1

Coastal

 Marine

 Institute

Minerals Management Service

Department of the Interior

and the

School of Fisheries & Ocean Sciences

University of Alaska Fairbanks

University of Alaska

Proceedings of a Workshop

on Hydrological Modeling of 

Freshwater Discharge from 

Alaska’s Arctic Coast

Jia Wang

Principal Investigator

Final Report

OCS Study MMS 2005-043

July 2006
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Contact information

 e-mail: carter@sfos.uaf.edu

 phone: 907.474.1811

 fax: 907.474.1188

 Coastal Marine Institute

 P.O. Box 757220

 School of Fisheries and Ocean Sciences

 University of Alaska Fairbanks

 Fairbanks, AK 99775-7220
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Table of Contents

Abstract ..........................................................................................................................................................6

Introduction ....................................................................................................................................................6

Objectives ......................................................................................................................................................8

Workshop Strategies ......................................................................................................................................8

Working Group Recommendations ..............................................................................................................13

General .........................................................................................................................................................13

Specifi c Themes Within the Discussion .......................................................................................................13

Suggested Work in the Alaska Region .........................................................................................................14

Discussion (See Appendix 3) .......................................................................................................................15

Scientifi c Objectives ....................................................................................................................................15

Recommendations ........................................................................................................................................15

Scientifi c Objectives/Questions ...................................................................................................................15

Needs from Other Themes ...........................................................................................................................15

Areas of Streamfl ow Prediction ...................................................................................................................15

Modeling Needs – Data ...............................................................................................................................16

Modeling Needs – Algorithm .......................................................................................................................16

Main Elements .............................................................................................................................................16

Executive Summary .....................................................................................................................................17

Acknowledgements ......................................................................................................................................19

References ....................................................................................................................................................19

Appendix 1. Workshop Agenda. ..................................................................................................................22

Appendix 2. Workshop Abstracts. ................................................................................................................26

Appendix 3. Themes for Workshop. ............................................................................................................47

Appendix 4. SIOM Workshop Participants ..................................................................................................52
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List of Figures 

Figure 1. A schematic diagram for coastal circulation in the Chukchi-Beaufort Seas (light blue: 

Alaskan Coast Current with the origin of freshwater). ..................................................................................7

Figure 2. River networks in the North Slope region and outer continental shelf in the Beaufort and 

Chukchi seas. ............................................................................................................................................... 8

Figure 3. The time series of Alaska North Slope precipitation simulated by the CCSR/NIES/FRCGC 

climate model of Japan. ................................................................................................................................9

Figure 4. A North Slope hydrological model grid. Right panel: Seasonal cycle (from January to 

December) of simulated freshwater runoff. 

Figure 5. The Gulf of Alaska watershed number at every grid point before the streams become 

constant in the study area. The watershed number denotes units of total grid point area (km2

). ................10

Figure 6. The U.S. Geological Survey streamgaging network of Alaska. Inserted is an example of the 

time series of Kuparuk River runoff onset date from 1972–2003. ..............................................................11

Figure 7. The Arctic coastal zone can be defi ned as a multi-phase boundary. ............................................12

Figure 8. Frequency of oceanic SST fronts in August as determined by remote sensing data. 9 km 

ice-ocean model-simulated coastal current and salinity in the section of Prudhoe Bay. .............................12

Figure 9. Western Arctic freshwater sources derived from δ18o and alkalinity. ........................................13

Figure 10. Proposed North Slope DEM-based hydrological model domain (upper panel) 0.1 

degree DEM (topography); (lower panel) River network derived from the DEM. .....................................18

Hydrological Modeling of the Mackenzie Basin using WATFLOOD and WATCLASS in 

MAGS: 

Figure 1. Land cover map for the Mackenzie basin derived from the Canada Centre for 

Remote Sensing. ..........................................................................................................................................36

Figure 2. WATFLOOD and WATCLASS results on the Mackenzie basin for the years 1994–2001. 

The gage on the Mackenzie River at Arctic Red River (top hydrograph) has the largest drainage area 

while the other three hydrographs represent the three main sub-basin. .......................................................36

A Hydrological Digital Elevation Model for Freshwater Discharge into the Gulf of Alaska: 

Figure 1. Elevation in the model study area, and the watershed of the line source and fi ve big rivers, 

which are named Susitna, Copper, Alsek, Taku, and Stikine rivers. ...........................................................39

Figure 2. Model simulated monthly climatology (seasonal cycle) of freshwater discharge derived 

from the 41-year simulations (1958–1998) and standard deviations. ..........................................................40

Appendices
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List of Tables

Appendices

Hydrological Modeling of the Mackenzie Basin using WATFLOOD and WATCLASS in MAGS:

Table 1. Water balance for seven water years using WATFLOOD 35

Table 2. Water balance for seven water years using WATCLASS 35

A Hydrological Digital Elevation Model for Freshwater Discharge into the Gulf of Alaska: 

Table 1. Newly acquired Russian CTD data set from the Siberian seas. 44

List of Acronyms

ETOPO5 – Eeath TOPOgraphy – (5 minute) digital average land and sea fl oor.

GTOPO30 – Global TOPOgraphy – (30 second) derived from digital elevation model (DEM) average 

land and sea fl oor.

NCEP/NCAR – National Center for Environmental Prediction/National Center for Atmospheric Research

ECMWF/ERA40 – European Center for Medium-range Weather Forecasts/ECMWF Re-Analysis of 40 

years.
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Abstract

A workshop on Hydrological Modeling of Freshwater Discharge from Alaska’s Arctic Coast was 

held at the International Arctic Research Center 

(IARC), University of Alaska Fairbanks (UAF) on 

7–8 October 2004 (http://www.iarc.uaf.edu/workshops/hydro_mod_workshop_04/). This workshop 

was sponsored by the Minerals Management 

Service (MMS)/U.S. Department of the Interior 

and Coastal Marine Institute/UAF, hosted by the 

IARC. One of the objectives of this workshop was 

to bring modelers and observationalists together to 

discuss strategies for state-of-the-art hydrological 

modeling north of the Brooks Range. The workshop 

highlighted approaches to medium-range, regional 

hydrological modeling that could be applied to the 

North Slope region, which drains into the nearshore Beaufort and Chukchi seas, affecting seasonal landfast ice, coastal circulation, and water 

mass properties. A second objective was to promote 

discussion of the following topics:

Climate variability and its impacts on hydrological cycle in the Arctic

Collection and archival of hydrological datasets in Alaska

Hydrological modeling approaches on the 

North Slope

Hydrology-related sea ice, oceanography, and 

geochemistry

The workshop had three research themes: 1) 

Climate and variability and its impacts, 2) Hydrological observations and modeling, and 3) Sea ice, 

oceanography, and geochemistry. There were 28 

participants (23 presentations). Each theme had a 

rapporteur to chair the discussions and summarize 

the recommendations at the end of the workshop. 

The workshop produced recommendations, which 

are valuable for MMS needs and may be used as 

a guideline to arctic hydrological modeling and 

related research areas.

The workshop focused on precedents in data 

processing, hydrological modeling, and fi eld observations: including needs, scientifi c and economic 

issues, and possible solutions in this region. The 

participants included scientists and managers from 

academic institutions, governmental laboratories, 

the state, borough, and local communities.

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Introduction

This hydrological modeling workshop is directly 

related to the MMS framework issue “Modeling studies of environmental, social, economic, 

or cultural processes related to OCS gas and oil 

activities in order to improve scientifi c predictive capabilities.” MMS is interested in freshwater 

runoff because it is important locally in the coastal 

Beaufort Sea: it enhances breakup of nearshore ice 

and affects release of spilled oil from landfast ice, 

water mass properties, and density-driven currents 

of the nearshore shelf, such as coastal current along 

Alaska’s Arctic coast (Weingartner et al. 1998; Fig. 

1). Furthermore, changes in timing and amounts 

of river runoff to the arctic shelves may affect the 

ocean circulation (Weingartner 1998; Wang et al. 

1999).

There are six sites in the North Slope region 

currently being gaged (http://akrfc.arh.noaa.gov/ 

view hydrology): the Kuparuk River, the Colville 

River at Umiat and at the river mouth, the Ikpikpuk River near Barrow, the Sagavanirktok River, 

and Fish Creek. Although a fairly comprehensive 

observation network has been put in place on the 

watersheds of these rivers, it covers a small portion of the entire Alaska Arctic coast. For example, 

the North Slope Hydrology Research Projects (the 

Kuparuk River Watershed Studies), conducted by 

the Water Environmental and Resources Center 

(WERC) of UAF, focuses on small-scale basin 

watershed (http://www.uaf.edu/water/projects/

NorthSlope/northslope.html). There have also been 

some small-scale observational studies in the North 

Slope Region (http://www.uaf.edu/water/faculty.

html). 

A vast region remains ungaged (see Fig. 2). The 

percentage of the discharge that is ungaged has not 

been quantifi ed; this portion drains into the Alaska’s Arctic in a manner of line sources (Wang et al. 

1999, 2004; Carmack 2000; Jin and Wang 2003). 

Thus, there is a great need to focus on the existing 

hydrological observation and known processes to 

quantify river runoff along the Arctic coast (Kane 

et al. 1996, 1997; McNamara et al. 1997, 1998; 

Hinzman et al. 2000). Known factors infl uencing 

runoff include climate variability (temperature and 

precipitation, etc.), terrain elevation, terrain ground 

cover (vegetation types), evaporation, soil type and 
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permafrost distribution, snow drifting and melting, 

and glacier melting. Unlike the steep terrain along 

the Gulf of Alaska (Wang et al. 2004), the North 

Slope of Alaska is more complex because there 

exists not only surface runoff, but also aquifer and 

ground water discharge due to its relatively fl at 

terrain. Although there have been digital elevation 

data collected with 1 km or fi ner spatial resolution from the North Slope and six hydrological 

gages north of the Brooks Range, there has not 

yet been a high resolution digital elevation model 

(DEM)-based hydrological model constructed for 

the region to calculate freshwater discharge into the 

Arctic Ocean from Alaska’s Arctic coast. 

Figure 1. A schematic diagram for coastal circulation in the Chukchi-Beaufort Seas (light blue: 

Alaskan Coast Current with the origin of freshwater). (Courtesy of Tom Weingartner).

Obviously, a high-resolution, large-scale DEMbased hydrological model (Fig. 2) would fi ll the 

gap between the small-scale observation studies 

and coarse-resolution pan-Arctic modeling. Therefore, it was an especially appropriate time for this 

workshop to put forward new ideas to stimulate a 

fresh modeling effort in the region. This interdisciplinary research would involve Arctic climate 

change (Thompson and Wallace 1998; Wang and 

Ikeda 2000, 2001; Ikeda et al. 2001; Wu et al. 

2006), atmospheric circulation, temperature and 

precipitation (Walsh et al. 1998; Yang et al. 2001, 

2002), hydrology (Kane et al. 1996, 1997, 1998; 

McNamara et al. 1997, 1998), and coastal circulation (Weingartner et al. 1998; Wang et al. 1999).
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Figure 2. (Left and right upper) River networks in the North Slope region and outer continental 

shelf in the Beaufort and Chukchi seas. (Right lower) Terrain distribution based on the ETOPO5 

(9.2-km) dataset, a much coarser resolution dataset than the GTOPO30 (1 km). The Brooks Range 

divides the North Slope watershed from the Yukon River watershed south of the Brooks Range. The 

Mackenzie River watershed in Canada is not shown.

Objectives

The objectives of this workshop were to: 

Review precedents in hydrological observations, data archives and analysis, and modeling of freshwater discharge (including river 

runoff from numerous creeks and streams due 

to snow and glacier melting). 

Review the impacts of climate variability on 

hydrology in the North Slope, and possible 

connections between hydrology, sea ice, and 

oceanography 

Lay out strategies for hydrological observation and modeling, particularly for 

implementing a high-resolution DEM-based 

hydrological model, which will incorporate 

the fi rst-order hydrological processes (precipitation, energy balance, aquifer/land processes) 

to estimate freshwater discharge into the 

Arctic Ocean primarily along the BeaufortChukchi sea coasts.

Make recommendations to MMS for future 

research, including atmospheric forcing, 

hydrology-related sea ice, oceanography, and 

biogeochemistry, which would also benefi t 

water resource management by the State of 

Alaska, North Slope Borough, and local communities.

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Workshop Strategies

Atmosphere, hydrology/oceanography, and sea 

ice are important components of the Arctic climate 

system. Atmospheric circulation, precipitation, and 

temperature fi elds are considered forcing functions 

of hydrological models in terms of heat budget 

and energy balance. Thus, atmospheric circulation variability or climate change will directly 

impact the hydrological cycle in regional, basin, 

and global scales. The commonly-used datasets are 

either the reanalysis (such as NCEP/NCAR and 

ECMWF/ERA40) or global model outputs. Figure 

3 shows a global model simulation and projection 

of precipitation in the North Slope for the period of 

1960–2100. It indicates the weather in North Slope 

became wetter in the last four decades (1960–

2000), and will continue so in the next 100 years. It 

is also seen that there is decadal and multi-decadal 

variability in precipitation, which will result in 

decadal and multi-decadal variability in freshwater 

runoff, and eventually lead to Arctic climate change 

on the similar quasi-decadal time scales (Wang et 

al. 2005).
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Figure 3. The time series of Alaska North Slope precipitation simulated by the CCSR/NIES/

FRCGC climate model of Japan (Courtesy of J. Walsh). (CCSR-Climate Change System ResearchTokyo University, NIES—National Institute of Environmental Studies, Japan, FRCGC—Frontier 

Research Center for Global Change, Japan).

Arctic warming may be an important trigger 

for wetter weather in the Arctic (Polyakov et al. 

2003). The warmer climate also melts North Slope 

glaciers (such as McCall Glacier) and thaws the 

frozen ground or permafrost, leading to an increase 

in freshwater runoff along the Arctic Coast. Other 

important parameters include snow cover and its 

albedo. Thus, measuring albedo of snow cover using satellites is important for having a large spatial 

coverage.

Reanalysis data and global model outputs are 

used for hydrological modeling in a downscaling 

process. Thus, an observational network using 

meteorology stations in the North Slope region 

becomes an urgent task for validating the downscaling forcing fi elds based on reanalysis and largescale model output.

Hydrological modeling of the North Slope 

watershed and in the pan Arctic region was conducted (Bowling et al. 2000) in a coarse resolution 

(50 km). In the North Slope an example can be 

shown in Figure 4 in a very coarse resolution grid. 

Seasonal cycle of freshwater runoff can be reasonably simulated (right panel of Fig. 4). Nevertheless, 

detailed watershed information, river network, and 

small-scale processes were missing. 

There are some small-scale basin simulations 

in a single small watershed on the North Slope 

(such as Kuparuk Basin), and also in a single large 

watershed elsewhere in the Arctic (such as Mackenzie Basin, Lena Basin, etc.) (Fig. 4). In the Gulf 

of Alaska watershed, a medium-scale DEM-based 

hydrological model was developed with a 4 km 

resolution (Wang et al. 2004), which (Fig. 5) successfully simulates the river runoff and line-source 

runoff (ungaged small streams, rivers, snow-melt 

discharge, etc.) from 1958–1998. The line-source 

runoff accounts for 76% of total runoff in the re-
10

0

100

200

300

400

R u n o ff 

0.0

2.1

4.2

6.3

8.4

R u n o ff 

)

)

m m

m 3 / s

(

(

Figure 4. Left panel: A North Slope hydrological model grid. Right panel: Seasonal cycle (from 

January to December) of simulated freshwater runoff. In Lakes and wetlands, 60 to 74% of snow 

melt water goes into storage and is not immediately available for runoff. Between 50 and 64% of 

the stored meltwater is depleted by evaporation from open water areas (Courtesy of L. Bowling).

Discharge (10 m /s) 4

T(month)

3

Number

Figure 5. The Gulf of Alaska watershed number at every grid point before the streams become 

constant in the study area. The watershed number denotes units of total grid point area (km2). 

The inserted fi gure shows the model simulated monthly climatology (seasonal cycle) of freshwater 

discharge derived from the 41-year simulations (1958–1998) and standard deviations (vertical bars). 

Black line is the total discharge into the Gulf of Alaska; the blue line is the line source, and the red 

is the point source (Wang et al. 2004).
11

gion, which is the major forcing mechanism forming and maintaining the Alaska Coastal Current. 

Observational studies of single river basins or 

the remote sensing technique that has been applied to measure runoff in one river can be used to 

validate a model. Data from the extensive USGS 

stream gaging network across Alaska (Fig. 6) can 

be used for analysis and validation of any hydrological models. Considering the limited coverage 

in the North Slope, a medium-scale hydrological 

model is an ideal candidate to fi ll the gap between 

the large-scale modeling and small-scale single 

basin modeling, given that observation in the 

North Slope is scarce. The North Slope model will 

consider fi rst-order hydrological processes, such as 

lakes and wetlands, snow albedo, energy balance, 

and aquifer/land processes, etc.

Dispersal of river discharge into the Arctic seas 

can signifi cantly infl uence the sea-ice environment, 

because the Arctic coastal zone is a multi-phase 

boundary: atmosphere-sea ice-ocean-land interfaces (Fig. 6; Eicken et al. submitted). Exchange 

of heat, moisture, and momentum between the inFigure 6. The U.S. Geological Survey streamgaging network of Alaska (Courtesy of D. Myers) 

Inserted is an example of the time series of Kuparuk River runoff onset date from 1972–2003.

terfaces is greatly altered by sea ice and freshwater 

conditions. In particular, landfast ice conditions are 

determined not only by the atmospheric conditions, 

but also by freshwater runoff and ocean circulation.

In addition, freshwater runoff can generate oceanic fronts in the Chukchi and Beaufort seas, and 

produce coastal ocean current due to the horizontal density gradient (Fig. 8). A coupled ice-ocean 

model can reproduce such coastal freshening and 

the coastal current (Maslowski et al. 2000; Wang et 

al. 2002). 

River discharge from the North Slope is a driver 

that brings terrestrial organic carbon and trace metals into the ocean, which modifi es the nearshore 

environment in terms of biogeochemistry. Sea ice 

formation and advection will further redistribute 

the materials into the deep basins (Wang et al. 

2003). Thus, sources of freshwater (Fig. 9) including river discharge, the Bering Sea infl ow, and sea 

ice melting are key dynamic processes for the nearshore Beaufort-Chukchi seas ice-ocean dynamics 

(Kawai et al. 2005; MacDonald et al. 1999, 2005).
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Pack ice Landfast ice 

1s to 100s km

Flaw lead 

1s to 10s km

Bottom fast ice 

<1-20 km

Ocean

Sea ice

Submarine

permafrost

Terrestrial

permafrost

Atmosphere

Advective

heat exchange

Coastal erosion: fluxes 

of particulates, carbon, 

greenhouse gases

Sediment-ice-water

heat flow: Thermal 

subrosion and icebonding of sediments

Atmosphere-ice-water

exchange of heat,

moisture, momentum

Atmosphere-land(+ice+water)

exchange of heat & moisture

Figure 7. The Arctic coastal zone can be defi ned as a multi-phase boundary (Courtesy of H. Eicken).

Prudhoe

Bay

Figure 8. Left panel: Frequency of oceanic SST fronts in August as determined by remote sensing 

data (Courtesy of I. Belkin); Right panel: 9 km ice-ocean model-simulated coastal current and 

salinity in the section of Prudhoe Bay (Courtesy of Maslowski).
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ice melt

rejected brine

Mackenzie runoff

Russian runoff + 

10 m Pacific FW

Distribution of freshwater sources in the Western 

Arctic Ocean: Derived from δ 18O and alkalinity

Figure 9. Western Arctic freshwater sources derived from δ18o and alkalinity (courtesy of M. 

Kawai).

Working Group Recommendations

Theme 1: Climate Variability and its Impacts 

(original records)

General

The discussion focused on the requirements of 

terrestrial hydrology for climate forcing parameters, in particular for the MMS context and in general for the broader Alaska context. Caryn Smith 

outlined general MMS requirements, which include 

nearshore management and near-coastal discharge 

and currents for the North Alaska regional setting. 

MMS is interested in work that provides insight 

into theoretical underpinnings as well as derivation 

of practical applications. Given the general workshop theme of terrestrial hydrological modeling 

on Alaska’s North Slope and elsewhere, discussion turned to identifying climate forcing data best 

suited to hydrological modeling needs, as well as 

how best to adapt climate data for these purposes. 

This included observational data and climate model 

data, both predictive and reanalysis.

Specifi c Themes Within the Discussion

Observational data: Climate observational data 

are important in hydrological monitoring for several reasons:

a. These data help to establish understanding 

of essential processes;

b. Data are used to build process and system 

models;

c. Data also serve to verify models. 

The arctic regions in general suffer from a lack 

of climate observational data. For the context of 

the North Slope, three specifi c shortcomings were 

identifi ed. 

The interior of the North Slope is virtually without climate observational support. 

Specifi c areas have been instrumented as part 

of projects, such as the Colville and McCall 

Rivers, but such efforts are limited spatially 

(by design they cover only a single catchment) as well as temporally. The interior of 

the North Slope requires the establishment of 

long-term monitoring, as well as identifi cation 

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and follow-up monitoring of possible different, previously un-instrumented, regional 

eco-types within the North Slope. 

The Brooks mountain range, which provides 

the source waters for many drainage basins in 

the North Slope, is also virtually without instrumentation. This, too, should be addressed 

with the establishment of an instrumentation/

monitoring program. 

The coastal region, although possessing several weather stations, is less of a problem than 

the interior, which still lacks suffi cient density 

to be fully descriptive. Furthermore, in some 

cases existing coastal stations, specifi cally the 

fi ve automatic weather stations operated by 

MMS in the Deadhorse vicinity, are due to be 

shut down in fall 2006. 

Scale discrepancies: Matching spatial and temporal scales between climate forcing data and the 

needs of the hydrological community is challenging because climate data is usually available only 

at broad spatial scales, whereas the interest of the 

hydrological community lies with processes operating at relatively fi ne spatial scales. This issue may 

be identifi ed for climate data obtained from both 

observational sources and modeled sources. 

Observational data can be scale limited due to 

a lack of site representativeness. For example, 

data obtained from a coastal station, a typical situation for northern Alaska, is ill-suited 

to drive an interior watershed model because 

the data do not represent conditions in the 

interior. 

Data from climate models are often scale 

limited due to coarse spatial resolution. In this 

case a single model data point might represent 

an area several degrees of latitude/longitude 

on a side and might entirely encompass the 

hydrological study area. This is also undesirable because local-scale climate heterogeneity 

is not refl ected. 

Poor coastal process understanding: Of less direct relevance to terrestrial hydrological modeling, 

but worth mentioning, is a poor understanding of 

coastal processes in general over the entire circumArctic domain. Coastal regions tend to be the “edge 

zones” for both oceanographic and land-surface 

models, and for theoretical studies, they tend to 

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fall into a jurisdictional “grey-zone” between terrestrial and marine researchers. Coastal processes 

represent the fi lter through which the results of 

terrestrial hydrological processes are passed before 

infl uencing the marine regime, and as such form an 

important component of the MMS general research 

mandate to achieve broader understanding of the 

north Alaska system. 

Suggested Work in the Alaska Region

Improve the basic data record by establishing 

instrumentation initiatives for the following areas, 

in order of importance:

The Brooks Range

The interior regions of the North Slope

The Alaskan Beaufort and Chukchi coasts

It is further suggested that such work be linked 

to the International Polar Year (IPY) initiative. During the previous IPY, efforts to begin monitoring 

large regions of low data availability were initiated. 

It is proposed that similar activities be conducted 

for the up-coming IPY, this time focusing on smaller areas. A project of simultaneous monitoring at 

multiple sites with a 10-year project horizon, such 

as is being proposed for IPY projects, would make 

substantial advances on the data gaps described 

above. Monitored parameters should also refl ect 

needs of the hydrological community and include 

parameters not normally measured at a climate station, such as full energy balance components. 

Climate model data should be downscaled to improve utility for the terrestrial hydrological modeling context. This includes:

Predictive scenarios generated using the largest scale general circulation models, downscaled using 

a. Topoclimatic approach

b. As boundary conditions to drive a regional 

climate model

Current and hindcast (reanalysis) data generated using higher resolution forecast models, 

such as 

a. NCEP/NCAR Eta model (32 km resolution)

b. Alfred Wegener Institute HIRHAM model 

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(20 km resolution)

The ultimate target scale for downscaling efforts should be on the order of 1 km, to be 

of use to the hydrological community. 

Accurate representation of precipitation for 

the Brooks Range/North Slope system must 

be established. In lieu of adequate observational data, a targeted regional modeling 

initiative should be undertaken for this region.

Assess general circulation model output for 

the Alaska region, in terms of 

a. Internal consistency (i.e., model to model)

b. Accuracy of representation (compared to 

observational data)

Along with this an exploration of reasons for 

observed discrepancies should be undertaken. This will provide information on the 

following:

a. It will indicate what aspects of the Alaska 

system present the greatest challenges for 

model representation. That is, what model 

sensitivities are activated by the Alaskan 

system

b. Assessment of current performance will 

indicate which model(s) are best suited as 

a basis for future scenario prediction in the 

Alaskan region

Establish an Alaska data clearing house. 

There are observational data sets that do exist; 

having them at one accessible portal would 

enhance future research efforts. Given the 

unique nature of Alaska in the U.S. arctic system, perhaps NSIDC could be approached to 

add an “Alaska” search term to their data sets. 

Evaluate the potential of satellite-derived 

data for providing climate fi elds relevant to 

hydrological modeling at the North Slope 

scale. Parameters could include surface and 

near-surface air temperature, snow water 

equivalent, or soil moisture content.

Theme 2: Hydrological Observations and 

Modeling (original records)

Discussion (See Appendix 3)

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Scientifi c Objectives

Combine USGS data, remote sensing of rivers 

and hydrologic modeling to produce continuous estimates of land surface discharge from 

the north slope into the Arctic Ocean 

Recommendations

Reestablish discontinued USGS gages

Establish a 2–3 year intensive observation 

period for a North Slope region to compile 

multiple observational datasets for model 

evaluation

a. Cover a gradient from the Brooks Range to 

the coastal region

b. Wish list of desired variables

c. Establish consistent gridded atmospheric 

dataset for all researchers

Develop a web site to compile available data 

sets for the North Slope

Require that all data generated by MMS-funded research be released after some time period

Scientifi c Objectives/Questions

Better understanding of thermokarst occurrence and potential for increase in sediment 

and carbon

Fluxes

Groundwater dynamics, aufeis fi elds, and 

permafrost distribution

Are better estimates of winter discharge possible through modeling?

Needs from Other Themes

What range of temperature changes are actually important?

Intelligent, physically-based scheme for 

downscaling course meteorology (e.g. ECMWF) or interpolating station observations in 

data sparse regions 

Areas of Streamfl ow Prediction

Observation-based methods

a. “Traditional” streamfl ow data collection

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b. Remote-sensing based methods

Hydrologic Modeling

a. Small-scale, fully-distributed (topofl ow)

b. Macroscale, semi-physical (VIC, WATCLASS, SVAT)

c. Intermediate Scale? 

Observational Directions and Needs

Expanded gages for hydrologically interesting 

rivers

Interest from the hydrological community

Re-creating historical records through analysis of archived images

High quality DEM

Modeling Needs – Data

Meteorological Datasets

a. Spatial patterns of T and P

b. Vapor pressure and windspeed

c. Radiation and cloud cover

Spatial characterization data

a. Soils, DEM, vegetation

Calibration concerns

a. More spatial datasets (SWE, soil moisture, 

ALD, river and lake break-up)

b. Improved calibration: time to explore current state of models

c. Storage terms

Modeling Needs – Algorithm

Permafrost algorithms

a. Varying levels of complexity exist

b. Runoff dynamics (interfl ow, inter-hummock surface fl ow)

Spatial variability in solar radiation and wind

Shrub tundra melt dynamics

Glaciers

Snow albedo

Snow damming/ice jams

Water temperature prediction

Main Elements

Reestablish USGS gages

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Cover a gradient from the Brooks Range to 

the coastal region

Establish web site that provides links for data 

sets

Combine different methods (RM, modeling, 

ground measurements)

Scientifi c objectives: thermokarst, carbon 

cycle

Groundwater dynamics, aufeis fi elds, and 

permafrost distribution

Theme 3: Priorities in Sea Ice, Oceanography, 

and Geochemistry (original records)

Ocean/sea ice modeling community needs 

realistic high resolution, space and time, gridded datasets of the atmospheric forcing and 

freshwater inputs/fl uxes, including error bars, 

std, rms

Need independent validation data for models 

(atmosphere, ocean, sea ice) 

Resolve coastal geometry and coastal processes governing sea ice, land fast ice, coastal 

fl ow, and dynamics

~10 km hydrological model outputs to drive 

Arctic ice-ocean models in 3–5 year scope; 

1–3 km for the North Slope for 10-year scope

Coordinated fi eld measurement and modeling 

studies: parallel efforts to maximize limited 

resources, use model guidance to choose ideal 

site for long-term/process studies

Mackenzie runoff is important to local and 

large-scale ice/ocean circulation and water 

mass distribution

Determine relative importance of North Slope 

vs. Mackenzie runoff impact on local iceocean dynamics; modeling sensitivity studies 

of the ocean dynamics response to freshwater 

inputs and pathways from the North Slope 

Freshwater dynamics in the upper 10 m 

coastal regime

Prediction of effects of severe/single storms 

on coastal erosion, sediment transport, ecosystem variability, impact on humans, and 

pollutant dispersal

Vertical transport/mixing, dense water forma-

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tion due to atmospheric forcing – process 

studies and their relevance to large scale 

dynamics

Remotely sensed data on surface ocean thermal fronts are useful for model

Validation/improvements (12-year at 10 km 

data are already available, 1 km for 20 years 

possible

Storm-induced coastal upwelling, changes in 

vertical structures of T&S

Focused process studies of landfast ice, coastal currents, and density-driven circulation

Improve/develop parameterizations of landfast ice in large scale ice-ocean models

Measurements of freshwater fl ux through 

Bering Strait (long-term monitoring needed) 

Executive Summary

Based on the working group recommendations 

listed above, a concise summary is drawn as follows:

Theme 1: Climate Variability and its Impacts

Improve the basic data record by establishing observations (temperature, precipitation, 

wind, etc.) for the following areas, in order of 

importance: 

a) The Brooks Range

b) The interior regions of the North Slope

c) The Alaska Beaufort and Chukchi coasts 

linked to the International Polar Year initiative

Climate model data should be downscaled to 

improve utility for the terrestrial hydrological 

modeling context. This includes 

a) Predictive scenarios generated using the 

largest scale general circulation models, 

downscaled using i) topoclimatic approach; 

ii) as boundary conditions, to drive a regional climate model

b) Current and hindcast (reanalysis) data 

generated using higher resolution forecast 

models, such as i) NCEP/NCAR Eta model 

(32 km resolution); ii) Alfred Wegener 

Institute HIRHAM model (20 km resolu-

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tion)

 The ultimate target scale for downscaling efforts to be of use to the hydrological 

community should be on the order of 1 km 

Assess general circulation model output for 

the Alaska region, in terms of a) Internal 

consistency (i.e., model to model), and b) Accuracy of representation (compared to observational data)

Establish an Alaska data clearing house. 

There are observational data sets that do exist; 

having them at one accessible portal would 

enhance future research efforts. Given the 

unique nature of Alaska in the U.S. arctic system, perhaps NSIDC could be approached to 

add an “Alaska” search term to their data sets

Evaluate the potential of satellite-derived 

data for providing climate fi elds relevant to 

hydrological modeling at the North Slope 

scale. Parameters could include surface and 

near-surface air temperature, snow water 

equivalent, or soil moisture content

Theme 2: Hydrological Observation and 

Modeling

Development of a fi ne-resolution (1–5 km) 

DEM-based hydrological model covering the 

entire North Slope from the Brooks Range 

to the coastal region (Fig. 10). The scientifi c 

objections are to understand 

a) thermokarst occurrence and potential for 

increase in sediment and carbon fl uxes

b) Groundwater dynamics, aufeis fi elds, and 

permafrost distribution

Combine different methods (remote sensing, 

modeling, ground measurements). In addition, 

an intelligent, physically-based scheme for 

downscaling course meteorology (e.g. ECMWF) or interpolating station observations in 

data sparse regions should be developed 

Reestablish USGS gages on the North Slope

Establish a web site that provides links for 

data sets

Theme 3: Sea Ice, Oceanography, and 

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Geochemistry

High-resolution (1–5 km) ocean-sea ice 

models in the Chukchi-Beaufort seas should 

be developed under forcing of freshwater 

discharge (both point source/rivers and line 

source) derived from a high-resolution panNorth Slope DEM-based hydrological model. 

Improve/develop parameterizations of landfast ice in large scale ice-ocean models.

Coordinated fi eld measurement and modeling 

studies: parallel efforts to maximize limited 

resources, use model guidance to choose ideal 

site for long-term/process studies, such as focused process studies of landfast ice, coastal 

currents, fronts and density-driven circulation, 

and measurements of freshwater fl ux through 

Bering Strait.

Determine relative importance of North Slope 

vs. Mackenzie runoff impact on local ice-

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_/North Slope/ 

0.1- degree resolution

Figure 10. Proposed North Slope DEM-based hydrological model domain (upper panel) 0.1 degree 

DEM (topography); (lower panel) River network derived from the DEM (Courtesy of X. Ma).

ocean dynamics; modeling sensitivity studies 

of the ocean dynamics response to freshwater 

inputs and pathways from the North Slope

Prediction of effects of severe/single storms 

on coastal erosion, sediment transport, ecosystem variability, impact on humans, and 

pollutant dispersal, such as storm-induced 

coastal upwelling, changes in vertical structures of T&S, and mixing

Remotely sensed data on surface ocean 

thermal fronts for model validation/improvements, landfast ice, and multi-year sea ice

In summary, the workshop proposed an interdisciplinary (hydrology, meteorology, oceanography, 

sea ice, and geochemistry) integration/synthesis 

(I/S) study in the pan-North Slope (and BeaufortChukchi seas) region, an important research platform for oil and gas exploration and development. 

The proposed I/S in the pan-North Slope region can 

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be linked to IPY 2007–2008. 

The strategy of this I/S will combine modeling (ice-ocean, hydrological, and downscaling of 

atmospheric forcing) with fi eld observations (river 

gages, meteorological measurements, satellite 

measurements, etc.). This can only be done through 

a strong project leadership (PI) with co-PIs in each 

component.

The approach of the I/S can be organized as a 

three-year project: 

Year 1:

Hydrological modeling 

Ice-ocean modeling with landfast ice and 

river runoff 

Atmospheric downscaling

Year 2:

Hydrological model-data fusion studies

Ice-ocean model-data fusion studies

Atmospheric downscaling model-data fusion 

studies

Year 3:

Couple the ice-ocean model to hydrological 

model forced by the downscaled atmospheric 

variables

Project Leader organizes an I/S with a fi nal 

report, and a possible special issue in a refereed journal or in a book

Acknowledgements

We sincerely thank the Coastal Marine Institute/

University of Alaska Fairbanks, Minerals Management Service/U.S. Department of the Interior, 

and the International Arctic Research Center and 

Frontier Research System for Global Change for 

jointly sponsoring the workshop. We appreciate 

Vera Alexander and Syun Akasofu of the University of Alaska Fairbanks for their support and the 

workshop participants for their contributions.

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22

Appendix 1. Workshop Agenda.

Workshop Agenda

Hydrological Modeling of Freshwater Discharge from Alaska’s Arctic Coast 

October 7–8, 2004 

International Arctic Research Center

Fairbanks, Alaska

Thursday, October 7 (IARC 401)

8:30–8:50 a.m. Opening remarks, Introductions, Orientation to Fairbanks

 Jia Wang Workshop Chair

 Paul Reichardt Provost of UAF

 Syun Akasofu Director of IARC

 Denis Wiesenburg Dean of SFOS

 Larry Merculieff Alaska Native Science Commission

8:50–9:00 a.m. “Workshop Expectations: MMS Perspective” by MMS Project 

 Manager, Richard Prentki, Ph.D.

 Theme 1 Chair: Larry Hinzman (Climate Variability and its Impacts)

(invited: 30 minutes, others: 25 minutes including 5 minutes questions; posters: 5 minutes)

9:00–11:05 a.m. 

 •John Walsh: “Arctic Hydrological Variations in Global Climate Models” (invited)

 •Keith Echelmeyer: “Wastage of Brooks Range Glaciers: Relation to North Slope 

 Hydrology”

 •David Atkinson: “Topoclimatic Modeling of Arctic Summer Screen-height Air 

 Temperature”

 •Shusun Li: “Modeling and Measuring Spectral Bidirectional Refl ectance Factor 

 (BRF) of Snow: An Intercomparison Study”

 •Jessica Cherry: “Reconstructing Solid Precipitation in the Arctic with a Land Surface 

 Hydrology Model”

11:05–11:20 a.m. Coffee break

 Theme 2 Chair: Steven Frenzel (Hydrological Observations and Modeling)
23

11:20–12:25 p.m.

 •Larry Hinzman: “Spatially Distributed Simulations of Permafrost Hydrology” (invited)

 •Laura Bowling: “Estimating the Freshwater Budget of High-latitude Land Surfaces”

 •W. Robert Bolton: “Simulation of the Infl uence of Discontinuous Permafrost On

 Hydrologic Processes” (5 mins, poster)

 •Kristin Susens: “Dynamic Hydrologic Processes on the Seward Peninsula” 

 (5 mins, poster)

12:25: 12:40 p.m. Group Photograph: Front steps of IARC Building

12:40–1:40 p.m. Lunch: IARC 5th fl oor

1:40–2:00 p.m. Personal Time. A computer is available in 401 to check your e-mail.

2:00–3:40 p.m.

 •David Bjerklie: “Development of a General Approach to Estimating Discharge in 

 Alaskan Arctic Rivers from Remotely Sensed Information”

 •Robert Carlson: “Exploration of Hydrograph Analysis Techniques for Alaskan Arctic 

 Coastal Watersheds”

 •Frank Seglenieks: “Hydrological Modeling of the Mackenzie Basin using 

 WATFLOOD and WATCLASS in MAGS”

 •Stefan Pohl: “Hydrologic Modeling in the Tundra Region of NW Canada”

3:40–4:00 p.m. Coffee break

4:00–5:50 p.m.

 •David Meyer: “USGS Streamfl ow Data Collection in Alaska”

 •Meibing Jin: “A Hydrological Digital Elevation Model for Freshwater Discharge 

 into the Gulf of Alaska”

 •Xieyao Ma: “Hydrological Modeling in the Lena River Basin”

 •Daqing Yang: “Changes in Lena River Streamfl ow Hydrology: Human Impacts vs. 

 Natural Variations” (5 minutes, poster)

 “Streamfl ow Response to Seasonal Snowcover Extent Changes in 

 Large Siberian Watersheds” (5 minutes, poster)

 •Imke Schramm “Hydrological Modeling of Imnavait Creek, Alaska’s North Slope”

6:15 p.m. Bus transportation departs for Pike’s Landing Lodge 

 (meet on front steps of IARC Building)
24

Friday, October 8

Theme 3 Chair: Nori Tanaka (Sea Ice, Oceanography and Geochemistry)

8:30–9:20 a.m. 

 •Hajo Eicken: “Dispersal of River Discharge in the Siberian Arctic and its Impact 

 on the Sea-ice Environment: Lessons to be Learned for the North 

 Slope Region?” (invited).

 •Igor Belkin: “Arctic Shelf Fronts and Their Relation to Freshwater Discharge”

 •Wieslaw Maslowski: “The Flow of Alaskan Coastal Current from the Gulf of Alaska to 

 the Beaufort Sea – Challenges and Opportunities”

9:20–9:30 a.m. Coffee break

9:30–10:45 a.m.

 •Robert Rember: “Riverine Transport and Dispersion of Freshwater, Suspended 

 Sediment, Organic Carbon and Trace Metals in the coastal Beau- 

 fort Sea During the Spring Floods”

 •Michiyo Kawai: “Distribution of Freshwater Sources in the Western Arctic Ocean 

 Derived From Chemical Tracers Oxygen Isotope Ratio and Alka- 

 linity”

 •Jia Wang: “Intraseasonal and Interdecadal Variability of Arctic Freshwater 

 and Heat Budget”

10:45–11:00 a.m. Coffee break

10:45–12:30 p.m. (35 minutes for each)

 Discussion of Theme 1: Moderator: David Atkinson

 Discussion of Theme 2: Moderator: Laura Bowling 

 Discussion of Theme 3: Moderator: Wieslaw Maslowski

12:30–1:40 p.m. Lunch, IARC 5th fl oor

1:40–2:00 p.m. Personal Time

2:00–3:45 p.m. Breakout group meetings to fi nalize recommendations that each theme 

 contributes: 

 IARC Room

Discussion of Theme 1: Rapporteur: David Atkinson 417

Discussion of Theme 2: Rapporteur: Laura Bowling 401

Discussion of Theme 3: Rapporteur: Wieslaw Maslowski 5th fl oor
25

3:45–4:00 p.m. Coffee break

4:00–5:00 p.m. Plenary session and summary by moderators of Themes 1–3 (Chair: Jia Wang)

David Atkinson (15 minutes)

Laura Bowling (15 minutes)

Wieslaw Maslowski (15 minutes)

5:15 p.m. Bus transportation departs for Pike’s Landing Lodge 

 (meet on front steps of IARC Building)

6:30–9:30 p.m. Workshop Dinner

 Pike’s Landing Lodge, Binkley Room

6:30–7:00 p.m. Appetizers and No Host Bar

7:00–9:00 p.m. Dinner

9:00–9:30 p.m. Invited speaker–Dr. Doug Kane: “Future Direction of Arctic Hydrologic Research” 
26

Appendix 2. Workshop Abstracts.

Theme 1: Climate Variability and its Impacts

Arctic Hydrological Variations in Global 

Climate Models

John E. Walsh

The simulated high-latitude precipitation in 

global coupled climate models generally exceeds 

the observational estimates, particularly over the 

terrestrial watersheds of the Arctic Ocean. The bias 

is larger in the coupled models than in uncoupled 

(atmosphere-only) models, and is strongest during 

the cold season, raising the possibility that the observational estimates may be too low. Multi-model 

means of the annual net surface moisture fl ux (P-E) 

are generally within the range of uncertainty of 

estimates of discharge from the rivers and streams 

of the Arctic terrestrial regions. The validity of the 

simulated values of evapotranspiration is not well 

known because of the large uncertainties in the 

observed values of E over the watershed scale.

The most carefully examined projections of 

changes in the 21st century in Arctic precipitation, evapotranspiration and river discharge are 

those in the soon-to-be-published Arctic Climate 

Impact Assessment. In general, the models project increases of precipitation over the Arctic. The 

areas of largest projected increase vary seasonally, 

from the North Pacifi c and North Atlantic storm 

tracks during winter to the inland Arctic terrestrial 

regions during summer. Depending on the scenario 

of increasing greenhouse gas concentrations, the 

projected increases of annual mean precipitation 

range from 5–15% (B2 scenario) to 10–30% (A2 

scenario, 1% increase of CO2

) by the end of the 

21st century. There is considerable disagreement 

among models concerning the projected changes 

of evapotranspiration, and even the sign of the 

projected change varies among models. There is 

a potentially important seasonality in the sense 

that the projected changes of P-E are generally 

smaller, and occasionally negative, over the major 

river basins during the warm season. The relative 

decrease of P-E during the summer is attributable 

to (1) an increase of E associated with the warming, and (2) the lengthening of the period with a 

snow-free surface and above-freezing temperatures in the upper soil layers. Despite the increase 

of summertime evapotranspiration, Arctic river 

discharge is projected to increase by 5–25% during 

the next century. The peak discharge occurs earlier 

in the year in response to the combination of earlier 

snowmelt, anticipated earlier river break-up, and 

increased summer evapotranspiration in the greenhouse scenarios.

Wastage of Brooks Range Glaciers: Relation to 

North Slope Hydrology

Keith Echelmeyer 

We have measured surface elevations of about 

100 glaciers throughout Alaska, NW Canada, andWashington using an airborne GPS/laser altimetry 

system and/or land-based profi ling. Profi les were 

made in the early 1990’s and many were repeated 

in 2000–2003, giving surface elevation, volume, 

and area changes over this time interval. These 

measurements were also compared to topographic 

maps made in the 1950s or 1970s to determine 

changes over these longer periods. Our fi ndings 

indicate that most of these glaciers are thinning, 

decreasing in volume, and retreating. We have also 

found that changes in surface elevation are small at 

high elevations on any given glacier, but are quite 

large near the terminus. 

The measured changes are generally larger 

during the period from the 1990s–2001 than those 

during the period 1950s–2001 or the period 1970s–

2001. The wastage of these glaciers indicates a 

recent increase in temperature and/or a decrease in 

precipitation in Alaska and western Canada. The 

thinning rates of these 100 glaciers are signifi cantly 

greater than previous estimates and are about twice 

the average thinning rate of the Greenland Ice 

Sheet over the recent period. 

Included in our profi ling measurements are 

eleven glaciers in the Brooks Range, all of which 

are located in the Arctic National Wildlife Refuge. 

Workshop Abstracts
27

These glaciers are relatively small in area, but their 

temporal changes are an important component of 

the hydrology of the North Slope, including river 

discharges there. Their input complements the hydrologic input from aufeis, snowmelt, and springfed rivers. The changes of Brooks Range glaciers 

are also a key factor in assessing the ongoing 

climate change in arctic Alaska. However, these 

changes provide an insignifi cant contribution to sea 

level rise due to their small areal extent. 

Topoclimatic Modeling of Arctic Summer 

Screen-height Air Temperature

David E. Atkinson

A DEM modeling solution is presented for 

climatological purposes, and could be applicable 

to hydrological issues either as an approach framework or to provide climate data at high spatial 

resolution as input to other hydrological models. 

Generally in arctic regions, weather observing 

stations are few and the spatial complexity of the 

region is high, which means temperature patterns are poorly resolved at the meso-scale. This 

issue was addressed over the Canadian Arctic 

Archipelago using a model to estimate surface air 

temperature. The effects on temperature due to site 

elevation and coastal proximity were selected for 

parameterization. The spatial basis is a 1 km resolution digital elevation model of the region (USGS 

GTOPO30 DEM) and the change in temperature 

with elevation was implemented in the model using 

derived environmental lapse rates. Advection effects were handled using resultant winds combined 

with air temperature above the ocean. Lapse rates 

and resultant wind estimates were obtained from 

rawinsonde ascents obtained at upper-air weather 

stations; however, multi-level modeled hindcast or 

forecast data could be utilized. Model results for 

14-day runs were compared to observed data. The 

model was sensitive to steep surface inversions and 

to low-level warming. Sensitivity analyses were 

performed on the model to determine response to 

alterations in lapse rate calculation, sea surface 

temperature, and wind fi eld generation. The model 

was most sensitive to the lapse rate calculation. The 

best results were obtained using a moderate lapse 

rate calculation, moderate wind fi eld, and variable 

sea-surface temperature. This model will soon be 

implemented over Alaska and the western Canadian mainland arctic. 

Modeling and Measuring Spectral Bidirectional 

Refl ectance Factor (BRF) of Snow: An 

Intercomparison Study

Shusun Li and Xiaobing Zhou

Broadband albedo is a very important geophysical parameter in the Earth surface-atmosphere 

interaction in either global climate change or 

hydrological cycle and snowmelt runoff studies. 

To derive the broadband albedo accurately from 

satellite optical sensor observation at limited bands 

and at a single observation angle, the bidirectional 

refl ectance factor (BRF) has to be quantitatively 

specifi ed. In the present albedo derivation algorithms from the satellite radiance data, BRF is 

either modeled or observed. Questions may arise as 

to how well a BRF model can be in the broadband 

albedo derivation. To help answer such questions, 

we studied the performance of a snow surface 

BRF model for two specifi c cases under large solar 

zenith angles (65 and 85). We measured snow 

surface spectral directional refl ectance under clear 

skies. The snow physical properties such as snow 

grain size and snow density at the same sites were 

also measured. In situ snow physical data are used 

to simulate the snow surface BRF and hemispherical directional refl ectance factor (HDRF) through 

a multi-layered azimuth- and zenith-dependent 

plane parallel radiative transfer model. While the 

fi eld measurements and BRF and HDRF simulations all reveal the forward-scattering nature of 

snow surface under large solar incidence angles, 

the BRF model results depict the strongest forward-scattering patterns under such solar zenith 

angles. Because the HDRF is simulated through 

coupling of surface BRF with radiative transfer 

in the atmosphere, the resulting HDRF patterns 

agree with the fi eld measurements better than the 

simulated BRF does. The deviation of the simulated HDRF from fi eld-based clear-sky directional 

refl ectance (FCDR) is within 10% for the central 

(viewing zenith angle & lt; 45) and lateral sides 

of the viewing hemisphere. This level of agreement between the simulated HDRF and FCDR also 

implies that the simulated BRF model can provide 

remote sensing estimates of spectral albedo with an 
28

uncertainty of 10% for the same part of the viewing 

hemisphere. Further improvement in BRF model 

performance requires better handling of single scattering properties of snow grains, surface roughness, 

and atmospheric correction. Also, better procedures 

and techniques in fi eld measurement are necessary 

for more accurate assessment of the performance of 

BRF models. 

Reconstructing Solid Precipitation in the Arctic 

with a Land Surface Hydrology Model

Jessie Ellen Cherry

Much of the uncertainty in land-based Arctic freshwater estimates relates to the diffi culty 

of measuring solid precipitation (Goodison et 

al. 1998). Precipitation gages that work well for 

liquid precipitation perform poorly for mixed and 

solid precipitation because the gage itself disrupts 

the boundary layer wind fl ow and causes snow to 

preferentially fall downwind from the gage (Sevruk 

1998). Another problem in the Arctic is the paucity 

of gages (compared to mid-latitudes) which is compounded by creating gridded products for use in 

climate studies when there are only a few points of 

observation (Bowling et al. 2000). Such products 

are even more misleading when different countries 

and regions use different kinds of gages, each kind 

with a unique bias toward undercatch.

The goal of this proposal is to reconstruct a 

century-long record of solid precipitation in the 

Arctic by running the NASA Seasonal to Interannual Prediction Project (NSIPP) land surface 

hydrology model in an inverse mode (Ducharne 

et al. 2000, Koster et al. 2000). To this end, the 

model is run using observations of snow depth and 

surface air temperature to reconstruct the precipitation that must have fallen to produce the observed 

snow depth. Transport of snow by wind on the 

Arctic prairies may be as much as 75% (Pomeroy 

and Gray 1995) and sublimation induced by strong 

winds may account for losses to the atmosphere 

of nearly 30% (Pomeroy et al. 1997). For these 

reasons, the model includes compaction, surface 

sublimation, and blowing snow. This reconstruction 

is based on simple snow depth measurements using 

a ruler, with minimal instrumental error and little 

or no destructive infl uence on the snowpack. The 

snow depth record is quite long (back to 1890 in 

some stations) and adds thousands of stations to the 

small number of precipitation gages in the Arctic 

(over 400 new stations in the Mackenzie catchment 

alone). By estimating the historical land-based 

solid precipitation in the Arctic, uncertainty in the 

Arctic freshwater budget associated with precipitation gages is signifi cantly reduced. 

The Reynolds Mountain station at Reynolds 

Creek Experimental Watershed (RCEW) in southwestern Idaho was chosen to calibrate and evaluate the method. Hourly measurements of all the 

relevant climate and hydrological variables for this 

study have been taken at RCEW since 1984 (Marks 

et al. 2001). Snow depth and snow water equivalent 

(SWE) are measured on several permanent snowcourses, twice monthly, and SWE is measured on 

an automatic snowpillow (pressure-based measurement of the weight of overlying snow) once hourly. 

RCEW was one site of the World Meteorological Organization’s Solid Precipitation Intercomparison Project from 1987–1994, a time during 

which instrumental biases associated with several 

solid precipitation gages were carefully evaluated 

against the double fenced inter-comparison reference (DFIR), considered the least biased snow gage 

available. Transfer functions were then developed 

to adjust the gage data for known biases. These 

adjustments are applied to the precipitation observations for the present study.

First, the model is forced with corrected observed precipitation, surface temperature, surface 

pressure, vapor pressure, wind, incoming shortwave and longwave radiation. The simulated snow 

depth is then compared to observed snow depth 

(from cumulative corrected gages, pillows, and 

courses) to demonstrate the ability of the model to 

reproduce the observed snowpack. Then the model 

is run in an inverse mode to reconstruct precipitation. In this way, snowdepth observations and 

modeled snowpack physics are used to calculate 

how much precipitation must have occurred to 

produce the observed snow depth. Results from a 

pilot run show excellent agreement (< 3% yearly 

SWE) between NSIPP reconstructed precipitation 

and adjusted observed precipitation.
29

References

Armstrong, R. 2001. Historical Soviet Daily Snow 

Depth Version 2 (HSDSD). Boulder, CO, 

USA: National Snow and Ice Data Center. 

CD-ROM.

Bowling, L.C., D.P. Lettenmaier, and B.V. Matheussen. 2000. Hydroclimatology of the Arctic 

Drainage 

Basin, in The Freshwater Budget of the Arctic 

Ocean. E.L. Lewis, ed. Dordrecht, Kluwer 

Academic Publishers, p. 57–90.

Brown, R.D., and R.O. Braaten. 1998. Spatial and 

temporal variability of Canadian monthly 

snow depths, 1946–1995. Atmosphere-Ocean, 

36, 37–45.

Dickson, R.R. and J. Brown. 1994. The production 

of north Atlantic deep-water: sources, rates, 

and pathways. J. Geophys Res-Oceans, 99 

(C6):12319–12341.

Ducharne, A, R.D. Koster, M.J. Suarez, et al. 2000. 

A catchment-based approach to modeling 

land surface processes in a general circulation model 2. Parameter estimation and model 

demonstration. J Geophys Res-Atmos, 105 

(D20):24823–24838.

Goodison, B.E., H.L. Ferguson, and G.A. McKay. 

1981. Measurement and Data Analysis, in 

Handbook of Snow. D.M. Gray and D.H. 

Male, eds. Ontario, Pergamon Press, pp. 

191–274.

Goodison, B.E., P.Y.T. Louie, and D. Yang. 1998. 

WMO solid precipitation measurement intercomparison, fi nal report. WMO/TD-No.872, 

WMO, Geneva, 212pp.

Hanover, N.H. Yang, D., and M.K. Woo. 1999. 

Representativeness of local snow data for 

large-scale hydrological investigations. Hydrological Processes, 13(2-13), 1977–1988.

Johnson, J.B. and G.L. Schaefer. 2002. The infl uence of thermal, hydrologic, and snow deformation mechanisms on snow water equivalent 

pressure sensor accuracy. Hydrol Process, 16 

(18):3529–3542.

Koster, R.D., M.J. Suarez, A. Ducharne, et al. 

2000. A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J Geophys 

Res-Atmos, 105 (D20):24809–24822.

Marks, D, K.R. Cooley, D.C. Robertson, et al. 

2001. Long-term snow database, Reynolds 

Creek Experimental Watershed, Idaho, United 

States. Water Resour Res, 37 (11):2835–2838.

Meteorological Service of Canada (MSC), 2000. 

Canadian Daily Climate Data (CDCD). 

Downsview, Ontario, CANADA: National 

Archives and Data Management Branch. CDROM.

Pomeroy, J.W. and D.M. Gray. 1995. Snowcover 

Accumulation, Relocation and Management. 

NHRI Science Rep. No. 7, Saskatoon, 144 pp.

Pomeroy, J.W., P. Marsh, and D.M. Gray. 1997. 

Microphysics of Clouds and Precipitation, 

2nd ed. Kluwer Academic Publishers, Dordrecht, 954 pp.

Serreze, M.C., J.E. Walsh, F.S. Chapin, et al. 2000. 

Observational evidence of recent change in 

the northern high latitude environment. Climatic Change, 46 (1–2):159–207.

Sevruk, B. 1998. Physics of precipitation gages, appendix in WMO solid precipitation measurement intercomparison, fi nal report. WMO/

TD-No.872, WMO, Geneva, 212 pp.

Work, R.A., H.J. Stockwell, T.G. Freeman, and 

R.T. Beaumont. 1965. Accuracy of fi eld snow 

surveys, western United States, including 

Alaska. Tech. Rep. 163, U.S. Army Cold Reg. 

Res. Eng. Lab. 

Theme 2: Hydrological Observations and 

Modeling

Spatially Distributed Simulations of Permafrost 

Hydrology

Larry Hinzman, W. Robert Bolton, Peter Prokein, 

Matt Nolan, and Kenji Yoshikawa

Permafrost is a strong factor in controlling many 

hydrologic processes including stream fl ow and 

soil moisture. Soil moisture, which displays a high 

spatial and temporal variability, is an important 

variable in understanding and predicting a large 

number of processes, including land-atmosphere 

interactions and permafrost aggradation/degradation. In order to understand and predict ecosystem 
30

response to a changing climate and resulting feedbacks, it is critical to quantify the interaction of soil 

moisture and meteorology as a function of climatic 

processes, landscape type, and vegetation.

The primary goal of our research is to describe, 

simulate, and predict soil moisture dynamics 

and all other hydrologic processes everywhere 

throughout both sub-arctic and arctic watersheds. 

The model we are developing, TopoFlow, is being 

used as a tool to better understand the effects of 

vegetation and soil type, presence or absence of 

permafrost, the amount and timing of precipitation, and disturbance (such as wildfi re) on soil 

moisture dynamics. Three small sub-basins of the 

Caribou-Poker Creeks Research Watershed (CPCRW), located 48 km north of Fairbanks, Alaska 

(65o 10’N, 147o 30’W) and three nested watersheds 

in the Kuparuk Watershed (~68o 37’N, 149o 19’W) 

are the areas selected for study.

The primary control on local hydrological 

processes is dictated by the presence or absence of 

permafrost, but is also infl uenced by the thickness 

of the active layer and the total thickness of the underlying permafrost. As permafrost becomes thinner or decreases in areal extent, the interaction of 

surface and sub-permafrost ground water processes 

becomes more important. The inability of soil 

moisture to infi ltrate to deeper groundwater zones 

due to ice rich permafrost maintains very wet soils 

in arctic regions. However, in the slightly warmer 

regions of the subarctic, the permafrost is thinner 

or discontinuous. In permafrost-free areas, surface 

soils can be quite dry as infi ltration is not restricted, 

impacting ecosystem dynamics, fi re frequency, 

and latent and sensible heat fl uxes. Other hydrologic processes impacted by degrading permafrost 

include increased winter stream fl ows, decreased 

summer peak fl ows, changes in stream water chemistry, and other fl uvial geomorphological processes. 

Hydrologic changes occurring in Alaska include 

drying of thermokarst ponds, increased active layer 

thickness, increasing importance of groundwater 

in the local water balance and differences in the 

surface energy balance.

Estimating the Freshwater Budget of High 

Latitude Land Surfaces

Laura C. Bowling, Jennifer Adam, Fengge Su, and 

Dennis P. Lettenmaier

Based on observations alone, we do not have 

the ability to close the water budget of the Alaskan 

North Slope, or to make predictions regarding its 

response to warmer temperatures. This presentation describes on-going research aimed at better 

estimating the North Slope regional water budget 

through the use of a large-scale, distributed hydrologic model, with a focus on the infl uence of 

surface water storage in wetlands and thaw ponds 

and sublimation from blowing snow. In the lowgradient watersheds of the coastal plain, permafrost 

contributes to the generation of extensive wetlands, 

ponds, and lakes in a semi-arid region of precipitation. In the Putuligayuk catchment, which drains 

into Prudhoe Bay, water balance calculations 

indicate that between 24 and 42% of snow melt 

water is not immediately available for runoff. This 

observed storage effect can be explained in large 

part by the excess of evapotranspiration over summer precipitation from open water areas, which results in a seasonal reduction in the extent of surface 

water of 58 to 73%. A lake and wetland algorithm 

added to the Variable Infi ltration Capacity (VIC) 

macroscale hydrology model is able to simulate 

this seasonal reduction in wetland extent.

An algorithm that parameterizes the topographically induced sub-grid variability in wind speed, 

snow transport, and blowing snow sublimation was 

also designed for use within the VIC model. The 

algorithm is intended to provide consistent estimates of the relative infl uence of sublimation from 

blowing snow for continental-scale river basins, 

while balancing the land surface water and energy 

budgets. In addition to the standard land surface 

scheme inputs, the model requires specifi cation of 

the standard deviation of terrain slope, the mean 

fetch, and the lag one autocorrelation of terrain 

gradients. Model predictions of end-of-season 

snow water equivalent (SWE) compare favorably 

with observations for Imnavait Creek and other 

sites throughout the Kuparuk River basin on the 

Alaskan North Slope. Annual average sublimation 

from blowing snow predicted by the model for the 

Kuparuk River watershed varies from 47 mm in the 

foothills of the Brooks Range to approximately 31 

mm on the Arctic coastal plain; sublimation was 

primarily controlled by topographic limitations on 
31

fetch in the foothills and by precipitation and vapor 

pressure on the coastal plain.

The results from these studies reveal a number of ongoing scientifi c challenges to accurately 

predict regional runoff. These include observational shortcomings associated with solid precipitation, relative humidity, and wind speed, as well 

as modeling challenges associated with the role of 

recharge to upland tundra areas and the pathways 

of water exchange between the tundra and surface 

water networks. These will be discussed within the 

context of strategies for prediction of runoff from 

the entire North Slope domain.

Simulation of the Infl uence of Discontinuous 

Permafrost on Hydrologic Processes

W. Robert Bolton and Larry Hinzman

In the sub-arctic environment, permafrost is a 

strong factor in controlling many hydrologic processes including stream fl ow and soil moisture. Soil 

moisture, which displays a high spatial and temporal variability, is important in understanding and 

predicting a large number of processes, including 

land-atmosphere interactions and permafrost aggradation/degradation. In order to understand and 

predict ecosystem response to a changing climate 

and resulting feedbacks, it is critical to quantify the 

interaction of soil moisture and meteorology as a 

function of climatic processes, landscape type, and 

vegetation.

The primary goal of our research is to describe, 

simulate, and predict soil moisture dynamics 

and all other hydrologic processes everywhere 

throughout a sub-arctic watershed. The model we 

are developing, TopoFlow, is being used as a tool 

to better understand the effects of vegetation and 

soil type, presence or absence of permafrost, the 

amount and timing of precipitation, and disturbance (such as wildfi re) on soil moisture dynamics. Three small sub-basins of the Caribou-Poker 

Creeks Research Watershed (CPCRW), located 

48 km north of Fairbanks, Alaska (65o

 10’N, 147o

30’W), are the areas selected for study. These 

small sub-basins, which are underlain with approximately 3, 19, and 53% permafrost, are simulated to 

explore differences in permafrost versus non-permafrost areas.

The primary control on local hydrological 

processes is dictated by the presence or absence of 

permafrost, but is also infl uenced by the thickness 

of the active layer and the total thickness of the underlying permafrost. As permafrost becomes thinner or decreases in areal extent, the interaction of 

surface and sub-permafrost ground water processes 

becomes more important. The inability of soil 

moisture to infi ltrate to deeper groundwater zones 

due to ice-rich permafrost maintains very wet soils 

in arctic regions. However, in the slightly warmer 

regions of the subarctic, the permafrost is thinner 

or discontinuous. In permafrost-free areas, surface 

soils can be quite dry as infi ltration is not restricted, 

impacting ecosystem dynamics, fi re frequency, 

and latent and sensible heat fl uxes. Other hydrologic processes impacted by degrading permafrost 

include increased winter stream fl ows, decreased 

summer peak fl ows, changes in stream water chemistry, and other fl uvial geomorphological processes. 

Hydrologic changes occurring in Alaska include 

drying of thermokarst ponds, increased active layer 

thickness, increasing importance of groundwater 

in the local water balance, and differences in the 

surface energy balance.

Dynamic Hydrologic Processes on the Seward 

Peninsula

Kristin Susens and Larry Hinzman 

The purpose of this project is to contribute to a 

more complete understanding of hydrologic and 

meteorological processes as impacted by varying 

proportions of permafrost due to climate change. 

We have been studying the hydrologic processes 

occurring in nested watersheds of the Kuparuk 

river basin on the North Slope of Alaska, U.S.A. 

since 1985. This project compares and contrasts 

watersheds in a slightly warmer region of the 

Arctic, the Seward Peninsula. The hydrology of 

the Seward Peninsula is simulated using a spatially 

distributed hydrologic model, TopoFlow, which 

was developed in our laboratory and verifi ed on 

watersheds in the Interior and the North Slope of 

Alaska. Complementary measurements of all the 

important components of the surface water and 

energy balances are currently being collected in 

two watersheds on the Seward Peninsula, Kuzitrin 

River near Kougarok (drainage area ~4450 km2

) 
32

and Snake River near Nome (~220 km2

). Water 

balance computations include the measurements or 

computations of precipitation, evapotranspiration, 

runoff, and storage change in snow, ice, groundwater, and soil water. These hydrological and meteorological elements vary spatially and temporally, 

and most water balance computations will deal 

with the computations of time-average. On large 

time and space scales, subpermafrost groundwater 

also becomes an important factor. With projected 

increases in surface temperature and decreases in 

surface moisture levels, the active layer thickness 

will probably increase and permafrost area extent 

will decrease, leading to subtle but predictable 

ecosystem responses such as vegetation changes. 

Permafrost in arctic regions exerts a signifi cant 

infl uence upon hydrologic and ecosystem dynamics through controls on vegetation and drainage. In 

relatively fl at areas where the frozen layer is near 

the surface, the soil moisture contents are usually 

quite high. These areas have relatively high evapotranspiration and sensible heat transfer, and a low 

conductive heat transfer due to the insulative properties of thick organic soils. The climax vegetative 

species and soil forming processes are dominantly 

controlled by the closely coupled permafrost and 

hydrologic conditions. As permafrost degrades, the 

soil moisture holding capacity increases, soil drainage improves and moisture is no longer held near 

the surface but percolates to deeper reservoirs. As 

permafrost becomes thinner or absent, groundwater 

contributions from springs become more important. 

Development of a General Approach to 

Estimating Discharge in Alaskan Arctic Rivers 

from Remotely Sensed Information

David Bjerklie

Previous studies suggest that remotely-sensed 

river hydraulic data could be used to directly 

estimate the discharge of rivers in specifi c reaches 

with typical accuracies within plus or minus 50%, 

and a mean accuracy within 10%. With limited 

ground information and appropriate regional river 

classifi cation schemes, this accuracy can likely be 

improved. The advantage of using remote sensing 

is that it has the capability to provide information 

over large areas including those that are diffi cult to 

access. Thus, if site specifi c discharge ratings can 

be developed from hydraulic information observed 

from remote platforms, discharge dynamics in 

rivers with little or no historical discharge records 

could be estimated from the historic record of remote imagery and other remote data sources.

The water surface width (estimated from water 

surface area), channel slope, and mean channel 

width (estimated from channel surface area) can all 

be obtained from existing remote sources, including topographic information, visible spectrum 

satellite and aerial images, and synthetic aperture 

radar (SAR) images. In some cases, the surface 

velocity of rivers can also be observed remotely using synthetic aperture radar. At least since the late 

1970’s, satellite and aerial imagery of the Alaskan 

arctic have been routinely collected and archived. 

Although a continuous time series of imagery 

for a specifi c river reach would generally not be 

available, a relatively long sequence of remotely 

sensed river widths could be assembled for analysis. These data combined with historic stream gage 

records for the Alaska arctic and sub-arctic would 

provide the necessary data to develop, test, and 

calibrate a general approach to estimating river 

discharge from remote data sources.

Unique hydrologic issues that would be addressed include the effects of ice and ice damming, 

and frozen channel bottoms. These issues would 

be addressed by using an unsteady fl ow model 

coupled with a general resistance equation that is 

calibrated using the Froude number. The general 

approach is based on new formulations of general 

fl ow-resistance equation(s) that do not require 

site-specifi c estimates of fl ow resistance as input. 

Instead, resistance is explained to the maximum 

extent possible by measurable hydraulic variables 

and channel morphology. The study will develop 

general relationships between observable hydraulic 

variables (width, slope) and fl ow resistance from 

existing regional discharge data (USGS NWIS 

data for Alaska), and incorporate these data into 

an unsteady fl ow model developed from channel 

geometry information. 

Exploration of Hydrograph Analysis Techniques 

for Alaskan Arctic Coastal Watersheds

Robert F. Carlson
33

Analysis of the daily streamfl ow hydrograph records of Alaskan arctic coast streams is a valuable, 

but underutilized, tool for understanding freshwater 

discharge. At the present time, most studies refer to 

only mean annual discharge or, perhaps, the single 

daily peak fl ow for the season. Yet, classic streamfl ow analysis provides a rich store of information 

for understanding modeling efforts of freshwater 

discharge. The USGS lists 16 stream gage sites 

with 8 sites of more than one year of record in the 

North Slope region. A typical analysis sequence 

will yield the following information–volume and 

seasonal pattern of groundwater fl ow; dates of 

break-up and freeze-up; volume and pattern of the 

spring breakup fl ow; number, volume, peak, and 

pattern of summer rainstorms; calculation of fl ow 

recession values of groundwater and surface runoff; and volume and pattern of winter fl ow (if the 

data is available). The annual streamfl ow pattern 

can also be subject to model-free time series analysis including auto regressive-integrated-moving 

average (ARIMA) analysis and singular spectrum 

analysis (SSA). These techniques may generate parameters that are instructive for understanding the 

nature of watershed runoff patterns within a season 

and from year to year. Finally, linear reservoir conceptual models, through numerical inversion with 

the streamfl ow data, can identify watershed parameters and inputs. These techniques are illustrated 

for several of the long-term streamfl ow records in 

the Arctic coastal region.

Hydrological Modeling of the Mackenzie Basin 

Using WATFLOOD and WATCLASS in MAGS

F. Seglenieks and E.D. Soulis

The objective of the Mackenzie Basin GEWEX 

Study (MAGS) is to understand the water and 

energy balance for the Mackenzie river basin. In 

such a data sparse area, modeling plays an important role by providing a framework for integration 

of observations into a consistent description of the 

budgets. The particular challenges of the Mackenzie are that the database is extremely limited, 

and the processes are poorly understood for the 

northern domains. Therefore the modeling effort 

must be as comprehensive as possible, involving 

atmospheric and hydrologic models that are tightly 

integrated and use all available data. 

The challenge for the Mackenzie is to conduct 

modeling in a data sparse environment. There are 

approximately 82 operational streamgages in the 

1.7 million square kilometer basin and a similar 

number of synoptic weather stations. Although not 

typical of the developed world, this low network 

density is characteristic of much of the globe. This 

gives special importance to work in the Mackenzie, 

as the techniques developed will be more appropriate globally than models developed in data rich 

environments. An extensive database has been 

developed to support the modeling program. 

Another challenge is the northern climate of the 

watershed. Although many existing hydrologic 

models have winter components, the parameterizations are usually not developed well enough to 

accommodate the extreme northern conditions in 

the Mackenzie. Also, physiographic considerations 

such as permafrost, frozen soils, snow sublimation, 

and snow redistribution are rarely incorporated in 

most hydrological models. There are major problems when models are transposed from temperate 

climates and are applied to northern environments.

The modeling system is built in stages by combining two well-established Canadian codes: WATFLOOD and CLASS. WATFLOOD has a welldeveloped routing scheme and provides a solid 

connection to the streamfl ow records. CLASS, the 

Canadian Land Surface Scheme, pays great attention to the vertical and water energy budget and 

has an appropriate interface to atmospheric models. 

The resulting code, WATCLASS, combines these 

strengths.

WATFLOOD was developed at the University 

of Waterloo starting in 1973 as a fl ood forecasting model. The WATFLOOD model divides a 

watershed into a number of Grouped Response 

Units (GRU) and discretizes the basins into a 

series of square grids. The surface water budget is 

computed for each GRU within a grid square and 

infi ltrated using the well-known Green-Ampt approach. When the infi ltration capacity is exceeded 

by the water supply, and the depression storage has 

been satisfi ed, the model then computes overland 

fl ow from the Manning equation. Infi ltrated water 

is stored in a soil reservoir referred to as the Upper Zone Storage (UZS). Water within this layer 

percolates downward or is exfi ltrated to nearby 
34

water-courses using simple storage-discharge relationships. 

The development of CLASS began in 1987 in 

response to the perceived need for second-generation land surface modeling within the Canadian 

GCM. In CLASS, land cover is dealt with using 

a patched landcover, or mosaic approach. Each 

modeled grid cell can have up to four subareas, representing bare soil, vegetation-covered, 

snow-covered, and snow-and-vegetation covered 

“patches” of the landscape. Inputs of meteorological variables at the bottom of the atmosphere are 

used to drive the energy and moisture balances for 

each of the subareas, and the resulting fl uxes to the 

atmosphere are passed back to the host atmospheric 

model. The surface energy balance is solved for 

each subarea by expressing the various fl uxes as 

functions of a single unknown, the surface temperature (of the vegetation, snow or soil as appropriate), and then solving iteratively. 

The coupling of WATFLOOD and CLASS 

was begun in 1997, however the process was not 

straightforward and it has taken many years to fully 

integrate the models. Both models are land-covered based, and have similar land cover categories 

but the treatments of mixed covers are signifi cantly 

different. For example, each blends different state 

variables within an element and many of the parametizations are slightly different. The combined 

model was constructed using CLASS for the vertical processes and WATFLOOD for the horizontal 

process. In the WATCLASS model, the generation 

of runoff is assigned to CLASS and the routing is 

carried out by WATFLOOD. 

Use of distributed hydrologic models requires a 

detailed description of topography and sub-basin 

boundaries for a watershed. These were compiled 

from the USGS GTOPO30 digital terrain model, 

with the basin being divided into a 20 km polarstereographic grid with 4700 elements. These 

models also require land cover information for the 

watershed. A land cover product developed by the 

Canada Center for Remote Sensing (CCRS) proved 

to be the most accurate in a previous study done for 

MAGS. The thirty-one land classes of the CCRS 

land-cover product were consolidated into 7 land 

classes: Barren, Coniferous Forest, Deciduous Forest, Cropland, Glacier, Wetland, and Water (Figure 

1).

WATFLOOD and WATCLASS require gridded 

surface meteorological data to drive their hydrologic calculations. Data for this purpose traditionally have been derived from interpolated measured 

station data and measured weather radar; however, 

more recently Global Circulation Model (GCM) 

and Numerical Weather Prediction (NWP) archive 

data have been used successfully. For this study, 

data were obtained from two models operated by 

the Canadian Meteorological Centre; the Regional 

Finite Element (RFE) model and the Global Environmental Multiscale (GEM) model. Output from 

the gridded forecast portion of the archive was used 

as the forcing data set for WATFLOOD and WATCLASS for the period of 1994–2001. Because of 

the inherently poor predictability of precipitation, 

the gridded precipitation data obtained from the 

NWP models were adjusted in an attempt to match 

observations from the climate network.

In order to improve the streamfl ows that were 

simulated, the parameter sets were calibrated for 

the Mackenzie basin. To begin, an initial parameter set was used that has been derived by running 

WATFLOOD and WATCLASS on many different 

watersheds both within Canada and around the 

world. This initial parameter set was then calibrated for two years (1995–1996) using measured 

fl ows from the Liard river basin. The Liard River 

was chosen as it is the largest watershed in the 

basin that does not have regulated fl ows or large 

lakes. To calibrate the model, the parameters were 

adjusted, within limits, to minimize the square of 

the difference between the measured and simulated 

fl ows. This calibrated parameter set was then used 

to run the models for the rest of the time period 

over the entire basin. Apart from those two years 

on the Liard river basin, all other simulated streamfl ows can be considered an evaluation (or validation) of the parameter set. Figures 1 and 2 show 

simulated and measured streamfl ows at various 

locations within the Mackenzie basin using WATFLOOD and WATCLASS after calibration. 

Along with streamfl ows, the models also calculate the components of the basin’s water budget: 

precipitation, evaporation, and runoff. The difference between the incoming precipitation and the 
35

combined evaporation and runoff can be considered the year-over-year change in storage. Tables 1 

and 2 show the water budget calculated using both 

the WATFLOOD and WATCLASS models.

The WATFLOOD and WATCLASS streamfl ow 

results show good overall agreement with the 

measured values. In general, both the timing and 

volume of the peak fl ows are well represented. 

The best results are for the Liard River where the 

parameter calibration was performed for two years. 

However, there are defi nitely improvements that 

can be made to the simulated fl ows of the Mackenzie River at Arctic Red River. Most of the fl ow 

at this station is dependent on the outfl ow of Great 

Slave Lake; hence improvement in the simulated 

streamfl ow will require an updated stage-discharge 

relationship for the outlet of the lake. There are 

also problems with too much fl ow volume at the 

Athabasca River station.

In general there is good agreement between the 

water budgets of the two models; the difference in 

the average yearly change in storage is less than 

1% of the incoming precipitation. The WATCLASS model shows higher evaporation and less 

runoff than the WATFLOOD model. Unfortunately, the values of the water budget components are 

diffi cult to measure, and as a result, other sources 

are being investigated to obtain independent measures of these components. These include output 

from other models, indirect measurements using 

remote sensing, and direct measurements made on 

a small scale. In the future these will be used to 

verify the components of the water budget and may 

lead to changes in the models in order to produce 

more realistic values.

Future modeling efforts will focus on the interaction of soil moisture and transpiration from 

vegetation, optimization of land-surface parameters 

Hydrologic Local Delta

Water Year Precipitation Evaporation P-E Runoff Storage

1994-1995 368.3 182.5 185.8 188.6 -2.8

1995-1996 460.1 188.2 272.0 248.8 23.2

1996-1997 472.2 205.3 267.0 265.1 1.8

1997-1998 370.4 185.6 184.8 202.9 -18.1

1998-1999 427.1 147.8 279.2 260.9 18.4

1999-2000 444.9 154.8 290.2 280.3 9.9

2000-2001 424.6 143.3 281.2 291.4 -10.1

Average 423.9 172.5 251.4 248.3 3.2

Table 1. Water balance for seven water years using WATFLOOD (all values in mm of water).

Hydrologic Local Delta

Water Year Precipitation Evaporation P-E Runoff Storage

1994-1995 358.5 204.1 154.4 173.6 -19.2

1995-1996 462.8 186.5 276.3 238.5 37.8

1996-1997 474.9 180.8 294.0 286.5 7.5

1997-1998 367.3 208.4 158.9 181.6 -22.8

1998-1999 425.9 180.8 245.1 243.5 1.6

1999-2000 436.2 171.2 265.1 246.5 18.5

2000-2001 420.2 167.4 252.8 259.2 -6.4

Average 426.1 185.3 240.8 239.5 -0.3

Table 2. Water balance for seven water years using WATCLASS (all values in mm of water).
36

using hydrographs, and the parameterization of 

snow processes including permafrost, frozen soil, 

snow sublimation, and snow redistribution.

Barren

Coniferous Forest 

Deciduous Forest 

Cropland

Glacier

Wetland 

Water 

Figure 1. Land cover map for the Mackenzie basin derived from the Canada Centre for Remote 

Sensing.

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

10000

20000

30000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

2000

4000

6000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

4000

8000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

4000

8000

12000

16000

WATFLOOD Results on the Mackenzie Basin

(black - measured, red - simulated)

MACKENZIE RIVER AT ARCTIC RED RIVER - 10LC014(1 680 000 km^2)

LIARD RIVER AT FORT LIARD - 10ED001 (222 000 km^2)

PEACE RIVER AT PEACE RIVER - 07HA001 (186 000 km^2)

ATHABASCA RIVER BELOW MCMURRAY - 07DA001 (133 000 km^2)

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

10000

20000

30000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

2000

4000

6000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

4000

8000

1994 1995 1996 1997 1998 1999 2000 2001 2002

0

4000

8000

12000

16000

WATCLASS Results on the Mackenzie Basin

(black - measured, red - simulated)

MACKENZIE RIVER AT ARCTIC RED RIVER - 10LC014(1 680 000 km^2)

LIARD RIVER AT FORT LIARD - 10ED001 (222 000 km^2)

PEACE RIVER AT PEACE RIVER - 07HA001 (186 000 km^2)

ATHABASCA RIVER BELOW MCMURRAY - 07DA001 (133 000 km^2)

Figure 2. WATFLOOD and WATCLASS results on the Mackenzie basin for the years 1994–2001. 

The gage on the Mackenzie River at Arctic Red River (top hydrograph) has the largest drainage 

area while the other three hydrographs represent the three main sub-basins of the Mackenzie River. 

Note that the Peace River contains a major hydroelectric dam and thus a majority of its fl ow is 

regulated.
37

Hydrologic Modeling in the Tundra Region of 

NW Canada

S. Pohl, P. Marsh, A. Pietroniro, B. Davison, C. 

Onclin, and M. Russell

This paper will consider various process and 

hydrologic modeling studies being carried out in 

NW Canada. These studies have utilized a variety 

of hydrologic models including WATFLOOD and 

WATCLASS and will utilize TopoFlow in the near 

future. This paper will concentrate on results to 

date and the physical process studies carried out in 

support of model development. Results reported 

in this paper are for a small research basin (less 

than 100 km2

), but this research is in support of a 

variety of larger scale modeling efforts, including: 

the Mackenzie GEWEX Study (MAGS); estimating fl ow from ungaged areas; and various climate 

change studies for example. 

The hydrology land-surface scheme WATCLASS has been tested in order to simulate spring 

snowmelt runoff in Trail Valley Creek (TVC) – a 

small arctic basin dominated by open tundra and 

shrub tundra vegetation. TVC enters the Beaufort 

Sea east of the Mackenzie Delta. WATCLASS calculates snowmelt rates from a full surface energy 

balance, and a three layer soil model is used to 

simulate the infi ltration into and the exchange of 

heat and moisture within the ground. The generated meltwater is delivered to the stream channel 

network by overland fl ow, interfl ow, and basefl ow 

and subsequently routed out of the catchment. Five 

spring snowmelt periods with a variety of initial 

end-of-winter snow cover and melt conditions were 

simulated and compared against observed runoff 

data. A main component of this stage of model 

validation considered the models ability to simulate 

spatially variable snow covered area (SCA) within 

the basin. Variable snow covered area is believed 

to be an important process in controlling both the 

fl uxes of energy between the landsurface and atmosphere, and snowmelt runoff. The ability of WATCLASS to model spatially variable snow cover 

was carried out by comparing model predictions to 

remotely sensed SCA. 

Results show that WATCLASS was able to fairly 

accurately predict runoff volumes, as well as timing 

of snowmelt and meltwater runoff, for open tundra. 

However, the model underestimated melt in the 

energetically more complex shrub tundra areas of 

the basin. This was not a major issue for the study 

basin, but would be for areas with larger areas 

of shrub tundra. This is especially a problem for 

climate change studies, as the area of shrub tundra 

is expected to increase under various future climate 

scenarios. Furthermore, the large observed spatial 

variability of the modeled SCA were not captured 

well at the 1 km model resolution. Recommendations to improve model performance in arctic basins include: a more realistic implementation of the 

gradual deepening of the thawed layer during the 

spring to improve runoff predictions, and the use 

of topographic information in the defi nition of land 

cover classes for the grouped response unit (GRU) 

approach employed by the model to handle subgrid 

variability. This would include a better prediction 

of the variability of both snow water equivalent 

(SWE) and energy fl uxes controlling snowmelt 

rates.

Subsequent research has considered the relative 

importance of variable SWE vs. variable energy 

fl uxes, including both solar radiation and turbulent 

fl uxes driven by spatially variable wind speed, in 

low-relief tundra basins. Small scale process models were developed to consider the spatial variability in solar radiation and latent/sensible heat fl ux 

during spring melt (controlled both by spatially 

variable wind speed and small scale advection of 

sensible heat from the snow-free to snow-covered 

patches). These model results are able to accurately 

predict both snow-covered area and the distribution 

of patches when compared to remote sensing images of snow covered area, and clearly demonstrate 

the relative importance of various processes in controlling the development of a patchy snow cover. 

In addition, recent work has demonstrated the role 

of shrubs in affecting snow melt. Ongoing work is 

concentrating on including these small scale processes into WATCLASS in order to better estimate 

both the exchange of energy between the snow 

covered terrain and the atmosphere, and runoff for 

both small and large scale model applications. 

USGS Streamfl ow Data Collection in Alaska

David F. Meyer

The U.S. Geological Survey (USGS) has collected streamfl ow information in Alaska since 1906. 
38

However, continuous records have been collected 

only since the 1970s. The stream gaging network in 

Alaska, and specifi cally in the Arctic, is sparse relative to the conterminous United States. The number 

of daily discharge stations in Alaska equates to 

about 1 station for every 5,100 square miles. In the 

“lower 48”, there is about one station for every 400 

square miles. On the North Slope, there have been 

only 15 continuous stream gages since streamfl ow 

data collection began, and only 6 are active today, about one for every 10,000 square miles. In a 

recently completed fl ood frequency study of Alaska 

rivers, only 24 gages with 10 or more years of peak 

fl ow record could be used for the arctic region of 

Alaska that includes drainages to the Arctic Ocean, 

and Norton and Kotzebue Sounds.

Currently, 122 continuous streamfl ow gaging 

stations and 70 partial-record gages are operating in 

Alaska. Stage and discharge data from 90 of these 

gages, as well as most historical data, are accessible in real-time through the Internet. These data 

are collected, reviewed, and archived to nationally consistent, documented standards. At many of 

these gages, rainfall and water temperature are also 

collected and transmitted. Most are operated year 

round, though determination of streamfl ow under 

ice is possible only by direct discharge measurement. Continuous streamfl ow must be estimated 

between visits.

Stream gages are funded through the USGS 

National Streamfl ow Information Program, which 

is a direct Congressional appropriation, through 

interagency agreements with other Federal Agencies, including the Corps of Engineers, Forest 

Service, and Bureau of Land Management, and 

through cooperative agreements with state and local agencies and Native Tribal Councils. In cases 

where private entities need hydrologic data that is 

also in the National interest, funding is frequently 

provided by various state agencies, which in turn 

sign agreements with those private entities. In 

many cases, USGS matches as much as 50% of 

the funding provided by state and local agencies 

through the USGS Cooperative Program. Funding for stream gaging, both direct and through the 

Cooperative Program has been declining over the 

past few years.

New techniques promise more effective and 

safer data collection. Non-contact stream stage 

measuring currently is being tested, and will provide more reliable records during breakup, when 

the Arctic rivers normally peak. Acoustic Doppler 

current profi ling to measure stream discharge is 

widespread, and can provide quicker, more accurate measurements of stream discharge. Helicopter-mounted ground-penetrating radar, coupled 

with Doppler radar, shows promise for making 

non-contact discharge measurements possible, 

even during periods when ice fl ow precludes more 

routine streamfl ow measurement. However, access 

to remote sites remains expensive and time-consuming. Even with these new techniques, fi nancial 

resources will likely limit hydrologic data coverage 

in remote areas.

A Hydrological Digital Elevation Model for 

Freshwater Discharge into the Gulf of Alaska

Jia Wang, Meibing Jin, David L. Musgrave, 

and Moto Ikeda 

Freshwater discharge into the Gulf of Alaska 

(GOA) has an important effect on coastal circulation. In order to incorporate freshwater discharge 

into a 3-D ocean circulation model with both point 

sources (big rivers) and line sources (gridded 

coastlines), a digital elevation model (DEM) was 

developed to simulate freshwater discharge into 

GOA under forcing of daily air temperature and 

precipitation data from NCEP/NCAR reanalysis 

during 1958–1998. This GOA-DEM includes glacier, snow storage, and melting processes. Coastal 

freshwater discharge into GOA displays a very 

strong seasonal cycle and interannual variability. 

The comparison of simulated runoff with gaged 

(observed) river discharge was conducted for two 

major rivers (Copper River and Susitna River), 

showing a good agreement on seasonal cycle and 

interannual variability. The simulated annual mean 

of the total freshwater discharge into GOA ranges 

from 19,000 to 31,000 m3

s

-1 (with a mean of 

23,100 m3s

-1 ) for the period of 1958–1998. In the 

winter season (November to April), precipitation 

is mainly stored as snow, and freshwater discharge 

remains as a small base fl ow with some occasional 

changes due to short-term temperature increase. 

Freshwater discharge starts to rise sharply from 

May due to increasing precipitation and above-
39

freezing temperatures, and remains high from June 

through September because of snow melt and some 

melting glaciers. In October, the discharge decreases rapidly, to a basic fl ow in December as the 

temperature drops below the freezing point.

Freshwater discharge into GOA can be divided 

into the point sources (big rivers) and the line 

sources (ungaged numerous small streams and 

creeks due to melting of snow and glaciers). The 

model shows that fi ve major rivers (point sources) 

account for about 50.6% of the total drainage areas, 

while the line source accounts for 49.4% of the 

drainage area. However, our new fi nding is that the 

point sources only account for 26%, while the line 

sources contribute 74% to the total runoff. Thus, 

discharge from line sources (ungaged small rivers, 

streams, and creeks) is 2.8 times greater than the 

point sources (fi ve large rivers). 

Figure 1. Elevation in the model study area (top panel), and the watershed of the line source (in 

yellow) and fi ve big rivers (in colors) (bottom panel), which are named (from left to right) Susitna, 

Copper, Alsek, Taku, and Stikine rivers. The “+” signs denote the NCDC stations available in 

the study region. Note that these rivers have interior drainages, while the line source has coastal 

drainages.
40

Discharge (10 m /s) 4

T(month)

3

Figure 2. Model simulated monthly climatology (seasonal cycle) of freshwater discharge derived 

from the 41-year simulations (1958–1998) and standard deviations (vertical bars). Black line is 

the total discharge into the Gulf of Alaska; the blue line is the line source and the red is the point 

source.

Hydrological Modeling in the Lena River Basin

Xieyao Maa, Tetsuzo Yasunaria, Tetsuo Ohatac,

and Yoshihiro Fukushimae

The Lena River is one of the four largest rivers 

fl owing into the Arctic Ocean. The river freezes 

over completely from early December to late April 

and the fl ood dominated by snowmelt occurs with 

the river break-up in May–June. In order to determine the mechanism of the runoff formation, 

a hydrological modeling investigation was carried out in the period of 1986–2000. The model 

is composed of four submodels: one-dimensional 

soil-vegetation-atmosphere transfer (SVAT) model, 

runoff formation model, river routing model, and 

river ice model, developed by Ma and Fukushima 

(2002). The domain of the study area was established between 52o–72o N and 100o–140o E. Forty 

meteorological gages’ data over the domain come 

from GAME-Siberia Committee. Three hydrological gages were selected to check the model performance. The results show that 1) the timing of fl ood 

rising and fl ood peak could be modeled for the 

selected hydrological stations, which present the 

entire basin (Kusur), the upper and midstream portions of the Lean (Tabaga), and a main tributary of 

the Lena (Verkhoyanskiy Perevoz); 2) river-freeze 

processes delay the spring, snowmelt-dominated 

fl ood by about 23 days at Tabaga; 3) estimates of 

annual runoff were suffi ciently accurate (within 20 

mm on average and 8.85%) at Kusur; 4) the total 

fl ood volume during May–July at Kusur is contributed by snowmelt.

Changes in Lena River Streamfl ow Hydrology: 

Human Impacts vs. Natural Variations

Daqing Yang and Douglas Kane

This study systematically analyzes long-term 

(1936–1999) monthly discharge records for the 

major sub-basins within the Lena River watershed 

in order to document signifi cant streamfl ow hydrology changes induced by human activities (particularly reservoirs) and by natural variations/changes. 

The results show that the upper streams of the watershed, without much human impact, experience a 

runoff increase in winter, spring, and (particularly) 

summer seasons, and a discharge decrease in the 

fall season. These changes in seasonal streamfl ow characteristics indicate a hydrologic regime 
41

shift toward early snowmelt and higher summer 

streamfl ow, perhaps due to regional climate warming and permafrost degradation in the southern 

parts of Siberia. The results also demonstrate that 

reservoir regulations have signifi cantly altered the 

monthly discharge regime in the lower parts of the 

Lena river basin. Because of a large dam in west 

Lena River, summer (high) fl ows at the outlet of 

the Vilui valley have been reduced by up to 55% 

and winter (low) fl ows have been increased by 

up to 30 times. These alterations, plus streamfl ow 

changes in the upper Lena regions, lead to strong 

upward trends (up to 90%) in monthly discharge 

at the basin outlet during the low fl ow months and 

weak increases (5–10%) in the high fl ow season. 

Monthly fl ow records at the basin outlet have been 

reconstructed by a regression method to reduce 

reservoir impacts. Trend analyses and comparisons 

between the observed and reconstructed monthly 

fl ows show that, because of reservoir regulations, 

discharge records observed at the Lena basin outlet 

do not always represent natural changes and variations. They tend to underestimate the natural runoff 

trends in summer and overestimate the trends in 

both winter and fall seasons. Therefore, cold season 

discharge increase identifi ed at the mouth of the 

Lena basin is not all caused naturally, but is the 

combined effect of reservoir regulation and natural 

runoff changes in the unregulated upper sub-basins. 

This study clearly illustrates the importance of human activities in regional and global environment 

changes, and points to a need to examine human 

impacts in other large high-latitude watersheds.

Streamfl ow Response to Seasonal Snowcover 

Extent Changes in Large Siberian Watersheds

Daqing Yang, David Robinson, Yuanyuan Zhao, 

and Thomas Estilow

This study uses remotely sensed long-term 

(1966–1999) weekly snowcover extent data to 

investigate snowmelt runoff response to seasonal 

snowcover change in the large Siberian watersheds 

(the Ob, Yenisei, and Lena basins). It quantifi ed 

the seasonal cycles and variations of snowcover 

extent and river streamfl ow, and identifi ed a clear 

correspondence of river streamfl ow to seasonal 

snowcover extent change, i.e. an association of low 

streamfl ow with high snowcover extent during the 

cold season, and an increase in discharge associated with a decrease of snowcover extent during the 

melt periods. This study also examined and compared the weekly mean streamfl ow with the weekly 

basin snowcover extent for the study period.

The results revealed a very strong linkage 

between the streamfl ow and snowcover extent 

change during the spring melt season over the large 

Siberian watersheds, and developed a statistically 

signifi cant weekly runoff-snowcover relation. This 

relation suggests a practical procedure of using 

remotely sensed snowcover information for snowmelt runoff forecasting over the large northern watersheds. Analyses of extreme (high/low) streamfl ow cases (years) and the associated snowcover 

conditions indicate an association of high (low) 

fl ood peak with late (early) snowmelt in the Ob and 

Yenisei basins. Comparisons of snowmelt timing 

with peak fl ow show different associations between 

these two variables among the large Siberian rivers. 

These results demonstrate that the NOAA weekly 

snowcover extent data are useful for understanding and predicting streamfl ow changes in the arctic 

regions. Snowcover water equivalent data/products 

obtained by remote sensing technology and in-situ 

snow observations are currently being examined 

for what we expect will eventually improve hydrologic forecasts over the large northern watersheds.

Hydrological Modeling of Imnavait Creek, 

Alaska’s North Slope

Imke Schramm

A new process-based, spatially distributed 

Hydrological Model (TopoFlow) is used to quantitatively simulate the water and energy fl uxes in 

an Alaskan watershed. Imnavait Creek is a small 

watershed (1.5 km2

) located in the northern foothills of the Brooks Range (680

30’ N, 149015’ W) 

and drains into the Kuparuk River. Two peculiar 

characteristics: (i) Imnavait creek is a beaded 

stream and (ii) water tracks are effi cient at conveying water down the slope during snowmelt and 

rainstorms.

Continuous permafrost underlies the active layer 

which, by the end of the summer season, reaches 

depths between 40 to 60 cm. Tussock tundra is the 

typical vegetation. The organic layer varies from 

about 50 cm in the valley bottom to around 10 cm 
42

on the slopes and ridges. The model is run using a collection of fi eld data: Hourly climate and 

soil data (precipitation, long wave and short wave 

radiation, and profi les of air and soil temperature). 

Other fi eld data collection includes discharge measurements (fl ume), snow survey for the calculation 

of snow water equivalent (SWE), soil physical 

properties, and channel characteristics.

The model simulates various physical processes 

including snow ablation, subsurface, overland and 

channel fl ow, soil thawing, and evapotranspiration. 

Predictions are made for the discharge at the outlet 

and will later include spatially-distributed soil 

moisture.

Results using TopoFlow are presented and 

compared to measured discharge and SWE. First 

results indicate that the onset of snowmelt does not 

correspond well with the measured data because 

the model does not include the snow damming effect. Simulated hydrographs for the summer runoff 

period 2001 with different settings demonstrate 

that the different components of the water circle are 

represented in the model. 

Theme 3: Sea Ice, Oceanography, and 

Geochemistry

Dispersal of River Discharge in the Siberian 

Arctic and its Impact on the Sea-ice 

Environment: Lessons to be Learned for the 

North Slope Region?

Hajo Eicken and Igor Dmitrenko

The interactions between river water discharged 

onto Arctic shelves and the sea-ice cover are manifold and complex. Following river break-up, part 

of the spring fl oodwaters typically fl ood the surface 

of landfast ice, accelerating ice decay and scouring 

under-ice sediments as a result of localized, highenergy drainage fl ow. These processes have been 

studied for some time in various parts of the Arctic 

and are fairly easy to detect and monitor with available remote sensing tools. Another type of interaction occurs during the freeze-up and winter periods, 

when river water that has not been fully dispersed 

and mixed into the offshore shelf waters can have 

signifi cant impacts on ice formation and growth. 

These processes are more diffi cult to observe 

because they are confi ned to the bottom of the ice 

cover and the waters underneath (and occur during a period where extreme weather and darkness 

hamper some remote-sensing approaches).

During the past decade, a detailed, large-scale 

Russian-German collaborational program (with 

some U.S. involvement) has examined hydrologic, 

oceanographic and geologic processes over the 

Laptev Sea shelf in central northern Siberia (e.g., 

Kassens et al., 1998). As the Laptev Sea shelf receives on the order of 500 km3

 of freshwater from 

the Lena River annually, it might serve as a model 

system, with the large amounts of data compiled 

for this region helping guide and validate freshwater discharge modeling and fi eld-observation 

efforts. 

Here, we report on recent studies of the impact 

of river discharge on the Laptev sea-ice environment (Eicken et al., submitted). A combination 

of remote sensing techniques (in particular synthetic aperture radar, SAR), fi eld observations and 

analysis of sea-ice core data yielded the following 

fi ndings: (1) The dielectric properties of low-salinity sea ice allow for mapping of brackish ice/water 

distribution as well as the detection of bottomfast 

sea ice in estuarine areas; (2) building on previous 

work by Macdonald et al. (1999) it was possible 

to obtain time series of under-ice water salinities 

through the water stable-isotope analysis of ice 

cores, allowing for derivation of under-ice water 

spreading rates (among the highest reported in the 

Arctic so far on this scale); (3) combining remotesensing and ice-core data, it was shown that of 

the Laptev landfast ice (ca. 160,000 km2

) roughly 

two thirds are composed of river water, locking up 

about one quarter of the annual Lena discharge and 

contributing to a mean inner shelf residence time of 

river water of more than a year. 

While the magnitude of freshwater discharge 

from Alaska North Slope rivers is signifi cantly 

smaller than that of the Lena, discharge dispersal 

modeling efforts need to address the question as 

to whether similar processes need to be taken into 

account in the coastal Beaufort and Chukchi seas. 

Here, remote sensing and ice-core analysis may 

help in quantitative assessments of the role of riverwater/sea-ice interaction.
43

References

Eicken, H., Dmitrenko, I., Tyshko, K., Darovskikh, 

A., Dierking, W., Blahak, U., Groves, J., 

and Kassens, H. (submitted) Zonation of the 

Laptev Sea landfast ice cover and its importance in a frozen estuary. Submitted to Global 

Planet. Change. 

Kassens, H., Dmitrenko, I., Rachold, V., Thiede, J., 

and Timokhov, L. (1998) Russian and German Scientists explore the Arctic’s Laptev 

Sea and its climate system. Eos, Transact. 

Am. Geophys. Union 79(27):317–323.

Macdonald, R.W., Carmack, E.C., Paton, D.W. 

(1999) Using the δ18o composition in landfast 

ice as a record of arctic estuarine processes. 

Mar. Chem. 65:3–24.

Arctic Shelf Fronts and their Relation to 

Freshwater Discharge

Igor Belkin, Nikolay Doronin, 

and Mikhail Kulakov

We will present a comparative analysis of the 

Arctic shelf fronts whose origins are largely related 

to freshwater discharge of northern rivers. Geographically, this survey is circumpolar and includes 

the following shelf seas and major rivers that 

empty into them:

Beaufort Sea (Mackenzie, Colville)

White Sea (Onega, Severnaya Dvina)

Barents Sea (Mezen’, Pechora)

Kara Sea (Ob’, Taz, Yenisei, Pyasina)

•

•

•

•

Laptev Sea (Khatanga, Lena, Yana)

East-Siberian Sea (Indigirka, Kolyma)

Bering Sea (Yukon, Kuskokwim)

We will present in situ and remote sensing data 

on the Arctic shelf fronts and their seasonal and 

interannual variability. Among the most valuable 

data sets, the newly acquired Russian CTD data 

from the Siberian seas (see Table) stands out since 

this data has not been previously analyzed to detect 

shelf fronts. To study these fronts from satellite 

data we used the Pathfi nder AVHRR SST 9 km resolution twice-daily data from January 1985 through 

December 1996 that has been processed, mapped, 

and analyzed at the University of Rhode Island. 

Seasonal and interannual variability of shelf 

fronts is related to respective variations in river 

discharge of adjacent rivers. Some rivers could 

control frontal characteristics far downstream from 

their estuaries, e.g. Yukon discharge is advected 

into the Chukchi Sea where it affects the Kotzebue 

Sound front and Barrow Canyon front, and infl uences shelf fronts of the Beaufort Sea. Similarly, 

the great Siberian rivers such as Ob’, Yenisei, and 

Lena affect shelf fronts located hundreds of miles 

away. Since the fronts’ location and structure exert 

signifi cant control over shelf sedimentation regime 

and cross-frontal exchange of nutrients, this study 

contributes to a wide range of applications, from 

marine geology and offshore technology to biological oceanography and fi sheries, through improvement of numerical models of the coastal Arctic 

Ocean.

•

•

•
44

Year Date Number of Area of survey 

CTD stations Latitude Longitude

Kara Sea

1993 16.09 - 13.10 145 70o 00’ - 78o 40’ 56o 00’ – 104o 00’ 

1994 23.08 – 24.09 142 68o 00’ – 78o 30’ 57o 50’ – 88o 10’ 

1994 17.06 – 28.06 

14.08 – 23.08 

6

4

70o 10’ – 77o 40’ 66o 20’ – 103o 45’ 

1995 17.8 – 7.09 108 68o 20’ – 80o 00’ 59o 40’ – 86o 00’ 

1995 10.08 – 14.08 

8.09 – 18.09 

6

20

69o 45’ – 77o 45’ 60o 30’ – 105o 40’ 

1997 13.09 – 25.09 59 72o 00’ - 74o 00’ 72o 40’ – 82o 50’ 

1999 24.08 – 8.09 37 72o 00’ – 74o 30’ 74o 00’ – 80o 00’ 

2000 19.08 – 24.08 15 57o 40’ – 84o 30’ 70o 40’ – 74o 50’ 

2000 4.09 – 19.09 24 70o 00’ – 77o 00’ 74o 10’ – 85o 45’ 

2001 14.08 – 11.09 83 69o 00’ – 78o 00’ 72o 15’ – 89o 20’ 

Laptev Sea 

1993 10.08 – 13.09 131 71o 40’ – 77o 10’ 114o 00’ –140o 40’ 

1994 3.09 – 26.09 102 71o 45’ – 76o 30’ 114o 15’ – 136o 45’ 

1994 29.06 – 8.07 

31.07 – 11.08 

4

2

74o 20’ – 76o 40’ 113o 25’ – 138o 00’ 

1995 6.10 – 23.10 65 71o 05’ – 80o 10’ 102o 30’ – 139o 15’ 

1995 14.08 – 20.08 

30.08 – 9.09 

9

25

71o 05’ – 77o 45’ 96o 45’ – 141o 30’ 

1998 1.09 – 18.09 55 73o 30’ – 77o 50’ 115o 50’ – 133o 50’ 

1999 27.08 – 8.09 30 71o 30’ – 76o 55’ 114o 35’ – 135o 40’ 

2000 3.09 – 15.09 48 71o 45’ – 77o 15’ 123o 45’ – 135o 30’ 

East-Siberian Sea 

1994 10.07 – 29.07 6 70o 15’ – 75o 40‘ 147o 10’ – 170o 25’ 

1995 20.08 – 30.08 30 69o 40’ – 76o 10’ 141o 15’ – 170o 30’ 

1995 24.08 – 29.08 45 69o 30’ – 71o 20’ 164o 00’ – 180o 00’ 

2003 10.09 – 20.09 44 70o 00’ – 74o 50’ 140o 20’ – 178o 30’ 

Chukchi Sea 

1990 3.09 – 23.09 122 65o 45’ – 74o 10’ 178o 00’E – 159o 40’W 

1990 4.10 – 20.10 141 65o 40’ – 74o 00’ 178o 00’E –159o 25’W 

1992 21.09 – 4.10 108 65o 00’ – 71o 10’ 177o 25’W –158o 50’W 

1993 21.09 – 10.10 120 64o 00’ – 74o 20’ 177o 50’ E –154o 45’W 

1994 22.09 – 10.10 60 65o 45’ – 71o 45’ 175o 00’W –159o 30’W 

1995 28.08 – 30.08 31 66o 40’ – 70o 00’ 179o 10’E-169o 00’W 

1995 10.09 – 30.09 208 64o 35’ – 73o 00’ 177o 40’E –157o 20’W 

Table 1. Newly acquired Russian CTD data set from the Siberian seas.
45

The Flow of Alaskan Coastal Current from the 

Gulf of Alaska to the Beaufort Sea–Challenges 

and Opportunities

Wieslaw Maslowski and Jaclyn Clement

The fl ow of relatively fresh water along the 

Alaskan coast from the North Pacifi c, through 

the Bering Sea, into the Arctic Ocean is typically 

defi ned as the Alaska Coastal Current (ACC). The 

southern portion of this current, from the Gulf of 

Alaska to the Aleutian Islands, has been most studied (Schumacher and Reed 1986; Stabeno et al., 

1995; Royer, 1998, Ladd et al., 2004). However, 

understanding of the important role it plays in the 

life history of juvenile salmon or Steller sea lions 

and in the transport and fate of spilled oil in the 

Gulf of Alaska requires further research. For example, improved knowledge of transient circulation 

features (e.g. eddies and meanders) may provide 

important insights into their effects on seasonal and 

interannual transport of nutrients, zooplankton, larvae, and fi sh across the front formed by the ACC.

Much less is known of the pathways and water 

properties of the ACC downstream of the Aleutian 

Islands, in the Bering, Chukchi, and Beaufort seas. 

Complex physical mechanisms involved in mixing and advection in the southeastern Bering Sea, 

tides, river runoff, and seasonal sea ice cover over 

the shelves, and strong atmospheric forcing during 

fall/winter storms make this region quite challenging for fi eld and modeling investigations. Understanding of these complex and variable interactions 

and feedbacks is critical to explaining the fl ow of 

ACC further north, in the Chukchi and Beaufort 

seas. The preconditioning of Pacifi c water passing 

Bering Strait and contributions from freshwater 

inputs and salt from coastal polynyas determine the 

dynamics and properties of the coastal fl ow along 

the northern Alaska coasts.

In this talk, we will outline the main physical 

and numerical challenges in modeling the ACC 

as a continuous fl ow from the Gulf of Alaska to 

the Beaufort Sea and will discuss opportunities to 

address some of them. Results from a regional iceocean model will be presented to illustrate potential 

capabilities for realistic modeling of the ACC at 

time scales from days to decades.

Riverine Transport and Dispersion of 

Freshwater, Suspended Sediment, Organic 

Carbon, and Trace Metals in the Coastal 

Beaufort Sea During the Spring Floods 

Robert Rember, J.H. Trefry, and R.P. Trocine 

Concentrations of suspended sediment, organic 

carbon, and dissolved metals in water from the 

Sagavanirktok and Kuparuk rivers were determined during the spring fl oods of 2001, 2002, and 

2004. All three of these components of river water 

increased in concentration during peak fl ow in 

response to the fl ushing of soils and surface ponds 

during the spring fl oods. Then, this large pulse of 

river water was tracked and detected under a 2 m 

thick lens of ice at distances of 10–20 km offshore 

in the coastal Beaufort Sea. Concentrations of dissolved metals below the turbid riverine discharge 

were very low and typical of levels found during 

the open water period in summer and fall. However, concentrations of suspended sediment in the 

deeper water under the ice were very low. During 

a three week study in May 2004, a more detailed 

study of offshore transport of river water under the 

ice was conducted by sampling through ~30 holes 

drilled offshore from the mouths of the Sagavanirktok and Kuparuk rivers. Sampling was designed 

to give both a temporal and spatial perspective to 

freshwater discharge under the ice. A CTD and 

current meter were deployed through the ice and 

multiple samples were collected from each hole 

for DOC, P, N, δ18O and a suite of dissolved trace 

metals.

Distribution of Freshwater Sources in the 

Western Arctic Ocean Derived from Chemical 

Tracers Oxygen Isotope Ratio, and Alkalinity

Michiyo Yamamoto-Kawai, Noriyuki Tanaka, 

Sergey Pivovarov, and Leonid Timokhov

Freshwater distribution in the Arctic Ocean has a 

key role in the regional and also the global climate 

through affecting the heat transport by changing 

surface stratifi cation in the Arctic Ocean and also 

the deep convection in the Greenland Sea. River 

runoff provides the largest freshwater amount to 

the Arctic Ocean. Precipitation–evaporation over 

the ocean and the Pacifi c water coming through the 

Bering Strait also provide additional freshwater. 

Furthermore, formation and melting of sea-ice can 
46

alter the distribution of freshwater. The western 

Arctic is the region where both massive freshwater 

input and active sea ice melting/formation occur. 

Two chemical tracers, oxygen isotope ratio (δ18O) 

and total alkalinity (TA) are used to distinguish 

freshwater sources and to investigate their distributions in the western Arctic Ocean. 

From horizontal distribution of freshwater sources and relationships between salinity, δ18O, and TA, 

the fl ow pattern of river water can be pictured as 

follows: Russian river water fl ows out of the shelf 

between the Mendeleyev Ridge and the Lomonosov Ridge, enters into the deeper part of the Ocean, 

and fl ows toward the western part of Fram Strait. 

Part of this freshwater enters into the Canadian 

basin to the east, where high fraction of river water 

is found but the fraction of Mackenzie River water 

is small. This may suggest that Mackenzie River 

water should exit from the basin relatively fast, 

probably through the Canadian Archipelago. 

Accumulation of freshwater in the Canadian 

Basin is maintained by advection of water from the 

adjacent shallow shelves. The dense shelf water 

formed during winter enters into the layer between surface and Atlantic origin water and can be 

identifi ed by the nutrient maximum. Although the 

high concentration of nutrients of the water points 

to the winter Pacifi c water as a major source, our 

analysis indicates signifi cant contributions of other 

shelf waters. Three types of shelf-derived water 

are suggested to form the nutrient maximum layer 

in the Canadian Basin. 1) Winter Pacifi c water 

with rejected brine during sea ice formation. 2) 

shelf water formed on the Beaufort shelf. 3) shelf 

water formed on the Chukchi/East Siberian seas, 

from fresher shelf water containing summer Pacifi c 

water and/or Russian river water. The third type 

of shelf water seems to contribute more than the 

second type of water. These shelf waters vertically 

transport freshwater from the surface to the deeper 

layer and accumulate in the Canadian basin.

We appreciate Drs. Murata and Shimada of 

JAMSTEC for providing unpublished data from the 

past Arctic expeditions by R/V Mirai.

Intraseasonal and Interdecadal Variability of 

Arctic Freshwater and Heat Budget

Jia Wang and Inna Shapiro

Using four decades (1950–1989) of hydrographic and sea-ice cover data in the Arctic Ocean 

and subpolar seas, we systematically examine the 

climatological mean, interdecadal, and interseasonal variability of freshwater storage in both liquid 

and solid (ice) forms. We found that the mean 

liquid freshwater storage is 201,400 (168,975) km3

for summer and (winter), which is about 2–2.5 

times the historical estimate (Arctic Ocean only) 

of 80,000 km3

 (Aagaard and Carmack 1989). The 

solid freshwater stored in the sea ice is estimated 

to be 23,228 (34,842) km3

 for 2 m (3 m) mean ice 

thickness, which is consistent with a previous estimate of 17,300 km3

. 

The intraseasonal variation indicates that the 

summer-winter freshwater storage difference is 

about 32,645 km3

. Inter-decadal variability in FS 

has a positive correlation with the 10-year averaged 

Arctic Oscillation index and fi rst SAT mode, while 

negative correlation with the sea-ice covers anomalies. Thus, the AO-related inter-decadal variability 

(or trend) is signifi cant. The intra-seasonal variation of solid freshwater storage is 13,359 km3

, the 

difference between the winter storage of 28,902 

km3 and the summer storage of 15,543 km3 (if the 

mean ice thickness is assumed 2 m). The Arctic 

Ocean is divided into fi ve sub-regions. Freshwater 

storages in both liquid and solid forms are estimated for the four decades. 
47

Appendix 3. Themes for Workshop.

Themes 1–3 Discussion (original notes)

Climate and Atmospheric Components

Introduced by David Atkinson

(Notes Jessie Cherry)

Jia: In global model hydrological scale is 200–400 

m, can climate models resolve?

Laura: Not in her large-scale model.

General question: How sensitive are models to timing and location of freshwater inputs and total 

quantity in OGCMs (ocean general circulation models) i.e. on NAO/AO scales?

Wieslaw: 1980s–1990s cyclonic shift (Canadian 

basin) changed the residence time of river 

runoff in the arctic...more sensitivity studies 

necessary...

David: Sea ice forced by atmospheric circulation 

and thermal forces, which is the stronger forcing? What is responsible for disappearing sea 

ice? 

Jia: Positive AO anomalous cyclonic circulation 

helps diverge sea ice out of the arctic into 

the GIN (Greenland, Island, and Norwegian) 

seas, negative AO anomalously anticyclonic 

circulation dominates, ice gets trapped in the 

Canada basin.

Seasonal cycle, winter Beaufort high, freshwater 

and sea ice trapped, summer, freshwater is 

relaxed and spreads out. 

Wieslaw: Recent AO is more neutral, yet most 

melting in recent years. That suggests that 

thermal coupling may be really important.

What about the residence time of ice?

Jia: In hydrological modeling, how sensitive is 

energy balance on snow albedo?

Laura: quite sensitive, changes in timing of melt 

onset

-----------------------------------------------------

Introduction by Laura Bowling

Talks in 3 areas:

Hydrology

USGS

Remote sensing techniques

Two tiers of model talks:

Small scale-topo model

Large scale-WATCLASS, Jia’s model

Wieslaw: How far away are we from 50 year runoffs of rivers w/temps?

Laura: What resolutions do you want information? Temp is not done, that will take a while. 

Course resolution 100 km grid is almost 

ready. 

Wieslaw: Ocean community needs location, daily 

temp, quant of runoff. 40–50 years. Line 

source is okay. 

Bob Carlson: What is needed to understand coastal 

runoff. NWS uses one approach, is Prentki’s 

[MMS’s] need a long range planning tool, 20–

30 years, versus, short term for use w/spills?

Dick: Yes, we need both, now. Use last year’s data 

for hindcast, that’s NOAA’s approach.

Bob Carlson: 100 km x 100 km not very useful. 

Stakeholders want to know specifi c places, 

oceanographers have other needs. 

Caryn: Need physical processes, goal is 1–3 km.

Dick: Some stakeholders need smaller scales

Bob Carlson: Oceanographers uncomfortable with 

coastal zone, so are hydrologists, we skip 

over that.

Dick: Landfast ice is a tricky area.

Wieslaw: Need more than one vertical grid cell, 

that’s not good enough for shallow 60 m area

Jia: What are differences between Japanese, Canadian models, Vic?

Frank: Treat different parts of the cycle differently, 

we have emphasis in Canada.

Laura: PILPS 2e analyzed this. CLASS has peat. 

She’s focused on lakes and wetlands and 

blowing snow (in VIC). Outliers in model 

intercomparisons are very useful to know 

what some models are doing right/wrong. 

Permafrost has not been tested well enough in 

the model. 
48

Frank: Do we have enough data to verify/input to 

see if Permafrost schemes are good/bad?

Laura: The lack of good permafrost dynamics will 

make the freshwater runoff dataset have big 

error bars.

Stephan: Getting the hydrograph right is a good 

place to start.

Dick: That would be a good recommendation.

Jia: Can Larry’s model be extrapolated to regional/

global scale?

Larry: No, we are process-oriented. Working w/

Lettenmier to see if we can nest our model in 

Vic. For example, testing vegetation changes 

helpful.

Bjerklie: Spring breakup key for heat transfer, nutrients. Models are focused on tundra regions, 

but what about mountains? 80% of Sag catchment is in the Brooks Range. Is it the same 

for other rivers? Is there a model that could 

handle the timing issues?

Larry: No, no work in the mountains, including 

measurements, we have no idea what is going 

on there.

Laura: another problem. No glaciers in the models!

Bjerklie: Lena? I don’t know about physiography 

of Russian rivers.

Laura: Headwaters in Siberia are as far South as 

45, steppes and high plateaus.

Igor: Reservoir on the rivers in Siberia? Effect of 

dams? Water mass transfer?

Dick: Reservoirs change heat regime, too.

Wieslaw: Hajo runoff onto land-fast ice, what is the 

residence time of river discharge onto landfast ice? 

Hajo: Ice decouples atmosphere dynamics from 

water, which is really forming a lake (like in 

Mack, Carmack’s work) during winter.

Wieslaw: Residence time of Lena River water in 

coastal area? How is it affected by land fast 

ice?

Hajo: Need tracers to be able to separate other river 

(Yenesei). Prevents cross-shelf transport, 

becomes along-coast transport.

Larry: Frazzle ice layer that Robert mentioned. Has 

that been documented before? How widespread? Does it help maintain stratifi cation?

It’s in Hajo’s book: Hajo: yes, it’s been reported by 

Russians on the Russian shelves and others. 

We don’t know how widespread.

Dick: Landfast ice off Prudue, no momentum transfer between atmosphere and ocean, once ice 

is there, runoff is going to stabilize that layer. 

Summer storms are what mixes it up. 

Jia: OGMS, do they input Freshwater velocity? 

Could they do this?

Wieslaw: Need the data! Resolution of grid cell 

50 km2

 ... how important is getting the runoff 

velocity right? He thinks there are more important problems.

Ron: Have you incorporated land fast ice?

Wieslaw: really diffi cult to get the anchor points, 

need to have the bathymetry features in the 

model, not resolved in the model. Need at 

least a climatology. This can probably be done 

once data is available. Flato’s way to deal 

w/problem is to use isobaths (10 m), say the 

ice isn’t moving. Most models have minimum 

shallow depth of 50 m, but mine is 10 m. 

Theme 2 Discussion (original records)

How do you know where there is continuous or 

discontinuous permafrost?

Model vs. observation: combine both to estimate 

discharge; modeling + remote sensing. 

Goal of models is to understand processes, but also 

good to distribute discharge.

Larry: Necessary to re-establish stream gages. And 

precipitation of course.

Dick: Important that experts say that more stations 

are needed.

Stefan: Snow course measurements important as 

well. Two classes of models: fi ne and course 

resolution. For large scale models resolution 

is coarser than measurements to compare to. 

E.g. every grid cell generates a runoff. If grid 

cells are bigger than streamfl ow measurements, there is no possibility to validate the 

model.

Laura: Large scale models cannot go into more 

detail. The problem is that there is no medium 

scale model, so there is a kind of grey zone 
49

in between both. The computation speed is a 

problem because most models run on PCs and 

cannot be run on supercomputers.

Frank: Concerning the validation of models: we 

should focus on a few basins to have a kind of 

research area with very detailed data. But if 

the model works for one watershed there is no 

way to conclude that it works in another area 

with e.g. another topography.

Larry: Excess to watersheds in mountain area is a 

problem. There are no roads and almost no 

glacier measurements. Is there so much water 

coming out of the glaciers in North Slope that 

it is important for hydrological modeling and 

climate change research?

Answer?

Frank: Data verifi cation: There was one big data 

collection for three years (BOREAS) which 

are used for all kinds of model verifi cation. 

That could be an idea for funding, to start 

one big data collection for a certain area and 

limited time.

Channel slopes: high resolution DEMs are not 

available for the whole North Slope area. But 

all models are based on DEMs, so that could 

be important.

Freeze up and break up of lakes in models? It is not 

represented in any model. There is no physical reason why one could not model it, but 

river ice is not included so far.

Radar data are used so far to monitor river break 

up.

Depletion snow cover: satellite images could be 

useful to monitor snow cover. 

Laura: Remote sensing for soil moisture, river 

break up, snow cover, active layer depth.

Larry: Soil moisture is very important. Continuous 

data at various data is needed. 

??: we can get soil wetness, but soil moisture is 

almost impossible. From SAR you can get ... 

best ones are???

Larry: Profi les of radiation are necessary.

Laura: δ apor fl ux over the whole winter would 

be great, but very diffi cult to collect and to 

maintain stations. There is no possibility so 

far to validate sublimation and snow distribution from model.

Larry: 10% of summer precipitation came as condensation at Betty Pingo site, so it is important to measure.

??: There are some data series for that in the North 

Slope.

Frank: it would be good to have an internet side or 

something to have an overview which data 

are available, at least for the North Slope. Everyone could put his data there or links where 

to fi nd them.

Dick: We try to do that, but ...

Frank: SWE and snow depth, soil moisture, and 

soil temperature at the same time.

Stefan: remote sensing does not give you any useful data without having ground measurements 

(concerning snow course). That would help to 

avoid errors from snow gages.

Frank: Runoff components measurements to differentiate between base fl ow, active layer fl ow...

Laura: Concerning lakes: There is a very complex 

drainage network especially during snow 

melt. And in the coastal region.

The storage component is important (in lakes...). 

How do you model lake depth?

We used radar and model simulations combined to 

get lake depth. 

Remote sensing of river ice: there is fl ow under the 

ice–how is this represented in models? Could 

winter fl ow be modeled? 

There are winter fl ow measurements, but the quality varies from station to station. It’s a problem 

that expensive equipment could be damaged.

Comment on winter measurement: (Rich) Zero 

fl ow because instruments are not located 

where fl ow occurs. 

Radar images where used to look at ice patches on 

rivers. 

Frank: Water temperature is the fi rst measurement 

that should be made in terms of chemistry.

2. Vorne links: we normally take temperature measurements when stream gaging.

Small temperature changes can be signifi cant for 

chemistry, species changes–there are biological reasons to monitor water temperature. 
50

Ratio of permafrost and non-permafrost region: 

more thermokarst results in more sediment 

transport. To model the carbon cycle, sediment transport is of importance.

Frank: Prediction of discharge in ungaged basins... 

If we did error bars on our predictions that 

would help, because to give out one specifi c 

number is not really true. We modelers should 

start doing that. 

It is diffi cult to include the errors in the input data.

There are several kinds of errors: in measurements, 

the model processing and ? – different error 

boxes.

Laura: Questions and recommendations related to 

the other groups?

Frank: Downscaling of precipitation (physical 

reason?).

Averaging of model predictions to limit the single 

model errors.

Laura: Data needs.

 Method recommendations?

Combining different methods of data collection: remote sensing, modeling, and ground measurements. Modeling is the only way to go back in 

time–and then a tool to go forward. 

Recommendation not only to focus on large basin 

gaging. Small basin measurements also 

important. Seward Peninsula and Kotzebue 

would be representative to calibrate the models for the north slope. Eastern Alaska is not 

covered yet. 

Larry: We don’t know a lot about aufeis fi elds. 

Laura: (Dis-) continuous permafrost represented in 

models?

Frank: Planned for the future to better represent.

Larry: Kind of representation in small-scale model 

(?)

Laura: Integrated Studies? Dealing with the grey 

zone?

Vorne links: How does road dust effect? 

Larry: Impact of road network on wildlife and ? 

USGS stations

High quality DEM

Validation Needs

Permafrost

Question: Discontinuous permafrost–how do 

you know where to put it in model, changes 

through time? Workshop on permafrost modeling in two weeks here.

Runoff fi elds could use mix of hydrographic stations and models.

Reestablish gages at long term sites.

Ideas-measure

Snow courses

Water temperatures

Precipitation

Large scale models could get to 1–2 km in theory 

with understanding of hydrology. Finer resolution is possible by computer.

Verifi cation 

Need more stations toward mountains.

Road dust and hydrology. Bird use in spring. 

Increasing glacier melt could relate to increasing 

Sag River fl ow through recent decades.

Suggestion for concentrated study on a few/couple 

watersheds: Active layer measurements, 

snow cover and depth, soil moisture profi le 

and temperature, total radiation balance, eddy 

covariance, runoff component rates/quantities, 

lake stage, lake area (storage in lakes, ponds, 

channels, soils). Lidar data being collected 

in NPRA, but not clear how these data being 

used. Mackenzie Delta Lidar, also. Expand 

Lidar or other to get consistent DEM across 

North Slope. Need fi ne resolution. 

AK North Slope, only Sag fl ows all winter. Canning has springs, could have fl ow?

USGS does measure temperature in discharge, but 

data hard to fi nd. 

Temperature changes could be biologically important; do not know what delta temperature 

becomes important oceanography. 

Uranium series signature appears to be different 

in permafrost vs. discontinuous permafrost 

areas. Perhaps a method to track changes in 

permafrost with global change?

Need to develop statistical error on hydrology data.

Seward Peninsula as a proxy North Slope—regage 

rivers. Redo in Canning and ANWR.
51

Aufeis fi elds in some areas.

Combine modeling with better gaging and remote 

sensing, some local. 

Models don’t get breakup and freeze up dates right. 

Remote sensing of snow cover.

Need web link for North Slope data—or make sure 

can fi nd existing one(s).

Develop methods to downscale large scale gridded 

data to hydrology scales.

Goal of real time model on web.

Discussion of themes (missed part)

Albedo set in Laura Bowling model

 Melting and accumulation time curves for 

albedo of snow.

Theme 2 Moderator:

Areas of hydro observations, remote sensing 

techniques, two tiers of modeling talks (small scale 

and large scale).

Q. How far from 50-year hydrology run with 

temperature, time, and volume to ocean.

R. Depends on resolution needed. Temperature 

not usually provided. 

Ocean community–at least daily on 10 km grid for 

50 years. Based on latlong–line source for 

ungaged OK.

Question–scale for MMS needs 20–30 year forecast, but hindcast needs basin to get there, 

want 1–3 km scale. Need basin to get physics 

right.

Different hydrology models depend on other prime 

issues,

For North Slope need permafrost. Moderator–best 

model not evaluated yet.

Comment: need agreement on what needed to test 

and needed to validate and compare. Mechanistic, but not tested against. 

Q. How best to convert small models to large 

scale–Not best approach. The small models are process models–not same purpose. 

Perhaps nested models best. (ex. Tussock to 

brush tundra). 

Q. Breakup–short with timing–heat and nutrients. But what’s happening in Brooks Range? 

Timing is different for different rivers. No 

data in mountains.

Q. What are Siberian Rivers like? Not Arctic, extend far south, have managed fl ow.

Theme 3: 

Capture of Lena freshwater in local sea ice–but ice 

melts. Yes, but long extent of landfast ice 

decouples with coastal circulation. Does a 

couple of months longer extent of freshwater 

nearby matter? It does spot cross shelf transport, keeps freshwater on the shelf. 

How stable is freshwater layer from rivers? No 

momentum transport from wind under landfast ice. Rivers breakup, May–June, Ocean 

July–layer stays until storms breakup in summer.

How to put fast ice in models? Problem with 

anchor points, not really good. Landfast ice 

zone is a problem.
52

Johnny L. Aiken David E. Atkinson 

Acting Land Management Regulations Manager Assistant Professor 

North Slope Borough Planning Dept IARC/Atmospheric Sciences 

1920 Takpuk St., P.O. Box 69 University of Alaska Fairbanks 

Barrow, Alaska 99723 930 Koyukuk Dr. / Box 757335 

Phone: 907-852-0440 Fairbanks, Alaska 99775-7335 

E-mail: Johnny.Aiken@north-slope.org Phone: 907-474-1126 

E-mail: datkinson@iarc.uaf.edu 

Igor M. Belkin Svetlana Berezovskaya 

Graduate School of Oceanography Water & Environmental Research Center 

University of Rhode Island University of Alaska Fairbanks 

215 South Ferry Road 305 Tanana Dr., 485 Duckering Bldg 

Narragansett, Rhode Island 02882 Fairbanks, Alaska 99775 

Phone: 401-874-6533 Phone: 907-474-2783 

E-mail: ibelkin@gso.uri.edu E-mail: ffslb2@uaf.edu 

Heather R. Best David M. Bjerklie 

Hydrologic Technician Hydrologist 

U.S.G.S. Fairbanks Field Headquarters U.S.G.S. 

3400 Shell Street 101 Pitkin Street 

Fairbanks, Alaska 99701 East Hartford, Connecticut 06108 

Phone: 907-479-5645 Phone: 860-291-6770 

E-Mail: hbest@usgs.gov E-mail: dmbjerkl@usgs.gov 

William R. Bolton Laura C. Bowling 

Graduate Assistant Assistant Professor 

Water & Environmental Research Center Department of Agronomy 

462 Duckering Purdue University 

University of Alaska Fairbanks Lilly Hall of Life Sciences 

Fairbanks, Alaska 99775 915 W. State Street 

Phone: 907-474-7975 West Lafayette, Indiana 47907-2054 

E-mail: ffwrb@uaf.edu Phone: 765-494-8051 

E-mail: bowling@purdue.edu 

Robert F. Carlson Jessica E. Cherry 

Doctor/Professor Graduate Research Assistant 

Dept. of Civil & Environmental Engineering Columbia University 

University of Alaska Fairbanks Lamont-Doherty Earth Observatory 

237 Duckering Bldg., P.O. Box 755900 61 Route 9W, Ocean 206 

Fairbanks, Alaska 99775-5900 Palisades, New York 10964 

Phone: 907-474-6120 Phone: 845-365-8327 

E-mail: ffrfc1@uaf.edu E-Mail: jcherry@ldeo.columbia.edu 

Appendix 4. SIOM Workshop Participants
53

Claude Duguay Keith Echelmeyer 

Professor Professor of Geophysics 

Geophysical Institute Geophysical Institute 

University of Alaska Fairbanks University of Alaska Fairbanks 

903 Koyukuk Drive 903 Koyukuk Drive 

Fairbanks, Alaska 99775 Fairbanks, Alaska 99775 

Phone: 907-474-6832 Phone: 907-474-7477 

E-mail: claude.duguay@gi.alaska.edu E-mail: kechel@gi.alaska.edu 

Hajo Eicken Susan Flora 

Associate Professor HazMats Contaminants - NPRA 

Geophysical Institute Bureau of Land Management 

University of Alaska Fairbanks 1150 University Avenue 

903 Koyukuk Dr./P.O. Box 757320 Fairbanks, Alaska 99709 

Fairbanks, AK 99775-7320 Phone: 907-474-2303 

Phone: 907-474-7280 E-mail: sflora@blm.gov 

E-mail: hajo.eicken@gi.alaska.edu 

Steven A. Frenzel Larry Hinzman 

Chief Water & Environmental Research Center 

Water Resources Office Institute of Northern Engineering 

USGS Alaska Science Center P.O. Box 755860 

U.S. Geological Survey 437 Duckering Building 

4230 University Dr., Suite 201 University of Alaska Fairbanks 

Anchorage, Alaska 99508 Fairbanks, Alaska 99775-5860 

Phone: 907-786-7107 Phone: 907-474-7331 

E-mail: sfrenzel@usgs.gov E-mail: ffdh@uaf.edu 

Meibing Jin Michiyo Kawaii 

Research Assistant Professor Post-Doc

International Arctic Research Center International Arctic Research Center 

University of Alaska Fairbanks University of Alaska Fairbanks 

P.O..Box 757335 / 930 Koyukuk Dr. P.O. Box 757335 / 930 Koyukuk Drive 

Fairbanks, Alaska 99775-7335 Fairbanks, Alaska 99775-7335 

Phone: 907-474-2442 Phone: 907-474-1524 

E-mail: mbj@iarc.uaf.edu E-mail: yamami@iarc.uaf.edu 

Joe Klein Richard Kemnitz 

Hydrologist Hydrologist - NPRA 

ADF&G, SF/RTS Bureau of Land Management 

333 Raspberry Road 1150 University Avenue 

Anchorage, Alaska 99518 Fairbanks, Alaska 99709 

Phone: 907-267-2148 Phone: 907-474-2225 

E-mail: joe_klein@fishgame.state.ak.us E-mail: rkemnitz@blm.gov 
54

Rebecca Kyle Ronald J. Lai 

Hydro Tech / Student Minerals Management Service/DOI 

USGS / FFO 381 Elden Street 

3400 Shell Street Herndon, Virginia 20170 

Fairbanks, Alaska 99701 Phone: 703-787-1714 

Phone: 907-479-5645 x232 E-Mail: ronald.lai@mms.gov 

E-mail: rkyle@usgs.gov 

Shusun Li Anna Liljedahl 

Research Professor Institute of Northern Engineering 

Geophysical Institute Water & Environmental Research Center 

University of Alaska Fairbanks Room 447 Duckering Building 

903 Koyukuk Drive University of Alaska Fairbanks 

Fairbanks, Alaska 99775 Fairbanks, Alaska 

Phone: 907-474-7676 Phone: 907-474-6243 

E-mail: sli@asf.alaska.edu E-mail: ftakl@uaf.edu or annald00@student.umu.se 

 

Michael R. Lilly Xieyao Ma 

President Frontier Research Center for Global Change 

Geo-Watersheds Scientific 3173-25 Showamachi, Kanazawa-ku, 

650 Eton Boulevard Yokohama 236-0001, Japan 

Fairbanks, Alaska 99709 Phone: 81-45-778-5546 

Phone: 907-479-8891 E-mail: xyma@jamstec.go.jp 

E-mail: mlilly@gwscientific.com 

Wieslaw Maslowski Larry Merculieff 

Research Associate Professor Alaska Native Science Commission 

Naval Postgraduate School Seven Generations Consulting 

Oceanography Department 429 L Street 

833 Dyer Road Anchorage, Alaska 99501 

Monterey, California 93943 Phone: 907-258-2672 

Phone: 831-656-3162 E-mail: lmerculieff@netscape.net 

E-mail: maslowsk@nps.edu 

David F. Meyer Edward Plumb 

Hydrologist, Data Section Chief Hydrologist/Meteorologist 

U.S. Geological Survey National Weather Service Forecast Office 

Alaska Science Center 930 Koyukuk Dr., Room 351 

4230 University Dr., Suite 201 Fairbanks, Alaska 99708 

Anchorage, Alaska 99508 Phone: 907-458-3714 

Phone: 907-786-7141 E-mail: edward.plumb@noaa.gov 

E-mail: dfmeyer@usgs.gov 
55

Stefan Pohl Richard Prentki 

National Water Research Institute Oceanographer 

11 Innovation Blvd. Minerals Management Service 

Saskatoon, Saskatchewan, 3801 Centerpoint Drive, Suite 500 

Canada S7N 3H5 Anchorage, Alaska 99503-5823 

Phone: 306-975-5752 Phone: 907-334-5277 

E-mail: Stefan.Pohl@ec.gc.ca E-mail: Richard.Prentki@mms.gov 

Robert Rember Imke Schramm 

Research Technician Water Environmental Research Center 

International Arctic Research Center University of Alaska Fairbanks 

University of Alaska Fairbanks Fairbanks, Alaska 99775 

P.O. Box 757335 / 930 Koyukuk Dr. Phone: 907-474-2783 

Fairbanks, Alaska 99775-7335 E-mail: fnis@uaf.edu or 

Phone: 907-474-2795 imkepots@rz.uni-potsdam.de 

E-mail: rrember@iarc.uaf.edu 

Frank Seglenieks Caryn Smith 

Research Engineering Oceanographer 

University of Waterloo Minerals Management Service 

200 University Avenue West 3801 Centerpoint Drive, Suite 500 

Waterloo, Ontario Anchorage, Alaska 99503-5823 

Canada N2L 3G1 Phone: 907-334-5248 

Phone: 519-88-4567 x6112 E-mail: caryn.smith@mms.gov 

E-mail: frseglen@uwaterloo.ca 

Kristin Susens Nori Tanaka 

Graduate Research Assistant Visiting Professor 

Water & Environment Research Centr International Arctic Research Center 

P.O. Box 755860 / 435 Duckering University of Alaska Fairbanks 

University of Alaska Fairbanks 930 Koyukuk Dr./ P.O. Box 757340 

Fairbanks, Alaska 99775-5860 Fairbanks, Alaska 99775-7340 

Phone: 907-474-2713 Phone: 907-474-2687 

E-mail: ftkms2@uaf.edu E-mail: norit@iar.uaf.edu 

John E. Walsh Jia Wang 

President's Professor of Climate Change & Research Associate Professor 

 Chief Scientist International Arctic Research Center 

International Arctic Research Center University of Alaska Fairbanks 

University of Alaska Fairbanks 930 Koyukuk Dr., P.O. Box 757335 

P.O. Box 757340 / 930 Koyukuk Drive Fairbanks, Alaska 99775-7335 

Fairbanks, Alaska 99775-7430 Phone: 907-474-2685 

Phone: 907-474-2677 E-mail: jwang@iard.uaf.edu 

E-mail: jwalsh@iarc.uaf.edu 
56

Matthew Whitman Daqing Yang 

Fisheries Biologist – NPRA Water & Environment Research Center 

Bureau of Land Management University of Alaska Fairbanks 

1150 University Avenue Fairbanks, Alaska 99775 

Fairbanks, Alaska 99709 Phone: 907-474-2468 

Phone: 907-474-2249 E-mail: ffdy@uaf.edu 

E-mail: mwhitman@blm.gov