Carbone, Emily F 1 ; Badger, Andrew 2 ; Livneh, Ben 3 ; Small, Eric E 4
1 University of Colorado Boulder
2 Cooperative Institute for Research in Environmental Sciences
3 Cooperative Institute for Research in Environmental Sciences
4 University of Colorado Boulder
Evapotranspiration (ET) is fundamental to the water, energy and carbon cycles. However, our ability to measure ET and partition the total flux into transpiration and evaporation from soil is limited. This project aims to generate a global, observationally-based soil evaporation dataset (E-SMAP), using SMAP soil moisture data in conjunction with models and auxiliary observations to observe or estimate each component of the surface water balance.
We first estimate the flux between the soil surface and root zone layers (qbot), which dictates the proportion of water that is available for soil evaporation to that which travels deeper and contributes to transpiration or groundwater recharge. The magnitude and direction of qbot are driven by gravity and the gradient in matric potential. We use a highly discretized Richards Equation-type model (e.g. Hydrus 1D software) with meteorological forcing from the North American Land Data Assimilation System (NLDAS), verified with SMAP L4 surface and root zone soil moisture data.
We next estimate transpiration from the surface layer using potential evapotranspiration, percentage of roots in the surface layer and a resistance calculation. Both bottom flux and transpiration from the surface layer are generally small at most times, but they cannot be ignored.
After qbot and transpiration are constrained, we can finally calculate the E-SMAP data product as the residual of the surface water balance. Preliminary calculations of E-SMAP give soil evaporation estimates of approximately 0-3 mm/day and less than ET estimates. E-SMAP is a unique approach to measure one component of ET, by combining L-band radiometer data with the surface water balance. This product will enable a better understanding of water balance processes and contribute to forecasts of water resource availability.