Institute: District of Columbia
Year Established: 2018 Start Date: 2018-03-01 End Date: 2019-02-28
Total Federal Funds: $10,000 Total Non-Federal Funds: $20,000
Principal Investigators: Leila Farhadi
Abstract: Spatially distributed soil moisture profiles are required for watershed applications such as drought and flood prediction, crop irrigation scheduling, pest management, and determining mobility with lightweight vehicles. Soil moisture is highly variable in space and time owing to the dynamics in soil hydraulic properties. Therefore, measurement and simulation of soil moisture pattern are of particular importance. Satellite-based soil moisture can be obtained from passive microwave, active microwave, and optical sensors, although the coarse spatial resolution of passive microwave and the inability to obtain vertically resolved information from optical sensors limit their usefulness for watershed-scale applications. Active microwave sensors such as synthetic aperture radar (SAR) currently represent the best approach for obtaining spatially distributed surface soil moisture at the fine spatial scale for watershed application. However, SAR provides surface soil moisture while the applications listed above require vertically resolved soil moisture profiles. In a synthetic study the potential of using surface soil moisture measurements obtained from different satellite platforms to retrieve soil moisture profiles and soil hydraulic properties will be explored using a model reduced variational data assimilation procedure and a 1D mechanistic soil water model. Four different homogeneous soil types will be investigated including loamy sand, loam, silt, and clayey soils. The forcing data including precipitation and potential evapotranspiration will be taken from the meteorological station in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site, located in a small catchment within the Anacostia River watershed. With the aid of the forward model run, a synthetic data set will be designed and observations will be generated. The virtual surface soil moisture observations will be then assimilated to estimate the soil moisture profile and soil hydraulic parameters of the model by means of a model reduced variational data assimilation method. The effect of assimilation strategy, measurement frequency, accuracy in surface soil moisture measurements, and soils differing in textural and hydraulic properties will be investigated. The approach will be able to assess the value of periodic space-borne observations of surface soil moisture for soil moisture profile estimation and for identifying the adequate conditions (e.g. temporal resolution and measurement accuracy) for remotely sensed soil moisture data assimilation. The results of this year-long study will provide the required foundation for a comprehensive research proposal focusing on estimation of soil moisture profile and soil hydraulic properties at watershed scales (ranging from 10m- 1km) from airbone and spaceborne measurements of surface soil moisture, for the Chesapeake Bay watershed.