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In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards' equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration. [ABSTRACT FROM AUTHOR]

In California and other regions vulnerable to water shortages, satellite-derived estimates of key hydrologic fluxes can support agricultural producers and water managers in maximizing the benefits of available water supplies. The Satellite Irrigation Management Support (SIMS) project combines NASA's Terrestrial Observation and Prediction System (TOPS), Landsat and MODIS satellite imagery, and surface sensor networks to map indicators of crop irrigation demand and develop information products to support irrigation management and other water use decisions. TOPS-SIMS provides the computing and data processing systems required to support automated, near real-time integration of observations from satellite and surface sensor networks, and generates data and information in formats that are convenient for agricultural producers, water managers, and other end users. Using the TOPS modeling framework to integrate data from multiple sensor networks in near real-time, SIMS currently maps crop fractional cover, basal crop coefficients, and basal crop evapotranspiration. Map products are generated at 30 m resolution on a daily basis over approximately 4 million ha of California farmland. TOPS-SIMS is a fully operational prototype, and a publicly available beta-version of the web interface is being pilot tested by farmers, irrigation consultants, and water managers in California. Data products are distributed via dynamic web services, which support both visual mapping and time-series queries, to allow users to obtain information on spatial and temporal patterns in crop canopy development and water requirements. TOPS-SIMS is an application framework that demonstrates the value of integrating multi-disciplinary Earth observation systems to provide benefits for water resource management. [ABSTRACT FROM PUBLISHER]

Satellite based retrievals of evapotranspiration (ET) are widely used for assessments of global and regional scale surface fluxes. However, the partitioning of the estimated ET between soil evaporation, transpiration, and canopy interception regularly shows strong divergence between models, and to date, remains largely unvalidated. To examine this problem, this paper considers three algorithms: the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Surface flux estimates from these three models, obtained via the WACMOS-ET initiative, are compared against a comprehensive collection of field studies, spanning a wide range of climates and land cover types. Overall, we find errors between estimates of field and remote sensing-based soil evaporation (RMSD = 90–114%, r 2  = 0.14–0.25, N = 35), interception (RMSD = 62–181%, r 2  = 0.39–0.85, N = 13), and transpiration (RMSD = 54–114%, r 2  = 0.33–0.55, N = 35) are relatively large compared to the combined estimates of total ET (RMSD = 35–49%, r 2  = 0.61–0.75, N = 35). Errors in modeled ET components are compared between land cover types, field methods, and precipitation regimes. Modeled estimates of soil evaporation were found to have significant deviations from observed values across all three models, while the characterization of vegetation effects also influences errors in all three components. Improvements in these estimates, and other satellite based partitioning estimates are likely to lead to better understanding of the movement of water through the soil-plant-water continuum. [ABSTRACT FROM AUTHOR]