The U.S. Bureau of Reclamation identified the remote sensing of evapotranspiration (ET) as an important water flux for study and designated a test site in the Lower Colorado River basin. A consortium of groups will work together with the goal to develop more accurate and cost effective techniques using the enhanced spatial and temporal coverage afforded by remote sensing. ET is a critical water loss flux where improved estimation should lead to better management of Reclamation responsibilities. There are several areas where NASA satellite and modeling data may be useful to meet Reclamation's objectives for improved ET estimation. In this paper we outline one possible contribution to use NASA's data integration capability of the Land Information System (LIS) to provide a merger of observational (in situ and satellite) with physical process models to provide estimates of ET and other water availability outputs (e.g., runoff, soil moisture) retrospectively, in near real-time, and also providing short-term predictions

Arsenault, Kristi

Engman, Ted

Houser, Paul

Kumar, Sujay

Nigro, Joe

Peters-Lidard, Christa

Pinheiro, Ana

Toll, David

Triggs, Jonathan

NASA Technical Reports Server

D. Toll, K. Arsenault, C. Peters-Lidard, P. Houser, S. Kumar, E. Engman, J. 
Nigro, and J. Triggs, NASA LIS Water Availability to Support Reclamation ET 
Estimation, 2005, US Bureau of Reclamation Evapotranspiration Workshop, Fort 
Collins, CO (March), Technical Paper, 6pp, Proceedings in Preparation. 
NASA Land Information System (LIS) Water Availability 
to Support Reclamation ET Estimation 
David Toll1, Kristi ~rsenault~,  Ana pinheiro3, 
Christa peters-~idard', Paul ~ o u s e r ~ ,  
Sujay Kumar', Ted ~ n ~ m a n ~ ,  Joe ~ i g r o ~  
& Jonathan Triggs2 
1 -Hydrological Sciences NASA/GSFC 
2- Univ. MD - GEST 
3- NRC Post-DOC, NASAKSFC 
4-George Mason University 
5-SAIC-NASMGSFC 
6- SSAI-NASMGSFC 
ABSTRACT 
The U.S. Bureau of Reclamation identified the remote sensing of evapotranspiration (ET) 
as an important water flux for study and designated a test site in the Lower Colorado 
River basin. A consortium of groups will work together with the goal to develop more 
accurate and cost effective techniques using the enhanced spatial and temporal coverage 
afforded by remote sensing. ET is a critical water loss flux where improved estimation 
should lead to better management of Reclamation responsibilities. There are several 
areas where NASA satellite and modeling data may be useful to meet Reclamation's 
objectives for improved ET estimation. In this paper we outline one possible contribution 
to use NASA's data integration capability of the Land Information System (LIS) to 
provide a merger of observational (in situ and satellite) with physical process models to 
provide estimates of ET and other water availability outputs (e.g., runoff, soil moisture) 
retrospectively, in near real-time, and also providing short-term predictions. 
INTRODUCTION 
Evapotranspiration (ET) or the water flux from the surface to the atmosphere is a crucial 
water availability loss that is measured in the western US by the Bureau of Reclamation 
(Reclamation) in a few selected locations. The amount of ET imparted to the atmosphere 
can account for 60% or more of surface water loss (e.g., Hartzell et al., 2000) of the 
western US and can critically affect local economies tied to agriculture, recreation, and 
hydroelectric power. The development of more accurate ET estimates could save 
Reclamation millions of dollars. Typically, Reclamation estimates ET from land cover 
classifications and weather station data using field based "transfer coefficients". 
Reclamation entered a partnership with Alliance Universities, NASA, USDA, USGS and 
other groups to study more accurate and cost effective methods to estimate ET. The 
initial test sites will be in the high water loss corridor of the Lower Colorado River basin 
https://ntrs.nasa.gov/search.jsp?R=20080030198 2019-08-30T05:00:40+00:00Z
characterized by irrigated agriculture and riparian vegetation such as the invasive species 
salt cedar. 
NASA CONTRIBUTIONS TO RECLAMATION ET 
Overall Contributions 
NASA Earth science satellite and modeling data may provide significant inputs to 
Reclamation's evapotranspiration and water supply and demand program. There are 
various satellite data such as afforded by NASA and other agencies that could benefit 
USBR evapotranspiration and water availability related models (Table 1). In addition to 
the long heritage of Landsat data, the MODerate resolution Imaging Spectroradiometer 
(MODIS) on the Aqua and Terra satellites provides global land, water and atmospheric 
data at a lkm resolution (2 bands at 250 m for land products) and up to four times per 
day. In addition, MODIS data are archived and processed as specific products (Table 2) 
for the user community. NASA with the University of Princeton is currently developing 
a MODIS based ET product for possible Reclamation use. Other higher resolution data 
sets may be useful, for example, including the Landsat Enhanced Thematic Mapper 
(ETM+), the EO-1 Advanced Land Imager (ALI), and the Terra ASTER. 
Land Information System for Reclamation ET 
We propose through the NASA Land Information System (LIS) (htt~:lllis.nsfc.nasa.nov) 
(Peters-Lidard et al. 2004) to use modeling and satellite data in conjunction with ancillary 
Earth science data, including Reclamation data, to provide spatially distributed (e.g., 
100m-lkm) evapotranspiration estimates at hourly time steps for selected test cases in the 
Lower Colorado. Table 3 summarizes the typical output products available from LIS. 
LIS will be used to integrate and assimilate various data sets using a suite of land surface 
models within a data integration framework. LIS provides water resources applications 
building on the heritage of the Global Land Data Assimilation System (GLDAS; Rodell 
et al. 2004) the North American Land Data Assimilation System (NLDAS; Mitchell et al. 
2004) developed primarily for weather and climate model initializations and predictions 
(http:l/ldas.nsfc.nasa.nov). 
LIS features a high-performance and flexible design, providing infrastructure for data 
integration and assimilation, and operates primarily on an ensemble of land surface 
models (e.g. CLM2, Noah, VIC) for execution over user-specified regions and time- 
frames (retrospective, real-time and predictive) throughout the global land surface. 
Meteorological forcing is provided by satellite-, radar- and gauge-based observations of 
precipitation (e.g. CPC, CMAP, CMORPH, TRMM MPA) and by satellite-based 
observations of radiation (e.g. GOES, AGRMET), while MODIS is used to supply many 
surface input parameters (land cover, albedo, temperature, LAI, vegetation indexes). In 
addition, Reclamation site specific meteorological data (e.g., Agrimet) may also be used 
to provide more accurate LIS forcing data. Higher resolution data such as from the 
Landsat ETM+ will be used for finer resolution estimates for land cover and leaf area 
index. Depending on data availability, LIS may be run retrospectively, in near real-time, 
and also provide short-term forecasts. 
Data Assimilation A key advantage to LIS is the data assimilation capability which 
combines land surface modeling with satellite and observational data to produce optimal 
ET estimates. LIS includes data assimilation methods such as the extended Kalman, 
ensemble Kalman and optimal insertion to merge observational and model data. This 
capability will enable spatially and temporally consistent fields of evapotranspiration 
from land surface models assimilated with surface ET measurements (e.g., eddy 
covariance, scintillometer, Bowen-ratio derived ET estimates). 
Prediction System The physical process modeling framework of LIS coupled with its data 
integration capability provides an excellent opportunity to provide forecasts of 
evapotranspiration and other water availability parameters such as soil moisture, snow 
water equivalent, and runoff. For example, short-term predictions may be derived from 
inputting NOAA weather prediction forcings (e.g., precipitation, radiation, wind, 
humidity) to drive LIS for several days in to the future (e.g., 1-14 days). 
REFERENCES 
Hartzell, C.L., Brower, LA. Hansen S. 2000. Agricultural Water Resources Decision 
Support System and Evapotranspiration Toolbox. 16"~ Conference on Hydrology, 14- 
17 January 2000. 
Mitchell, K. E., and coauthors, 2004: The multi-institution North American Land Data 
Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a 
continental distributed hydrological modeling system. J. Geophys. Res., 109, 
D07S90, doi: lO.l029/2003JD003823. 
Peters-Lidard, C.D., S.V. Kumar, Y. Tian, J.L. Eastman, and P.R. Houser, 2004: Global 
urban-scale land-atmosphere modeling with the Land Information System. Presented 
at the Symposium on Planning, Nowcasting, and Forecasting in the Urban Zone 
during the 84th Annual Meeting of the American Meteorological Society, 10-16 
January 2004, Seattle WA. 
Rodell, M., and coauthors, 2004: The Global Land Data Assimilation System. Bull. 
Amer. Meteor. Soc., 85,38 1-394. 
Tablel. Satellite based products for input and/or assimilation in to LIS. 
Land 
States 
Land 
Fluxes 
profiles 
Near- surface solar radiation 
Near-surface LW radiation 
Soil moisture 
Temperature 
Themlal anomalies 
Snow cover or water 
equivalent 
Freezehaw 
Ice cover 
Inunddon 
Total water storage 
Cloud water 
Cloud type 
Evapotranspiration 
Streadow 
Carbon flux 
Solar radialion 
Longwave radiation 
Sensible heat flux 
opticaVlR 
IR 
actnrdpassiie microwave, 
IR change 
IR, in-situ 
IR, MIR, optical 
optical, microwave 
Radar 
radar, lidar 
opticahicrowave 
Gravi i  
Radar 
IR, optical, MI 
optaNR, in-si i  
microwawe, laser 
optical, IR 
optical, IR 
IR 
AMSR 
GOES, MODIS, CERES, ERBS 
GOES, MODIS, CERES, ERBS 
SSMI, AMSR, HYDROS, SMOS, 
NPOESS, TRMM 
IR-GEO, MODIS, AVHRR, TOWS 
AVHRR, MODIS, TRMM 
SSMI, TM, MODIS, AMSR, AVHRR, 
NPOESS, GEO-IR, SMMR, future ESSP 
Quickscat, HYDROS, IceSAT, ClyoSAT 
IceSAT, GLlMS 
MODIS, ESSP Surface Water Mission 
GRACE 
CLOUDSAT 
TRMM, GPM 
MODIS, GOES 
ERS2, TOPEWPOSEIDON, future ESSP, 
GRDC 
MODIS, GOES, CERES, ERBS 
MODIS, GOES 
MODIS, ASTER, GOES 
hourly-weekly 
houdyweekly 
3-30 day 
hourlyinonthly 
daily-weekly 
weeklynonthly 
weekly 
weeklyinonthly 
weeklymonthly 
monthly 
hourly-weekly 
hourly-weekly 
hourly-weekly 
weeklyinonthly 
hourlymonthly 
hourly-monthly 
hourlyinonthly 
Ikm 
I krn 
10100 km 
1h-4km 
250 m - 
Ikm 
3km 
100m 
1 OOOkm 
Ihllkm 
10h-lkm 
lhQlrm 
lh-4km 
Ihh 
Table 2. Summary of MODIS available products. 
Table 3. Typical LIS forcing and output products. 
Land 
*MOD 09 - Surface Reflectance 
*MOD 11 - Land Surface Temperature & Emissivity 
*MOD 12 - Land CoverILand Cover Change 
*MOD 13 - Gridded Vegetation Indices (Max NDVI 
& Integrated MVI) 
*MOD 14 - Thermal Anomalies, Fires & Biomass 
Burning 
*MOD 15 - Leaf Area Index & WAR 
*MOD 16 - Evapotranspiration (Under development) 
*MOD 17 - Net Photosynthesis and Primary 
Productivity 
*MOD 43 - Surface Reflectance 
*MOD 44 - Vegetation Cover Conversion 
ATMOSPHERIC LAND SURFACE AND SUBSURFACE 
Cryosphere 
*MOD 10 - Snow Cover 
*MOD 29 - Sea Ice Cover 
Atmosphere 
*MOD 04 - Aerosol Product 
*MOD 05 - Total Precipitable Water (Water Vapor) 
*MOD 06 - Cloud Product 
*MOD 07 - Atmospheric Profiles 
*MOD 08 - Gridded Atmospheric Product 
*MOD 35 - Cloud Mask 
Net Shortwave Radiation (W/m2) 
Net Longwave Radiation (w/m2) 
Downward Solar Radiation Flux 
(w/m2) 
Downward Longwave Radiation 
Flux (w/m2) 
Snowfall, Frozen Precipitation 
@dm2) 
Rainfall, Unfrozen (kg/m2) 
Surface Pressure (Pa) 
Air Temperature, 2m (K) 
Specific Humidity, 2m (kgkg) 
U Wind Component ( d s )  
V Wind Component (mls) 
Convective Precipitation (kg/m2) 
Snowpack Water Equivalent Top 1 m Soil Moisture (kg/m2) 
(kg/m2) Layer 2 Soil Moisture (kg/m2) 
Snow Depth (m) Layer 3 Soil Moisture (kg/m2) 
Snow Cover (%) Total Soil Column Wetness (%) 
Snowmelt (kg/m2) Root Zone Wetness (%) 
Surface Runoff (kg/m2) Root Zone Soil Moisture (kg/rn2) 
Subsurface Runoff (kg/m2) Total Column Soil Moisture (kg/m2) 
Average Sfc Temperature (K) Plant Canopy Surface Water Storage 
Surface Albedo (%) (kg/m2> 
Canopy Surface Water Canopy Transpiration (w/m2) 
Vegetation Greenness (%) Aerodynamic Conductance (mls) 
Leaf Area Index Canopy conductance ( d s )  
Evaporation (w/m2) Sensible Heat Flux (w/m2) 
Deep Soil Temperature (K) Latent Heat Flux (w/m2) 
Canopy Temperature (K) Ground Heat Flux (w/m2) 
Figure 1. Flow diagram showing data integration of the Land Information System to 
provide evapotranspiration and other water availability parameters. 
NASA Land Information System Diagram 


NASA Land Information System (LIS) Water Availability to Support Reclamation ET Estimation

NASA Land Information System (LIS) Water Availability to Support Reclamation ET Estimation

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