> NOAA Technical Report NWS 45
>
> **NOAA NWS Distributed Hydrologic Modeling Research and Development**
>
> Michael Smith
>
> Victor Koren
>
> Ziya Zhang
>
> Seann Reed
>
> Dongjun Seo
>
> Fekadu Moreda
>
> Vadim Kuzmin
>
> Zhengtao Cui
>
> Richard Anderson
>
> April 2004
>
> U.S. DEPARTMENT OF COMMERCE
>
> National Oceanic and Atmospheric Administration
>
> National Weather Service

NOAA Number range CHAPTERNWS Distributed Hydrologic Modeling Research
and Development

![](media/image1.wmf){width="2.8368055555555554in"
height="2.129861111111111in"}

![](media/image4.wmf){width="3.377083333333333in"
height="2.535416666666667in"}

![](media/image3.wmf){width="2.7916666666666665in"
height="2.095833333333333in"}![](media/image3.wmf){width="3.6875in"
height="2.7270833333333333in"}

![](media/image6.wmf){width="2.9965277777777777in" height="2.25in"}

Executive Summary

The Hydrology Laboratory (HL) of the NOAA National Weather Service
Office of Hydrologic Development (NWS OHD) began its distributed
modeling research in 1992. It has followed a strategy defined by two
phases. In Phase 1, the research focused on developing methods of fully
realizing the potential information in the NEXRAD precipitation
estimates using models within the existing NWS river forecast system. In
Phase 2 of this research, HL has aggressively pursued the development of
a fully distributed parameter hydrologic model. Phase 2 is characterized
by the concurrent in-house development of the NWS's first distributed
model and the launching of the Distributed Model Intercomparison Project
(DMIP). Twelve groups participated in this highly successful
intercomparison, including representatives from China, New Zealand,
Denmark, Canada, as well as leading institutions in the U.S. The
distributed model developed by HL performed well in DMIP, leading to the
conclusion that HL is on a sound path of research and development for
operational distributed hydrologic modeling.

In the course of HL research, at least 25 peer-reviewed papers have been
published in leading scientific journals, attesting to the scientific
validity of the HL R&D. Numerous US and international oral and poster
presentations have been made, as well as five NWS-organized sessions at
AGU conferences on the comparison of lumped and distributed models and
related issues. HL scientists have been invited to give oral
presentations on two occasions, and one has been invited by the Journal
of Hydrology to be Guest Editor of a Special Issue of the Journal on the
DMIP project. Along the way, new tools and procedures have been
consistently delivered to the field.

Research and development of distributed hydrologic models has progressed
to the point of prototype deployment of the NWS distributed model at the
Arkansas-Red Basin, Colorado Basin, Mid-Atlantic, and West Gulf River
Forecast Centers. Moreover, HL is launching an effort to begin in-house
runs of its distributed model executed over the CONUS domain. In
addition, a significant software engineering project is underway to
deliver a fully supported distributed model compatible with the NWS
officially-supported computational environment for forecast operations.

Contents

[1 Introduction 7](#introduction)

[2 Purpose 7](#purpose)

[3 Motivation for Distributed Modeling Research and Development.
7](#motivation-for-distributed-modeling-research-and-development.)

[4 Overall Research and Development Strategy
8](#overall-research-and-development-strategy)

[5 Phase 1 Research 9](#phase-1-research)

[5.1 Phase 1 R&D Strategy 9](#phase-1-rd-strategy)

[5.2 Phase 1 Goals 9](#phase-1-goals)

[5.3 Phase 1 Constraints 11](#phase-1-constraints)

[5.4 Major Issues Investigated in Phase 1
13](#major-issues-investigated-in-phase-1)

[5.5 Major Findings from Phase 1 13](#major-findings-from-phase-1)

[5.6 Tools and Models Developed in Phase 1
20](#tools-and-models-developed-in-phase-1)

[5.7 Phase 1 Associated Collaborative Research
20](#phase-1-associated-collaborative-research)

[6 Phase 2 Research and Development
21](#phase-2-research-and-development)

[6.1 Phase 2 Scope 21](#phase-2-scope)

[6.2 Phase 2 Goals 21](#phase-2-goals)

[6.3 Phase 2 Constraints 22](#phase-2-constraints)

[6.4 Phase 2 R&D Strategy 22](#phase-2-rd-strategy)

[6.4.1 Internal HL Distributed Model Development
23](#internal-hl-distributed-model-development)

[6.4.1.1 HL-RMS parameterization using DEM-GIS data
25](#hl-rms-parameterization-using-dem-gis-data)

[6.4.1.2 Estimation of SAC-SMA parameters
27](#estimation-of-sac-sma-parameters)

[6.4.1.3 Estimation of routing parameters using DEM and channel
hydraulic data
28](#estimation-of-routing-parameters-using-dem-and-channel-hydraulic-data)

[6.4.1.4 Large Area applications of HL-RMS
29](#large-area-applications-of-hl-rms)

[6.4.1.5 Prototype Application of HL-RMS
32](#prototype-application-of-hl-rms)

[6.4.2 The Distributed Model Intercomparison Project (DMIP)
35](#the-distributed-model-intercomparison-project-dmip)

[6.4.2.1 Background for DMIP 35](#background-for-dmip)

[6.4.2.2 DMIP Schedule 36](#dmip-schedule)

[6.4.2.3 DMIP Study Area 37](#dmip-study-area)

[6.4.2.4 DMIP Participants 37](#dmip-participants)

[6.4.2.5 Major Findings from DMIP 38](#major-findings-from-dmip)

[6.4.2.6 Future DMIP Phases 42](#future-dmip-phases)

[6.5 Tools and Procedures from Phase 2 R&D
42](#tools-and-procedures-from-phase-2-rd)

[6.6 Collaborative Research Associated with Phase 2 R&D
43](#collaborative-research-associated-with-phase-2-rd)

[7 Distributed Modeling for Flash Flood Forecasting
44](#distributed-modeling-for-flash-flood-forecasting)

[8 Distributed Modeling Software Engineering and Implementation
46](#distributed-modeling-software-engineering-and-implementation)

[9 References 47](#references)

[Appendix I: Publications and Presentations from Phase 1 Research
52](#appendix-i-publications-and-presentations-from-phase-1-research)

[Appendix II: Publications and Presentation from Phase 2
54](#appendix-ii-publications-and-presentation-from-phase-2)

[Appendix III: Acronyms 58](#appendix-iii-acronyms)

[Appendix IV: Proposed Field Office Transition from Lumped to
Distributed Modeling
59](#appendix-iv-proposed-field-office-transition-from-lumped-to-distributed-modeling)

[Appendix V: Needs for Continuing HL R&D
63](#appendix-v-needs-for-continuing-hl-rd)

**List of Tables**

[Table 1. Tools, Procedures, and Guidelines developed from Phase 1 R&D.
20](#__RefHeading___Toc68408868)

[Table 2 Schedule for Major DMIP Activities
36](#__RefHeading___Toc68408869)

[Table 3. Tools developed and delivered as a result of Phase 2 research
and development 43](#__RefHeading___Toc68408870)

**List of Figures**

[Figure 1. Overall timeline and major tasks for Phase 1 (yellow) and
Phase 2 (green) of NWS HL distributed modeling research and development.
10](#__RefHeading___Toc68409891)

[Figure 2. Location of NEXRAD radar sites and coverage of Phase 1 and
Phase 2 study basins. Circles denote the coverage of each radar. Radius
of the radar umbrella is 230 km. These basins were also used in the
Distributed Model Intercomparison Project (DMIP). (Source: Smith et al.,
2004a.) 11](#__RefHeading___Toc68409892)

[Figure 3. Components of the NWSRFS (Source: Smith et al. 2003; Johnson
et al.,1999) 12](#__RefHeading___Toc68409893)

[Figure 4. (Following pages). Spatially-aggregated Stage III
precipitation field over Northeastern Oklahoma, January 16, 1994 at
20:00Z. 15](#__RefHeading___Toc68409894)

[Figure 5. Relative sensitivity of SAC-SMA runoff components to size of
computational area, keeping model parameters constant. Runoff values are
normalized by the value at the 8x8 HRAP bin scale. (Source: Finnerty et
al., 1997) 18](#__RefHeading___Toc68409895)

[Figure 6. Scale dependency of total runoff, simulated by different
models, and expressed in percent change in surface runoff as compared to
the finest scale value. (Source: Fig 4 of Koren et al., 1999; Figure
2-15 of Smith et al. 1999- NWS 44: Distributed Modeling: Phase 1 Results
18](#__RefHeading___Toc68409896)

[Figure 7. Relative simulation improvement versus level of sub-basin
scale (disaggreation) for various degrees of noise in input forcing.
(Source: Koren et al., 2003) 19](#__RefHeading___Toc68409897)

[Figure 8. HL-RMS treatment of computed flows. A. Cell flow directions
and connectivity for the Blue River basin in Oklahoma. Drainage area is
476 sq. mi. B. Conceptual representation of overland flow segments
feeding a single channel in each HL-RMS computational cell.
24](#__RefHeading___Toc68409898)

[Figure 9. Schematic for parameterizing the water balance and channel
routing components of HL-RMS 26](#__RefHeading___Toc68409899)

[Figure 10. A priori estimates of the SAC-SMA model upper zone tension
water depth in mm for the CONUS domain. Cell resolution is 1km^2^
29](#__RefHeading___Toc68409900)

[Figure 11 Large-area implementation of HL-RMS over the Arkansas River
basin. 30](#__RefHeading___Toc68409901)

[Figure 12. Observed and derived fields from the large-area application
of HL-RMS. 31](#__RefHeading___Toc68409902)

[Figure 13. Large-Area Application of HL-RMS: Initial discharge
simulation of the entire Arkansas River Basin
32](#__RefHeading___Toc68409903)

[Figure 14. Location Map of Sites for HL-RMS Prototype Implementation.
Colored areas are the domain extents of the NWS River Forecast Centers.
33](#__RefHeading___Toc68409904)

[Figure 15. Location of the Susquehanna River and its sub-basins for
prototype implementation of HL-RMS. 34](#__RefHeading___Toc68409905)

[Figure 16 Study basins used in DMIP (source: Smith et al., 2004a)
38](#__RefHeading___Toc68409906)

[Figure 17. Improvement of Distributed Model over Lumped SAC-SMA for
different DMIP basins and models. Results are for calibrated lumped and
distributed models. The basins are segregated into parent basins and
interiorcomputational points. Positive values of improvement indicate
the distributed model performed better than the NWS calibrated lumped
SAC-SMA model 40](#__RefHeading___Toc68409907)

[Figure 18. Relationship between storm rainfall center-of-mass (rainfall
location index) and a basins ability to dampen the input rainfall signal
(dampening ratio) for three DMIP basins. (Source: Smith et al. 2004b)
42](#__RefHeading___Toc68409908)

[Figure 19. Application of the Distributed model HL-RMS to the Blue
River, OK. Approximately 80 computational elements are used in this
application. 44](#__RefHeading___Toc68409909)

[Figure 20. Hydrograph Generation at various points in the Blue River
basin illustrating the use of a distributed model for flash flood
forecasting at interior locations. 45](#__RefHeading___Toc68409910)

# Introduction

The National Weather Service (NWS) has a mandate to provide forecasts
for the Nation's rivers. To fulfill this mission, the NWS uses its River
Forecast System (NWSRFS) at 13 River Forecast Centers (RFCs) to provide
daily stage forecasts at over 4,000 points. Within the NWSRFS are
algorithms for hydrologic and hydraulic models as well as procedures for
data ingest, display and analysis of results, and other functions.
Research and development to support the NWSRFS is conducted within the
Hydrology Lab (HL) of the NWS Office of Hydrologic Development (OHD).
Interested readers are referred to Fread et al. (1995), Larson et al.
(1995), and Stallings and Wenzel (1995) for more information regarding
the forecast mission of the NWS and the structure of the NWSRFS.

The NWS HL of conducts scientific research, data archival and analysis,
and software engineering to support the river and flash flood mission of
the NWS. HL initiated research and development into distributed
hydrologic modeling in 1992. Since that time, HL has directed increasing
resources toward this important effort. Significant research and
development is taking place in HL in conjunction with major
collaborative efforts with leading universities and other institutions.

# Purpose

This document serves as both an overview of past research and
development as well as a plan for continued work. While this report will
present an overview of the findings of the research to date, details of
the findings and conclusions can be found in the cited journal papers.

# Motivation for Distributed Modeling Research and Development.

Beven (1985) and numerous other authors since then have described and
promoted the advantages of distributed modeling. Indeed, distributed
hydrologic models have been in development for almost three decades.
Early efforts to develop distributed hydrologic models were hampered by
limited computational capacities and seemingly huge storage
requirements. Currently, exponentially increasing computing speeds and
storage capacities have largely removed the traditional barriers to
distributed modeling, bringing them ever closer to the operational
environment.

The NWS recognizes these advantages and views distributed modeling as a
critical pathway through which new scientific advances can be infused
into the NWSRFS (Carter, 2002; Koren et al., 2001). Recently, the
National Academy of Sciences listed hydrologic forecasting as one of the
grand challenges in environmental sciences (NAS, 2001). The NWS sees
distributed modeling as one important approach to meeting this
challenge. Moreover, distributed modeling is identified as one of the
ways the NWS can improve the accuracy of its river and flash flood
forecasts so as to meet stated goals (NWS, 1999). The NWS also
recognized the recommendations by the NAS review (1996) regarding the
need to develop topographically-based models. Moreover, distributed
modeling is a more scientifically valid approach to the prediction of
flash floods than the current fielded tools.

Given the scale of the NWS mission and the recommendations from external
reviewers, it was clear that an accelerated program was needed to move
the NWS research in the proper direction for operational distributed
modeling. While numerous distributed models exist and indeed some are
moving into the operational forecasting environment (e.g., Koren and
Barrett, 1994; Turcotte et al. 2003) it is not clear from the literature
which distributed model or modeling approach is best to improve the NWS
forecasting capabilities. Therefore, a significant effort was begun to
investigate distributed models for operational river and flash flood
forecasting.

# Overall Research and Development Strategy

HL's strategy for developing distributed models for operational river
and flash flood forecasting has involved two primary phases as shown in
Figure 1. Plans for Phase 1 Research were initiated by Lindsey (1993,
1994) and later modified by Smith (1996). In Phase 1, we focused on the
questions: "What can we learn about distributed data sets and current
capabilities in NWSRFS? How can we take advantage of the NEXRAD
precipitation information to improve forecasting using
[existing]{.underline} NWSRFS models and procedures? Can we improve
lumped simulations using NEXRAD data? Is semi-distributed modeling of
existing forecast basins a reasonable alternative?

In Phase 2 of the distributed modeling R&D, the concept of distributed
parameters as well as distributed inputs is investigated, capitalizing
on the significant experience gained in Phase 1. Phase 2 R&D embodies a
major effort of scientific development not constrained by the structure
of the current NWSRFS.

As shown in Figure 1, HL research has been augmented by collaborative
research with MIT, the University of Arizona, and the Hydrologic
Research Center in both phases of work.

Figure 2 presents the location of the study basins used in Phase 1,
Phase 2, and also the Distributed Model Intercomparison Project (DMIP).
These basins were selected for their good radar coverage, the lack of
complications such as snow and orographics, and the availability of USGS
observed hourly discharge data at basin outlets and a few interior
points.

# Phase 1 Research

## Phase 1 R&D Strategy

In Phase I of this research we investigated lumped and semi-distributed
applications of the Sacramento Soil Moisture Accounting Model (SAC-SMA)
(Burnash, et al., 1973) using basin and sub-basin mean areal averages of
the NEXRAD 4km gridded precipitation estimates. In the semi-distributed
scenario, existing NWS forecast basins are disaggregated into a number
of sub-basins, each having a SAC-SMA model and a unit hydrograph.
Channel routing was accounted for in two ways; an extended unit
hydrograph and explicit Muskingum channel routing from the outlet of one
sub-basin to the next downstream outlet. In this way, minimal system
changes would be needed but a great deal of scientific understanding
could be gained.

##  Phase 1 Goals

Several goals were outlined for Phase 1 research with the anticipation
that much would be learned that would provide a strong foundation for
Phase 2 R&D:

> 1\. Develop a methodology to use and evaluate the utility of NEXRAD
> gridded rainfall data with the models currently available in NWSRFS.
> Lumped and semi-distributed approaches were investigated
>
> 2\. Study the effects of increasing the spatial and temporal
> resolution on hydrologic model response and parameters.
>
> 3\. Develop guidelines, tools, and recommendations to help the RFC's
> use gridded rainfall data within the NWSRFS framework.
>
> 4\. Modify the lumped Sacramento Soil Moisture Accounting Model
> (SAC-SMA) to account for a statistical representation of the rainfall
> spatial variability.

<table>
<colgroup>
<col style="width: 7%" />
<col style="width: 6%" />
<col style="width: 5%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 5%" />
<col style="width: 0%" />
<col style="width: 3%" />
<col style="width: 2%" />
<col style="width: 6%" />
<col style="width: 7%" />
<col style="width: 5%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 0%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td>‘92</td>
<td>‘93</td>
<td>‘94</td>
<td>‘95</td>
<td>‘96</td>
<td>‘97</td>
<td>‘98</td>
<td>‘99</td>
<td colspan="3">‘00</td>
<td>‘01</td>
<td>‘02</td>
<td>‘03</td>
<td colspan="3">‘04</td>
<td>‘05</td>
<td>‘06</td>
<td>Beyond</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="3"></td>
<td></td>
<td></td>
<td></td>
<td colspan="3"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td colspan="14"><strong>Phase 1: Use of NEXRAD data with existing
NWSRFS models</strong></td>
<td colspan="3"></td>
<td></td>
<td colspan="2"></td>
</tr>
<tr class="even">
<td colspan="2">Project plan</td>
<td colspan="3"><p>Basin selection</p>
<p>Data collection</p></td>
<td colspan="5"><p>Lumped modeling</p>
<p>-existing SAC-SMA</p>
<p>-reformulated SAC-SMA</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="3"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="5">Scaling studies</td>
<td colspan="5"></td>
<td colspan="3"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="8"><p>Semi-distributed modeling</p>
<p>-UHG routing</p>
<p>-Muskingum-Cunge channel routing</p></td>
<td colspan="3"></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td colspan="13"><p>Colloborative Research</p>
<p>-MIT: development of complex physically-based distributed model</p>
<p>-HRC: development of SAC-SMA based distributed model, sensitivity to
radar, parameter uncertainty</p>
<p>-Arizona: lumped and semi-distributed model calibration</p></td>
<td colspan="6"></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="14"><p><strong>Phase 2: Full Distributed
Modeling</strong></p>
<p>continuing</p></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="10"><p>Parameterization: soils-based SAC-SMA parameters</p>
<p>-STATSGO data</p>
<p>-SSURGO data</p></td>
<td colspan="2"></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="13"><p>HL-RMS development</p>
<p>-basic version</p>
<p>-Continuous API model</p>
<p>-snow</p>
<p>- Muskingum Cunge</p></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="6">DMIP Phase 1</td>
<td colspan="5">DMIP Phase 2</td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="9"><p>Collaborative Research</p>
<p>MIT: state-of-art physically based</p>
<p>HRC: effects of parameteric, NEXRAD uncertainty</p>
<p>Arizona: calb. of semi-distributed models</p>
<p>U. Minn: probabilistic channel routing parameters</p></td>
<td colspan="5"><p>Future Collaborative Research</p>
<p>NWS competitive program</p></td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td colspan="6"></td>
<td colspan="5"></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

[]{#__RefHeading___Toc68409891 .anchor}Figure 1. Overall timeline and
major tasks for Phase 1 (yellow) and Phase 2 (green) of NWS HL
distributed modeling research and development.

![](media/image5.wmf){width="6.519444444444445in"
height="4.902777777777778in"}

[]{#__RefHeading___Toc68409892 .anchor}Figure 2. Location of NEXRAD
radar sites and coverage of Phase 1 and Phase 2 study basins. Circles
denote the coverage of each radar. Radius of the radar umbrella is 230
km. These basins were also used in the Distributed Model Intercomparison
Project (DMIP). (Source: Smith et al., 2004a.)

## Phase 1 Constraints

Phase 1 R&D operated under several constraints. The structure and
philosophy of the NWSRFS places unique constraints on the development of
distributed modeling approaches.

To begin, a brief review of the current forecast system is warranted
here. Figure 3 presents the major components of the NWSRFS. In the
Calibration System (CS), time series of historical forcings are prepared
and model parameters are calibrated. In the Operational Forecast System
(OFS), real time data are used with the calibrated hydrologic and
hydraulic models to produce forecast river stages several days into the
future. The Interactive Forecast Program (IFP) allows the hydrologist to
make manual run-time adjustments to account for non-standard conditions.
The historical time series of precipitation, temperature, and potential
evaporation are used to generate a suite of long term probabilisitic
forecasts weeks or months into the future in the Ensemble Streamflow
Prediction system (ESP). Initial conditions for ESP simulations are
taken from the current model states as computed by the OFS. Statistical
procedures are used to quantify the uncertainty of these forecasts
within a designated window.

![](media/image6.wmf){width="6.1090277777777775in"
height="4.594444444444444in"}

[]{#__RefHeading___Toc68409893 .anchor}Figure 3. Components of the
NWSRFS (Source: Smith et al. 2003; Johnson et al.,1999)

As envisioned in Phase 1, any lumped and semi-distributed modeling
techniques had to fit into the existing framework of the NWSRFS. Gridded
data fields can be input into the NWSRFS and spatially averaged to
create a time series of mean-areal values of precipitation. However,
NWSRFS cannot currently pass gridded data and parameters through its
models to generate gridded output fields to support fully distributed
modeling. Also, a question facing Phase 1 (and still faces Phase 2
research) is: Can a distributed model used for RFC basin outlet
forecasts and interior flash flood forecasts also be used for long term
ensemble streamflow forecasts? Computational efficiencies cloud the
issue here.

Other constraints include the necessary around-the-clock operations
schedule of the NWS field offices. Models must be able to run
continuously, with many built in fail-safe procedures.

## Major Issues Investigated in Phase 1

The following issues were investigated in Phase 1 research:

1\. Comparison of rain gage precipitation and radar precipitation
estimates. The issue underlying this effort was the differences in the
data used for model calibration and operational forecasting. Is
recalibration necessary if a model is calibrated using data from a
network of rain gages while using NEXRAD radar precipitation estimates
for operational forecasting?

2\. Sensitivity of the SAC-SMA parameters to different space-time scales
of precipitation forcing.

3\. Sensitivity of different hydrologic models to different space-time
scales of precipitation forcing. As shown in Figure 4, spatially
averaging a typical precipitation field from the NEXRAD radar will
result in a smoothed input signal to hydrologic models operating at the
scale of averaging.

4\. Development of local area unit hydrographs using GIS data.

5\. Level of disaggregation to achieve improved basin outlet
simulations.

6\. Development of a reformulated version of the lumped SAC-SMA model to
account statistically for the spatial distribution of rainfall.

## Major Findings from Phase 1

We summarize the major findings of our research here:

1\. Rain gage and radar estimates of precipitation have different
statistical properties. Moreover, these statistical properties of the
radar precipitation estimates change in time with the algorithmic
changes in the NEXRAD processing system. Care must be taken when
calibrating a model with rain gage data and forcing with another data
source at the same spatial scale (corroborated later by Stellman and
Fuelberg, 2001; Bradley and Kruger, 1998; and Young et al. 2000 (with
credit given to HL staff D. Johnson, Mike Smith, D.-J.Seo). This finding
underscores the great need to perform re-analysis of the NEXRAD data to
produce stationary data sets for research and model calibration.

2\. The conceptual SAC-SMA model is sensitive to the time and spatial
scale of the precipitation inputs. Therefore, the NWS cannot transition
from forecasting using rain gage derived 6-hour precipitation estimates
to hourly lumped forecasting without model recalibration. Recalibration
is necessary when transitioning from a 6 hour time step to a one hour
time step, or when disaggregating a lumped basin into a number of
constituent sub-basins for semi-distributed modeling. Figure 5 shows the
relative sensitivity of the SAC-SMA runoff components. Simulations were
conducted by examining the runoff behavior from a 64x64 HRAP bin
synthetic basin in the ABRFC domain shown in Figure 4. This basin was
modeled as a series of coarser and coarser computational elements for
which the rainfall was averaged as seen in Figure 4.

3\. Semi-distributed modeling is a valid approach to improve the
accuracy of forecasts at NWS forecast points compared to lumped
modeling. This was shown for the Blue River in Oklahoma using an 8
sub-basin approach combined with extended unit hydrographs or
Muskingum-Cunge channel routing (Boyle et al., 2001; Zhang et al., 2003;
Smith et al., 2000). Semi-distributed modeling is feasible using
existing models in NWSRFS.

4\. Reformulating the upper zones of the SAC-SMA results in a less scale
dependent model (Koren et al.1999). Bradley and Kruger (1998) supported
this finding. As seen in Figure 6, the reformulated SAC-SMA model is the
least scale dependent of the 4 tested models. The reformulated SAC-SMA
is a viable alternative and represents a transition from lumped to
distributed models.

5\. Some of the basins investigated in Phase 1 did not show improvements
from semi-distributed models. Overall run-period statisitics did not
show that semi-distributed modeling provides more accurate basin-outlet
streamflow simulations. However, statistics from specific events showed
improvement. The Blue River showed more improvement from
semi-distributed modeling approaches. Specific causes for the
improvements were later investigated by Zhang et al. (2004) and Smith et
al. (2004b) in DMIP follow-on research.

1.  There may be a lower limit to the level of disaggregation of a basin
    beyond which simulations are degraded and not improved. This is
    especially true in cases of input data error. Figure 7 presents the
    relative simulation sensitivity as a lumped basin is disaggregated
    into a number of computational elements. The y axis of Figure 7 is
    the relative improvement of a distributed model over a lumped model
    for the synthetic basin. In this case the 'truth' is the simulation
    generated at the finest scale of basin disaggregation. For the case
    of zero error, distributed simulations get better as modeling
    resolution gets higher. However, as the level of noise increases in
    the precipitation forcing, the benefits from distributed modeling
    are reduced as modeling resolution increases. Eventually, the
    simulations can be worse than a lumped model.

[]{#__RefHeading___Toc68409894 .anchor}Figure 4. (Following pages).
Spatially-aggregated Stage III precipitation field over Northeastern
Oklahoma, January 16, 1994 at 20:00Z.

\(a\) Stage III 1-hour precipitation field in units of millimeters (z
axis) over a spatial extent of 64 x 64 bins. Each bin has an individual
value relative to its neighbors, and is used as input to the lumped
SAC-SMA hydrologic model. Thus, 64^2^ or 4096 individual SAC-SMA model
run are used over this domain every hour of the model simulation.

\(b\) Same data as shown in (a) accept they have been averaged in 2 x 2
bins. This field has 64^2^ /2^2^ =1024 individual values and will
require 1024 SAC-SMA model runs for analysis. Notice that the averaging
procedure reduces the peaks of actual values shown in (a).

\(c\) Same data as in (a) have been average over 4 x 4 bins. This field
has 64^2^ /4^2^ =256 individual values.

\(d\) The data in (a) have been averaged over an 8 x 8 bin area. This
field has 64^2^ /8^2^ =64 individual values. Notice that this field only
very coarsely resembles the original field shown in (a).

\(e\) The data in (a) have been averaged over a 16 x 16 bin area. This
field has 64^2^/16^2^ =16 individual values. The maximum rainfall depth
is 6.62mm.

\(f\) The data in (a) have been averaged over a 32 x 32 bin area. This
field has 64^2^ /32^2^ =4 individual values. This field is arguably a
poor representation of the original spatial distribution of data.

\(g\) The data in (a) have been averaged over a 64 x 64 bin area. This
field has only one value.

(Source: Finnerty et al., 1997; Smith et al., 1999)

![](media/image7.wmf){width="7.985416666666667in"
height="6.018055555555556in"}

![](media/image8.wmf){width="8.0in" height="6.040972222222222in"}

![](media/image9.wmf){width="4.7340277777777775in" height="3.525in"}

> []{#__RefHeading___Toc68409895 .anchor}
>
> Figure 5. Relative sensitivity of SAC-SMA runoff components to size of
> computational area, keeping model parameters constant. Runoff values
> are normalized by the value at the 8x8 HRAP bin scale. (Source:
> Finnerty et al., 1997)
>
> []{#__RefHeading___Toc68409896 .anchor}Fgure 6. Scale dependency of
> total runoff, simulated by different models, and expressed in percent
> change in surface runoff as compared to the finest scale value.
> (Source: Fig 4 of Koren et al., 1999; Figure 2-15 of Smith et al.
> 1999- NWS 44: Distributed Modeling: Phase 1 Results

![](media/image12.wmf){width="6.625in" height="4.96875in"}

> []{#__RefHeading___Toc68409897 .anchor}Figure 7. Relative simulation
> improvement versus level of sub-basin scale (disaggreation) for
> various degrees of noise in input forcing. (Source: Koren et al.,
> 2003)

## Tools and Models Developed in Phase 1

Practical tools and guidelines for using NEXRAD precipitation estimates
were developed in Phase 1 R&D that were made available to the NWS River
Forecast Centers. Table 1 presents a summary of these tools.

[]{#__RefHeading___Toc68408868 .anchor}Table 1. Tools, Procedures, and
Guidelines developed from Phase 1 R&D.

  ----------------- ------------------------ -----------------------------
  Name              Description              Status

  Muskingum-Cunge   Routing algorithm        Development version
  channel routing   compatible with current  incorporated into NWSRFS,
                    NWSRFS structure         used for semi-distributed
                                             modeling (Smith et al. 2000),
                                             user documentation being
                                             written, used in NWS training
                                             workshop for routing (Sept.
                                             2002)

  Procedures for    UHG development          Being revised according to
  synthetic unit    guidelines for 1 hour    Phase 2 research
  hydrograph        lumped and               
  development       semi-distribued modeling 

  Reformulated      Modified SAC-SMA model   Incorporated into existing
  SAC-SMA model     compatible with current  NWSRFS structure. Parameter
                    NWSRFS structure         calibration needs to be
                                             finalized.

  Mean Areal        Stand alone program to   Delivered to RFCs as an
  Precipitation     compute time series of   officially supported NWSRFS
  from NEXRAD       mean-areal precipitation component
  preprocessor      from NEXRAD data for     
                    model calibration.       

  Guidance for      Description of           Delivered to RFCs as part of
  calibration with  implications of          NWS technical report (Smith
  NEXRAD            calibrating with 6 hour  et al., 1999b). Taught at NWS
                    rain gage data the       calibration workshops.
                    forecasting using hourly 
                    NEXRAD gridded data.     
  ----------------- ------------------------ -----------------------------

## Phase 1 Associated Collaborative Research

While conducting Phase 1 R&D, HL also collaborated with leading
institutions. HL provided modest financial support to MIT in their
development of a complex physically-based distributed model using a
triangulated irregular network (TIN) as the computational domain. MIT's
model represents the 'state-of-the-art' of the class of physically
based, high resolution distributed models. HL highly values the work of
MIT because their modeling strategy helps address a very important
question for the NWS: what level of model complexity is necessary to
achieve improvements in river and flash flood simulation and
forecasting? Also, HL supported the Hydrologic Research Center (HRC).
Under the direction of Konstantine Georgakakos, HRC developed a
distributed model using the SAC-SMA model and kinematic routing. Monte
Carlo analyses with this model considered the effects of parametric and
radar rainfall uncertainty. These detailed analyses supported HL's
findings that for some basins, semi-distributed models may not provide
improvement over lumped models given uncertainty in model parameters and
NEXRAD forcing (Carpenter et al., 2001; Carpenter et al., 1999). HL also
collaborated with the University of Arizona to develop automatic
procedures for the calibration of lumped and semi-distributed models.
This work culminated with the work of Boyle et al. (2001), and U. of
Arizona's participation in the DMIP project (Khodatalab et al., 2004).
Hogue et al.(2000) also developed a multi-step automatic calibration
procedure designed to combine the power of automatic search algorithms
with the insights of manual calibration.

# Phase 2 Research and Development

Building on the experience gained in Phase 1, HL embarked on an
aggressive campaign to develop a distributed parameter hydrologic model
for NWS river and flash flood forecasting. This effort was fueled by the
hiring of several key PhD-level researchers.

## Phase 2 Scope

Phase 2 R&D is aimed at a very broad scope of topics that are recognized
in the distributed modeling research and operational community: proper
model structure, rainfall-runoff partitioning, streamflow routing,
analysis of forcings, determination of optimum scale, parameterization,
calibration, updating, and issues of conceptual versus so-called
'physically-based' modeling.

## Phase 2 Goals

> The NOAA NWS/OHD identified the following goals of Phase 2 R& D:

1.  Investigate the utility of distributed parameters [and]{.underline}
    distributed forcings (in contrast to Phase 1, which focused only on
    distributed inputs).

2.  Develop prototype version of a distributed model for real time
    forecasting.

HL identifies the following performance parameters for a distributed
model:

> a\. The distributed model should perform at least as well in an
> overall sense as the current operational lumped model. Simulation
> improvement should be achieved in cases of non-uniform rainfall
> patterns.
>
> b\. The distributed model should be operationally feasible in current
> and anticipated computational environments.
>
> c\. The distributed model should have procedures for parameterizaion,
> calibration, and state updating.

## Phase 2 Constraints

In this phase, HL faces the anticipated and traditional constraint of
computational limitations imposed by computer processing speeds and
software architecture. Recall that the current NWSRFS cannot currently
support gridded distributed modeling. HL scientists nonetheless embarked
on the development of a distributed model in the expectation that a
major software engineering effort would be directed towards making the
distributed model operationally viable as part of the
officially-released NWSRFS. Indeed, in September, 2002, an effort named
Distributed Modeling System (DMS) 1.0 was launched. This software
engineering project is designed to implement the HL-RMS model as an
officially-supported NWSRFS component. This effort is described more
fully in section 8.

I

##  Phase 2 R&D Strategy 

Phase 2 R&D builds upon the knowledge gained in Phase 1. In this second
phase, the broader question of distributed parameters was added to the
investigation whereas only distributed inputs were investigated in
Phase 1. While HL could have selected any one (or more) of the existing
distributed models for testing, it was not clear from the literature
which would be the best in light of the NOAA NWS forecasting mission. HL
did not want to solely develop its own model in a vacuum, only to find
that after significant time it would not be the best for RFC river and
flash flood forecasting. Therefore, within Phase II, two concurrent
major thrusts were launched to maximize HL's resources and to capitalize
on the considerable research performed at leading institutions:

1)  internal HL development of a distributed modeling system

2)  extensive evaluation/comparison of distributed models from leading
    institutions. This effort is called the Distributed Model
    Intercomparison Project (DMIP)

The following rationale shows the concurrent and interwoven parts of
this research strategy:

-   HL ***develops*** a research modeling system, (HL-RMS) capable of
    testing model components at different scales (lumped,
    semi-distributed, gridded distributed). This system is modular, so
    that as the DMIP results appear, HL would be ready to test
    algorithms and components that are deemed beneficial. HL develops
    the NWS's first distributed model within HL-RMS.

-   HL ***leads*** DMIP to identify models and or components that appear
    advantageous for NWS river and flash flood forecasting. DMIP was
    also envisioned as a vehicle to identify institutions for continued
    collaborative research.

-   HL ***participates*** in DMIP. HL generates simulations as an
    official DMIP participant. In this way, HL not only leads DMIP, but
    is a major contributor as well.

### Internal HL Distributed Model Development

HL has developed the Hydrology Laboratory Research Modeling System
(HL-RMS). This modeling system combines lumped conceptual and
distributed model features. This formulation is meant to capitalize on
HL's considerable understanding of and experience with the SAC-SMA
model. Moreover, this model philosophy was designed to address the
concern of Robinson and Sivapalan (1995), who stated that not enough
analyses are being performed to explore the connections between
physically-based and conceptual models, even though they stated this is
'precisely what is required for the advancement of hydrological modeling
for predictive purposes'.

Koren et al. (2003, 2004) describe the details of HL-RMS. However, a
brief overview of HL-RMS is warranted here.

Number range CHAPTERHL-RMS is a flexible modeling system, able to use
grid cells or sub-basins as the computational elements for
rainfall-runoff modeling. Currently, HL-RMS is defined on a regular
rectangular grid as shown in Figure 8. Each grid cell consists of a
water balance component and a hillslope and channel routing component. A
number of conceptual hillslopes are defined to make overland flow
distances physically realistic for the relatively large (\~ 16 km^2^)
cell size. A drainage density parameter is used to subdivide a cell into
equally sized overland flow planes as seen in Figure 8. Conceptual
hillslopes drain water to a conceptual channel within the same grid
cell. A conceptual channel usually represents the highest order stream
of a selected grid cell. It is assumed that all hillslopes have the same
properties inside each grid cell but they may be different from cell to
cell. The main channel length within each cell is assumed to be equal to
the cell diagonal distance. Cell-to-cell channel routing is done using a
flow direction grid like that illustrated in Figure 8. A modified
version of the algorithm described by Wang et al. (2000) was developed
to generate the flow direction grid. The algorithm automatically
generates a coarser resolution flow direction grid from higher
resolution DEM data. As a result, the basin boundaries and channel
structure match reasonably well with high resolution basin properties
(see Figure 8). To facilitate efficient routing calculations, the
drainage network depicted in Figure 8 is translated into a computational
sequence of grid cells in an upstream to downstream order. The same
method for storing the computational grid sequence was used in the Nile
Forecast System (Koren and Barrett, 1995).

Each grid cell consists of a water balance component and a hillslope and
channel routing component. Fast response runoff from the water balance
model is routed over conceptual hillslopes within each cell to a
conceptual channel. Slow response runoff is assumed to enter the channel
system directly from the soil and therefore bypass the hillslope
routing. There is no physical connection between soil moisture states in
adjacent grid cells. The conceptual channel is the only source of water
exchange between neighboring pixels. While this may be considered a
weakness in the current distributed system, some field data support this
'zero lateral conductivity' assumption (Watson et al., 1998).

![](media/image13.wmf){width="2.459055118110236in"
height="2.106299212598425in"}![](media/image14.png){width="3.0543307086614173in"
height="3.3326771653543306in"}

[]{#__RefHeading___Toc68409898 .anchor}Figure 8. HL-RMS treatment of
computed flows. A. Cell flow directions and connectivity for the Blue
River basin in Oklahoma. Drainage area is 476 sq. mi. B. Conceptual
representation of overland flow segments feeding a single channel in
each HL-RMS computational cell.

The water balance component of the current version of the HL-RMS first
used the Sacramento Soil Moisture Accounting Model (SAC-SMA). Recently,
the Continuous Antecedent Precipitation Index (API) model was added
specifically for the domain of the Mid-Atlantic River Forecast Center
(MARFC). In all cases, the hillslope-channel routing component employs
the kinematic wave model. Several factors played a role in the selection
of the SAC-SMA model. Use of the SAC-SMA is a practical choice because
NWS hydrologists have great experience with lumped applications of the
model. Also, the work of Koren et al. (2000) established relationships
between SAC-SMA parameters and soil properties, making it possible to
run simulations using parameter estimates that vary within a basin. The
kinematic wave model is well tested, and it is successfully used in
watershed modeling (Goodrich et al., 1991; Willgoose and Kuczera, 1995;
Koren and Barrett, 1995; Georgakakos, 2002; Vieux and Moreda, 2003).
Although the accuracy of the kinematic model is reduced in hydraulically
mild slopes (Fread, 1993; Singh, 1996), it is appropriate to use it in
the first version of the HL-RMS mainly for two reasons: (1) it will be
used mostly for headwater basins where lateral inflow effects dominate
over wave propagation effects, and (2) flood prediction (the main goal
of the HL-RMS) is most critical in regions of rather steep topography
regions (e.g., dominant slopes in basins studied here were well above
0.005). Another consideration was high computational efficiency and
flexibility of this algorithm as it followed from its application to the
Nile basin (Koren and Barrett, 1995). We anticipate that in operational
applications over large river basins the HL-RMS will be combined with a
dynamic routing model which is now available in the NWS River
Forecasting System.

#### HL-RMS parameterization using DEM-GIS data 

Water balance and routing model parameters are assumed to be constant
within each grid cell, however, they can vary from cell to cell. Thus,
spatially variable parameter grids should be generated over the area of
interest. As shown in Figure 9, the approach adopted in HL-RMS is a two
step procedure: derivation of *a priori* parameter grids, and adjusting
of these grids using observed outlet hydrographs. Derivation of *a
priori* parameter grids is a critical step to guarantee the success from
a parameter adjusting step based on a basin response. Rinaldo et al.
(1995) found that the inverse procedure of obtaining the local basin
properties (in their case basin width function) from the basin response
is not reliable. Therefore, reasonable spatial parameter patterns should
be defined independently from basin properties. The basic idea used in
this study is to combine distributed grid cell data (e.g. slope, soil
properties) with integrated basin properties observed at the outlet
(e.g. rating curve data). While this idea was applied in the first step
of the routing parameter estimation procedure, it was used only in the
second step of water balance model parameterization.

![](media/image15.wmf){width="6.4743055555555555in"
height="4.876388888888889in"}

[]{#__RefHeading___Toc68409899 .anchor}Figure 9. Schematic for
parameterizing the water balance and channel routing components of
HL-RMS

#### Estimation of SAC-SMA parameters

Although there are strong physical arguments to support the SAC-SMA
model, its 16 parameters cannot be measured directly. Manual and
automatic procedures to estimate SAC-SMA parameters for lumped model
applications are well defined (Burnash, 1995; Smith et al., 2003; Boyle
et al., 2000; Hogue et al., 2000). The procedures are based solely on
input-output data analysis. Consequently, they do not provide any
information on intra-basin parameter variability, which is desirable
information for the implementation of a distributed model. To account
for the spatial variability within basins, *a priori* SAC-SMA parameter
grids developed by Koren et al. (2000) were used.

Koren et al. (2000) pioneered a set of equations to derive 11 major
SAC-SMA parameters from soil texture, hydrological soil group, and soil
depth. (This work was later reported on by Duan et al., 2001). These
equations were developed based on both physical reasoning and empirical
relationships. The main assumption was that tension water storages of
the SAC-SMA model were related to available soil water (difference
between field capacity and wilting point), and that free water storages
were related to gravitational soil water. Available soil water and
gravitational soil water were derived from soil properties which could
be inferred from soil texture: porosity, field capacity, wilting point,
and hydraulic conductivity. Using 1-km soil texture data estimated for
11 soil layers (Miller and White, 1999), Koren et al. (2000) generated
*a priori* SAC-SMA parameter grids covering the conterminous United
States. These have been made available to the NWS RFCs via the
Calibration Assistance Program (CAP). An example of the parameter grids
is shown in Figure 10, which shows the value of the SAC-SMA upper zone
tension water depth for the entire Nation.

Results from lumped simulations using basin-averaged *a priori*
parameters (Koren et al., 2000; Duan et al., 2001; Koren et al., 2002)
suggest that while *a priori* estimates cannot outperform results from
well calibrated parameters on gaged basins, their values are reasonable
initial estimates for manual or automatic calibrations.

Koren et al. (2003) discussed the limitations of the SAC-SMA parameters
derived from the STATSGO data. To address these limitations, research is
underway in HL to adapt the theory developed by Koren et al. (2000) to
use the high resolution county-level NRCS SSURGO data (Anderson, et al.,
2003).

As with other distributed hydrologic models, significant effort is
needed to develop advanced parameter calibration techniques for HL-RMS.
Work is currently underway to investigate automatic calibration
strategies for use with the SAC-SMA parameters in HL-RMS. A
newly-developed simplified search algorithm called Step-Wise Line Search
(SLS) combined with the a priori parameters of Koren et al. (2000) has
shown to achieve parameter sets equivalent to those derived from the
Shuffled Complex Evolution (SCE) developed by Duan et al. (1993).
However, this new approach requires far fewer function evaluations than
the SCE algorithm. For example, the SLS procedure requires only hundreds
of function evaluations to optimize 11of the SAC-SMA parameters in a
lumped application, whereas SCE needs upwards of 10,000 evaluations to
achieve the global optimum solution. Given the efficiency of this
approach for lumped basins, it is hoped that the SLS procedure can be
applied to the simultaneous optimization of the SAC-SMA parameters in
the dozens of computational elements in a typical application of HL-RMS.
SCE would simply be too computationally intensive in a distributed model
application.

#### Estimation of routing parameters using DEM and channel hydraulic data

[Hillslope routing parameters]{.underline}*.* Three parameters are
defined for overland flow routing: hillslope slope, roughness, and
drainage density (or hillslope length if available). Note that in
current model structure, hillslope slope and roughness may vary from
cell to cell, but not among the conceptual hillslopes within a cell.
Representative hillslope slopes are estimated from DEM data (30-m DEM
data for basin scale applications, and 400-m DEM data for regional scale
applications) by first computing the local slope of each DEM cell in the
study domain, and then averaging all of the DEM cell slopes in each
model cell (Reed et al., 2002). Spatially variable hillslope roughness
values can be related to land use data based on a lookup table (e.g.,
Skahill and Johnson, 1999). However, a lookup table is very subjective,
and it offers limited guidance in defining spatial variability because
within a given land use category, published roughness values cover wide
ranges of possible values that often overlap with the ranges assigned to
other land use categories. Initial HL-RMS tests have shown more
sensitivity to channel routing parameters than hillslope parameters; the
tests also suggest that using spatially consistent hillslope roughness
has been satisfactory. Therefore, a constant value of hillslope
roughness (0.15) has been assigned for all model cells in the current
HL-RMS implementation.

For drainage density, Dingman (1993) notes that values ranging from 2
km^-1^ to 100 km^-1^ have been reported in the literature, and that
drainage density varies depending on climate and geology. For areas we
are modeling in the dry Southern Great Plains region of the United
States, a spatially constant value of drainage density (2.5 km^-1^) has
been assumed.

Current work in HL involves the addition of a Muskingum-Cunge routing
algorithm for HL-RMS application in flatter-sloped basins. Future ideas
include the linking of HL-RMS with a full dynamic wave channel routing
algorithm.

![](media/image16.png){width="6.393055555555556in"
height="3.6819444444444445in"}

[]{#__RefHeading___Toc68409900 .anchor}Figure 10. A priori estimates of
the SAC-SMA model upper zone tension water depth in mm for the CONUS
domain. Cell resolution is 1km^2^

#### Large Area applications of HL-RMS

We believe that a valid development component for distributed modeling
for river and flash flood forecasting and water resources modeling is to
execute a large area or CONUS implementation of HL-RMS. This strategy is
meant to mimic the development path of numerical weather prediction
models. Early in their history, these models were established to run on
a CONUS domain. Such an implementation attracted numerous researchers
who contributed new components or algorithmic improvements. In this
large-area application, real time precipitation and temperature data
feeds into HL would be established so that the HL-RMS could be executed
each day at an hourly time step. To date, a large area application has
been launched that covers the Arkansas River basin in the domain of the
Arkansas-Red Basin River Forecast Center (ABRFC) as shown in Figure 11.
In this application, HL-RMS was executed over 25,000 computational
elements (4km^2^). Figure 12 shows more of the details of the
implementation: (a) spatial variability of one of the SAC-SMA
parameters, (b) input precipitation, (c) one of the SAC-SMA states, (d)
HL-RMS computed flow. A test of this application revealed that a 10-day
simulation executing at an hourly time step took just under 5 minutes on
an HP workstation. Thus, recent exponential advances in computer
processing speeds and storage capabilities have largely removed the
traditional computational barriers to operational distributed modeling.
Figure 13 shows the good agreement between the observed and simulated
flows for the Arkansas River.

![](media/image17.wmf){width="2.8633858267716534in"
height="2.12992125984252in"}![](media/image18.wmf){width="3.267322834645669in"
height="1.550787401574803in"}![](media/image19.wmf){width="3.248425196850394in"
height="1.55in"}

[]{#__RefHeading___Toc68409901 .anchor}Figure 11 Large-area
implementation of HL-RMS over the Arkansas River basin.

![](media/image20.png){width="6.479166666666667in"
height="4.083333333333333in"}

[]{#__RefHeading___Toc68409902 .anchor}Figure 12. Observed and derived
fields from the large-area application of HL-RMS.

\(a\) Value of the SAC-SMA parameter for the upper zone free water
reservoir; (b) NEXRAD precipitation for an event in February, 1997. (c)
Amount of computed water in SAC-SMA upper zone free water reservoir; (d)
Compute channel flow. (Source: Reed et al., 2002)

![](media/image21.wmf){width="6.789583333333334in"
height="5.105555555555555in"}

[]{#__RefHeading___Toc68409903 .anchor}Figure 13. Large-Area Application
of HL-RMS: Initial discharge simulation of the entire Arkansas River
Basin

#### Prototype Application of HL-RMS

Development and testing of HL-RMS has progressed to the point that
operational testing at several RFCs would provide valuable insight into
its performance. Four RFCs volunteered to implement prototype versions
of HL-RMS: ABRFC, WGRFC, CBRFC, and MARFC shown in Figure 14. (Note:
here, we use the term 'prototype' to signify that it has not been
officially released as part of NWSRFS software builds). In ABRFC, WGRFC,
and CBRFC, HL-RMS is being run on individual NWS forecast basins. In
MARFC, HL-RMS is being implemented over the entire Juniata River basin
as shown in Figure 15. In several ways, the MARFC prototype application
is spearheading HL-RMS development. First, a continuous antecedent
precipitation index (API) model has been incorporated into HL-RMS for
use in this area to match the model used operationally by the RFC.
Research and development for this effort has led to a strategy to define
the API model parameters from GIS-based soils information (Moreda et
al., 2003). The inclusion of another model into HL-RMS (in addition to
the SAC-SMA) has proven the robustness and modularity of the HL-RMS
design. Second, the operational NWS snow model developed by Eric
Anderson (1968) has also been incorporated into HL-RMS for this
application. Initial tests of this version of HL-RMS have shown that
this version of HL-RMS is computationally correct and robust Much work
remains, however, in the parameterization and calibration of distributed
API and snow models.

![](media/image2.wmf)

[]{#__RefHeading___Toc68409904 .anchor}Figure 14. Location Map of Sites
for HL-RMS Prototype Implementation. Colored areas are the domain
extents of the NWS River Forecast Centers.

![](media/image3.wmf){width="6.413888888888889in"
height="4.834722222222222in"}

[]{#__RefHeading___Toc68409905 .anchor}Figure 15. Location of the
Susquehanna River and its sub-basins for prototype implementation of
HL-RMS.

### The Distributed Model Intercomparison Project (DMIP)

#### Background for DMIP

Concurrent with the in-house development of HL-RMS, HL also initiated
DMIP (Smith et al., 2004a) (see also
http://www.nws.noaa.gov/oh/hrl/dmip/index.html). This effort arose out
of the convergence of several factors. First, NOAA NWS realized the need
to infuse new science into its river forecasting capability. Second, the
continued proliferation of geographic information system (GIS) data sets
and exponential increases in computer capabilities have largely removed
historical barriers from the path for development of complex distributed
models. Finally, but certainly not the least important, large questions
remain in the scientific literature regarding the effect of the
variability of precipitation and basin properties on runoff response.
Related to these questions is the choice of model or approach to best
exploit variability information to generate improved outlet simulations
and potentially useful information at ungaged interior points.

Given the scale of the NWS mission and the recommendations from external
reviewers, it was clear that an accelerated program was needed to move
the NWS research in the proper direction for operational distributed
modeling. While numerous distributed models exist and indeed some are
moving into the operational forecasting environment (e.g., Koren and
Barrett, 1994; Turcotte et al., 2003) it is not clear from the
literature which distributed model or modeling approach is best suited
to improve the NWS forecasting capabilities. With guidance from several
outside organizations, the NWS formulated DMIP as a method to capitalize
on the formidable distributed modeling research being conducted at
academic institutions and other organizations around the world. DMIP can
also be considered as an extension to the model comparison research
being performed in a joint collaborative research effort between MIT and
HL.

With the advent of 4km spatial resolution Next Generation Radar (NEXRAD)
rainfall estimates in many parts of the US, the NWS and the research
community at large have access to gridded rainfall estimates at
unprecedented spatial and temporal resolution. Other parts of the world
have similar quality radar data available (e.g., Moore and Hall, 2000).
Also, the proliferation of GIS data sets and ever-increasing
capabilities of computer systems have continued to push distributed
modeling to the forefront of hydrologic research and application. In
light of these developments, the major question facing the NWS and
perhaps other operational organizations is: what is the best way to
exploit the information in high resolution radar rainfall estimates and
GIS data sets to improve river and flash flood forecasting? Or, in the
words of Beven (1985), under what conditions and for what type of
forecasting is it profitable to implement a distributed model?

A review of the scientific literature did not provide clear guidance for
the NOAA NWS. A comprehensive comparison of lumped and distributed
modeling techniques has not been published. It is encouraging that in
the development and testing of their distributed models, several authors
have included a comparison of their results to those using lumped inputs
or from simpler lumped approaches (Bell and Moore, 2001; Boyle et al.,
2001; Smith et al., 1999; Michaud and Sorooshian, 1994b; Obled et al.,
1994; Pessoa et al., 1993; Naden, 1992; Loague and Freeze, 1985). In
addition, Carpenter et al., (2001) used Monte-Carlo analysis to evaluate
distributed versus lumped model gains in light of parametric and radar
rainfall data uncertainty.

However, HL felt that a more organized and controlled comparative effort
is required to guide NWS distributed modeling research and development.
The emergence of high resolution data sets, GIS capabilities, and
rapidly increasing computer power has maintained distributed modeling as
an active area of research. While the utility of distributed models to
predict interior hydrologic processes is well known, few studies have
specifically addressed the improvement of distributed models over lumped
models for predicting *basin outflow* hydrographs of the type useful for
flood forecasting. As a consequence, the hypothesis that distributed
modeling using higher resolution data will lead to more accurate outlet
hydrograph simulations remains largely untested.

#### DMIP Schedule

HL guided DMIP according to the schedule in Table 2. An initial science
plan was developed in HL then sent to many institutions for comment.
Data were collected from contributors and HL archives and placed on an
ftp site. DMIP was officially launched with a town-hall meeting at the
Spring Meeting of the AGU in Washington, DC on May 31, 2000. While
follow-on research continues, this first phase of DMIP culminated in the
publication of a special issue of the Journal of Hydrology to be
released in the spring of 2004.

[]{#__RefHeading___Toc68408869 .anchor}Table 2 Schedule for Major DMIP
Activities

+----------------+-----------------------------------------------------+
| Date           | Task                                                |
+----------------+-----------------------------------------------------+
| January, 2000  | Basic DMIP plan approved by NWS/HL                  |
+----------------+-----------------------------------------------------+
| May 31, 2000   | General Announcement of DMIP at Town Hall Meeting,  |
|                | AGU spring Meeting, Washington, DC                  |
+----------------+-----------------------------------------------------+
| June 1, 2000   | DMIP plan completed                                 |
+----------------+-----------------------------------------------------+
| December 2000  | General Announcement to participate in DMIP         |
|                |                                                     |
|                | DMIP web site officially opened.                    |
+----------------+-----------------------------------------------------+
| January 1,     | 1\. All data in place for Illinois River Basins,    |
| 2001           | Elk River Basin and Blue River Basin                |
|                |                                                     |
|                | 2\. Metadata and utilities in place                 |
+----------------+-----------------------------------------------------+
| March 31, 2002 | Participants send results to HL for analysis        |
+----------------+-----------------------------------------------------+
| August 22-23,  | DMIP summary workshop at NWS/HL with all            |
| 2002           | participants                                        |
+----------------+-----------------------------------------------------+
| September 30,  | Participants verify that analyzed simulations are   |
| 2002           | correct                                             |
+----------------+-----------------------------------------------------+
| January 31,    | Deadline for any follow-up submissions.             |
| 2003           |                                                     |
+----------------+-----------------------------------------------------+
| Spring, 2004   | Publication of J. Hydrology DMIP Special Issue      |
+----------------+-----------------------------------------------------+

#### DMIP Study Area

The DMIP study basins in Figure 16 were selected for several reasons.
First, these basins had the data required to conduct the
intercomparison, beginning with the longest and highest quality archive
of NEXRAD radar-based precipitation estimates in the U.S. The NWS began
measuring precipitation with NEXRAD radars in this region in 1993,
providing the DMIP project with nearly 8 years of continuous hourly
gridded precipitation estimates. The NEXRAD radars in this area provide
good coverage of the study basins as shown in Figure 2. Also, several
pertinent studies of the quality of the NEXRAD precipitation estimates
in this region have been performed. (e.g., Young et al., 2000; Wang et
al., 2000; Smith et al., 1999; Johnson et al., 1999; Finnerty and
Johnson, 1997; and Smith et al.,1996). Concurrent time series of hourly
discharge data were also available for the basin outlets and selected
interior points.

Another critical criterion for selecting basins in this region is the
lack of complications such as significant snow accumulation, orographic
influences, and modification of the streamflow due to reservoirs.
Moreover, the selected parent basins contain several internal points
having observed streamflow data, allowing the DMIP program to develop
study questions regarding the prediction of interior hydrologic
processes.

The Illinois River flowing through Arkansas and Oklahoma presented a
good opportunity for participants to test their models on nested basins
as seen in Figure 16. The Eldon basin has an interior gage on Peacheater
Creek at Christie, OK. Next to the Eldon basin is the Watts basin, which
contains the catchment draining to the USGS gage at Savoy, AR. Both the
Watts and Kansas basins are nested within the largest basin, the
Illinois River above Tahlequah, OK. Thus, the Tahlequah basin contains
three interior gage locations.

#### DMIP Participants

The following institutions and lead investigators participated in DMIP:

1\. Massachusetts Institute of Technology, Dr. Rafael Bras

2\. Hydrologic Research Center, Dr. Konstantine Georgakakos

3\. DHI Water and Environment, Dr. Michael Butts

4\. University of Arizona, Dr. Hoshin Gupta

5\. NCEP/EMC, Dr. Kenneth Mitchel, Dr. Dag Lohman, Dr. Christa
Peters-Lidard

6\. University of Oklahoma, Dr. Baxter Vieux

7\. University of Waterloo, Ontario, Dr. Allyson Bingeman

8\. University of Utah, Dr. David Tarboton and National Institute of
Water Research, (NIWR), New Zealand, Dr. Ross Woods.

9\. NWS Hydrology Lab, Dr. Mike Smith

10\. USDA ARS, Dr. Jeff Arnold and TAES Blackland Research Center, Dr.
Mauro Di Luzio

11\. University of California at Berkeley, Dr. Xu Liang

12\. The Hydraulic and Electrical College of WuHan University, China,
Dr. Li Lan

All participants followed the DMIP modeling instructions and submitted
simulations to HL for analysis. In August, 2002, HL hosted a three-day
meeting of all participants and presented extensive analyses of the
results.

![](media/image4.wmf){width="6.624305555555556in"
height="4.988888888888889in"}

[]{#__RefHeading___Toc68409906 .anchor}Figure 16 Study basins used in
DMIP (source: Smith et al., 2004a)

#### Major Findings from DMIP

HL scientists concluded from the DMIP results that both the HL-RMS and
our proposed research path were scientifically sound and that no major
changes need to be made at the present time. However, HL is committed to
evaluating new rainfall-runoff and routing models in order to improve
its services to the Nation. The following major DMIP conclusions are
taken from Reed et al. (2004):

1.  Although the lumped model outperformed distributed models in more
    cases than distributed models outperformed the lumped model, some
    calibrated distributed models can perform at a level comparable to
    or better than a calibrated lumped model (the current operational
    standard). The wide range of accuracies among model results suggest
    that factors such as model formulation, parameterization, and the
    skill of the modeler can have a bigger impact on simulation accuracy
    than simply whether or not the model is lumped or distributed.

2.  Clear gains in distributed model performance can be achieved through
    some type of model calibration. On average, calibrated models
    outperformed uncalibrated models during both the calibration and
    validation periods.

3.  Gains in predicting peak flows for calibrated models were most
    noticeable in the Blue and Christie basins as shown in Figure 17.
    The Blue basin has shape, orientation, and soil characteristics that
    are distinguishable from other basins in the study. The Blue results
    are consistent with those of previous studies and indicate that the
    gains from applying a distributed simulation model at NWS forecast
    basin scales (on the order of 1000 km2) will depend on the basin
    characteristics. Christie is distinguishable in this study because
    of its small size.

4.  The Christie basin had distinguishable results from the larger
    basins studied, not just in overall statistics, but in relative
    inter-model performance compared with larger basins. One explanation
    offered for the improved calibrated, peak flow results is that the
    lumped "calibrated" model parameters (from the parent basin
    calibration, Eldon) are scale dependent and distributed model
    parameters that account for spatial variability within Eldon are
    less scale dependent. Some caution is advised in interpreting the
    results for Christie for model submissions with a relatively coarse
    cell resolution compared to the size of the basin. Since no other
    basins in DMIP are comparable in size to Christie, more studies on
    small, nested basins are needed to confirm and better understand
    these results.

5.  Among calibrated results, models that combine techniques of
    conceptual rainfall-runoff and physically-based distributed routing
    consistently showed the best performance in all but the smallest
    basin. Gains from calibration indicate that determining reasonable a
    priori parameters directly from physical characteristics of a
    watershed is generally a more difficult problem than defining
    reasonable parameters for a conceptual lumped model through
    calibration.

![](media/image5.wmf){width="6.972222222222222in"
height="5.072222222222222in"}

[]{#__RefHeading___Toc68409907 .anchor}Figure 17. Improvement of
Distributed Model over Lumped SAC-SMA for different DMIP basins and
models. Results are for calibrated lumped and distributed models. The
basins are segregated into parent basins and interior computational
points. Positive values of improvement indicate the distributed model
performed better than the NWS calibrated lumped SAC-SMA model

6.  Simulations for smaller interior basins where no explicit
    calibration was done exhibited reasonable performance in many cases,
    although not as good statistically as results for larger, parent
    basins. The relatively degraded performance in smaller basins
    occurred both in cases when parent basins were calibrated and when
    they were uncalibrated, so the degraded performance was not simply a
    function of the fact that no explicit calibration at interior points
    was allowed.

7.  Distributed models designed for research can be applied successfully
    using operational quality data. Several models responded similarly
    to long term biases in archived multi-sensor precipitation grids.
    Ease of implementation could not be measured directly. However, an
    indirect indicator of operational practicability is that several
    participants were able to submit a full set or nearly a full set of
    simulations in a relatively short time.

In addition, DMIP follow-on research in HL investigated the
identification of basins that will benefit from distributed models for
basin-outlet simulations. Smith et al. (2004b) and Zhang et al. (2004b)
explored time series analysis techniques to derive diagnostic indicators
from concurrent time series of observed basin precipitation and
discharge. They concluded that one of the basins in the DMIP suite had
unique characteristics regarding the spatial variability of
precipitation and the ability of a basin to filter or dampen the input
precipitation signal. Figure 18 shows the relationship between the
center of mass of storm precipitation (location index on abscissa) and a
measure of the basin's filtering effect (dampening ratio on ordinate).
It is clear from Figure 18 that the Blue River is unique amongst the
study basins by having the greatest spatial variability of precipitation
while exerting the least filtering on the input rainfall signal.

Finally, Michael Smith of HL was invited by the Chief Editor Roman
Krzysztofowicz of the Journal of Hydrology to be Guest Editor of a
special issue of the journal dedicated to DMIP. This special issue will
appear in the spring of 2004. Dr. Konstantine Georgakakos of HRC and Dr.
Xu Liang of the University of California Berkeley will be co-Guest
Editors. This issue will feature 14 papers from the DMIP participants.

![](media/image6.wmf){width="6.34375in" height="4.144444444444445in"}

[]{#__RefHeading___Toc68409908 .anchor}Figure 18. Relationship between
storm rainfall center-of-mass (rainfall location index) and a basins
ability to dampen the input rainfall signal (dampening ratio) for three
DMIP basins. (Source: Smith et al. 2004b)

#### Future DMIP Phases

HL is now in the preliminary planning stages for a second phase of DMIP.
Initial reaction from the hydrologic modeling community for another DMIP
phase has been very positive. Current activities include developing the
science questions to be addressed. We anticipate that DMIP II will
examine complexities such as snow and orographics. At present, there is
some discussion on organizing DMIP II as part of the Prediction in
Ungaged Basins (PUB) initiative.

## Tools and Procedures from Phase 2 R&D

As with Phase 1 R&D, Phase II has provided a steady stream of tools and
other practical items delivered to the RFCs. Table 2 shows that items
ranging from DEM processing tools to the full prototype version of the
HL-RMS distributed model have been delivered throughout the progression
of this phase.

[]{#__RefHeading___Toc68408870 .anchor}Table 3. Tools developed and
delivered as a result of Phase 2 research and development

  ------------------ ----------------------------- -----------------------
  Name               Description                   Status

  Prototype version  HL-RMS distributed model that Used by 3 RFCs on
  of HL-RMS          can be used for operational   selected basins
                     forecasting                   

  SAC-SMA parameters National data sets in         Delivered to RFCs as
                     Arc/View format of all 11     part of Calibration
                     SAC-SMA parameters.           Assistance Tool

  Statistics program Stand-alone program to        Linux version
                     compute statistics from time  available. Ready for
                     series for model calibration  RFC use

  Advanced tools for Excel and MATLAB programs     Excel programs
  channel routing    developed.                    delivered to RFCs
  parameterization                                 involved in prototype
                                                   testing.

  DEM processing     Advanced flow direction       Used in HL to process
  tools              strategy (Reed, 2003) for 4km DEMs for RFC useage.
                     cells                         

  Distributed Model  Strategy to transition from   Delivered to RFCs
  Calibration        lumped 6hr model to hourly    involved in prototype
  Strategy           dist. model                   testing.

  DMIP results       Evaluation of 12 distributed  Draft copies of two
                     models to guide NWS R&D       Journal of Hydrology
                                                   papers sent to RFCs

  UHG development    New procedure uses            Prototype delivered to
  tool               Variational assimilation      several RFCs for
                     (VAR)                         evaluation.
  ------------------ ----------------------------- -----------------------

## Collaborative Research Associated with Phase 2 R&D

As with Phase 1, a significant emphasis is placed on extramural research
and collaboration. In this phase, we extend work with our previous
partners MIT, U. of Arizona, and HRC. We continue to value the work of
MIT as we consider their model to represents the state-of-the-art in
complex so-called 'physically-based' distributed models. Their model
provides valuable insight into the computation of the major types of
runoff including infiltration-excess and saturation excess. MIT's work
continues to help the NWS address the question of how much model
complexity is warranted to achieve a certain prediction accuracy. HRC
continues to examine the effects of parameter and NEXRAD rainfall
uncertainty on gains realized from distributed versus lumped models.
Recently they have been characterizing the uncertainty in the NEXRAD
precipitation at the bin scale rather than at the scale of the sub-basin
(computational element) scale. An intriguing result of this work is the
identification of a relationship between basin area and simulation
uncertainty (Carpenter and Georgakakos, 2004, 2003). HRC is also
examining distributed modeling from an end user standpoint such as for
water resource management. The University of Arizona continues to
perform research into optimization schemes for lumped and distributed
models and has recently requested the HL-RMS distributed model code for
their research.

This year, HL intends to add a new collaborative partner for distributed
modeling. The University of Minnesota proposes research into a
probabilistic method for generating channel routing parameters for the
kinematic wave routing component in HL-RMS.

# Distributed Modeling for Flash Flood Forecasting

We propose that distributed modeling forms the most scientifically valid
procedure for flash flood forecasting over a wide domain. Figures 19 and
20 illustrate our proposed strategy. First, we envision that HL-RMS
would be implemented at a typical RFC forecast basin to generate
standard basin-outlet forecasts. For example, Figure 19 shows that
HL-RMS applied to the Blue River (drainage area 476 sq. mi.) in Oklahoma
would require approximately 80 computational elements.

![](media/image7.wmf){width="6.559027777777778in"
height="4.936111111111111in"}

[]{#__RefHeading___Toc68409909 .anchor}Figure 19. Application of the
Distributed model HL-RMS to the Blue River, OK. Approximately 80
computational elements are used in this application.

Given that the basin is modeled as in Figure 19, it is possible to
predict the hydrologic activity at any of the 80 computational cells.
Thus, if rainfall is sufficiently concentrated at any point in the basin
to cause alarm, the forecaster would be able to generate a forecast for
that specific area. Figure 20 illustrates this idea. From Figure 19, we
see that in an actual even in April, 1999, the rainfall was concentrated
near the basin outlet. Such a concentration would alert a forecaster
that further investigation is warranted. The forecaster could generate
forecasts at points A and B in Figure 20 and determine that no
significant river rise will occur. However, the response at Point C, the
basin outlet, will be quite extreme. Figure 20 also clearly illustrates
how a distributed model provides improved simulations compared to a
lumped model in cases of non-spatially-uniform precipitation. The
distributed model simulation agrees well with the observed discharge,
while the lumped model has a dampened response. We anticipate using the
work of Carpenter and Georgakakos (2004, 2003) to incorporate
uncertainty estimates for interior simulations given parametric and
NEXRAD precipitation uncertainty. They identified a relationship between
basin scale and simulation uncertainty. Thus, given uncertainty defined
ab basin outlets, and estimate of uncertainty could be derived for basin
interior flash flood forecasts.

![](media/image8.wmf){width="6.324305555555555in"
height="4.748611111111111in"}

[]{#__RefHeading___Toc68409910 .anchor}Figure 20. Hydrograph Generation
at various points in the Blue River basin illustrating the use of a
distributed model for flash flood forecasting at interior locations.

As an intermediate step to this strategy that can potentially use
distributed model results without requiring calibration, Reed et al.
(2004a) is proposing a statistical-distributed modeling approach.
Perhaps the biggest potential gain from the use of distributed
hydrologic models in flood forecasting is to increase the spatial
resolution of forecasts. Higher resolution hydrologic forecasts can
provide information in flash flood situations; however, an important
question to consider in evaluating higher resolution forecasts is
whether or not larger simulation uncertainties at smaller scales will
diminish the utility of these forecasts, and, if so, to what degree? The
statistical-distributed modeling approach is proposed to simulate floods
on small basins and account for hydrologic modeling uncertainty. This
modeling approach should enhance our ability to predict the occurrence
of small stream and flash flooding. The approach offers an alternative
to the current (NWS) Flash Flood Guidance (FFG) system and inherently
addresses FFG limitations. Two goals of this study are to:

1.  Define, develop, and evaluate a statistical- distributed framework
    for predicting relative flood severity at flash flood scales
    (typically ungauged locations).

2.  In doing so, characterize hydrologic simulation uncertainties across
    a wide range of scales using operational quality radar-based
    precipitation data.

# Distributed Modeling Software Engineering and Implementation

As mentioned before, a significant software engineering project is
underway to develop a methodology to implement HL-RMS as an officially
released and supported NWSRFC component. This is a major effort in that
NWSRFS is only designed to pass time series of basin mean areal values
such as precipitation, temperature, and model parameters. While NEXRAD
gridded precipitation inputs can be passed into NWSRFS, these are used
to create mean areal time series of precipitation which are then used as
model forcing. There is no current option to generate gridded output
fields in the NWSRFS.

The Distributed Modeling Systems (DMS) project was conceived to study
the options for operational implementation of the HL-RMS distributed
model science. A team has been formed consisting of HL scientists,
software engineers, and outside software engineering contractors. The
DMS project will rely heavily on the experience gained with the
prototype implementations of HL-RMS at the WGRFC and ABRFC discussed in
section 6.4.1.5

The interested reader is referred to the following web site for more
information:

<http://www.nws.noaa.gov/oh/hrl/currentprojects/dist_model/index.htm>

# References

Anderson, E.A., 1968. Development and testing of a snow pack energy
balance equations. Water Resources Research. Vol. 4, No. 1, 19-37.

Anderson, R., Koren, V., Smith, M., and Seo, D.-J., 2003. On the
importance of linear vs. nonlinear response in estimating base flow
features for ungauged catchments in Southern Texas. Proceedings of the
Fall Meeting of the AGU, December.

Bell, V.A., and Moore, R.J., 1998. A grid-based distributed flood
forecasting model for use with weather radar data: Part 2. Case studies.
Hydrology and Earth System Sciences, Vol. 2, No. 3, 283-298.

Bell, V.A., Carrington, D.S., and Moore, R.J., 2001. Comparison of
rainfall-runoff models for flood forecasting. Part 2: Calibration and
evaluation of models. Environment Agency R&D Technical Report W242,
Research Contractor: Institute of Hydrology, September 2001, Environment
Agency, 239pp.

Beven, K.J., 1985. Chapter 13, Distributed Models, in 'Hydrological
Forecasting', M.G. Anderson, and T.P. Burt, editors, Wiley-Interscience
Publication.

Boyle, D.B., Gupta, H.V., Sorooshian, S., Koren, V., Zhang, Z., and
Smith, M., 2001. Toward improved streamflow forecasts: Value of
semi-distributed modeling. Water Resources Research, 37(11), 2749-2759.

Number range CHAPTERBoyle, D. P., Gupta, H. V., and Sorooshian, S.,
2000. Toward improved calibration of hydrologic models: Combining the
strengths of manual and automatic methods. Water Resources Research,
Vol. 36, No. 12, 3663-3674.

Number range CHAPTERBradley A. A., and Kruger, A., 1998. Recalibration
of hydrologic models for use with WSR-88D precipitation estimates. Proc.
Special Symposium on Hydrology, American Meteorological Society,
Phoenix, Arizona, pp. 302-305.

Burnash, R.J.C., Ferral, R.L., and McGuire, R.A., 1973. A generalized
streamflow simulation system -- Conceptual modeling for digital
computers', Technical Report, Joint Federal and State River Forecast
Center, U.S. National Weather Service and California Department of Water
Resources, Sacramento, California, 204pp.

Carpenter, T.M., and Georgakakos, K.P., 2004. Impacts of parametric and
radar rainfall uncertainty on the ensemble streamflow simulations of a
distributed hydrologic model. DMIP Special Issue of the Journal of
Hydrology, accepted.

Carpenter, T.M., and Georgakakos, K.P., 2003. Assessing the Effects of
Scale in Operational Hydrologic Modeling. HRC Limited Distribution
Report No. 15, July 31, 177 pp.

Carpenter, T.M., Georgakakos, K.P., Spersflage, J.A., 2001. On the
parametric and NEXRAD-radar sensitivities of a distributed hydrologic
model suitable for operational use. Journal of Hydrology, 253, 169-193.

Carpenter, T.M., Georgakakos, K.P., Spersflage, J.A., 1999. Distributed
Hydrologic Modeling for operational use. HRC Technical Report No. 3,
August. 224 pp.

Carter, G.C., 2002. Infusing new science into the National Weather
Service River Forecast System. Second Federal Interagency Hydrologic
Modeling Conference, Las Vegas, Nevada, July 28-August, 10 pages.
http://hsp.nws.noaa.gov/oh/hrl/presentations/fihm02/pdfs/carterpwp.pdf

Number range CHAPTERDingman, S. L., 1993. Physical Hydrology, Prentice
Hall, Englewood Cliffs, NJ.

Duan, Q., Gupta, V.K., and Sorooshian, S., 1993. A shuffled complex
evolution approach for effective and efficient global optimization.
Journal of Optimization Theory and its Applications, Vol. 76, No. 3,
501-521.

Duan, Q., Schaake, J., and Koren, V., 2001. A priori estimation of land
surface model parameters, in Laskhmi, V. et al., (Eds.), Land Surface
Hydrology, Meteorology, and Climate: Observations and Modeling, Water
Science and Applications 3, AGU, Washington, D.C., 77-94.

Fread, D.L., Shedd, R.C., Smith, G.F., Farnsworth, R., Hoffeditz, C.N.,
Wenzel, L.A., Wiele, S.M., Smith, J.A., and Day, G.N., 1995.
Modernization in the National Weather Service River and Flood Program.
Weather and Forecasting, 10(3), 477-484.

Number range CHAPTERGeorgakakos, K. P., 2002. Hydrometeorological Models
for Real Time Rainfall and Flow Forecasting. In: Singh, V. P. and
Frevert, D. K., (Eds.) Mathematical Models of Small Watershed Hydrology
and Applications, Water Resources Publications, LLC, 593-655.

Glaudemans, M.J., Erb, R.A., Wells, E.B., Zimmerman, J., Hill-Maxwell,
J., Mack, K., 2002. Overview and Status of the Hydrologic Forecast
System in the National Weather Service Weather Forecast Offices.
Interactive Symposium on the Advanced Weather Interactive Procesing
System, (AWIPS), Orlando, Floriday, 13-17 January, pp J173-178.

Number range CHAPTERHogue, T. S., Sorooshian, S., Gupta, H., Holz, A.,
and Braatz, D., 2000. A multistep automatic calibration scheme for river
forecasting models. J. of Hydrometeorology, Vol. 1, 524-542.

Khodatalab, N., Gupta, H. Wagener, T., and Sorooshian, S., 2004.
Calibration of a semi-distributed hydrologic model for streamflow
estimation along a river system. Journal of Hydrology, DMIP Special
Edition, submitted.

Koren, V., Reed, S., Smith, M., Zhang, Z., Seo, Dong-Jun, 2004.
Hydrology Laboratory Research Modeling System (HL-RMS) of the National
Weather Service. Journal of Hydrology, in press.

Koren, V., Reed, S., Smith, M., and Zhang, Z., 2003. Combining
physically-based and conceptual approaches in the development and
parameterization of a distributed system. Information from Weather Radar
and Distributed Hydrological Modeling, Proceedings of international
symposium, IAHS Publication no. 282, Editors Tachikawa, Y., Vieux, B.,
and Georgakakos, K.P., 101-108.

Koren, V., Smith, M., Reed, S., and Zhang, Z., 2001. Transition from
lumped to distributed modeling systems. Session H32C, AGU Spring
Meeting, May 29-June 2, Boston, Mass.

Koren, V.I., Smith, M., Wang, D., Zhang, Z. 2000. Use of Soil Property
Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters,
15^th^ Conference on Hydrology, 80th Annual Meeting of the AMS, Long
Beach, Ca. January, 103-106.

Koren, V., and Barrett, C.B., 1994. Satellite based distributed
monitoring, forecasting, and simulation (MFS) system for the Nile River.
Kite, G.W., Pietroniro, A., and Pultz, T.J. (Eds) Applications of Remote
Sensing in Hydrology - Proceedings of the Second International Workshop,
NHRI Symposium No. 14, 18-20 October, Saskatoon, Saskatchewan, Canada,
187-200.

Kuzmin, V., Koren, V., and Seo, D.-J., 2004. Calibration of the
Sacramento model: Step-wise line search, in preparation.

Larson, L.W., Ferral, R.L., Strem, E.T., Morin, A.J., Armstrong, B.,
Carrol, T.R., Hudlow, M.D., Wenzel, L.A., Schaefer, G.L., and Johnson,
D.E., 1995. Operational responsibilities of the National Weather Service
river and flood program. Weather and Forecasting, 10(3), 465-476.

Lindsey, S.D., 1994, 'Distributed Modeling Project Plan', Internal
Publication, Hydrology Lab, NWS, February.

Lindsey, S.D., 1993, 'Strategy for Utilizing Radar-Based Precipitation
Estimates for River Forecasting', Proceedings of the Symposium on
Engineering Hydrology, ASCE, C.Y. Kuo, Editor, 940-945.

Loague, K.M., and Freeze, R.A., 1985. A comparison of rainfall-runoff
modeling techniques on small upland catchments. , Water Resources
Research, 21(2), 229-248.

Michaud, J., and Sorooshian, S., 1994b. Comparison of simple versus
complex distributed runoff models on a midsized semiarid watershed,
Water Resources Research, 30(3), 593-605.

Number range CHAPTERMiller, D. A. and White, R. A., 1999. A Conterminous
United States multi-layer soil characteristics data set for regional
climate and hydrology modeling. Earth Interactions, 2, (available at
[[http://EarthInteractions.org).]{.underline}]()

Moore, R.J., and Hall, M.J. eds., 2000. HYREX: the Hydrology Radar
Experiment , Special Issue of Hydrology and Earth System Sciences,
European Geophysical Society, 4(4).

Number range CHAPTERMoreda, F.G., Zhang, Z., Koren, V., Reed, S., and
Smith, M., 2003. On the parameterization of distributed models. Abstract
HW08/08A/C28-005, XXIII General Assembly of the International Union of
Geodesy and Geophysics-IUGG 2003 Sapporo, Japan, June 30-July 11. Full
paper to be submitted to a special issue of the Journal of Hydrology
dedicated to MOPEX.

Naden, P. S., 1992. Spatial Variability in flood estimation for large
catchments: the exploitation of channel network structure. Hydrological
Sciences Journal, 37(1),53-71.

National Academy of Sciences, 2001. Grand Challenges in Environmental
Sciences, Committee on Grand Challenges in Environmental Sciences,
Oversight Commission for the Committee on Grand Challenges in
Environmental Sciences, National Research Council, National Academy
Press.

National Research Council, 1996. Toward a New National Weather Service:
Assessment of Hydrologic and Hydrometeorologic Operations and Services.
National Weather Service Modernization Committee, Commission on
Engineering and Technical Systems, National Academy Press.

National Weather Service, 1999: Vision 2005, National Weather Service
Strategic Plan for Weather, Water, and Climate Services 2000-2005. US
Department of Commerce, NOAA, Silver Spring, Maryland, 24 pp.

Obled, C. H., Wendling, J., and Beven, K., 1994. The sensitivity of
hydrological models to spatial rainfall patterns: an evaluation using
observed data. Journal of Hydrology, 159, 305-333.

Pessoa, M. L., Bras, R.L., and Williams, E.R., 1993. Use of weather
radar for flood forecasting in the Sieve river basin: a sensitivity
analysis. Journal of Applied Meteorology, 32 (3), 462-475.

Reed, S., J. Schaake, V. Koren, D.J. Seo, M. Smith, 2004a. A
Statistical-distributed Modeling Approach for Flash Flood Prediction,
Paper Presented at the 84^th^ Annual Meeting of the AMS, Seattle, WA,
Jan. 11-15.

Reed, S.M., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.-J, and
DMIP participants, 2004b. Overall Distributed Model Intercomparison
Project Resutls, Journal of Hydrology DMIP Special Issue, accepted.

Reed, S., 2003. Deriving flow directions for coarse resolution (1 - 4
km) gridded hydrologic modeling. Water Resources Research, Vol. 39, No.
9.

Number range CHAPTERReed, S., Koren, V., Zhang, Z., Smith, M., Seo,
D.-J., 2002. Distributed modeling for improved NWS river forecasts.
Proc. the Second Federal Interagency Hydrologic Modeling Conference, Las
Vegas, NV.

Number range CHAPTERRinaldo, A., Vogel, G. K., Rigon, R., and
Rodriguez-Iturbe, I., 1995. Water Resource Research, Vol. 31, No. 4,
1119-1127.

Robinson, J.S., and Sivapalan, M., 1995. Catchment-scale runoff
generation model by aggregation and similarity analysis. In: Scale
Issues in Hydrological Modeling, J.D. Kalma and Sivapalan, M., Eds.
311-330. Wiley, New York, USA.

Number range CHAPTERSingh, V. P., 1996. Kinematic Wave Modeling in Water
Resources, Surface-Water Hydrology, Wiley.

Number range CHAPTERSkahill, B. S., Johnson, L. E., 1999. Evaluation of
Finite Difference Methods for a Distributed Rainfall-Runoff Model. NOAA
Technical Memorandum ERL FSL-23, Forecast System Lab., Boulder,
Colorado.

Smith, M., 1996. Distributed Modeling Project Plan, Internal NWS/OHD/HL
document.13pp.

Smith, A. A., 1980. A generalized approach to kinematic flood routing.
J. of Hydrology, 45, 71-89.

Smith, M.B., Seo, D. -J., Koren, V. I., Reed, S., Zhang, Z., Duan,
Q.-Y., Moreda, F., and Cong, S., 2004a. The distributed model
intercomparison project (DMIP): Motivation and experiment design.
Accepted for publication in Journal of Hydrology DMIP Special Issue.

Smith, M.B., Koren, V.I., Zhang, Z., Reed, S.M., Pan, J. --J., and
Moreda, F., 2004b. Runoff response to spatial variability in
precipitation: An analysis of observed data. Accepted for publication in
Journal of Hydrology DMIP Special Issue.

Smith, M. B., Laurine, D. P., Koren, V.I., Reed, S.M., and Zhang, Z.,
2003. Hydrologic model calibration in the National Weather Service. In:
Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N., Turcotte, R.,
(Editors.) Calibration of Watershed Models, Water Science and
Application 6, American Geophysical Union, Wash. D.C., 133-152.

Smith, M., Koren, V.I, Zhang, Z., Wang, D., and Reed, S., 2000.
Semi-distributed vs Lumped model simulations: Comparison using observed
data for RFC scale basins, H32F-03, Spring Meeting, Washington, DC, May
30-June 3.

Smith, M.B., Koren, V., Zhang, Z., and Wang, D., 1999a. (Invited)
Semi-Distributed and Lumped Modeling Approaches: Case Study of NEXRAD
Data Application to Large Headwater Basins in the Arkansas River Basin.
Spring Meeting of the AGU, Boston.

Smith, M.B., Koren, V., Finnerty, B., and Johnson, D., 1999b.
Distributed Modeling: Phase 1 Results. NOAA Technical Report NWS 44, US
Dept of Commerce, 250 pages. Copies available upon request.

Smith, J.A., Seo, D.-J., Baeck, M.L., and Hudlow, M.D., 1996. An
intercomparison study of NEXRAD precipitation estimates. Water Resources
Research, 32(7), 2035-2045.

Smith, J.A., Bradley, A.A., and Baeck, M.L., 1994. The space-time
structure of extreme storm rainfall in the Southern Plains. Journal of
Applied Meteorology, Vol. 33, December, 1402-1417.

Stallings, E.A., and Wenzel, L.A., 1995. Organization of the river and
flood program in the National Weather Service. Weather and Forecasting,
10(3), 457-464.

Stellman, K.M., and Fuelbert, H.E., 2001. An examination of radar and
rain gauge-derived mean areal precipitation over Georgia Watersheds.
Weather and Forecasting, Vol. 16, 133-144.

Turcotte, R., Rousseau, A.N., Fortin, J.-P., Villeneuve, J.-P, 2003. A
process oriented, multiple-objective calibration strategy accounting for
model structure. In Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau,
A.N., Turcotte, R., (Eds.), Calibration of Watershed Models, Water
Science and application Volume 6, American Geophysical Union, 153-163.

Number range CHAPTERWang, M., Hjelmfelt, A. T., and Garbrecht, J., 2000.
DEM Aggregation for Watershed Modeling, Journal of the American Water
Resources Association, 36, 3, 579-584.

Number range CHAPTERWatson, F. G. R., Gryson, R. B., Vertessy, R. A.,
and McMahon, T. A., 1998. Large scale distribution modeling and the
utility of detailed data. Hydrological Processes, 12(6),

873-888.

Number range CHAPTERWillgoose, G. And Kuczera, G., 1995. Estimation of
Subgrid Scale Kinematic Wave Parameters for Hillslopes. In: Kalma, J. D.
And Sivapalan, M., (Eds.) Scale Issues in Hydrological Modeling,
Advances in Hydrological Processes, Wiley, 227-240.

Young, C, B., Bradley, A.A., Krajewski, W.F., and Kruger, A., 2000.
Evaluating NEXRAD Multisensor Precipitation Estimates for Operational
Hydrologic Forecasting. Journal of Hydrometeorology, Vol. 1, June,
241-254.

Zhang, Z., Koren, V., Smith, M.B., Use of NEXRAD data and basin
disaggregation to improve continuous hydrologic simulations. Journal of
Hydrologic Engineering Vol. 9, No. 2, March, 2004, 103-115.

Zhang, Z., Smith, M.B., Koren, V., Reed, S.M., Moreda, F., Kuzmin, V.,
and Anderson, R., 2004b. A study of the relationship between rainfall
variability and the success of a distributed model. GIS and Remote
Sensing in Hydrology, Water Resources, and Environment (Proceedings of
ICGRHWE held at the Three Gorges Dam, China, 2003) IAHS Publication 289.

# Appendix I: Publications and Presentations from Phase 1 Research {#appendix-i-publications-and-presentations-from-phase-1-research .unnumbered}

[Peer Reviewed Papers from Phase 1 Research]{.underline} (HL Scientists
in Boldface type)

Boyle, D.B., Gupta, H.V., Sorooshian, S., **Koren, V., Zhang, Z., and
Smith, M**., 2001. Toward improved streamflow forecasts: Value of
semi-distributed modeling. Water Resources Research, 37(11), 2749-2759.

**Duan, Q., Schaake, J.,** **and Koren, V.**, 2001. Number range
CHAPTERA Priori Estimation of Land Surface Model Parameters, in book
'Land Surface Hydrology, Meteorology, and Climate: Observations and
Modeling' (Editors: V. Lakshmi, J. Albert, J. Schaake), Water Science
and Application, Vol. 3, pp. 77-94, AGU, 2001'

**Johnson, D., Smith, M., Koren, V., and Finnerty, B**., 1999. Comparing
mean areal precipitation estimates from NEXRAD and rain gauge networks.
Journal of Hydrologic Engineering, Vol. 4, No. 2, April, 117-124.

**Koren, V.I., Finnerty, B.D., Schaake, J.C., Smith, M.B., Seo, D.-J.,
Duan, Q.-Y**., 1999. Scale dependencies of hydrologic models to spatial
variability of precipitation. Journal of Hydrology, 217, 285-302.

**Finnerty, B.D., Smith, M.B., Seo, D.-J., Koren, V., and Moglen, G.E.**
1997. Space-time scale sensitivity of the Sacramento model to radar-gage
precipitation inputs. Journal of Hydrology, 203, 21-38.

**Schaake, J.,** **Koren, V., Duan, Q.-Y**., Mitchell, K., and Chen, F.,
Number range CHAPTERSimple water balance model for estimating runoff at
different spatial and temporal scales, 1996. JGR, Vol. 101, No. D3,
7461-7475.

**Zhang, Z., Koren, V., Smith, M., Reed, S., Wang, D**., 2004. Use of
NEXRAD data and basin disaggregation to improve continuous hydrograph
simulations. ASCE Journal of Hydrologic Engineering, Vol. 9, No. 2,
March-April, 103-115.

[Other Documents Resulting from Phase 1]{.underline}

Smith, M.B., Koren, V., Finnerty, B., Johnson, D., 1999. Distributed
Modeling: Phase 1 Results, NOAA Technical Report NWS 44, February,
250pp.

Wang, D., Smith, M.B., Zhang, Z., Koren, V., Reed, S., 2001. Statistical
Comparison of Mean Areal precipitation Estimates from WSR-88D,
Operational, and Historical Gage Networks, Hydrology Lab. Technical
Report, August.

[Oral and Poster Presentations at Conferences from Phase 1
Research]{.underline}

Finnerty, B., and Johnson, D., 1997. Comparison of mean areal
precipitation estimates derived from NEXRAD radar vs. rain gauge
networks. Proceedings of Theme A, Water for a Changing Global Community,
The 27^th^ Congress of the Int'l Assoc. for Hydraulic Research, ASCE
Water Resources Engineering Division, held August 10-15, San Francisco,
CA., 601-606.

Koren, V., 1993. The potential for improving lumped parameter models
using remotely sensed data, 1993. 13th Conference on Weather Analysis
and Forecasting including Symposium on Flash Floods, Vienna, Virginia,
2 - 6 August, 1993, AMS/WMO/NWA, 397-400.

Koren, V., J. Schaake, M. Smith, B. Finnerty, D.J. Seo, and S. Tokar,
1998. Number range CHAPTERDistributed/Semi-distributed and lumped
approaches in runoff simulations: Numerical experiments using NEXRAD
data. T*he Fourth International Symposium on Hydrologic Applications of
Weather Radar. 5-9 April 1998, San Diego, California.*

Koren, V. I., J. C. Schaake, M. Smith, 1999. Number range CHAPTEREffects
of Scale Aggregation and Noise in High Resolution Data on Distributed
Model Results, Abstract H52A-19, *AGU Spring Meeting, Boston,
Massachusetts, June 1-4, 1999.*

Koren, V. I., M. Smith, D. Wang, Z. Zhang, 2000. Number range CHAPTERUse
of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff
Model Parameters. 15^th^ Conference on Hydrology, 10-15 January 2000,
Long Beach, CA, AMS, 103-106.

Koren, V., M. Smith, Q. Duan, Z. Zhang, 2000. Number range CHAPTERUse of
a Priori Parameter Estimates in the Derivation of Spatially Consistent
Parameter Sets. AGU Fall Meeting, San-Francisco, California, December
2000, H72C-08, F478. INVITED

Lindsey, S.L., 1993. Strategy for utilizing radar-based precipitation
estimates for river forecasting. Proceedings of the symposium
Engineering Hydrology, Hydraulics Division, ASCE, San Francisco, CA,
July 25-30, 940-945.

Smith, M., Koren, V.I, Zhang, Z., Wang, D., and Reed, S., 2000.
Semi-distributed vs Lumped model simulations: comparison using Observed
data for RFC scale basins, H32F-03, Spring Meeting, Washington, DC, May
30-June 3.

Smith, M.B., Koren, V., Zhang, Z., and Wang, D., 1999. (Invited)
Semi-Distributed and Lumped Modeling Approaches: Case Study of NEXRAD
Data Application to Large Headwater Basins in the Arkansas River Basin.
Spring Meeting of the AGU, Boston.

Smith, M., Koren, V., Zhang, Z., and Wang, D., 1999. Lumped and
semi-distributed modeling using NEXRAD Stage III data: Results from
continuous multi-year simulations. Proceedings, Georgia Water Resources
Conference, Kathryn J. Hatcher, Editor, March 30,31, University of
Georgia, Athens. 359-362.

Smith, M.B., Seo, D.-J., Finnerty, B.D., Koren, V., 1996. Distributed
Parameter Hydrologic Modeling and NEXRAD for River Forecasting: Scale
Issues Facing the National Weather Service. Presented at ASCE Conference
North American Water and Environment Congress \'96 Anaheim, CA, June
24-28.

Wang, D., Smith, M.B., Zhang, Z., Reed, S., and Koren, V.I., 2000.
Statistical comparison of mean areal precipitation estimates from
WSR-88D, operational, and historical gage networks. 15th Annual
Conference on Hydrology, 80^th^ Meeting of the AMS, Long Beach, CA.,
January 10-14, J2.17

#  {#section .unnumbered}

#  {#section-1 .unnumbered}

# Appendix II: Publications and Presentation from Phase 2 {#appendix-ii-publications-and-presentation-from-phase-2 .unnumbered}

Peer Reviewed Publications from Phase 2 Research. (HL scientists in bold
type).

Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, **V. Koren**,
G. Gayno, and Tarpley, 2004. Implementation of Noah land-surface model
advances in the NCEP operational mesoscale Eta model. GCIP3 Special
Issue, JGR, 2004, accepted.

**Koren, V., Reed, S., Smith, M., Zhang, Z., and Seo, Dong-Jun**, 2004.
Hydrology laboratory research modeling system (HL-RMS) of the National
Weather Service. Journal of Hydrology, in press, to appear in Special
Issue.

**Number range CHAPTERKoren, V., Reed, S., Smith, M., and Zhang,** Z.,
2003. Combining physically based and conceptual approaches in the
development and parameterization of a distributed system. In:
Information from Weather Radar and Distributed Hydrologic Modeling, IHAS
Publication No. 282, Sapporo, Japan, pages 101-108, Editors Tachikawa,
Y., Vieux, B., and Georgakakos, K.P.

**Koren, V., Smith, M., and Duan, Q**., 2003. Use of a-priori parameter
estimates in the derivation of spatially consistent parameter sets of
rainfall-runoff models. In: Duan, Q., Gupta, H.V., Sorooshian, S.,
Rousseau, A.N., Turcotte, R., (Editors.) Calibration of Watershed
Models, Water Science and Application 6, American Geophysical Union,
Wash. D.C., 239-254.

**Koren, V.,** Kogan, F. and Barrett, C., 1997. Number range
CHAPTERParameterization of hydrological model using NOAA/AVHRR data.
Adv. Space Res., Vol. 19, No. 3, 507-510, Pergamon.

**Koren, V.,** and Barrett, C.B., 1995. Satellite -based, distributed
monitoring, forecast, and simulation (MFS) system for the Nile River.
In: 'Appications of Remote Sensing in Hydrology', Canada, 187-200.

Mitchell, K. E., D. Lohmann, P. R. Houser, E. F. Wood, **J. C.
Schaake**, A. Robock, B. A. Cosgrove, J. Sheffield, Q. Duan, L. Luo, R.
W. Higgins, R. T. Pinker, J. D. Tarpley, D. P. Lettenmaier, C. H.
Marshall, J. K. Entin, M. Pan, W. Shi, **V. Koren**, J. Meng, B. H.
Ramsay, A. A. Bailey, 2004. The Multi-institution North American Land
Data Assimilation System (NLDAS): Utilizing Multiple GCIP Products and
Partners in a Continental Distributed Hydrological Modeling System.
GCIP3 Special Issue, JGR, 2004, *accepted*.

**Reed, S.M., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.-J**,
and DMIP participants, 2004. Overall distributed model intercomparison
project results, Journal of Hydrology DMIP Special Issue, accepted.

**Reed, S**., 2003. Deriving flow directions for coarse resolution (1 -
4 km) gridded hydrologic modeling. Water Resources Research, Vol. 39,
No. 9.

**Reed, S., D. Johnson, T. Sweeney**, Application and National
Geographic Information System Database to Support Two-Year Flood and
Threshold Runoff Estimates, Journal of Hydrologic Engineering, May/June
2002.

**Seo, D.-J., Koren, V., and Reed, S.,** 2003. Improving a priori
estimates of hydraulic parameters in a distributed routing model via
variational assimilation of long-term streamflow data. In: Information
from Weather Radar and Distributed Hydrologic Modeling, IHAS Publication
No. 282, Sapporo, Japan, pages 109-113, Editors Tachikawa, Y., Vieux,
B., and Georgakakos, K.P.

**Seo, D., V. Koren, N. Cajina**, 2003. Real-time assimilation of
radar-based precipitation data and streamflow observations into a
distributed hydrologic model. In: Information from Weather Radar and
Distributed Hydrological Modeling (Eds. Tachikawa Y., Vieux. B E.,
Georgakakos K. P) , IAHS Publ. No. 282, Sapporo, Japan, 2003, 138-142.

**Seo, D.-J., V. Koren, and N. Cajina**, 2003. Real-Time Variational
Assimilation of Hydrologic and Hydrometeorological Data into Operational
Hydrologic Forecasting. Journal of Hydrometeorology, Vol. 4, 2003,
627-641.

**Smith, M.B**., Seo, D.-J., Koren, V.I., Reed, S., Zhang, Z., Duan,
Q.-Y., Moreda, F., and Cong, S., 2004a. The distributed model
intercomparison project (DMIP): Motivation and experiment design.
Journal of Hydrology DMIP Special Issue, accepted

**Smith, M.B., Koren, V**., Pan, J.J., **Zhang, Z**. , 2004b. Runoff
response to spatial variability in precipitation: An analysis of
observed data. Journal of Hydrology DMIP Special Issue, accepted.

Vieux, B and **Moreda, F**. Ordered Physics-Based parameter adjustment
of a distributed model: In AGU Monograph advances in calibration of
watershed models. In Calibration of watershed models, water science and
Application Volume 6. Copyright 2003 by the American Geophysical Union,
pp 267-281.

Vieux, B., and **Moreda, F.**, Nutrient Loading Assessment in the
Illinois River Basin Using a Synthetic Approach. Journal of American
Water Resources Association. *.* Vol. 39 4. P. 757-769. August 2003.

**Zhang, Z., Smith, M.B., Koren, V., Reed, S.M., Moreda, F., Kuzmin, V.,
and Anderson, R**., 2004b. A study of the relationship between rainfall
variability and the success of a distributed model. GIS and Remote
Sensing in Hydrology, Water Resources, and Environment (Proceedings of
ICGRHWE held at the Three Gorges Dam, China, 2003) IAHS Publication 289.

[Oral presentations]{.underline}.

Anderson, R., Koren, V., Smith, M., and Seo, D.-J., 2003. On the
Importance of Linear vs. Non-Linear Response in Estimating Base Flow
Features for Ungauged Catchments in Southern Texas. AGU Presentation at
American Geophysical Union, San Francisco, December 9.

Anderson, R.M., Koren, V., Reed, S., Smith, M., and Kuzmin, V., 2003.
Regionalization of Rainfall-Runoff Model Parameters. Presentation at
International Union of Geodesy and Geophysics, Sapporo, Japan

Koren, V., S. Reed, M. Smith, Z. Zhang, 2003. Combining physically-based
and conceptual approaches in the development and parameterization of a
distributed system. International Union of Geodesy and Geophysics,
Sapporo, Japan Sapporo.

Koren, V., M. Smith, S. Reed, Z. Zhang, 2001. Number range
CHAPTERTransition from Lumped to Distributed Modeling Systems. Eos,
Transactions, AGU Spring Meeting, Vol. 82, No. 20, Boston,
Massachusetts, *May - June 2001,* *H32C-01.* INVITED.

Koren, V., M. Smith, S. Reed, Z. Zhang, 2001. 2001. Number range
CHAPTERUse of NEXRAD Precipitation Estimates in Distributed Modeling:
Parameterization, Aggregation Scale, and Data Uncertainties. Eos,
Transactions, AGU Fall Meeting, Vol. 82, No. 47, San-Francisco,
California, December 2001, H11A-0210*.* INVITED

Koren, V., S. Reed, Z. Zhang, D. Seo, F. Moreda, Kuzmin V., 2003. Use of
spatially variable data in river flood prediction. AGU-EGS-EUG Assembly,
April 9-14, 2003, Nice, France.

Moreda, F., Smith, M., Cong, S., Seo, D.-J., Zhang, Z., Reed, S., Koren,
V., and Smith, M. 2002. Evaluation of NEXRAD Rainfall Data in a lumped
and Distributed Hydroloic Models for a Mountainous Watershed. Eos.
Trans. AGU, 83(19), Spring Meet. Supl., Abstract H51b-05.

Reed, S., J. Schaake, V. Koren, D.J. Seo, M. Smith, 2004a. A
Statistical-distributed Modeling Approach for Flash Flood Prediction,
Paper Presented at the 84^th^ Annual Meeting of the AMS, Seattle, WA,
Jan. 11-15.

Reed, S., J. Schaake, V. Koren, M. Smith, Z. Zhang, FFG/ThreshR: Current
Issues and Future Possibilities. Poster Presented at the First National
Hydrologic Program Managers Conference, New Orleans, LA, Dec. 3 -- 6,
2002.

Reed, S., V. Koren, Z. Zhang, M. Smith, D.J. Seo, Distributed Modeling
for Improved NWS River Forecasts, Proceedings of the Second Federal
Interagency Hydrologic Modeling Conference, Las Vegas, NV, July
28-August 1, 2002.

Reed, S., V. Koren, Z. Zhang, M. Smith, Assessment of Distributed
Hydrologic Routing Parameter Estimates used in NWS-HL-RMS, Poster
Presentation at the 2002 Spring Meeting of the American Geophysical
Union, H51B-12, May 28-31, Washington, D.C.

Reed, S., V. Koren, Z. Zhang, M. Smith, \"Derivation of Routing
Parameters for a Gridded Runoff Model,\" Oral Presentation at the 2001
Spring Meeting of the American Geophysical Union, H32C-08, Boston, MA,
May 30, 2001.

Seo, D., V. Koren, L. Cajina, R. Corby, B. Finn, F. Bell, 2003.
Real-time variational assimilation of streamflow and radar-based
precipitation data into operational hydrologic forecasting. AGU-EGS-EUG
Assembly, April 9-14, 2003, Nice, France.

Smith, M., Seo, D.J., Koren, V., Reed., S., Zhang., Z., and Moreda, F.,
2003. Results from the Distributed Model Intercomparison Project (DMIP).
AGU-EGS-EUG Assembly, April 9-14, 2003, Nice, France. (presented by
Victor Koren)

Smith, M. Koren, V., Seo, D.-J., Reed, S., Zhang, Z., Moreda, F., Duan,
Q.-Y. 2003. Distributed Models for Operational River Forecasting:
Research, Development, and Implementation, Session NH-4, INVITED
(presented by Victor Koren) AGU-EGS-EUG Assembly, April 9-14, 2003,
Nice, France.

Vieux, B. J. Eshleman and F. Moreda**.** Assessing Radar to gage
calibration in a distributed modeling context In the Proceedings of The
Fifth International Symposium on Hydrological Application of Weather
Radar. Raddar Hydrology- November 19 -22, 2001 Heian- Kaikan, Kyoto,
Japan

Zhang, Z., Smith, M., Koren, V., Reed, S., Moreda, F., Kuzmin, V., and
Anderson, R., 2004. A study of the relationship between rainfall
variability and the improvement of using a distributed model,
Proceedings of the International Conference of GIS and Remote Sensing in
Hydrology, Water Resources and Environment, Three Gorges Dam site,
China, 16-19 September 2003. IAHS Publication 289.

Zhang Z., Koren, V., Smith, M., and Reed., S., 2001. Application of a
Distributed Modeling System Using Gridded NEXRAD Data, Proceedings,
Fifth International Symposium on Hydrological Applications of Weather
Radar -- Radar Hydrology, 19-22, November 2001, Heian-Kaikan, Kyoto,
Japan.

Zhang, Z., Koren, V., Reed, S., and Smith, M., 2002. Results from NWS
Hydrology Lab's Research Modeling System for DMIP Basins, *Eos*,
*Transactions*, AGU, 83(7), 7 May 2002. Poster and oral presentations
for 2002 AGU Spring Meeting (28-31 May 2002, Washington, D. C.).

[Poster Presentations]{.underline}

Smith, M., Koren, V., Seo, D., Reed, S., Zhang, Z., Moreda, F., and
Duan, Q.-Y., 2002. Evaluating the results of DMIP: How the NWS will move
forward with Distributed Modeling. Poster H51B-01, Spring Meeting of the
AGU, Washington, D.C., May 28-31.

Smith, M. B., Koren, V., Seo, D.-J., Reed, S., Zhang, Z., and Duan, Q.,
2001. Intermediate Results from the Distributed Model Intercomparison
Project (DIMP). (INVITED) Poster H12C-0310, Fall Meeting of the AGU, San
Francisco, December 10-14.

Wang, D., Smith, M., Reed, S., Koren, V., and Zhang, Z., 2001.
Comparison of Mean areal precipitation estimatesfrom WSR-88D and
historical gage networks over Cheat River, W. Va., poster H22A-05,
Spring Meeting of the AGU, Boston, May 29-June 2.

[Conference Sessions Organized]{.underline}

Hypothesis, Theories, and Applications of Distributed Modeling and the
Initial Results of the Distributed Model Intercomparison Project (DMIP)
Oral. H42G, Presiding: **Mike Smith**, NWS, and Xu Liang, U. California
Berkeley, Spring Meeting of the AGU, Washington, DC., May 28-31

Hypothesis, Theories, and Applications of Distributed Modeling and the
Initial Results of the Distributed Model Intercomparison Project (DMIP)
II posters. H51B , Presiding: **Mike Smith**, NWS, and Xu Liang, U.
California Berkeley, Spring Meeting of the AGU, Washington, DC., May
28-31.

Lumped versus Distributed Modeling: Issues in Real World Applications.
H32C, Presiding: **Michael Smith**, NWS, and Doug Boyle, Desert Research
Institute, Spring Meeting of the AGU, Boston, May 29-June 2, 2001.

Lumped Versus Distributed Modeling: Issues in Real World Applications.
H32F, Presiding, **Michael Smith** and **Dong Jun Seo**, NWS, Spring
Meeting of the AGU, Washington D.C., May 30-June 3, 2000.

Real Time Flood Forecasting, Presiding: **Victor Koren**, NWS and Gabor
Balint, Water Resources Research Centre (VITUKI), AGU-EGS-EUG Assembly,
April 9-14, 2003, Nice, France.

# Appendix III: Acronyms {#appendix-iii-acronyms .unnumbered}

ABRFC Arkansas-Red Basin River Forecast Center

API Antecedent Precipitation Index

CBRFC Colorado Basin River Forecast Center

CONUS Continental United States

DEM Digital Elevation Model

DMIP Distributed Model Intercomparison Project

DMS 1.0 Distributed Modeling System version 1.0

ESP Ensemble Streamflow Prediction

EMC Environmental Modeling Center of NCEP

FFG Flash Flood Guidance

HL Hydrology Laboratory

HL-RMS Hydrology Lab Research Modeling System

MAPX Mean Areal Precipitation estimates from NEXRAD radars

MARFC Mid-Atlantic River Forecast Center

MCP3 Manual Calibration Program version 3

MOPEX Model Parameter Estimation Experiment

MPE Multisensor Precipitation Estimator

NCEP NWS National Centers for Environmental Prediction

NWSRFS National Weather Service River Forecast System

OHD NWS Office of Hydrologic Development

RFC River Forecast Center

SAC-SMA Sacramento Soil Moisture Accounting model

WGRFC West Gulf River Forecast Center

#  {#section-2 .unnumbered}

# Appendix IV: Proposed Field Office Transition from Lumped to Distributed Modeling {#appendix-iv-proposed-field-office-transition-from-lumped-to-distributed-modeling .unnumbered}

We propose a methodical transition from traditional lumped modeling to
distributed hydrological modeling for RFC forecast operations. Our
proposed methodology considers steps already being taken by many RFCs so
that the implementation of an operational distributed model should not
represent a significant departure from current RFC activities. For
example, in many RFCs, basins are already being re-calibrated so that
the models can be run on a lumped basis at a 1-hour time step rather
than a 6-hour time step. Prior to the implementation of the NEXRAD
series of radar platforms, RFCs were limited to a minimum 6-hour time
step because of the constraints of the prevailing rain gage network. The
availability of hourly rainfall estimates from NEXRAD radar platforms
and in come cases dense rain gage networks has promoted this transition
to a finer time scale for lumped hydrologic modeling.

As seen in step 13 of Table IV-1, a lumped hourly calibration of a basin
is a desired prerequisite for the calibration of HL-RMS. Moreover, we
foresee no need to discontinue the lumped hourly forecasts after a
distributed model is implemented. Rather, like Bell and Moore (1998), we
believe that there is merit in operating a distributed model along side
a lumped model as part of a decision-support system approach to river
forecasting. In a sense, the forecaster would be provided a two-member
ensemble to provide guidance in the issuance of a river forecast.

Tables IV-1 and IV-2 compare the steps required to calibrate and set up
a traditional lumped model within NWSRFS and the HL-RMS distributed
model. These tables show that the steps required to calibrate and
operationally set up a distributed model are logical, feasible, and do
not represent a significant departure from current RFC procedures.

Table IV-1. Comparison of steps needed for calibration of traditional
lumped model and HL-RMS distributed model.

  -------------- ------------------------------- ----------------------------------
  Calibration                                    
  Mode Steps                                     
  Using                                          
  StageIII/MPE                                   
  Input Data                                     

  1              Distributed modeling steps      Lumped modeling equivalent
                 performed at beta test sites    (NWSRFS)
                 (HLRMS)                         

  2              Get observed streamflow data    Same

  3              Ensure archived StageIII/MPE    Same
                 data are in an accessible       
                 location                        

  4              Get outlet lat-lon, HRAP        Get drainage area and lat-lon
                 coordinates, lat-lon, drainage  boundaries (or line segment
                 area                            definition, for MAPX)

  5              Estimate channel routing        Estimate unit graph (manually from
                 parameters at outlet            hydrograph or using empirical
                                                 method, e.g. IHABBs)

  6              Update channel routing          Enter unit graph into MCP3 deck
                 parameter grids (genpar         
                 utility)                        

  7              Add outlet to connectivity file Add outlet/time series information
                                                 to MCP3 deck; define segment in
                                                 OFS

  8              Adjust pixel areas to match     Not required
                 USGS areas (cell-areas utility) 

  9              Edit HLRMS input deck (define   Edit MCP3 Deck
                 start and end time, other       
                 options)                        

  10             Not required                    Run CAP to get mean a-priori
                                                 parameter estimates (SAC and PE);
                                                 enter values into MCP3 deck

  11             Not required                    Run MAPX

  12             Run HLRMS                       Run MCP3

  13             Iteratively adjust gridded      Using ICP, iteratively adjust
                 parameters for the selected     parameter values to meet
                 basin using the scale factor    calibration objectives
                 option to meet calibration      
                 objectives (ideal situation     
                 would be to be able to scale    
                 initial estimates based on      
                 results from a lumped           
                 calibration)                    

  14             Plot mean precipitation,        Same, using ICP
                 simulated, and observed time    
                 series to assist with parameter 
                 adjustments. (using XDMS or     
                 ICP)                            

  15             No equivalent currently         Using ICP, examine the time
                 available                       variation of model states,
                                                 percolation curve, and unit graph
                                                 values to help with parameter
                                                 adjustments

  16             Visually examine the spatial    No AWIPS-supported way to examine
                 patterns of inputs, parameters, basin-to-basin variability of
                 and model results.              parameters.
  -------------- ------------------------------- ----------------------------------

Table IV-2 Comparison of steps required for the operational set-up of
HL-RMS and a traditional lumped model

  ----------------------------------- -----------------------------------
  Forecast Mode Steps Using           
  StageIII/MPE Input Data             

  **Distributed modeling steps        **Lumped modeling equivalent
  performed at beta test sites        (NWSRFS)**
  (HLRMS)**                           

  Complete calibration mode steps     Same

  Define a new time series in OFS     ??
  (PRDUTIL); edit the OFS segment     
  definition to display the new time  
  series (FCINIT)                     

  Edit the input deck to include the  ??
  new basin                           

  Run HL-RMS (typically in batch      Run OFS (typically in batch mode)
  mode)                               

  Display results in IFP or XDMS      Display results in IFP

  Limited. The equivalents to         Make run time modifications
  run-time mods could be made to      
  runoff states, routing states,      
  runoff parameters, and routing      
  parameters. Requires manual         
  modifications to the ASCII decks    
  and user must remember to re-enter  
  or re-copy original values after    
  the mods are made.                  

  ??                                  Issue forecast
  ----------------------------------- -----------------------------------

# Appendix V: Needs for Continuing HL R&D {#appendix-v-needs-for-continuing-hl-rd .unnumbered}

Aside from the major need for continued annual funding for HL scientists
and extramural research collaborations, we identify several practical
items needed to continue an effective R&D program:

1.  As discussed earlier in Section 5.5.1, there is a great need to
    perform a re-analysis of the NEXRAD precipitation data to produce a
    high-quality data set to be used for research and calibration.
    Numerous authors have commented on the inconsistencies and biases in
    the data due to changes and updates in the NEXRAD processing
    algorithms. (e.g., Bradley and Kruger, 1998; Smith et al., 1999;
    Young et al., 2000; Stellman and Fuelberg, 2001).

2.  Updates are needed for the Interactive Calibration Program (ICP)
    (Smith et al., 2003). HL scientists are heavily reliant on this
    display and analysis tool. While originally designed as an RFC tool
    for calibration of the SAC-SMA, SNOW-17, and other NWSRFS models, it
    is quite often used as a research tool because of its flexibility
    and ability to plot numbers of multi-year time series. The
    importance of ICP was clearly demonstrated at the August, 2002 DMIP
    workshop when all participants simulations were displayed
    simultaneously, facilitating an accurate assessment of model
    performance.

However, ICP has never ranked very high in the prioritization process
for providing updates to NWSRFS software. As a result, while a few bugs
have been fixed, ICP development as a research tool has been restricted.
A new approach to providing updates to R&D software programs is needed
that is not restricted by field-requested updates to other software
systems.