An Integrated Variational Framework for Coupled Estimation of Evapotranspiration and Recharge by Assimilating Land Surface Soil Moisture and Soil Temperature Data

Evapotranspiration (ET) and recharge fluxes are fluxes at the land-atmosphere interface. Evaporative flux links the surface and atmospheric systems and the recharge flux links the surface and subsurface systems. These are two critical fluxes in the water cycle that have major impact on agriculture, water supply, climate, biogeochemical cycles and etc. These fluxes are interconnected and depend on the soil moisture content.  In situ measurements of ET and recharge are costly, limited and cannot be readily scaled to regional scales relevant to weather and climate studies. Sequence of land surface state observations of moisture (SM) and temperature (LST), widely available from remote sensing across a range of scales, contain implicit information that can be used for characterization and mapping of evapotranspiration and recharge fluxes.

In this work, A variational data assimilation (VDA) framework is developed to estimate key parameters of ET and recharge flux by assimilating Soil Moisture Active Passive (SMAP) soil moisture and Geostationary Operational Environmental Satellite (GOES) land surface temperature data into a coupled dual-source energy and water balance model. These parameters include neutral bulk heat transfer coefficient (CH_N) and evaporative fraction from soil (EF_S) and canopy (EF_C)) that regulate the partitioning of available energy, and the effective saturated hydraulic conductivity (Ks) and bore size index (B) that regulate the movement of moisture into the soil column. The uncertainties of the retrieved parameters are estimated through the inverse of the hessian of the cost function, obtained using the lagrangian methodology. Analysis of the second-order information provides a tool to identify the optimum parameter estimates and guides towards a well-posed estimation problem.

The proposed framework is implemented over the US Southern great plain (SGP) and Oklahoma Panhandle region (with computational grid size of 0.05 degree) to map evapotranspiration and recharge fluxes across a range of temporal scales. Comparison with in-situ observations from the USCRN and the Mesonet sites show that the proposed assimilation framework can accurately estimate the temporal variability of root zone soil moisture profile. The evapotranspiration estimates show good agreement with the in-situ data from Atmospheric Radiation Measurement (ARM) sites at different locations and the estimated annual recharge flux values are within the range suggested in the literature for this region. Results demonstrate the success of the proposed assimilation framework in estimating key water cycle components and their interrelations across a range of spatial and temporal scales from remotely sensed near surface soil moisture and temperature data.