 

the sulfate model is capable of capturing the spatial, interannual, and seasonal variations in sulfate concentrations in the 

Refuge. Our simulations indicate that sulfate-elevated canal 

water intrusion into the marsh determines the surface-water 

sulfate levels in the Refuge marsh areas. Our compartmental 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 45

hydrological and water-quality modeling approach is a useful 

tool for estimating sulfate reduction rates and understanding 

the implications of water management decisions on Refuge 

resources. 

Variations in Pesticide Leaching 

Related to Land Use, Pesticide 

Properties, and Unsaturated Zone 

Thickness in an Agricultural 

Watershed (Poster) 

By Richard Webb1

1

U.S. Geological Survey, Denver, Colo.

Pesticide leaching through variably thick soils beneath 

agricultural fields in Morgan Creek, Md., was simulated 

for water years 1995 to 2004 using the Leaching Estimation and Chemistry Model (LEACHM). Fifteen individual 

models were constructed to simulate five depths and three 

crop rotations with associated pesticide applications. Unsaturated zone thickness averaged 4.7 meters (m) but reached a 

maximum of 18.7 m. The average annual recharge to ground 

water decreased from 15.9 centimeters (cm) to 11.1 cm as the 

unsaturated zone increased in thickness from 1 m to 10 m. 

These recharge estimates are at the lower end of previously 

published values, which indicate overestimates of surface 

runoff and (or) the importance of preferential flow paths not 

considered in the piston-flow models like LEACHM. The 

total amounts of applied and leached masses for five parent 

pesticide compounds and seven metabolites were estimated 

for the 32-square-kilometer (km2) Morgan Creek watershed 

by associating each hectare to the closest one-dimensional 

model analog of model depth and crop rotation scenario as 

determined from land use surveys. LEACHM parameters were 

set such that the branched, serial, first-order decay of pesticides and metabolites was realistically simulated. Leaching 

is predicted to be greatest for shallow soils and for persistent 

compounds with low sorptivity. Based on simulation results, 

percent parent compounds leached within the watershed can 

be described by a regression model of the form exp(-depth)

(a ln(t1⁄2) - b ln(KOC)) where t1⁄2 is the degradation half-life in 

aerobic soils, KOC is the organic carbon normalized sorption 

coefficient, and a and b are fitted coefficients. 

The USA National Phenology Network: 

Towards an Integrative Assessment of 

Global Change Impacts at the National 

Scale (Poster)

By Jake F. Weltzin1 and Mark Losleben1

1

U.S. Geological Survey, Tucson, Ariz.

Phenology is the study of the timing of recurring biological phases, the causes of their timing with regard to biotic and 

abiotic forces, and the interrelation among phases of the same 

or different species. Although phenology is a far-reaching 

component of environmental science, it is poorly understood relative to other ecological patterns and processes. For 

example, it is unclear how climatic attributes affect the phenology of different organisms and how those attributes vary in 

importance on different spatial and temporal scales. We know 

that phenology affects the abundance and diversity of organisms and their function and interactions in the environment, 

especially their effects on fluxes in water, energy, and chemical elements at various scales. With sufficient observations and 

understanding, phenology can be used as a predictor for other 

processes and variables of importance at local to global scales, 

and phenology could drive a variety of ecological forecast 

models with both scientific and practical applications.

The USA National Phenology Network (NPN) (which 

is accessible at http://www.usanpn.org) is a new enterprise—

a national network of integrated phenological observations 

essential to evaluate ongoing environmental changes. The 

NPN will be integrated with other observation networks, 

including regional phenology networks, remote sensing 

products, emerging technologies, and data management 

capabilities, and will capitalize on myriad educational 

opportunities and a new readiness of the public to participate in investigations of nature on a national scale. This 

poster illustrates how phenology is an emerging integrative science for assessing impacts of global change, and for 

increasing citizen awareness and participation in understanding environmental impacts of human activities. Further, the 

poster highlights the need to develop tools, protocols, and 

models for the scaling of phenological data, from embedded sensors (for example, on an individual or in a particular 

habitat) to individuals, to patches and landscapes, to regions 

and the globe.
46 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Application of Bayesian Hierarchical 

and Information Theoretic Approaches 

in Model-Facilitated Recovery of 

Endangered Fish Populations Based 

on Behavior and Physiology as Part of 

an Individual-Based Spatially Explicit 

Model (Poster) 

By Mark L. Wildhaber1, Thomas W. Bonnot2, 

Ali Arab3

, Scott H. Holan1, Joshua J. Millspaugh1, 

Christopher K. Wikle1

, and Ginger M. Davis4

1

U.S. Geological Survey, Columbia, Mo.

2

University of Missouri, Columbia, Mo.

3

Georgetown University, Washington, D.C.

4

University of Virginia, Charlottesville, Va.

Endangered fish species recovery efforts require detailed 

understanding of the life history of the species, the environments in which it lives, and the aquatic community with which

it interacts. The ability of fish to meet requirements of different life stages determines the restrictive nature of potential 

ecological bottlenecks at each life stage. Individually based 

models used in conjunction with explicit models of space that 

incorporate spatial and temporal variation could be used to 

predict the physiology, behavior, growth, survival, and reproduction of fishes under changing environmental conditions to 

estimate the restrictive nature of each bottleneck. One possible approach is to integrate knowledge across disciplines to 

develop an individual-based spatially and temporally explicit 

forecasting tool based on an effective set of mathematical, 

statistical, and empirical models that describe fish community 

dynamics on the basis of fish physiological and behavioral 

responses and the physical environment in which these fish 

live. Such forecasting tools should provide an excellent source 

for understanding the dynamics of fish populations and communities and the nature of ecological bottlenecks. The ultimate

value of this approach is a better understanding of the causes 

behind and an improved ability to predict the short-term 

and long-term distribution and population trends of fish in 

response to management decisions. 

In developing such a model for sturgeon, we are applying a combination of hierarchical Bayesian and information 

theoretic approaches to model behavior and spawning success of the gravid shovelnose sturgeon in the Lower Missouri 

River. We are developing a hybrid physical/statistical model 

of general fish movement based on an advection-diffusion 

partial differential equation in a hierarchical Bayesian modeling framework. We are also describing the selection of 

physical habitat by gravid shovelnose sturgeon during the 

breeding season by evaluating discrete choice models, based 

on telemetry data, within an information theoretic framework. 

Using measurements of biological variables associated with 

readiness to spawn as well as longitudinal behavioral data 

collected using telemetry and data storage device sensors, we 

introduce a hierarchical Bayesian model for predicting spawning success. This model uses an eigenvalue predictor from the 

transition probability matrix in a two-state Markov switching 

model with generalized autoregressive conditional heteroskedasticity (GARCH) dynamics as a generated regressor in a 

linear regression model. 

Designing Sustainable Landscapes for 

Bird Populations in the Eastern United 

States: A Collaborative Approach 

(Poster) 

By James B. Grand1, Steven G. Williams2, 

Jaime A. Collazo3

, Alexa McKerrow4, 

Andrew Milliken5

, Craig Watson6, and 

Malcolm T. Jones7

1

U.S. Geological Survey, Auburn, Ala.

2

North Carolina State University, Raleigh, N.C.

3

U.S. Geological Survey, Raleigh, N.C.

4

Southeast Gap Analysis Program, Raleigh, NC

5

U.S. Fish and Wildlife Service, Hadley, Mass.

6

U.S. Fish and Wildlife Service, Charleston, S.C.

7

U.S. Fish and Wildlife Service, Laurel, Md.

The overall objective of the proposal described in this 

poster is to develop a consistent methodology for—and to 

enhance the capacity of—States, joint ventures and other 

partners to assess and design sustainable landscape conservation plans for birds and other wildlife in the eastern United 

States. Specifically, this project would develop and implement 

a framework and tools to (1) assess the current capability of 

habitats in ecoregions in the eastern United States to support sustainable bird populations; (2) predict the impacts of 

landscape-level changes (for example, from urban growth, 

conservation programs, and climate change) on the future 

capability of these habitats to support bird populations; 

(3) target conservation programs to effectively and efficiently 

achieve objectives in State wildlife action plans and bird 

conservation plans and evaluate progress under these plans; 

and (4) enhance coordination among partners during the planning, implementation, and evaluation of habitat conservation 

through conservation design. 

Conservation planning for migratory birds has been 

occurring at the continental, national, regional and State 

scales for a number of years but, for the most part, conservation design tools that allow managers to determine how 

much, what type of and where specific habitats are needed 

on the landscape to sustain priority bird populations have not 
Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008 47

been developed consistently and are not widely available, 

especially in the eastern United States. With the completion 

of national and regional bird conservation plans and of State 

wildlife action plans in each State, there is an immediate need 

for a consistent conservation design framework and set of 

tools at State and ecoregional scales that allow managers to 

make scientifically based decisions about habitat conservation 

and to evaluate progress relative to objectives stated in these 

plans. Current bird conservation planning does not allow for 

a quantitative assessment of the capability of landscapes to 

sustain populations at objective levels or of the net impact of 

land use change. Current bird conservation planning also is 

hampered by the inability to assess holistically (that is, for all 

species-habitat suites) the current or likely future landscape 

condition or its ability to support sustainable bird populations. 

In the eastern United States, bird conservation plans have been 

developed for several Bird Conservation Regions (BCRs) that 

have stepped down continental priorities and identified priority 

species and habitats, threats, limiting factors, population and 

habitat objectives, and coarse focus areas for bird conservation. A U.S. Geological Survey Regional Gap Analysis project 

recently completed for the southeastern States and underway 

in the northeastern States will result in up-to-date, uniform, 

habitat classifications and maps. This proposed project would 

meet the need for consistent, widely available conservation 

design tools; draw from the priorities in continental, BCR, and 

State wildlife action plans: and use the Regional Gap Analysis habitat data layers to build a framework and specific tools 

for conservation design that can be applied across the eastern 

United States and, eventually, across the country. 

This project would be a collaboration between two 

universities, the Atlantic Coast Joint Venture, and many 

Federal, State, and nongovernmental organization (NGO) 

partners involved in bird conservation in the eastern United 

States. The universities would be primarily responsible for the 

development of the models, maps, and decision support tools 

as described below, and the joint venture would be responsible primarily for collaborating with partners throughout the 

project. 

The products developed though this project will build on 

several regional efforts that are currently developing or have 

recently completed spatial data, most notably the Gap Analysis Program (GAP), which will be delivering a land cover 

map of the southeastern United States based on ecological 

systems (Corner et al., 2003). This will be the most detailed 

land cover map to date at this resolution. GAP also will deliver 

interim mapping products for the northeastern United States to 

cooperators by 2009 and will have a consistent, seamless land 

cover product by 2011 for the entire eastern United States. The 

use of ecological systems has become a de facto standard for 

land cover classification for remote sensing specialists and a 

growing number of habitat modelers. This consistent approach 

is critical given the myriad habitat classification systems and 

descriptions across multiple States, Federal agencies, and 

NGOs). In addition to land cover, GAP products include predicted habitat models for terrestrial vertebrate species that will 

provide a solid foundation for the development of abundance/

population models through the supporting ancillary datasets 

and extensive habitat relationship database used in their development (accessible at http://www.basic.ncsu.edu/segap). This 

project also will draw on other relevant bird habitat modeling 

efforts from the eastern United States. 

Several other regional spatial analysis efforts will be integral to the completion of this project. This project will build 

upon the single-habitat longleaf pine decision support tool 

being developed by the East Gulf Coastal Plain Joint Venture 

by integrating the needs of priority species across the suite of 

habitats they use, thereby incorporating the needs of such species as the Northern Bobwhite, which uses a variety of habitats 

that often occur as mosaics in the landscape. This project will 

also build upon the Northeast Association of Fish and Wildlife 

Agencies (NEAFWA) Northeast Habitat Classification and 

Mapping project that is developing a habitat classification that 

crosswalks classifications from wildlife action plans of the 

northeastern States with the ecological systems classification 

and is developing a common basis for habitat-species models 

and decision support tools. 

Reference Cited

Corner, P., Faber-Langendoen, D., Evans, R., Gawler, 

S., Josse, C., Kittel, G., Menard, S., Pyne, M., Reid, 

M., Schulz, K., Snow, K., and Teugue, J., 2003, 

Ecological systems of the United States—A working 

classification of U.S. terrestrial systems: NatureServe; 

available online at http://www.natureserve.org/library/

usEcologicalSystems.pdf.

Geospatial Modeling to Assess the 

Risk of American Ginseng Poaching in 

Shenandoah National Park (Poster)

By John A. Young1

1

U.S. Geological Survey, Kearneysville, W.Va.

We used an expert-assisted model in combination 

with a geographic information system (GIS) to examine potential geographic factors that could influence 

poaching of American ginseng (Panax quinquefolius) in 

Shenandoah National Park, Va. Following principles of the 

Analytic Hierarchy Process, we identified a hierarchy of 11 

geographic risk factors deemed important to poaching and 

asked law enforcement personnel of the National Park Service 

to rank those factors in a series of pair-wise comparisons. We 

used those comparisons to determine the statistical weightings 

of each factor. We then standardized the weights to a common 

measurement scale and combined them using a weighted linear function. Finally, we applied that linear function to the 11 
48 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

spatial data layers to map the relative risk of poaching for each 

0.01-hectare (ha) pixel within the study area. Experts ranked 

the quality of ginseng habitat as the most important variable, 

determining that it accounted for 73.4% of the weighting of 

the final model. Other factors that ranked high in the model 

were density of hiking trails, presence of geographic barriers, 

and proximity to park boundary. We tested the model based on 

69 locations of previous American ginseng poaching incidents 

recorded by law enforcement personnel. The occurrence of 

those locations corresponded well with areas predicted by the 

model to have a high risk of poaching. The results of our study 

can be used to evaluate resource protection strategies and to 

target law enforcement activities. Given adequate spatial data 

and reliable expert opinion, similar models could be constructed for other national parks and other species at risk of 

illegal exploitation.

Empirically Modeling Carbon Fluxes in 

the Northern Great Plains Grasslands 

(Poster) 

By Li Zhang1, Bruce K. Wylie1, Lei Ji1, Tagir Gilmanov2, 

and Larry L. Tieszen3

1

Science Applications International Corporation, Sioux Falls, S. Dak.

2

South Dakota State University, Sioux Falls, S. Dak.

3

U.S. Geological Survey, Sioux Falls, S. Dak.

The grassland ecosystem in the Great Plains occupies 

more than 1.5 million square kilometers of land area and 

is the primary resource for livestock production in North 

America. However, the contribution of grasslands to local 

and regional carbon budgets remains uncertain because of the 

lack of carbon flux data for the expansive grassland ecosystems that have various land management oversight, land uses, 

and climate variability. Understanding carbon fluxes across 

these systems is essential for developing regional, national, 

and global carbon budgets and providing guidance to policymakers and managers when substantial biomass conversion 

to biofuels is implemented. We develop a remote-sensingbased Piecewise Regression (PWR) model to estimate the 

grassland carbon fluxes in the northern Great Plains using 

the 8-day, 500-meter (m) Moderate Resolution Imaging 

Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. We estimate the carbon fluxes using 

integrated spatial databases and extrapolations of flux tower 

data to regional scales. The PWR model is applied to derive 

an empirical relationship between environmental variables 

and tower-based measurements. The model equations are then 

applied to estimate spatio-temporal carbon fluxes across the 

study area. We incorporate the actual vegetation evapotranspiration data derived from a vegetation evapotranspiration 

(VegET) model, which takes into account soil moisture and 

land-surface phenology. We also consider the lag response 

of vegetation production to precipitation. We model and map 

8-day and 500-m carbon fluxes for the years 2000 to 2006 in 

the northern Great Plains grasslands. These maps are then used 

to assess the regional and temporal trends of carbon fluxes 

in this region, identify carbon sink and source areas, and 

determine important transitions and environmental drivers of 

carbon sinks and sources. Carbon flux estimates will also provide insights into how the grassland ecosystem will respond to 

future climate and what systems are sustainable and offer net 

carbon sinks. 
Appendix 1. Conference Agenda 49

Appendix 1. Conference Agenda

Sunday, February 10, 2008

6:00–7:00 p.m. Registration 

Monday, February 11, 2008

Noon–1:00 p.m. and 5:00–7:00 p.m. Registration

8:30 a.m.–5:00 p.m. Working Group

 Leanne Hanson River Ecosystem Modeling in USGS: Where Are We and Where Should We Go?

U.S. Geological Survey

1:00–5:00 p.m. Optional Workshop(s)

James Grace An Introduction to Structural Equation Modeling

U.S. Geological Survey

Kate Flynn MapWindow GIS—Integrating Modeling Into a Customizable, Open-Source GIS

U.S. Geological Survey

Alyssa M. Dausman An Introduction to Using PEST—A Model Independent Parameter Estimation and Uncertainty Analysis 

U.S. Geological Survey Code

Tuesday, February 12, 2008 

7:30–8:00 a.m. Registration

8:00–8:30 a.m. Welcome and Opening Remarks—Linda Gundersen and Thomas Casadevall

8:30–9:50 a.m. Session 1: Integrated Landscape Monitoring—Oral Presentations A

Chair: Kenneth Odom, U.S. Geological Survey

8:30–8:50 a.m. Peter R. Claggett Toward a National Land-Change Community Model

U.S. Geological Survey 

8:50–9:10:00 a.m. Alicia Torregrosa Linking Plots to Landscapes: A Synthetic Framework for Monitoring Change in the 

U.S. Geological Survey

9:10–9:30 a.m. Stephen P. Faulkner 

Great Basin Ecosystem

Conceptual and Rapid Prototype Models for Evaluating the Effects of Land-Use and 

U.S. Geological Survey

9:30–9:50 a.m. Terry L. Sohl 

Land-Cover Changes on Ecosystem Services in the Lower Mississippi Valley (LMV)

An Integrated Modeling Approach for Analyzing the Effects of an 

EROS-SAIC Expanding Biomass-for-Biofuel Economy in the Northern Great Plains

9:50–10:00 a.m. BREAK

10:00–11:20 a.m. Session 1: Integrated Landscape Monitoring—Oral Presentations B

Chair: Kenneth Odom, U.S. Geological Survey

10:00–10:20 a.m. Christine E. Turner The FRAME (Framing Research in Support of the Adaptive Management of EcosysU.S. Geological Survey

10:20–10:40 a.m. Paul A. Conrads 

tems) Project: A Collaborative Modeling Approach to Natural Resource Management 

Using Data Mining Techniques and Artificial Neural Network Models to Integrate 

U.S. Geological Survey

10:40–11:00a.m. Eric D. Swain 

Hydrology, Ecology, and Biology—Two Case Studies

Applications of the FTLOADDS Coupled Code and Linkages to Hydrologic and 

U.S. Geological Survey Ecological Models

11:20 a.m.–2:20 p.m. Panel Discussion: Integrated Lanscape Monitoring

Panel members will share their knowledge and apply their diverse expertise to address multifaceted modeling issues being 

faced in the four pilot areas of the Integrated Landscape Monitoring Thrust, discuss partnerships that have been developed, and 

highlight ongoing and proposed multidisciplinary modeling approaches.

Chair: Paul Dresler, U.S. Geological Survey

Panel Members

Great Basin Alicia Torregrosa, U.S. Geological Survey

Puget Sound Susan Benjamin, U.S. Geological Survey

Lower Mississippi Valley Bogdan Chivoiu, ASci Corporation

Prairie Potholes Shuguang Liu, EROS-SAIC
50 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

12:20–1:30 p.m. LUNCH

1:30–2:50 pm Session 2: Global Climate Change—Oral Presentations A

 Chair: Linda Gundersen, U.S. Geological Survey

1:30–1:50 p.m. K. Bruce Jones Modeling Species Responses to Historical Climate Change: From Molecules to 

U.S. Geological Survey

1:50–2:10 p.m. Jill S. Baron 

Continental Landscapes

Addressing the Vulnerability of Western Mountain Ecosystems and Water Resources to 

U.S. Geological Survey

2:10–2:30 p.m. Lauren E. Hay 

Climate Change with RHESSys, the Regional Hydro-Ecological Simulation System

Hydrologic Effects of Climate Change in the Yukon River Basin

U.S. Geological Survey

2:30–2:50 p.m. Dave Douglas Modeling Past, Present, and Future Polar Bear Habitats

U. S. Geological Survey

2:50–3:00 p.m. BREAK

 3:00–4:20 p.m. Session 2: Global Climate Change—Oral Presentations B

Chair: Linda Gundersen, U.S. Geological Survey

3:00–3:20 p.m. Eric T. Sundquist Modeling the Historical Influence of Soil and Water Management on Sediment and 

U.S. Geological Survey Carbon Budgets in the United States

3:20–3:40 p.m. Neil K. Ganju CASCaDE: Computational Assessments of Scenarios of Change for the San Francisco 

U.S. Geological Survey Bay-Delta Ecosystem 

3:40–4:00 p.m. Mark A. Chandler EdGCM: An Interactive Climate Modeling Framework for Educators and Scientists

Columbia University

4:00–4:20 p.m. Randy Hunt Hydrologic Climate Change From a Deterministic View: Using GSFLOW to Simulate 

U.S. Geological Survey Climate Change in a Northern Temperate Climate

4:20–5:20 p.m. USGS Town Hall Meeting on Global Climate Change: Modeler’s Input

Moderator: Lauren E. Hay, U.S. Geological Survey

The town hall forum encourages audience participation to address a series of questions with 20 minutes of discussion per 

question:

1. What are the Global Climate Change (GCC) model inputs needed to address global climate change from the five USGS 

disciplines (Water, Biology, Geology, Geography, and Geospatial Information)?

• If you were designing a data retrieval tool for global climate change application, what would be your highest data priorities for that tool (quality control, formats that feed directly into your model, or distributed and central source)? 

• Does USGS have the expertise to handle the quality assurance and quality control (QA/QC), reformatting, 

and redistribution of the identified datasets (National Water Information System, National Map, Consortium of 

Universities for the Advancement of Hydrologic Science, Inc.)? 

2. Do we need standardization of general circulation model (GCM) climate change scenarios for USGS modelers and should 

we be considering the same group of scenarios? 

3. What model outputs are being produced that could be used by other GCC model applications?

5:30–7:00 p.m. DINNER
Appendix 1. Conference Agenda 51

7:00–9:30 p.m. Evening Poster Session I

Integrated Landscape Monitoring Theme

Presenter Title

Susan Benjamin The Puget Sound Integrated Landscape Monitoring Project

Norman B. Bliss A New Method of Multiscale Topographic Analysis: Extracting Landforms

Jacoby Carter MOAB: Modeling Individual Movement and Behavior Using an Expert System Approach

Thomas H. Cecere Communicating Remote Sensing Requirements and Working Together to Satisfy Project Needs

Richard A. Champion Cost Benefit Analysis of Computer Resources for Machine Learning

Paul A. Conrads Integrating 3-D Hydrodynamic Transport and Ecological Plant Models of the Savannah River Estuary Using 

Artificial Neural Network Models

Michael Fienen Implementation of a Bayesian Geostatistical Parameter Estimation Module into PEST

Michael J. Friedel

John D. Guthrie

Stochastic Modeling to Forecast Likely Effects of a Proposed Reservoir on Colorado River Quantity and Quality 

near Grand Junction, Colorado

Using Such Web Services as StreamStats to Increase Your Modeling Productivity

James R. Hatten Using GIS/Remote Sensing Models to Quantify and Monitor Southwestern Willow Flycatcher Breeding Habitat

Edward J. Laurent Developing an Infrastructure for Knowledge-Based, Spatially Explicit Population Modeling of Multiple Species

Steve Markstrom GSFLOW—Coupled Ground-Water and Surface-Water Flow Model

Leonard Pearlstine

Christopher Polloni

Spatially Continuous Interpolation of Water Stage and Water Depths Using the Everglades Depth Estimation 

Network (EDEN)

Animation and Publication of 3-D and 4-D Physical Models

Gabriel B. Senay

Kathryn A. Thomas

Landscape Evapotranspiration Estimation Using a Simplified Surface Energy Balance Approach for Watershed 

Water Balance Modeling

Predictive Models of Suitable Habitat for the Mojave Desert Tortoise: “Truthiness” in Pseudo-Absence

Nguyen Hieu Trung

Dalia E. Varanka

Credibility and Acceptability of Landuse Planning Approaches: A Case Study in the Coastal Area of the Mekong 

Delta, Vietnam

Modeling and Generalizing Population Pressure at the Regional Scale by Interpolating a Kriging Surface

Richard Webb Variations in Pesticide Leaching Related to Land Use, Pesticide Properties, and Unsaturated Zone Thickness in an 

Agricultural Watershed

Global Climate Change Theme

Presenter Title

Guleid A. Artan Monitoring Snow Water Status with a Distributed Snowmelt Model

Erik A. Beever Extirpations and Elevational-Range Changes of the American Pika Linked to Climate Warming through ClimateEnvelope Modeling

Jacoby Carter Modeling Nutria (Myocastor coypus) Invasions Across Regional and National Landscapes
52 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Dave Douglas Forecasting the 21st century World-Wide Status of Polar Bears Using a Bayesian Network Modeling Approach

Thomas W. Doyle SELVA-MANGRO: Landscape Model for South Florida Mangrove Ecosystems

Thomas W. Doyle SLRRP: Sea-Level Rise Rectification Program and Seawater Inundation Model under Climate Change

James B. Grand Designing Sustainable Landscapes for Bird Populations in the Eastern United States: A Collaborative Approach

Catherine S. Jarnevich Advanced Species Modeling

John F. Walker Processing Time-Series Data to Calibrate the GSFLOW (Coupled Ground-Water and Surface-Water Flow) Model 

in the Trout Lake Watershed, Northern Wisconsin

Wednesday, February 13, 2008 

Field Trip

Mississippi Sandhill Crane National Wildlife Refuge, Grand Bay, 5 Rivers Delta Resource Center and Bon Secour National 

Wildlife Refuge

The field trip will be conducted in two groups. Each group will spend 1 hour each at Mississippi Sandhill Crane, Grand Bay, 

and the 5 Rivers Delta Resource Center, and approximately 40 minutes each at the beach and in the upland forest of Bon Secour. 

One group will have an early lunch at the 5 Rivers Delta Resource Center and the second group will have a late lunch there.

DINNER

7:00–9:30 p.m. Evening Poster Session II

Ecosystem Modeling Theme

Presenter Title

Nate Booth Taking SPARROW Predictions to the Web: A Tool for Research and Resource Management

Bruce Campbell Numerical Simulation of Ground-Water Flow within the Atlantic Coastal Plain Aquifers of North Carolina and 

South Carolina

Chunfang Chen

Mingshi Chen

Hydrodynamic and Water Quality Modeling of the A.R.M. Loxahatchee National Wildlife Refuge, North Everglades, Florida

Mutually Interactive State-Parameter Estimation of a Carbon Cycle Model Using a Smoothed Ensemble Kalman 

Filter

Michael E. Colvin

Donald L. DeAngelis

A Proposed Ecosystem Simulation Model to Predict the Effects of Common Carp and Zebra Mussels in Shallow 

Lake Ecosystems

Modeling an Aquatic Trophic Chain in a Two-Dimensional Topography in a Seasonally Varying Wetland

Michael J. Friedel

Brian R. Gray

Application of Stochastic Modeling to Forecast Likely Urbanization Effects on Ecological Integrity in the Upper 

Illinois River Basin, USA

Addressing Among-Group Variation in Covariate Effects Using Multilevel Modeling Approaches With Emphasis 

on Multilevel Count Models

Timothy Green Spatial and Age-Structured Population Model of the American Crocodile for Comparisons of CERP Restoration 

Alternatives

Lauren E. Hay Evaluation of Environmental Flow Components Produced Using a Distributed Watershed Model of the Flint 

River Basin, Georgia
Appendix 1. Conference Agenda 53

Ecosystem Modeling Theme - Continued.

Presenter Title

Shuguang Liu De-Trending for Climatic Variations to Reveal Stressed Ecosystems

Ken Lubinski Modeling with Stakeholders: When the Process is as Valuable as the Product

Monica Palaseanu-Lovejoy Delineation of Vegetation Communities Using Airborne Lidar Data at Jean Lafitte National Park, Louisiana

Daniel H. Slone A Spatially Explicit Stage-Based Population Model of American Alligators in Support of Everglades Restoration

Brad Stith Integrating Hydrology and Individual-Based Models: Issues and General Approaches to Simulating Animal 

Movement Using Artificial Intelligence and Navigation Theory

Zhengxi Tan Ecosystem Carbon Budgets and Crop Yields in a Tropical Savanna Ecosystem as Related to Changes in Climate 

and Management

Adam Terando Seeing the Forest Fire through the Trees: Integrating Mulitple Data Streams to Model the Pre-Settlement Fire 

Regime in the Longleaf Pine Ecosystem

Alicia Torregrosa A Knowledge Management Approach for Complex Regional Ecosystem Modeling

Roland Viger Formalizing Model-Specific GIS Methodology with the GIS Weasel

Roland Viger The Integration of Environmental Simulation Models and GIS Using Metadata

Hongqing Wang Modeling Surface-Water Sulfate Dynamics in a Northern Everglades Wetland

Mark L. Wildhaber Application of Bayesian Hierarchical and Information Theoretic Approaches in Model-Facilitated Recovery of 

Endangered Fish Populations Based on Behavior and Physiology as Part of an Individual-Based Spatially 

Explicit Model John A. Young Geospatial Modeling to Assess the Risk of American Ginseng Poaching in Shenandoah National Park

Hazards and Risks Theme

Presenter Title

Thomas W. Doyle CONGFLO: Floodplain Inundation Model of Congaree National Park

Thomas W. Doyle HURASIM: Hurricane Simulation Model for Reconstructing Wind Fields and Landfall Frequencies of Historic 

Storms

Thomas W. Doyle Landscape Simulation Model for Invasive Species Spread and Detection

Michael J. Friedel

Michael J. Friedel

Robert Klinger

Joint Inverse Modeling to Estimate Extreme Rainfall Events and their Uncertainty in Ungaged Coastal Basins, 

El Salvador, Central America

Joint Inverse Modeling to Estimate Flood-Flow Depths and Their Uncertainty in Ungaged Coastal Basins, 

El Salvador, Central America

Limitations to Modeling Nonnative Plant Species Distributions for Early Detection Monitoring in 

Arid Environments

Bruce H. Pugesek Modeling Nonlinear Time Sequences in Earth Change and Human Disease Pandemics

Vivian R. Queija Elevation Derivatives for Modeling Postwildfire Debris Flows

Christopher R. Sherwood Community Sediment-Transport Modeling Project
54 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Thursday, February 14, 2008

7:45–8:00 a.m. Remarks

8:00–9:20 a.m. Session 3: Ecosystem Modeling—Oral Presentations A

Chair: Mark Wildhaber, U.S. Geological Survey

8:00–8:20 a.m. Laura A. Brandt Strategic Habitat Conversation and the Importance of Ecosystem Models

U.S. Fish and Wildlife 

Service

8:20–8:40 a.m. Roger G. Sayre Modeling Standardized, Meso-Scale Ecosystem Distributions for the United States and 

U.S. Geological Survey the Planet

8:40–9:00 a.m. Melannie D. Hartman Using a Coupled Ecosystem-Geochemical Model to Assess Ecological, BiogeochemiColorado State University cal, and Hydrologic Processes and Their Responses to Atmospheric Deposition and 

9:00–9:20 a.m. William B. Labiosa 

Climate Change for Diverse Ecosystems Within the United States

The South Florida Ecosystem Portfolio Model: Integrating Ecological, Community, 

U.S. Geological Survey and Economic Values for Decision Support 

9:20–9:40 a.m. BREAK

9:40–11:00 a.m. Session 3: Ecosystem Modeling—Oral Presentations B

Chair: Mark Wildhaber, U.S. Geological Survey

9:40–10:00 a.m. Guy Gelfenbaum Modeling the Hydrodynamic and Morphologic Response of an Estuary Restoration

U.S. Geological Survey

10:00–10:20 a.m. William R. Green The Future: Data-Driven Lake and Reservoir Monitoring Using Real-Time 3-D HydroU.S. Geological Survey dynamic and Water Quality Simulations 

10:20–10:40 a.m. Jon C. Cline Application of a Multimodeling Framework to Linking Ecosystem Pattern and Process 

Case Western Across Scales: A Tale of Two Ecosystem Models 

10:40–11:00 a.m. Reserve University Travis S. Schmidt Utilizing Structural Equation Modeling Techniques to Model Watershed Structure and 

U.S. Geological Survey Land Use Effects on Stream Geochemistry and Ecological Responses

11:00 a.m.-Noon Panel Discussion: Ecosystem Modeling

Panel members will share their knowledge and apply their diverse expertise to address multifaceted modeling issues being faced 

in ecosystem modeling, discuss partnerships that have been developed, and highlight ongoing and proposed multidisciplinary 

modeling approaches.

Chair: Laura Brandt, U.S. Fish and Wildlife Service

Panel Members

James T. Peterson, U.S. Geological Survey

Roger G. Sayre, U.S. Geological Survey

Daniel H. Slone, U.S. Geological Survey

Adam Terando, U.S. Geological Survey

Noon-1:15 p.m. LUNCH

1:20–2:20 p.m. Session 4: Hazards and Risks-Oral Presentations A

Chair: K. Bruce Jones, U.S. Geological Survey

1:20–1:40 p.m. E. Lynn Usery Elements of a Global Model: An Example of Sea-Level Rise and Human Populations 

U.S. Geological Survey

1:40–2:00 p.m. James L. Coleman 

at Risk

Modeling Natural Resource Management Decision Scenarios Using Decision Analysis: 

U.S. Geological Survey

2:00–2:20 p.m. Jerad Bales 

The Flow of Selenium in Appalachian Watersheds from Rocks to Ducks

Three-Dimensional Circulation and Transport Modeling in Devils Lake, North Dakota

U.S. Geological Survey

2:20–2:40 p.m. BREAK
Appendix 1. Conference Agenda 55

2:40–4:00 p.m. Session 4: Hazards and Risks—Oral Presentations B

Chair: K. Bruce Jones, U.S. Geological Survey

2:40–3 p.m. Laura Dinitz The Land Use Portfolio Model: A Tool for Natural-Hazards Risk Analysis

U.S. Geological Survey

3–3:20 p.m. Larry Garver Mastin Use of Operational and Research Models to Assess Volcanic Tephra Hazards

U.S. Geological Survey

3:20 3:40 p.m. Peter F. Cervelli Data Management and Interoperability in the USGS Volcano Hazards Program

U.S. Geological Survey

3:40–4 p.m. Gregory J. Smith The Delta Research and Global Observation Network

U.S. Geological Survey

4:00–5:00 p.m. Panel Discussion: Hazards and Risks

Panel members will share their knowledge and apply their diverse expertise to address multifaceted modeling issues being faced 

in hazards and risks modeling, discuss partnerships that have been developed, and highlight ongoing and proposed multidisciplinary modeling approaches.

Co-Chairs: K. Bruce Jones and Richard Bernknopf, U.S. Geological Survey

Panel Members

Introduction K. Bruce Jones, U.S. Geological Survey

Risk Analysis Process Anne Wein, U.S. Geological Survey

Floods Michael Friedel, U.S. Geological Survey

Fire Matthew Rollins, U.S. Forest Service

Climate Change DeWayne Cecil, U.S. Geological Survey

Multihazards Rich Bernknopf, U.S. Geological Survey

5:10–5:30 p.m. Closing Remarks—Linda Gundersen and Thomas Casadevall
56 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Appendix II. List of Conference Participants 

Erik A. Beever 

Ecologist 

U.S. Geological Survey 

USGS-ASC 

1011 E. Tudor Rd. 

Anchorage, AK 99503 

907-786-3954 

EBeever@usgs.gov 

Susan Benjamin 

Director, WGSC 

U.S. Geological Survey 

MS 531 

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-5049 

sbenjamin@usgs.gov 

Charles Berenbrock 

Hydrologist 

U.S. Geological Survey 

National Center, MS 415

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-6876 

ceberenb@usgs.gov 

Richard Bernknopf

U.S. Geological Survey

MS 531

345 Middlefield Rd.

Menlo Park, CA 94025

650-329-4951

rbern@usgs.gov

Kyle Blasch 

Research Hydrologist 

U.S. Geological Survey 

520 N Park Ave., Suite 221 

Tucson, AZ 85719 

520.670.6671 ext 283 

kblasch@usgs.gov 

Norman B. Bliss 

Principal Scientist, SAIC 

U.S. Geological Survey - SAIC 

47914 252nd St. 

Sioux Falls, SD 57198 

605-594-6034 

bliss@usgs.gov 

Daniel P. Ames 

Professor 

Idaho State University 

1784 Science Center Dr. 

Idaho Falls, ID 83402 

208-282-7851 

amesdani@isu.edu 

Guleid A. Artan 

Scientist 

Science Applications International 

Corporation

U.S. Geological Survey EROS Data 

Center

Sioux Falls, SD 57198 

605-594-6195 

gartan@usgs.gov 

Nicole Athearn 

Biologist 

U.S. Geological Survey 

505 Azuar Dr. 

Vallejo, CA 94592 

707-562-2002 

nathearn@usgs.gov 

Scott Ator 

Hydrologist 

U.S. Geological Survey 

5522 Research Park Dr. 

Baltimore, MD 21228 

443-498-5564 

swator@usgs.gov

Jerad Bales 

Director, NC WSC 

U.S. Geological Survey 

3916 Sunset Ridge Rd. 

Raleigh, NC 27607 

919-571-4048 

jdbales@usgs.gov 

Jill S. Baron 

Research Ecologist 

U.S. Geological Survey 

Natural Resource Ecology Laboratory 

Colorado State University 

Fort Collins, CO 80523-1499 

970-491-1968 

jill@nrel.colostate.edu 

David Boldt 

Computer Scientist 

U.S. Geological Survey 

National Center, MS 445

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5679 

dboldt@usgs.gov 

Nate Booth 

U.S. Geological Survey 

8505 Research Way 

Middleton, WI 53562 

608-821-3822 

nlbooth@usgs.gov 

Kip Bossong 

Hydrologist 

U.S. Geological Survey 

MS 415 

Lakewood, CO 80470 

303-236-4882 x 317 

cbossong@usgs.gov 

Shailaja (Sally) R. Brady 

Geologist

U.S. Geological Survey 

National Center, MS 911 

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-6882 

srbrady@usgs.gov 

Laura A. Brandt 

Team Leader 

U.S. Fish and Wildlife Service 

3205 College Ave. 

Davie, FL 33314 

954-577-6343 

Laura_Brandt@fws.gov 

Bruce Campbell 

Hydrologist 

U.S. Geological Survey 

720 Gracern Rd. 

Suite 129 

Columbia, SC 29210 

803-750-6161 

bcampbel@usgs.gov 
Appendix II. List of Conference Participants 57

Darren Carlisle

U.S. Geological Survey

National Center, MS 413

12201 Sunrise Valley Dr. 

Reston, VA 20192

703-648-6890

dcarlise@usgs.gov

Jacoby Carter 

Research Ecologist 

U.S. Geological Survey 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8620 

jacoby_carter@usgs.gov 

Thomas J. Casadevall 

Regional Director 

U.S. Geological Survey 

Box 25046 

Denver Federal Center, MS 150 

Lakewood, CO 80225-0046 

303-202-4740 

tcasadev@usgs.gov 

Thomas H. Cecere 

Land Remote Sensing Requirements 

U.S. Geological Survey 

National Center, MS 517 

12201 Sunrise Valley Dr.

Reston, VA 20192 

703-648-5551 

tcecere@usgs.gov 

Peter F. Cervelli 

Research Geophysicist 

U.S. Geological Survey 

4200 University Dr. 

Anchorage, AK 99508 

907-786-7446 

pcervelli@usgs.gov 

Richard A. Champion 

Mathematician 

U.S. Geological Survey 

MS 531 

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-4260 

rchampio@usgs.gov 

Mark A. Chandler 

Research Scientist 

Columbia University, NASA/GISS 

4717 Fond Du Lac Trail 

Madison, WI 53705 

608-270-9645 

mac59@columbia.edu 

Colleen Charles

U.S. Geological Survey

National Center, MS 300

12201 Sunrise Valley Dr.

Reston, VA 20192

508-335-3029

colleen_charles@usgs.gov

Chunfang Chen 

Postdoctoral Research Associate 

U.S. Fish and Wildlife Service 

P.O. Box 4031 

Lafayette, LA 70504 

337-482-0694 

ccflian@ufl.edu 

Bogdan Chivoiu 

ASci Corporation 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8669 

chivoiub@usgs.gov 

Peter R. Claggett 

Research Geographer 

U.S. Geological Survey 

410 Severn Avenue, Suite 109 

Annapolis, MD 21403 

410-267-5771 

pclaggett@usgs.gov 

Athena P. Clark 

Director 

U.S. Geological Survey 

75 TechnaCenter Dr.

Montgomery, AL 36117 

334-395-4141 

athclark@usgs.gov 

Jon C. Cline 

Case Western Reserve University 

10900 Euclid Ave.

Cleveland, OH 44106 

216-368-8996 

jon.cline@case.edu 

Brian E. Cole 

Regional Chief Scientist 

U.S. Geological Survey 

MS 150

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-4593 

becole@usgs.gov 

James L Coleman 

Team Chief Scientist 

U.S. Geological Survey 

National Center, MS 956 

12201 Sunrise Valley Drive 

Reston, VA 20192 

703-648-6400 

jlcoleman@usgs.gov 

Michael E. Colvin 

Iowa State University Coop Unit 

339 Science II 

Ames, IA 50010 

515-520-0564 

mcolvin@iastate.edu 

Paul A. Conrads 

Hydrologist 

U.S. Geological Survey 

720 Gracern Rd. 

Columbia, SC 29210 

803-750-6140 

pconrads@usgs.gov 

Alyssa M. Dausman

Hydrologist 

U.S. Geological Survey 

3110 SW 9th Ave. 

Fort Lauderdale, FL 33315 

954-377-5972 

adausman@usgs.gov 

Warren C. Day 

Rocky Mountain Area Science 

Program Officer

U.S. Geological Survey 

Bldg. 20

Denver Federal Center, MS 911 

Lakewood, CO 80225 

303-236-6484 

wday@usgs.gov 
58 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Robert Deibel 

U.S. Forest Service 

2150 Centre Ave. 

Building A 

Fort Collins, CO 80526 

970-295-5982 

rhdeibel@fs.fed.us 

Patrick N. Deliman 

Technical Director, Environmental 

Modeling

U.S. Army Corps of Engineers 

3909 Halls Ferry Rd. 

CEERD-EV-E 

Vicksburg, MS 39180 

601-634-3623 

pdeliman@earthlink.net 

Charles R. Demas 

U.S. Geological Survey 

3535 S. Sherwood Forest Blvd. 

Suite 120 

Baton Rouge, LA 70816 

225-298-5481, ex 3117 

crdemas@usgs.gov 

Laura Dinitz 

OR Analyst 

U.S. Geological Survey 

MS 531

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-4953 

ldinitz@usgs.gov 

David I. Donato 

Computer Scientist 

U.S. Geological Survey 

National Center, MS 521 

12201 Sunrise Valley Dr. 

Reston, VA 20192

703-648-5772 

didonato@usgs.gov 

Therese Donovan

Assistant Unit Leader 

U.S. Geological Survey 

311 Aiken Center 

University of Vermont 

Burlington, VT 05405 

802-656-2516 

tdonovan@uvm.edu 

Dave Douglas

U.S. Geological Survey 

3100 National Park Rd. 

Juneau, AK 99801 

907-364-1576 

ddouglas@usgs.gov 

Thomas W. Doyle 

Research Ecologist 

U.S. Geological Survey 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8647 

tom_doyle@usgs.gov 

Paul V. Dresler 

Program Coordinator 

U.S. Geological Survey 

National Center, MS 301 

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-4114 

paul_dresler@usgs.gov 

Jeff Duda 

Research Ecologist 

U.S. Geological Survey 

6505 NE 65th St. 

Seattle, WA 98115 

206-526-6282 x233 

jeff_duda@usgs.gov 

Ned H. Euliss 

Research Biologist 

U.S. Geological Survey 

8711 37th St. SE 

Jamestown, ND 58401 

701-253-5564 

ceuliss@usgs.gov 

Stephen P. Faulkner 

Research Ecologist 

U.S. Geological Survey 

700 Cajundome Blvd 

Lafayette, LA 70506 

337-266-8648 

sfaulkner@usgs.gov 

Michael Fienen 

U.S. Geological Survey 

8505 Research Way 

Middleton, WI 53562 

608-821-3894 

mnfienen@usgs.gov 

Kate Flynn 

Information Technology Specialist 

U.S. Geological Survey 

National Center, MS 415 

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5313 

kmflynn@usgs.gov 

Mary C. Freeman 

Research Ecologist 

U.S. Geological Survey 

Odum School of Ecology 

University of Georgia 

Athens, GA 30602-2202 

706-583-0978 

mcfreeman@usgs.gov 

Michael J. Friedel 

U.S. Geological Survey 

Box 25046 

Denver Federal Center, MS 964

Lakewood, CO 80225 

303-236-7790 

mfriedel@usgs.gov 

Janice M. Fulford

Senior Hydrologist 

U.S. Geological Survey 

Bldg. 2101 

Stennis Space Center, MS 39529 

228-688-1501 

jfulford@usgs.gov 

Neil K. Ganju 

U.S. Geological Survey 

Placer Hall 

6000 J St. 

Sacramento, CA 95819 

916-278-3117 

nganju@usgs.gov 

Leila Gass 

U.S. Geological Survey 

520 N. Park Ave. 

Suite 106 

Tucson, AZ 85719 

520-670-5005 

lgass@usgs.gov 
Appendix II. List of Conference Participants 59

Guy Gelfenbaum 

Oceanographer 

U.S. Geological Survey 

MS 999 

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-5483 

ggelfenbaum@usgs.gov 

James Grace 

Research Scientist 

U.S. Geological Survey 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8632 

gracej@usgs.gov 

Brian R. Gray 

Statistician 

U.S. Geological Survey 

2630 Fanta Reed Rd. 

La Crosse, WI 54603 

608-781-6234 

brgray@usgs.gov 

William R. Green 

Hydrologist - Assistant Director 

U.S. Geological Survey 

401 Hardin Rd. 

Little Rock, AR 72211 

501-228-3607 

wrgreen@usgs.gov 

Linda C. Gundersen 

Chief Scientist for Geology 

U.S. Geological Survey 

National Center, MS 911

12201 Sunrise Valley Dr. 

Reston, VA 20192

703-648-6641 

lgundersen@usgs.gov 

Thomas M. Gunther

U.S. Geological Survey 

National Center, MS 159

12201 Sunrise Valley Dr.

Reston, VA 20192

703-648-4708 

thomas_gunther@usgs.gov 

John D. Guthrie 

U.S. Geological Survey 

Box 25046 

Denver Federal Center, MS 516 

Denver, CO 80225 

303-202-4289 

jdguthrie@usgs.gov 

Stephen E. Hammond 

Acting Chief, Science Information & 

Education Office

U.S. Geological Survey 

National Center, MS 159

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5033 

sehammon@usgs.gov 

Leanne Hanson 

Biologist 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg. C 

Fort Collins, CO 80526-8118 

970-226-9262 

hansonl@usgs.gov 

Melannie D. Hartman 

Colorado State University 

Natural Resource Ecology Laboratory 

Fort Collins, CO 80523-1499 

970-491-1609 

melannie@nrel.colostate.edu 

James R. Hatten 

Research Geographer 

U.S. Geological Survey 

Columbia River Research Lab 

5501 A Cook Underwood Rd. 

Cook, WA 98605 

509-538-2299 x252 

jhatten@usgs.gov 

Lauren E. Hay 

Hydrologist 

U.S. Geological Survey 

Box 25046 

Denver Federal Center, MS 412 

Denver, CO 80225 

303-236-7279 

lhay@usgs.gov 

Matthew C. Hayne 

Senior Scientist 

U.S. Geological Survey 

National Center MS 917

12201 Sunrise Valley Dr. 

Reston, VA 20192-0002 

703-648-6065 

mhayne@usgs.gov 

Heather S. Henkel 

Information Technology Specialist 

U.S. Geological Survey 

600 4th St. South 

St. Petersburg, FL 

727-803-8747 x3028 

hhenkel@usgs.gov 

William B. Hughes 

Assistant Director 

U.S. Geological Survey 

3039 Amwiler Rd., Suite 130 

Atlanta, GA 30360 

770-903-9162 

wbhughes@usgs.gov 

Randy Hunt 

U.S. Geological Survey 

8505 Research Way 

Middleton, WI 53562 

608-821-3847 

rjhunt@usgs.gov 

Elise Irwin 

U.S. Geological Survey 

Alabama Cooperative Fish and 

Wildlife Research Unit 

119 Swingle Hall 

Auburn, AL 36849 

334-844-9190 

elise_irwin@usgs.gov 

James D. Jacobi 

Botanist 

U.S. Geological Survey 

677 Ala Moana Blvd., Suite 615 

Honolulu, HI 96813 

808-587-7456 

jim_jacobi@usgs.gov 

Catherine S. Jarnevich 

Ecologist 

U.S. Geological Survey 

2150 Centre Ave., Bldg. C 

Fort Collins, CO 80526 

970-226-9439 

jarnevichc@usgs.gov 
60 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

K. Bruce Jones

Chief Scientist for Geography 

U.S. Geological Survey 

National Center, MS 300

12201 Sunrise Valley Dr.

Reston, VA 20192

kbjones@usgs.gov 

Sonya A. Jones 

Program Officer 

U.S. Geological Survey 

3850 Holcomb Bridge Rd., Suite 160 

Norcross, GA 30092 

770-409-7705 

sajones@usgs.gov 

Robert L. Joseph 

Director 

U.S. Geological Survey 

USGS Texas Water Science Center 

8027 Exchange Dr. 

Austin, TX 78754 

512-927-3502 

rljoseph@usgs.gov

Julie E. Kiang 

Hydrologist 

U.S. Geological Survey 

National Center, MS 415

12201 Sunrise Valley Dr. MS415 

Reston, VA 20192 

703-648-5364 

jkiang@usgs.gov 

Suzette Kimball

Regional Director, Eastern Region 

U.S. Geological Survey 

National Center, MS 150

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5817 

Suzette_Kimball@usgs.gov 

Jack L. Kindinger

Associate Director 

U.S. Geological Survey 

600 Fourth St. South 

St. Petersburg, FL 33701 

727-803-8747 X 3002 

jkindinger@usgs.gov 

Robert Klinger

Ecologist 

U.S. Geological Survey 

Yosemite Field Station - Bishop Office 

RR2 

14 Utah St. 

Bishop, CA 93514 

760-387-2482 

rcklinger@usgs.gov 

William B. Labiosa 

Research Physical Scientist 

U.S. Geological Survey 

MS 531 

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-4279 

blabiosa@usgs.gov 

Catherine A. Langtimm 

Research Wildlife Biologist 

U.S. Geological Survey 

2201 NW 40 Terr. 

Gainesville, FL 32605 

508-335-3029 

Cathy_Langtimm@usgs.gov 

Dawn L. Lavoie 

Gulf of Mexico Coordinator 

U.S. Geological Survey 

2045 Lakeshore Dr. 

University of New Orleans, Lakefront 

Campus 

New Orleans, LA 70148 

504-280-4054 

dlavoie@usgs.gov 

Linda E. Leake 

Deputy Center Director 

U.S. Geological Survey 

2630 Fanta Reed Rd. 

LaCrosse, WI 54636 

608-781-6269 

lleake@usgs.gov 

Ken J. Leib 

Hydrologist 

U.S. Geological Survey 

764 Horizon Dr. 

Grand Junction, CO 81506 

970-245-5257 

kjleib@usgs.gov 

William A. Lellis 

Branch Chief 

U.S. Geological Survey 

176 Straight Run Rd. 

Wellsboro, PA 16901 

570-724-3322 

wlellis@usgs.gov 

Shuguang Liu 

Senior Principal Scientist 

U.S. Geological Survey EROS - SAIC 

Science Directorate 

Sioux Falls, SD 57198 

605-594-6168 

sliu@usgs.gov 

Ken Lubinski

River Ecologist 

U.S. Geological Survey 

Upper Midwest Environmental Sciences 

Center 

2630 Fanta Reed Rd. 

La Crosse, WI 54603 

608-781-6297 

klubinski@usgs.gov 

Peter Lyttle 

Program Coordinator 

U.S. Geological Survey 

National Center, MS 908

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-6943 

plyttle@usgs.gov 

Steve Markstrom 

U.S. Geological Survey 

Box 25046

Denver Federal Center, MS 412

Denver, CO 80225

303-236-3330

markstro@usgs.gov 

Luke Marzen 

Auburn University 

210 Petrie Hall 

Auburn University, AL 36849 

334-844-3462 

marzelj@auburn.edu 
Appendix II. List of Conference Participants 61

Robert Mason 

Deputy Chief 

U.S. Geological Survey 

National Center, MS 415

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5305 

rrmason@usgs.gov 

Larry G. Mastin 

Research Hydrologist 

U.S. Geological Survey 

1300 SE Cardinal Court 

Bdlg. 10, Suite 100 

Vancouver, WA 98683 

360-993-8925 

lgmastin@usgs.gov 

Gerard McMahon 

Research Geographer 

U.S. Geological Survey 

3916 Sunset Ridge Rd. 

Raleigh, NC 27607 

919-571-4068 

gmcmahon@usgs.gov 

Sergio Merino 

Biologist 

U.S. Geological Survey 

NWRC - IAP World Services 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8641 

sergio_merino@usgs.gov 

Mark J. Mihalasky 

Research Geologist/Spatial Data 

Analyst

U.S. Geological Survey 

904 W. Riverside Ave., Room 202 

Spokane, WA 99201 

509-368-3118 

mjm@usgs.gov 

Cheryl Morris 

Regional Geospatial Information 

Officer

U.S. Geological Survey 

Denver Federal Center, MS 306 

Denver, CO 80225 

303-202-4500 

cmorris@usgs.gov 

Jonathan Nelson

U.S. Geological Survey

MS 413

4620 Technology Dr.

Golden, CO 80401

303-278-7957

jmn@usgs.gov

Peter Ng 

Computer Scientist 

U.S. Geological Survey 

MS 531

329 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-5541 

png@usgs.gov 

Kenneth R. Odom 

Hydrologic Studies Chief 

U.S. Geological Survey 

75 TechnaCenter Dr.

Montgomery, AL 36117 

334-395-4140 

krodom@usgs.gov 

Monica Palaseanu-Lovejoy 

Physical Scientist 

U.S. Geological Survey 

600 4th St. South 

St. Petersburg, FL 33701 

727-803-8747 

mpal@usgs.gov 

Michael Pantea 

U.S. Geological Survey 

Box 25046

Denver Federal Center, MS 913 

Denver, CO 80225 

303-236-5554 

mpantea@usgs.gov 

Leonard Pearlstine 

Landscape Ecologist 

U.S. National Park Service 

950 N Krome Ave. 

Fort Lauderdale, FL 33030 

305-224-4228 

leonard_pearlstine@nps.gov 

Charles A. Peters 

Director 

U.S. Geological Survey 

8505 Research Way 

Middleton, WI 53562 

608-821-3810 

capeters@usgs.gov 

James T. Peterson

U.S. Geological Survey

401 Hardin Rd.

Little Rock, AR 72211

501-228-3620

Petersen@usgs.gov

Nathaniel Plant 

Oceanographer

U.S. Geological Survey 

600 4th St. South

St. Petersburg, FL 33701

nplant@usgs.gov 

Christopher Polloni 

Information Technology Specialist 

U.S. Geological Survey 

Woods Hole Science Center 

384 Woods Hole Rd. 

Woods Hole, MA 02543 

508-457-2280 

cpolloni@usgs.gov 

Stanley L. Ponce 

Acting Regional Executive 

U.S. Geological Survey 

Box 25046

Denver Federal Center, MS 300 

Denver, CO 80225 

303-236-2730 x246 

sponce@usgs.gov 

Bruce H. Pugesek 

Research Statistician 

U.S. Geological Survey 

1648 S. 7th St.

Montana State University

Forest Science Lab 

Bozeman, MT 59717 

406-994-6144 

bruce_pugesek@usgs.gov 

Vivian R. Queija 

Geographer 

U.S. Geological Survey 

909 1st Ave., Suite 800 

Seattle, WA 98104 

206-220-4565 

vqueija@usgs.gov 

Kenneth G. Rice

Section Leader 

U.S. Geological Survey 

7920 NW 71 St.

Gainesville, FL 32653 

352-264-3544 

krice@usgs.gov 
62 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Susan Russell-Robinson 

Associate Program Coordinator 

U.S. Geological Survey 

National Center, MS 915-B 

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-6682 

srussell@usgs.gov 

Roger G. Sayre 

Senior Scientist 

U.S. Geological Survey 

National Center, MS 519

Reston, VA 20192 

703-648-4529 

rsayre@usgs.gov 

Travis S. Schmidt 

Mendenhall Fellow/Research 

Ecologist

U.S. Geological Survey 

Box 25046

Denver Federal Center, MS 973 

Denver, CO 80225 

970-226-9470 

tschmidt@usgs.gov 

Robin Schrock 

Assistant Program Coordinator 

U.S. Geological Survey 

National Center, MS 301

12201 Sunrise Valley Dr.

Reston, VA 20192 

703-648-4066 

robin_schrock@usgs.gov 

Craig Seaver 

Alabama Geospatial Liaison 

U.S. Geological Survey 

75 AUM TechnaCenter Dr. 

Montgomery, AL 36117 

334-395-4129 

cseaver@usgs.gov 

Darius J. Semmens 

U.S. Geological Survey 

Rocky Mountain Geographical Science 

Center 

Box 25046

Denver Federal Center, MS 516 

Denver, CO 80225 

303-202-4331 

dsemmens@usgs.gov 

Gabriel B. Senay 

Principal Scientist 

U.S. Geological Survey 

47914 252nd St. 

Sioux Falls, SD 57198 

605-594-2758 

senay@usgs.gov 

Mark Sherfy

U.S. Geological Survey 

Northern Prairie Wildlife Research 

Center

8711 37th St. SE

Jamestown, ND 58401

701-253-5504

msherfy@usgs.gov

Christopher R. Sherwood 

Oceanographer 

U.S. Geological Survey 

USGS Woods Hole Science Center 

384 Woods Hole Rd. 

Woods Hole, MA 02543 

csherwood@usgs.gov 

Frank S. Shipley 

Discipline Coordinator for Biology 

U.S. Geological Survey 

909 First Ave., 8th Floor 

Seattle, WA 98104 

206-220-4600 

frank_shipley@usgs.gov 

Karen Siderelis 

U.S. Geological Survey

National Center, MS 108 

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-5747 

ksiderelis@usgs.gov 

Daniel H. Slone 

Research Ecologist 

U.S. Geological Survey 

2201 NW 40th Terr. 

Gainesville, FL 32605 

352-264-3551 

dslone@usgs.gov 

Jonathan H. Smith 

Program Coordinator, Geographic 

Analysis and Monitoring

U.S. Geological Survey 

National Center, MS 519

12201 Sunrise Valley Dr. 

Reston, VA 20192 

703-648-4516 

Gregory J. Smith 

Director 

U.S. Geological Survey 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8501 

gregory_smith@usgs.gov 

Stan Smith 

Sr. Data Modeler 

U.S. Geological Survey 

4230 University Dr., Suite 201 

Anchorage, AK 99508 

907-786-7072 

stansmith@usgs.gov 

Terry L. Sohl 

U.S. Geological Survey - SAIC 

USGS EROS Data Center 

Sioux Falls, SD 57198 

605-594-6537 

sohl@usgs.gov 

Brad Stith 

ASci / U. S. Geological Survey 

2201 NW 40 Terr. 

Gainesville, FL 32605 

352-264-3556 

bstith@usgs.gov 

Eric W. Strom 

Director, South Carolina Water 

Science Center

U.S. Geological Survey 

720 Gracern Rd., Suite 129 

Columbia, SC 29210 

803-750-6109 

ewstrom@usgs.gov 

Eric T. Sundquist 

Research Geologist 

U.S. Geological Survey 

384 Woods Hole Rd. 

Woods Hole, MA 02543 

508-457-2397 

esundqui@usgs.gov 
Appendix II. List of Conference Participants 63

Eric D. Swain 

Research Hydrologist 

U.S. Geological Survey 

3110 SW 9th Ave. 

Ft. Lauderdale, FL 33315 

954-377-5925 

edswain@usgs.gov 

Adam Terando 

Geographer/Modeler 

Southeast Gap Analysis Program 

216 David Clark Labs

North Carolina State University 

Department of Zoology

Campus Box 7617 

Raleigh, NC 27695-7617 

919-513-7292 

adam_terando@ncsu.edu 

Kathryn A. Thomas 

Ecologist 

U.S. Geological Survey 

115 Biological Sciences East 

Sonoran Desert Research Station 

University of Arizona 

Tucson, AZ 85721 

520-670-5534 

kathryn_a_thomas@usgs.gov 

Larry L. Tieszen 

Project Manager 

U.S. Geological Survey 

Mundt Federal Building 

Sioux Falls, SD 57198 

605-594-6056 

tieszen@usgs.gov 

Alicia Torregrosa 

Physical Scientist 

U.S. Geological Survey 

MS 531

345 Middlefield Rd. 

Menlo Park, CA 94025 

650-329-4091 

atorregrosa@usgs.gov 

Maurice Treece 

Hydrologist 

U.S. Geological Survey 

75 AUM TechnaCenter Dr. 

Montgomery, AL 36117

334-395-4126 

mwtreece@usgs.gov 

Michael J. Turco 

Chief - USGS TX-WSC Gulf Coast 

Program Office

U.S. Geological Survey 

19241 David Memorial Dr., Suite 180 

The Woodlands, TX 77385 

936-271-5300 

mjturco@usgs.gov 

Christine E. Turner 

Geologist 

U.S. Geological Survey 

Denver Federal Center, MS 939 

Box 25046 

Denver, CO 80225 

303-236-1561 

cturner@usgs.gov 

E. Lynn Usery 

Research Geographer 

U.S. Geological Survey 

1400 Independence Rd. 

Rolla, MO 65401 

573-308-3837 

usery@usgs.gov 

Beatrice Van Horne 

Ecosystem Program Coordinator 

U.S. Geological Survey 

1829 Elgin Dr. 

Vienna, VA 22182 

703-938-1686 

bvanhorne@usgs.gov 

Dalia E. Varanka 

Research Geographer 

U.S. Geological Survey 

1400 Independence Rd. 

Rolla, MO 65401 

573-308-3897 

dvaranka@usgs.gov 

Roland Viger 

U.S. Geological Survey 

Box 25046

Denver Federal Center, MS 412 

Denver, CO 80225 

303-236-5030 

rviger@usgs.gov 

Hardin Waddle 

Research Ecologist 

U.S. Geological Survey 

700 Cajundome Blvd. 

Lafayette, LA 70506 

337-266-8671 

waddleh@usgs.gov 

Terry Waddle 

U.S. Geological Survey 

2150 Centre Ave., Bldg. C 

Fort Collins, CO 80526 

970-226-9386 

waddlet@usgs.gov 

Michael G. Waldon 

Hydrologist 

U.S. Fish and Wildlife Service 

ARM Loxahatchee National Wildlife 

Refuge 

646 Cajundome Blvd., Suite 400 

Lafayette, LA 70506

337-291-3133 

mike_waldon@fws.gov 

John F. Walker 

Research Hydrologist 

U.S. Geological Survey 

8505 Research Way 

Middleton, WI 53562 

608-821-3853 

jfwalker@usgs.gov 

Hongqing Wang 

Assistant Professor Research 

University of Louisiana at Lafayette 

249 Ivory St. 

Lafayette, LA 70506 

850-443-7870 

hxw7894@louisiana.edu 

Jess D. Weaver 

Regional Executive 

U.S. Geological Survey 

3850 Holcomb Bridge Rd. 

Suite 160 

Norcross, GA 30092 

770-409-7701 

jdweaver@usgs.gov 

Richard Webb 

Research Hydrologist 

U.S. Geological Survey 

Box 25046 

Denver Federal Center, MS 413 

Denver, CO 802215 

303-236-5025 

rmwebb@usgs.gov 
64 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

Jake F. Weltzin 

Executive Director 

U.S. Geological Survey 

USA-NPN National Coordinating 

Office

1955 East 6th St.

Tucson, AZ 85705 

520-626-3821 

jweltzin@usgs.gov 

Emily Wheeler 

Graduate Student 

University of Illinois at 

Urbana-Champaign

1604 S. Vine St.

Urbana, IL 61801 

217-390-6401 

erwheele@uiuc.edu 

Helen Whiffen 

Geographer 

U.S. Geological Survey 

2524 S Frontage Rd., Suite C 

Vicksburg, MS 39180 

601-629-6610 

hwhiffen@usgs.gov 

Mark L. Wildhaber 

Research Ecologist 

U.S. Geological Survey 

4200 New Haven Rd. 

Columbia, MO 65201 

573-876-1847 

mwildhaber@usgs.gov 

K. Van Wilson 

Hydrologist, P.E. 

U.S. Geological Survey 

308 South Airport Rd. 

Jackson, MS 39208 

601-933-2922 

kvwilson@usgs.gov 

Scott Wilson

U.S. Geological Survey

700 Cajundome Blvd.

Lafayette, LA 70506

337-266-8644

scott.wilson@usgs.gov

Robert A. Winfree 

Science Advisor 

National Park Service 

240 West 5th Ave., Suite 521 

Anchorage, AK 99501-2327 

907-644-3516 

Robert_Winfree@nps.gov 

John A. Young 

Research Biologist (Biogeography) 

U.S. Geological Survey 

11649 Leetown Rd. 

Kearneysville, WV 25430 

304-724-4469 

jyoung@usgs.gov 

Ronald B. Zelt 

Associate Director for NAWQA 

U.S. Geological Survey 

USGS Nebraska Water Science Center 

5231 South 19th St. 

Lincoln, NE 68521 

402-328-4140 

rbzelt@usgs.gov 
Appendix III. Acronyms 65

Appendix III. Acronyms

2-D two-dimensional

3-D three-dimensional

4-D four-dimensional

ADCP Acoustic Doppler Current Profiler

AIBI Alternate Index of Biotic Integrity

AICc Akaike Information Criterion (second order biased corrected)

AMLI Abandoned Mine Lands Initiative

ANN artificial neural network

ArcGIS Arc Geographic Information System

ASCII American Standard Code for Information Interchange

ATLSS Across Trophic Levels System Simulation

BASINS Better Assessment Science Integrating Point and Nonpoint Sources

BCNP Big Cypress National Preserve

BCR Bird Conservation Region

BDRW San Francisco Bay–Delta River Watershed

BE bare earth

BIA Bureau of Indian Affairs

BLM Bureau of Land Management

BN Bayesian network

BRD Biological Resources Discipline

CANERM Canadian Emergency Response Model

CBLCM Chesapeake Bay Land-Change Model

CERP Comprehensive Everglades Restoration Plan

CH canopy height

CIDR Commercial Remote Sensing Space Policy (CRSSP) Imagery-Derived Requirements tool

CIR color infrared

CMF Completely Mixed Flow

COOP Cooperative Observer Program [National Weather Service] 

CRMS Coastwide Reference Monitoring System

CRR canopy-reflection ratio

CRSSP Commercial Remote Sensing Space Policy

CRU Climate Research Unit

CSTM Community Sediment-Transport Modeling project

DA decision analysis

DEFS Deschutes Estuary Feasibility Study

DEM digital elevation model
66 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

DEVS Discrete Event System Specifications

DHI Danish Hydraulic Institute

DNA deoxyribonucleic acid

DRAGON Delta Research and Global Observation Network

DST decision support tool

EAARL Experimental Advanced Airborne Research Lidar

EC-LUE eddy covariance-light use efficiency

EDCM Erosion Deposition Carbon Model

EDEN Everglades Depth Estimation Network

EdGCM Education Global Climate Modeling

EDNA Elevation Derivatives for National Application

EFC environmental flow component

ENFA Ecological Niche Factor Analysis

ENP Everglades National Park

EPA U.S. Environmental Protection Agency

EPM Ecosystem Portfolio Model

EROS Center for Earth Resources Observation and Science

ESA Endangered Species Act

ESM environmental simulation model

ET evapotranspiration

FAO Food and Agriculture Organization [United Nations]

FAO-MCE Food and Agriculture Organization multicriteria evaluation

FORE-SCE Forecasting Scenarios model

FPI Fire Potential Index

FRAME Framing Research in Support of the Adaptive Management of Ecosystems

FTLOADDS Flow and Transport in a Linked Overland/Aquifer Density-Dependent System

FTP File Transfer Protocol 

FWS U.S. Fish and Wildlife Service

GAP Gap Analysis Program

GARCH generalized autoregressive conditional heteroskedasticity

GCM general circulation model or global circulation model or global climate model

GDAS Global Data Assimilation System

GEFISH Greater Everglades Fish

GEMS General Ensemble Biogeochemical Modeling System

GEOSS Global Earth Observation System of Systems

GIS geographic information system

GPP gross primary production

GPS global positioning system

GSFLOW Ground-Water and Surface-Water Flow model
Appendix III. Acronyms 67

HAZUS-MH Hazards U.S. Multi-Hazard

HOME height of the median energy

HURASIM Hurricane Simulation Model

HYSPLIT Hybrid-Single Particle Lagrangian Integrated Trajectory

IC independent component

ICA independent component analysis

IOP Interim Operating Plan

IPCC Intergovernmental Panel on Climate Change

JELA Jean Lafitte National Park

LEACHM Leaching Estimation and Chemistry Model

lidar light detection and ranging

LMV Lower Mississippi Valley

LTRMP Long Term Resource Monitoring Program

LUP land use planning

LUPAS land use planning and analysis system

LUPM Land Use Portfolio Model

MAU multiattribute utility

MAXENT maximum entropy method

MM5 Mesoscale Model, version 5

MMS Modular Modeling System

MOAB Model of Animal Behavior

MODFLOW U.S. Geological Survey Modular Ground-Water Flow Model

MODFLOW-2000 U.S. Geological Survey Modular Ground-Water Flow Model-2000

MODFLOW-2005 U.S. Geological Survey Modular Ground-Water Flow Model-2005

MODIS Moderate Resolution Imaging Spectroradiometer

MSM marsh succession model

MT3D Modular Three-Dimensional Transport model

MT3DMS Modular Three-Dimensional Multispecies Transport Model

MVNP Mesa Verde National Park

NASA National Aeronautics and Space Administration

NAVD88 North American Vertical Datum of 1988

NAWQA National Water Quality Assessment Program

NB negative binomial

NDVI Normalized Difference Vegetation Index

NEAFWA Northeast Association of Fish and Wildlife Agencies

NEE net ecosystem exchange

NEXRAD Next-Generation Radar

NGO nongovernmental organization

NLCCM National Land-Change Community Model
68 Proceedings of the Second All-USGS Modeling Conference, February 11-14, 2008

NPN National Phenology Network

NPP net primary production

NPS National Park Service

NRI National Resources Inventory

NSM Natural Systems Model

NWIS National Water Information System

NWRC National Wetlands Research Center

NWS National Weather Service

OTEC Ocean Thermal Energy Conversion model

OTIS One-Dimensional Transport with Inflow and Storage model

PC principal component

PCA principal component analysis

PEST Parameter Estimation code

PLUP participatory land use planning

PNN probabilistic neural network

PRISM Parameter-Elevation Regressions on Independent Slopes Model 

PRMS Precipitation Runoff Modeling System

PS-ILM Puget Sound Integrated Landscape Monitoring project

PUFF Puff Dispersion Model

PWR Piecewise Regression model

Re ecosystem respiration

RECOVER Restoration Coordination and Verification

RHESSys Regional Hydro-Ecological Simulation System

ROMS Regional Oceanographic Modeling System

RPM rapid prototype model

RSF resource selection function

RWQM Refuge Water Quality Model

SAR synthetic aperture radar

SDM species distribution model

SELVA Spatially Explicit Landscape Vegetation Analysis

SELVA-MANGRO Spatially Explicit Landscape Vegetation Analysis-Mangrove model

SEM structural equation modeling

SEnKF Smoothed Ensemble Kalman Filter

SFWMD South Florida Water Management District

SFWMM South Florida Water Management Model

SHC Strategic Habitat Conservation

SICS Southern Inland and Coastal System

SIMPPLLE Simulating Patterns and Processes at Landscape Scales

SLEUTH Slope, Land cover, Exclusions, Urban Areas, Transportation Hydrologic model
Appendix III. Acronyms 69

SLRRP Sea-Level Rise Rectification Program

SOC soil organic carbon

SPARROW Spatially Referenced Regressions On Watershed Attributes model

SQL Structured Query Language

SRES-A1B Special Report on Emissions Scenarios -A1B

SSEB simplified surface energy balance

SWARM Seismic Wave Analysis and Real-time Monitor

SWFL Southwestern Willow Flycatcher

SWIFT2D Surface Water Integrated Flow and Transport in 2-Dimensions

TIME Tides and Inflows in the Mangroves of the Everglades model

TM Thematic Mapper [Landsat]

TSPROC time-series processing

USFS U.S. Forest Service

USGS U.S. Geological Survey

VAAC Volcano Ash Advisory Center

VALVE Volcano Analysis Visualization Environment

VegET vegetation evapotranspiration

VHP Volcano Hazards Program

VR virtual reality

WASP Water Quality Analysis Simulation Program

WASP (EPA) Water Quality Analysis Simulation Program (Environmental Protection Agency)

WB water balance

WEBB Water, Energy and Biogeochemical Budgets program

WRD Water Resources Discipline

XML Extensible Markup Language
Printed on recycled paper

Brady, ed.—Proceedings of the Second All-USGS Modeling Conference, February 11–14, 2008—Scientific Investigations Report 2009–5013