J5.13 APPLYING HIGH RESOLUTION LAND SURFACE DATA, MODELING AND

ASSIMILATION TECHNIQUES TO WATER SUPPLY AND DEMAND FORECASTS

Curtis L. Hartzell*, Tom Pruitt*, Steven M. Hunter*, David A. Matthews* and Warren Sharp**

U.S. Department of the Interior, Bureau of Reclamation

*Technical Service Center, Denver, Colorado

**Upper Columbia Area Office, Yakima, Washington

Kristi R. Arsenault and Paul R. Houser

NASA’s Goddard Space Flight Center, Hydrology Sciences Branch

1. INTRODUCTION

There is a critical need to improve water supply and

demand forecasting for water resources managers in the

Upper Columbia River Basin. The Bureau of

Reclamation (Reclamation) teamed with the Land Data

Assimilation System (LDAS) group at NASA’s Goddard

Space Flight Center (GSFC) to address this need. The

objectives of the joint research include improving

snowpack water supply forecasts, snowmelt runoff

forecasts, and agricultural and riparian water

consumptive use forecasts. The research integrates the

improved water supply and demand forecasts into

decision support tools to improve water resources

management. 

Reclamation is developing an Agricultural Water

Resources Decision Support (AWARDS) -

Evapotranspiration (ET) Toolbox system in portions of

the Upper Columbia Basin for automatically inputting

daily riparian/wetlands and crop water use estimates,

open water evaporation estimates, and WSR-88D-based

rainfall estimates to RiverWare, which is a daily river

modeling and accounting system for decision makers.

The rainfall estimates, as well as cool season

precipitation estimates, are from the Reclamationdeveloped Precipitation Accumulation Algorithm (PAA).

Recently consumptive use forecasts out to 72 hours have

been added to the ET Toolbox. These forecasts are

based on the NCEP Eta model (12-km grid) weather

forecast parameters. This water resources decision

support system is being used operationally.

The NASA GSFC LDAS group is heavily involved in

the development and testing of the uncoupled NorthAmerica Land Data Assimilation System (NLDAS). The

NLDAS uses four physically-based, land-surface models

(LSMs) running on a common grid and driven by

common surface forcing and observation-based

precipitation and radiation fields. The study approach for

the Upper Columbia River Basin is to incorporate NLDAS

products into the ET Toolbox and RiverWare. Upon

integration, then evaluate the resulting products and

value added to the water operations in terms of additional

conservation storage, and sustainable river basin

ecosystems.

* Corresponding author address: Curtis L. Hartzell.

Bureau of Reclamation, Denver Federal Center, PO Box

25007, D-8510, Denver, CO, 80225-0007; phone: 303-

445-2482; e-mail: chartzell@do.usbr.gov

 There are two ways in which collaboration efforts

between the LDAS team and Reclamation have to be

made, viz., (1) the exchange of data to help each other

evaluate the ability of the current LDAS and its suitability

to fit into Reclamation’s water management operations

and decision support tools, and (2) through such

validation, the LDAS team’s main goal is to improve the

parameterization and land surface model physics,

incorporating the scaling issues, to render better output

specific to water supply and demand forecasting needs.

On the applications side, Reclamation’s main goal is to

improve water resources management through the

development of decision support tools like the ET

Toolbox and RiverWare.

2. AWARDS - ET TOOLBOX

The AWARDS - ET Toolbox system (ET Toolbox) is

an automated information system to assist water users

by providing easy access to rainfall and daily crop water

use estimates (Hartzell et al., 2002). These estimates

are based on real-time data obtained from WSR-88D

(Weather Surveillance Radar - 1988 Doppler) radar

systems and automated weather stations. The purpose

of the ET Toolbox is to improve the efficiency of water

management and irrigation scheduling by proving

guidance on when and where to deliver water, and how

much to apply.

Reclamation’s Yakima, Columbia Basin and Umatilla

projects in the Upper Columbia Basin are developing and

deploying RiverWare, an object-oriented reservoir and

river modeling framework for both planning and daily

operational use. However, the current water supply and

demand accounting systems in their RiverWare systems

model fail to take advantage of the latest technologies for

precipitation estimation, agricultural and riparian water

use ET calculations, open water evaporation estimates,

and meteorological model forecasts. This applied

research effort will improve operational efficiency and

water conservation for water deliveries, and help

maintain reservoir levels and an equitable supply of

water for in-stream flows and endangered species during

periods of critically short water supplies. The goal of this

ongoing research project is to improve the efficiency of

water management by using the ET Toolbox to calculate

daily crop ET estimates for input to RiverWare. As the

decision support tools and products are being developed,

they are posted on an Internet Web page for use by

water managers (http://www.usbr.gov/rsmg/nexrad).
Figure 1. Kittias Division showing canals and drains for

irrigation water, field polygons, and 12-km NCEP Eta

model grid. Clicking on the white dots will pop up a table

with daily water use by individual crop and in total for the

subareas.

Figure 2. Example of a comparison of one Eta model 12-

km grid cell forecast parameters with measurements from

a weather station located within the grid cell.

Figure 3. Lind, Washington AgriMet weather station.

The primary purpose of the ET Toolbox is to

estimate high-resolution (nominal 2km x 2km cells)

radar-based daily rainfall and crop water depletions

within specified subareas of irrigation projects (an

example of such a project is shown in Figure 1). The

rainfall estimates are based on Reclamation’s

Precipitation Accumulation Algorithm (PAA). The daily

net crop water use requirements calculated by the ET

Toolbox for each subarea are available for input for

Depletion Requested values in RiverWare. A Data

Management Interface (DMI) was developed to

exchange data between the ET Toolbox and the Upper

Columbia Area Office's (UCAO) Hydrologic Engineering

Center - Data Storage System (HEC-DSS).

3. ETA MODEL FORECASTS

There are irrigation projects within the Upper

Columbia Basin (e.g. see Figure 1) that do not have a

representative real-time automatic weather station to

provide the meteorological parameters needed for the

calculation of daily reference ET. In these areas, the ET

Toolbox system is using the National Centers for

Environmental Prediction (NCEP) Eta model weather

forecast parameters at 12-km grid resolution. The ET

Toolbox uses the Eta model 0600 UTC (2300 PDT) run’s

3-hr forecasts for temperature, relative humidity, and

wind speed, plus the forecast cumulative solar radiation

and precipitation for 0600-0600 UTC. From the 3-hr

forecast values, the ET Toolbox selects the maximum

and minimum temperatures, and calculates the average

relative humidity and wind speed. The same Eta model

runs are also used to calculate 48-hr ET forecasts.

Figure 2 shows a comparison of the forecast total

solar radiation, maximum and minimum temperatures,

and average relative humidity, with measurements from

the Lind, WA AgriMet weather station (elevation 1475 ft)

located within the 12-km Eta grid cell (mean elevation

1539 ft). The Lind AgriMet weather station is shown in

Figure 3. 
Figure 4. Example of the PAA-based 24-hr QPE for the

Upper Columbia Basin area. For this image, QPEs from

four WSR-88Ds (Spokane, Pendleton, Portland, Seattle)

are merged; 24-hr gage accumulations are plotted for

comparison.

The data sample used in the comparison was 166

days from April 1 through September 29, 2002 (there

were a few days with missing Eta model data). Table 1

shows the results of a comparison of data from the Lind,

WA AgriMet weather station and the 12-km Eta Model

grid cell it is located in. The comparison numbers are

average daily values for the 166 day sample. 

Table 1. Comparison of 12-km Eta model 24-hr forecasts

with surface weather station data. 

Label Tmx

F

Tmn

F

RH 

%

Solar

L

WS

mph

12 Eta 76.6 54.5 35.1 641.5 7.9

Wx Stn 75.9 48.2 48.8 544.2 5.1

Diff (%) 0.9 13.1 -28.1 17.9 54.9

Corr (r) 0.982 0.958 0.873 0.790 0.829

The Eta positive bias in surface solar insolation was

expected as it has been documented by a number of Eta

model verification studies (Mitchell, et al., 2000). For this

sample, the maximum temperatures were in good

agreement, there was a positive bias in the minimum

temperature, and a negative bias in the average relative

humidity. The difference in relative humidity is likely due

to the mean height of the Eta grid cell being about 64 feet

higher than the weather station. The bias in wind speed

(ws) is due to comparing 10-m Eta model wind forecasts

with 2-m weather station measured data. Adjusting the

10-m ws forecast to 2 m using: ws2 = (ws2/ws10)

0.2 where

the exponent is an estimate of the surface roughness,

reduces the 7.9 mph average to 5.7 mph, which

compares closely with the measured 5.1 mph average.

4. PRECIPITATION ESTIMATES

High resolution rainfall estimates based on WSR88D Level 3 data are incorporated into the AWARDS - ET

Toolbox system to offset vegetation water use and open

water evaporation. The 24-hr rainfall for each HRAP grid

cell located over the irrigated and riparian/wetlands

acreage along river systems in the Upper Columbia Basin

is used in the water use calculations.

The accuracy of standard WSR-88D precipitation

products such as Stage III, based on the default Ze =

300R1.4 relationship with no range correction, was found

to be insufficient for Reclamation’s operational needs.

Therefore, a Ze = "R$

 precipitation accumulation

algorithm (PAA) based on WSR-88D Level 3 base

reflectivity data was developed to improve the

quantitative precipitation estimates (QPE). The first

version of the PAA developed for the Pacific Northwest

allowed site specific selected " and $ values, minimum

and maximum precipitation thresholds, and range

correction factor (Hartzell et al., 2001).

Improvements were made to the PAA that restores

the range correction function with a true vertical profile of

reflectivity, based on clearance between a radar beam

center and the terrain. Also, the precipitation type in the

radar beam is classified as dry snow, melting snow, and

rain, based on model soundings. The three classes will

have a different ", but $ will remain 2.0 (Hunter and

Holroyd, 2002). This version of the PAA was

implemented for operational testing in the Upper

Columbia Basin (and elsewhere) during late April 2002.

An example of the PAA-based QPE is shown in Figure 3.

PAA research work is ongoing to refine parameters

within the algorithm and to adapt it for the Upper

Columbia Basin area. Specifically, PAA QPE are being

compared against 24-hr precipitation amounts from

Reclamation’s AgriMet weather station network and the

National Weather Service Cooperative Observing

network within the Spokane, Washington WSR-88D

(KOTX) radar umbrella. This comparison will enable

accurate QPE in the Upper Columbia Basin area.

5. NLDAS PRODUCTS

The greatest priority of water supply management is

having accurate snowpack amounts and runoff forecasts.

Water managers must have timely data from remote

areas that are often inaccessible in winter, and they need

a means of quickly analyzing the impacts of precipitation

and snowmelt on streamflow for routine river system
Figure 5. Mosaic LSM Snow Water Equivalent (mm) of

the snowpack as of 00Z on 03/29/01.

Figure 7. NLDAS Mosaic LSM vs Lind SCAN 1m soil

moisture comparison for June - August 2001.

Figure 6. NLDAS Mosaic LSM vs Lind SCAN 1m soil

moisture comparison for March - May 2001.

management, and emergency responses to extreme

events. Therefore, accurate estimates and forecasts of

the snow water equivalent (SWE), antecedent soil

moisture, surface energy budget forecasts, and the

evolution of the snowpack into hydrologic runoff are very

important for accurate flood forecasts.

Reclamation researchers are collaborating with

scientists at the Hydrologic Sciences Branch, NASA’s

GSFC, on studies that support Reclamation priorities by

assessing the importance of orographic processes on

water cycle predictability through the use of snowfall

remote sensing and data assimilation in the western

United States and applications of model forecasts in

water resources management decision making.

The objective of this research collaboration is to

develop and demonstrate the value added from various

satellite, radar, and other in-situ observations in the North

American Land Data Assimilation System (NLDAS) to

improve water supply forecasts for water resource

managers, initially in the Columbia River Basin. This

research partnership among scientists/researchers at

NASA and Reclamation will test and evaluate different

land surface models (LSMs) and hydrologic runoff

models. These models will use satellite and radar data

and conventional surface observations to demonstrate

the value added to improved streamflow simulations and

forecasts.

Figure 5 shows an example of the snow water

equivalent (SWE) depth product from the Mosaic LSM

(Koster and Suarez, 1996). Bi-monthly Snotel SWE data

for two Natural Resources Conservation Service (NRCS)

Snotel sites were used for comparison purposes.

Manual versus pillow correlations for the two Snotel sites

have high correlations being 0.963 and 0.952. So far the

comparisons with the Mosaic near real-time and forecast

runs have shown that the snow cover is reasonably

captured, but snow depth proves to be the challenge due

to SWE being underestimated when compared to the

Snotel sites.

The cooperative study is also evaluating how

estimated soil moisture fields from NLDAS LSMs can be

used to improve the efficiency of water management.

Soil Moisture is crucial to land-atmosphere and surface

water interactions, and hence to forecast models. In this

study, forcing and output data for the Mosaic LSM

retrospective model were compared to and correlated

with actual soil moisture observations from the NRCS

Soil Climate Analysis Network (SCAN) site near Lind,

Washington (close to the Reclamation Lind AgriMet

weather station). Figures 6 and 7 show LDAS-SCAN 1m

depth soil moisture comparisons for Mar-May 2001 and

Jun-Aug 2001, respectively. The correlations of 0.730

and 0.920 were fair to good.

The future course of the cooperative research will

include continuing to evaluate the near real-time and

forecast run output from the Mosaic LSM and potentially

output from other LSMs at Reclamation facilities. For

both operational and forecast settings, various NLDAS

LSM variables will be tested and used in Reclamation’s

water resource and river management support tools.

6. SUMMARY

• The AWARDS - ET Toolbox system being developed

for the Upper Columbia Basin demonstrates a

methodology that integrates high resolution WSR88D 1-hr rainfall estimates from Reclamation’s PAA,

weather station data, atmospheric model forecasts,

crop and riparian/wetlands ET requirements, GIS
information, and land usage, with modern computer

communication, and Internet technologies for

improved water resources management.

• The land use data are combined with the NCEP

12-km Eta model data-based ET estimates as

calculated using the Kimberly-Penman equation to

develop agricultural water use estimates in acre-feet

(and as flows in cfs). When available, weather

station data are used in place of the Eta model data.

• A Data Management Interface (DMI) was developed

to exchange data between the ET Toolbox and a

database. Currently, only an aggregated output

from the ET Toolbox to the Upper Columbia Area

Office's (UCAO) Hydrologic Engineering Center -

Data Storage System (HEC-DSS) DMI exists. There

is a need to build a prototype RiverWare model with

a data object to transmit the ET Toolbox daily net

water use requirements for the subareas - thus

creating one time series each. 

• Pop-up charts and graphs of weather station data,

streamflow and river stage, and the 1-hr radar-based

PAA images are updated in near real time.

• Forecast parameters at 12-km grid resolution from

the NCEP Eta model are being used in the ET

Toolbox for calculating daily ET, and to make 24-hr

and 48-hr subarea consumptive use forecasts. 

• The PAA is being operationally tested. It appears

that the PAA is presently over-forecasting

precipitation during light events, but is doing well

during widespread moderate to heavy events. Case

studies using 24-hr accumulations from AgriMet

weather stations and NWS Cooperative observation

sites are in progress. Results from these studies will

be used to improve the PAA-based QPE for the

Upper Columbia Basin.

• Reclamation AgriMet weather station measurements

were compared with NLDAS Mosaic LSM near realtime and 00Z forecast simulations. The results show

strong correlations for maximum and minimum

temperatures, and good correlations for relative

humidity, downward shortwave radiation, and wind

speed. The forecast runs reveal a certain amount of

accuracy, but with weaker correlations for

precipitation and downward shortwave radiation

when compared with the near real-time runs.

• Comparisons were made between snow depth

(SWE) and soil moisture outputs from the NLDAS

Mosaic LSM to observed data. The SWE are being

underestimated when compared to Snotel data, but

there are good correlations with soil moisture.

ACKNOWLEDGMENTS

Funding for the development of the AWARDS - ET

Toolbox system in the Upper Columbia Basin area is

being provided by the Upper Columbia Area Office, and

Reclamation’s Science and Technology Program.

Special thanks goes to Shannon Cunniff, Chuck Hennig

and Tim Personius (Science and Technology Program),

and Norbert Ries (Upper Columbia Area Office) for their

support. The following team members provided

significant contributions to this project: Al Brower

(retired), Ra Aman, Verne Leverson, Richard Stodt, and

Steffen Meyer.

Data from the AgriMet automated weather stations

are provided by the Pacific Northwest Cooperative

Agricultural Weather Network (Peter Palmer). The WSR88D Level 3 data are obtained via FTP from the NWS

TGFTP server (Alan Darling). The 12-km Eta model

forecast parameters are obtained via FTP from NCEP’s

Environmental Modeling Center (Dr. Geoff DiMego).

REFERENCES

Arsenault, K.R., P.R. Houser, C.L. Hartzell, and D.A.

Matthews, 2002: Using Land Data Assimilation

Systems Products to Improve Streamflow Forecasts.

Preprints, Mississippi River Climate & Hydrology

Conference, GCIP/GAPP, New Orleans, LA, pg 176

& poster. .

Hartzell, C.L., S.M. Hunter, and E.W. Holroyd, III, 2001:

Development of a WSR-88D Based Precipitation

Accumulation Algorithm for Quantitative Precipitation

Estimates Over Northwest Oregon. Preprints, 17th

International Conference on Interactive Information

and Processing Systems (IIPS) for Meteorology,

Oceanography, and Hydrology, Amer. Meteor. Soc.,

Albuquerque, NM, pp 146-150.

Hartzell, C.L., L.A. Brower, and S Hansen, 2002:

Agricultural Water Resources Decision Support

System and Evapotranspiration Toolbox. Preprints,

16th Conference on Hydrology, Amer. Meteor. Soc.,

Orlando, FL, pp. J198-J203.

Hunter, S.M., and E.W. Holroyd III, 2002: Demonstration

of Improved Operational Water Resources

Management Through Use of Better Snow Water

Equivalent Information. U.S. Bureau of Reclamation

Report R-02-02, 75 pp. Also available online at

http://www.usbr.gov/rsmg/awards/publ/R-02-02.pdf

Koster, R., and M.J. Suarez, 1996: Modeling the land

surface boundary in climate models as a composite

of independent vegetation stands. J. Geophys. Res.,

97,2697-2715.

Mitchell, K., et al. 2000: The Collaborative GCIP Land

Data Assimilation (LDAS) Project and Supportive

NCEP Uncoupled Land-Surface Modeling Initiatives.

Preprints, 15th Conference on Hydrology, Amer.

Meteor. Soc., Long Beach, CA, pp. 1-4.