Institute: District of Columbia
Year Established: 2020 Start Date: 2020-03-01 End Date: 2021-02-28
Total Federal Funds: $10,000 Total Non-Federal Funds: $20,000
Principal Investigators: Leila Farhadi
Abstract: Quantifying the spatial patterns and determining the magnitude of groundwater recharge is important for understanding and managing groundwater systems. Groundwater recharge is a complex process, which depends on several factors, including the hydraulic properties of soils in the vadose zone. On the other hand, the rate of recharge is one of the main input data in hydrogeological and water quality models for saturated groundwater flow. Despite the importance of this fluxes there are no direct measurements â€“ in situ or by remote sensing â€“ that can allow any mapping and regional estimation of the rate of recharge. Long-term and spatially explicit (mapped) monitoring of recharge flux has been elusive goals and a grand challenge for hydrologists (NRC 2012). The research objective of this proposal is to develop a framework to quantify/map the patterns and dynamics of recharge flux using remotely sensed land surface state observations that are widely available across a range of spatial and temporal scales, landscapes and climates. In order to achieve this objective, the PI will develop and integrate state-of-the-art computational and data driven techniques to yield first order accurate estimates of key state and parameters (e.g., estimation control variables) of recharge flux from implicit information contained in the remotely sensed Land Surface state observations of Soil Moisture (SM). The developed approach is based on the reduced order variational data assimilation (VDA) scheme that assimilates state SM into a 1D mechanistic soil water model. 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. Given effective soil hydraulic parameters and the profile of soil moisture, diffusive recharge flux can be estimated following the Darcy equation or zero-flux plane techniques (Scanlon et al. 2002; Scott et al. 2000; Rushton et al. 2006). 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 recharge estimation (i.e. soil hydraulic parameters and profile of soil moisture) 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 recharge at watershed scales (ranging from 10m- 1km) from air-bone and space-borne measurements of surface soil moisture, at the Chesapeake Bay watershed.