Soil moisture conditions influence practically all aspects of Army activities and are increasingly affecting its systems
and operations. Regional distributions of high resolution soil moisture data will provide critical information on
operational mobility, penetration, and performance of landmine and UXO sensors. The US Army Corps of Engineers
(USACE) developed the Gridded Surface/Subsurface Hydrologic Analysis (GSSHA), which is a grid-based two-dimensional
hydrologic model that has been effectively applied to predict soil moisture conditions. GSSHA computes
evapotranspiration (ET) using the Penman-Monteith equation. However, lack of reliable spatially-distributed
meteorological data, particularly in denied areas, makes it difficult to reliably predict regional ET and soil moisture
distributions. SEBAL is a remote sensing algorithm that computes spatio-temporal patterns of ET using a surface
energy balance approach. SEBAL has been widely accepted and tested throughout the world against lysimeter, eddy-covariance
and other field measurements. SEBAL estimated ET has shown good consistency and agreement for
irrigated fields, rangelands and arid riparian areas. The main objective of this research is to demonstrate improved
GSSHA soil moisture and hydrological predictions using SEBAL estimates of ET. Initial results show that the use of
SEBAL ET and soil moisture estimates improves the ability of GSSHA to predict regional soil moisture distributions,
and reduces uncertainty in runoff predictions.
NASA/GSFC has developed with other groups a Land Data Assimilation System (LDAS) to output water and energy budgets for the primary purpose of improving weather and climate prediction. However, LDAS water and energy cycle outputs also may be coupled with other information to help with a wide range of water resources applications. For example, LDAS results may be used for water availability and quality, agricultural management and forecasting, assessment and prediction of snowmelt runoff, and flood and drought impact and prediction. Specifically, LDAS uses various satellites and ground based observations within a land surface modeling and data assimilation framework to produce optimal output fields of terrestrial energy, water and carbon fluxes. Current land surface outputs are gridded at 1/4° resolution globally and 1/8° for North America with work in progress to convert to a 1-km global grid. Integrated modeling, observations and data assimilation at various spatial and temporal scales helps LDAS to quantify terrestrial water, energy and biogeochemical processes. LDAS applications described in this paper are aimed at improving weather and seasonal forecasts. In addition, we also summarize the use of LDAS data to assist critical needs specified by the U.S. Bureau of Reclamation water resources management for selected basins in the western U.S.
We studied the importance of land surface heterogeneity on climate models using the MOSIAC Land-Surface Model (LSM). Preliminary analysis of results indicated there were errors in surface heat fluxes for certain geographical regions with contrasting cover such as forests, grasses, and crops when using only one cover class per grid. For spatially varying areas, two to four classes per grid typically captured most of the variation in surface energy and water fluxes. A Minimum Percent Cutoff approach to select the number of classes per grid (or tiles) was found the most efficient in terms of computer time and accuracy. In a comparison between 1/8 degree versus 1-degree grid resolutions, the finer resolution land cover data were more important than finer resolution atmospheric forcing data (e.g. precipitation and radiation) on latent heat flux estimation.