Division (IPAD) is responsible for forecasting and assessing global crop production and agricultural yields. IPAD uses a combination of satellite-derived data and land surface and crop modeling for these assessments, particularly in regions that lack traditional ground sensing data. From these analyses, IPAD provides a timely and standardized estimate of the status of global crop production -an essential part of international food security and management in areas and times of agricultural drought or stress. Soil moisture is a critical variable in the IPAD crop forecasting system because crop growth cycles are very dependent upon the near surface soil moisture amounts, particularly for root zone. However, soil moisture is a difficult to sample globally and at present, uncertainty in these soil moisture estimates lead to errors in crop forecasting ability and accuracy.In this study, we have presented the results from a study evaluating a soil moisture data assimilation system designed to integrate satellite-derived soil moisture estimates into a water balance model for improved root-zone soil moisture estimates. Our analysis primarily involves the comparison of multiple soil moisture model estimates with and without the integrated satellite observations over the conterminous United States. From this analysis we can quantitatively evaluate the performance of the data assimilation system and the root-zone soil moisture estimates which will be delivered to IPAD, Our results indicate that the system provides improved root-zone soil moisture estimates over most of the US with some degradation in the northeast and semi-arid areas of the southwest, which may be the result of inappropriate model parameters or poor satellite-derived products over vegetation regions.https://ntrs.nasa.gov/search.jsp?R=20100031160 2018-05-11T07:30:50+00:00Z
Comparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a thermal remote sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on thermal remote sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for the ESI were identified, incorporating a Penman–Monteith reference ET scaling flux and implementing a temporal smoothing algorithm at the pixel level. Of all indices evaluated, anomalies in the NLDAS ensemble-averaged SM provided the highest correlations with USDM drought classes, while the ESI yielded the best performance of the remote sensing indices. The VHI provided reasonable correlations, except under conditions of energy-limited vegetation growth during the cold season and at high latitudes. Change indices computed from ESI and SM time series agree well, and in combination offer a good indicator of change in drought severity class in the USDM, often preceding USDM class deterioration by several weeks. Results suggest that a merged ESI–SM change indicator may provide valuable early warning of rapidly evolving “flash drought” conditions.
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