SEARCH · Search NASA

Results for “Terrestrial Water Storage”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS2011

Using Enhanced Grace Water Storage Data to Improve Drought Detection by the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations of the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including groundwater. The U.S. and North American Drought Monitors rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors by filling this observational gap. GRACE TWS data were assimilating into the Catchment Land Surface Model using an ensemble Kalman smoother enabling spatial and temporal downscaling and vertical decomposition into soil moisture and groundwater components. The Drought Monitors combine several short- and long-term drought indicators expressed in percentiles as a reference to their historical frequency of occurrence. To be consistent, we generated a climatology of estimated soil moisture and ground water based on a 60-year Catchment model simulation, which was used to convert seven years of GRACE assimilated fields into drought indicator percentiles. At this stage we provide a preliminary evaluation of the GRACE assimilated moisture and indicator fields.

Houborg, Rasmus↗

NASA NTRS2015

Groundwater Storage Changes: Present Status from GRACE Observations

Satellite gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) provide quantitative measurement of terrestrial water storage (TWS) changes with unprecedented accuracy. Combining GRACE-observed TWS changes and independent estimates of water change in soil and snow and surface reservoirs offers a means for estimating groundwater storage change. Since its launch in March 2002, GRACE time-variable gravity data have been successfully used to quantify long-term groundwater storage changes in different regions over the world, including northwest India, the High Plains Aquifer and the Central Valley in the USA, the North China Plain, Middle East, and southern Murray-Darling Basin in Australia, where groundwater storage has been significantly depleted in recent years (or decades). It is difficult to rely on in situ groundwater measurements for accurate quantification of large, regional-scale groundwater storage changes, especially at long timescales due to inadequate spatial and temporal coverage of in situ data and uncertainties in storage coefficients. The now nearly 13 years of GRACE gravity data provide a successful and unique complementary tool for monitoring and measuring groundwater changes on a global and regional basis. Despite the successful applications of GRACE in studying global groundwater storage change, there are still some major challenges limiting the application and interpretation of GRACE data. In this paper, we present an overview of GRACE applications in groundwater studies and discuss if and how the main challenges to using GRACE data can be addressed.

Australia↗

NASA NTRS2012

Conceptual Problems in Land Surface Data Assimilation

A land data assimilation system (LDAS) merges observations (or satellite retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, several conceptual problems can interfere with realizing the potential improvements from data assimilation. Of particular concern is the frequent mismatch between the assimilated observations and the land surface model variables of interest. The seminar will discuss recent research with the ensemble-based NASA GEOS-S LDAS to address various aspects of this mismatch. These aspects include (i) the assimilation of coarse-scale observations into higher-resolution land surface models, (ii) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (iii) the forward modeling of microwave brightness temperatures over land for radiance-based land surface data aSSimilation, and (iv) the selection of the most relevant types of observations for the analysis of a specific water cycle variable (such as root zone soil moisture). At its core, the solution to the above challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Reichle, Rolf↗

NASA NTRS2013

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Land data assimilation↗

NASA NTRS2019

Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models

This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5-7 decades with modeled rates ranging from 0.01 to 2.18 mm year-1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.

Groundwater storage↗

NASA NTRS2019

Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models

This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate experiment (GRACe) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5–7 decades with modeled rates ranging from 0.01 to 2.18 mm year−1. this large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.

Bailing Li↗

NASA NTRS2007

Improving Land-Surface Model Hydrology: Is an Explicit Aquifer Model Better than a Deeper Soil Profile?

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the storage and movement of water (including soil moisture, snow, evaporation, and runoff) after it falls to the ground as precipitation. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy. Hence LSMs have been developed to integrate the available information, including satellite observations, using powerful computers, in order to track water storage and redistribution. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. Recently, the models have begun to simulate groundwater storage. In this paper, we compare several possible approaches, and examine the pitfalls associated with trying to estimate aquifer parameters (such as porosity) that are required by the models. We find that explicit representation of groundwater, as opposed to the addition of deeper soil layers, considerably decreases the sensitivity of modeled terrestrial water storage to aquifer parameter choices. We also show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a harmonious set.

Gulden, L. E.↗

NASA NTRS2016

Extreme Water Deficit in Brazil Detected from Space

Extreme droughts have caused significant socioeconomic and environmental damage worldwide. In Brazil, ineffective energy development and water management policies have magnified the impacts of recent severe droughts, which include massive agricultural losses, water supply restrictions, and energy rationing. Spaceborne remote sensing data advance our understanding of the spatiotemporal variability of large-scale droughts and enhance the detection and monitoring of extreme water-related events. In this study, data derived from the Gravity Recovery and Climate Experiment (GRACE) mission are used to detect and quantify an extended major drought over eastern Brazil and provide estimates of impacted areas and region-specific water deficits. Two structural breakpoint detection methods were applied to time series of GRACE-based terrestrial water storage anomalies (TWSA), determining when two abrupt changes occurred. One, in particular, defines the beginning of the current drought. Using TWSA, a water loss rate of 26.1 cmyr21 over southeastern Brazil was detected from 2012 to 2015. Based on analysis of Global Land Data Assimilation System(GLDAS) outputs, the extreme drought is mostly related to lower-than-usual precipitation rates, resulting in high soil moisture depletion and lower-than-usual rates of evapotranspiration. A reduction of 2023 of precipitation over an extended period of 3 years is enough to raise serious water scarcity conditions in the country. Correlations between monthly time series of both grid-based TWSA and ground-based water storage measurements at 16 reservoirs located within southeastern Brazil varied from 0.42 to 0.82. Differences are mainly explained by reservoir sizes and proximity to the drought nucleus.

terrestrial↗

NASA NTRS2018

UAS-Based P-Band Signals of Opportunity for Remote Sensing of Snow and Root Zone Soil Moisture

We have developed the P-band Signals of Opportunity (SoOp) sensor based on the Unmanned Aircraft System (UAS) to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The P-band UAS SoOp sensor for Hydrology (UASHydro) would operate on the S2 aircraft developed by Black Swift Technologies for sensing of SWE and RZSM with a spatial resolution of about 10m. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The long-term goal of our development would be to use small UAS to perform regional high resolution observation of two key hydrological measurements to improve the estimation of terrestrial water storage for water management, crop production and forecasts of natural hazard. The UASHydro concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals at the 360-380 MHz band from geostationary Mobile Use Objective System (MUOS) communication satellites launched by the US Navy. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE, while the soil moisture can be retrieved from the reflectivity at the P-band frequencies for MUOS. We have been conducting ground-based campaigns to test the instrumentation and data processing methods at the Fraser Experimental Forest in Colorado since February 2016. The field campaign data has provided support to the measurement concept. To install the SoOp technologies on the UAS, a lightweight antenna has been built and interfaces with the S2 built by Black Swift Technologies have been completed. A set of flights have been planned starting April 2018 through the end of 2018 in Colorado.

Elston, Jack↗

NASA NTRS2022

Using Sentinel-1 and GRACE Satellite Data to Monitor the Hydrological Variations Within the Tulare Basin, California

Subsidence induced by groundwater depletion is a grave problem in many regions around the world, leading to a permanent loss of groundwater storage within an aquifer and even producing structural damage at the Earth’s surface. California’s Tulare Basin is no exception, experiencing about a meter of subsidence between 2015 and 2020. However, understanding the relationship between changes in groundwater volumes and ground deformation has proven difficult. We employ surface displacement measurements from Interferometric Synthetic Aperture Radar (InSAR) and gravimetric estimates of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellite pair to characterize the hydrological dynamics within the Tulare basin. The removal of the long-term aquifer compaction from the InSAR time series reveals coherent short-term variations that correlate with hydrological features. For example, in the winter of 2018–2019 uplift is observed at the confluence of several rivers and streams that drain into the southeastern edge of the basin. These observations, combined with estimates of mass changes obtained from the orbiting GRACE satellites, form the basis for imaging the monthly spatial variations in water volumes. This approach facilitates the quick and effective synthesis of InSAR and gravimetric datasets and will aid efforts to improve our understanding and management of groundwater resources around the world.

Donald W. Vasco↗

NASA NTRS2012

Application of GRACE for Monitoring Groundwater in Data Scarce Regions

In the United States, groundwater storage is somewhat well monitored (spatial and temporal data gaps notwithstanding) and abundant data are freely and easily accessible. Outside of the U.S., groundwater often is not monitored systematically and where it is the data are rarely centralized and made available. Since 2002 the Gravity Recovery and Climate Experiment (GRACE) satellite mission has delivered gravity field observations which have been used to infer variations in total terrestrial water storage, including groundwater, at regional to continental scales. Challenges to using GRACE for groundwater monitoring include its relatively coarse spatial and temporal resolutions, its inability to differentiate groundwater from other types of water on and under the land surface, and typical 2-3 month data latency. Data assimilation can be used to overcome these challenges, but uncertainty in the results remains and is difficult to quantify without independent observations. Nevertheless, the results are preferable to the alternative - no data at all- and GRACE has already revealed groundwater variability and trends in regions where only anecdotal evidence existed previously.

Rodell, Matt↗

NASA NTRS2019

Quantifying Water Storage Change and Land Subsidence Induced by Reservoir Impoundment Using GRACE, Landsat, and GPS Data

The construction of hydropower dams is a common strategy to support a country's increasing need for electricity and river water management for industry and agriculture. Although the hydrological and geophysical impacts of water relocation are usually assessed prior to impoundment, their accuracy is generally limited due to the lack of in situ observations, especially in a remote area. This study presents a workflow to quantify the terrestrial water storage change (TWS) and land subsidence induced by a reservoir's water impoundment using multiple satellite observations (GRACE, Landsat), land surface models (CABLE, GLDAS, NCEP, ECMWF), and GPS data. The study site is the Bakun Dam, located in Sarawak, Malaysia, which is the largest hydropower dam in Southeast Asia. Commencing operation in late 2010, the dam induced a change of water mass and lake surface area that was clearly observed by GRACE and Landsat observations, respectively. During the 17-month impounding period (from August 2010 to December 2011), GRACE observed a dramatic increase of approximately 200 mm equivalent water height, while Landsat detected an increased lake extent of around 600 km2. In this paper, a forward model is developed to determine the increased water surface level corresponding to GRACE observations, estimated to be about 120 m. In contrast to GRACE, the TWS derived from land surface models cannot capture the increased TWS, due to the lack of reservoir routing algorithms in the models. In addition, the land subsidence was calculated using the disk load model constructed based on the GRACE-derived lake level and Landsat-derived lake extent; the result is validated with the GPS data from BIN1 station, located at the western coast of Borneo. The commencement stage of the Bakun Dam induces the large-scale land subsidence, which causes the GPS-BIN1 station to subside by ~9 mm, and move toward the Bakun Lake by ~4 mm. Computation of the surface displacements directly from GRACE spherical harmonic coefficient data fails to capture the subsidence feature, mainly due to the truncation error. Overall, this study demonstrates that evaluating GRACE in conjunction with Landsat, LSMs, and GPS data allows the exploitation of the gravity signal at a much smaller spatial scale than its intrinsic resolution. Benefiting from global coverage, the newly developed satellite-based algorithm is a valuable tool for assessing the impacts of reservoir operation on hydrological and geophysical changes from local to regional scales.

Natthachet Tangdamrongsub↗

NASA NTRS2023

Water Cycle Science Enabled By the GRACE and GRACE-FO Satellite Missions

Satellite observations of the time-variable gravity field revolutionized the monitoring of large-scale water storage changes, beginning with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE) mission. Most hydrologists were sceptical of the satellite gravimetry approach at first, but validation studies assuaged their concerns and high-profile, GRACE-based groundwater depletion studies caused an explosion of interest. The importance of GRACE observations for hydrologic and cryospheric science became so great that GRACE Follow-On (GRACE-FO) jumped the National Aeronautics and Space Administration’s Earth science mission queue and launched in 2018. A third mass change mission is currently under development. Here, we review key milestones in satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources studies and the sole source of observations of what is now an ‘essential climate variable’, terrestrial water storage.

climate change↗

NASA NTRS2015

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

Monitoring groundwater drought using land surface models is a valuable alternative given the current lack of systematic in situ measurements at continental and global scales and the low resolution of current remote sensing based groundwater data. However, uncertainties inherent to land surface models may impede drought detection, and thus should be assessed using independent data sources. In this study, we evaluated a groundwater drought index (GWI) derived from monthly groundwater storage output from the Catchment Land Surface Model (CLSM) using a GWI similarly derived from in situ groundwater observations. Groundwater observations were obtained from unconfined or semi-confined aquifers in eight regions of the central and northeastern U.S. Regional average GWI derived from CLSM exhibited strong correlation with that from observation wells, with correlation coefficients between 0.43 and 0.92. GWI from both in situ data and CLSM was generally better correlated with the Standard Precipitation Index (SPI) at 12 and 24 month timescales than at shorter timescales, but it varied depending on climate conditions. The correlation between CLSM derived GWI and SPI generally decreases with increasing depth to the water table, which in turn depends on both bedrock depth (a CLSM parameter) and mean annual precipitation. The persistence of CLSM derived GWI is spatially varied and again shows a strong influence of depth to groundwater. CLSM derived GWI generally persists longer than GWI derived from in situ data, due at least in part to the inability of coarse model inputs to capture high frequency meteorological variability at local scales. The study also showed that groundwater can have a significant impact on soil moisture persistence where the water table is shallow. Soil moisture persistence was estimated to be longer in the eastern U.S. than in the west, in contrast to previous findings that were based on models that did not represent groundwater. Assimilation of terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission improved the correlation between CLSM based regional average GWI and that based on in situ data in six of the eight regions. Practical issues regarding the application of GRACE assimilated groundwater storage for drought detection are discussed. An important conclusion of this study is that model parameters that control the depth to the water table, including bedrock depth, strongly influence the evolution and persistence of simulated groundwater and require careful configuration for drought monitoring.

Evaluation↗

NASA NTRS2014

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

hydrology↗

NASA NTRS2019

Evaluating the Uncertainty of Terrestrial Water Budget Components over High Mountain Asia

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Terrestrial water budget↗

NASA NTRS2009

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Reichle, Rolf↗

NASA NTRS2010

A Summary of NASA Related Contributions for the Remote Sensing of Evapotranspiration in Support of Water Management and Agriculture

The amount of evapotranspiration (ET) to the atmosphere can account for 60% or more of the water loss in many semi-arid locations, and can critically affect local economies tied to agriculture, recreation, hydroelectric power, ecosystems, and numerous other water-related areas. NASA supports many activities using satellite and Earth science data to more accurately and cost effectively estimate ET. NASA ET related work includes the research, development and application of techniques. The free and open access of NASA satellite data and products now permits a much wider application of ET mapping. Typically the NASA supported approaches ranges from large regional and continental ET mapping using MODIS (also with AIRS and CERES), GRACE (gravimetric water balance), geostationary (e.g., GOES and Meteosat for near continental sca|e), land surface modeling (i.e, Land Data Assimilation Systems) to fine scale mapping such as provided bvLandsatdata(<100 m). Usually satellite or airborne thermal imagery are used as input to an ET estimated surface energy balance based approach. There are currently several of these ET approaches under development and implementation including 'METRIC', 'SEBS', 'ALEXI/DisALEXI', etc.. One exception is an approach using GRACE satellite data that estimates the terrestrial water storage using gravimetric data over large areas and estimates ET indirectly. Also land surface modeling within the context of data assimilation and integration schemes provides the capability to integrate in situ, ancillary and satellite together to provide a spatially and synoptic estimates of ET also for use to provide for short-term ET predictions. We will summarize NASA related activities contributing to the improved estimation of ET for water management and agriculture with an emphasis on the Western U3.. This summary includes a description of ET projects in the Middle Rio Grande, Yakima, North Platte and other selected basins in the western US. We will also discuss plans to further address ET applications through working with the USDA and the Group on Earth Observations (GEO) to extend and evaluate western U.S. ET mapping to other parts of the U.S. and internationally.

Toll, David↗