Global Evaporation Estimates from SMAP Passive Microwave Soil Moisture Retrievals Using Conditional Sampling.

Abstract:

Evaporation links the water, energy and carbon cycles over land yet even its climatology on global scale is not observed. Tower-based flux measurements are sparse and do not cover diverse biomes and climates. In the last decades, many strategies to derive evaporation based on remote sensing measurements have been developed. However, these methods are dependent on a variety of assumptions and auxiliary data, making them more prone to error propagation. A more data-driven method was developed by Salvucci (2001), who found that under statistical stationary conditions the expected change in soil moisture storage is zero when conditioned to a certain storage for a certain time interval. Consequently, using the water balance, precipitation conditionally averaged to the soil moisture storage is equal to the total loss: evaporation and drainage. Using only soil moisture and precipitation data as model inputs reduces the sources of uncertainty.

In this presentation we provide the first estimates of global evaporation from NASA's Soil Moisture Active Passive mission by applying the conditional sampling method to passive microwave soil moisture time series and in situ precipitation data. The obtained evaporation estimates show a good correspondence to measured evaporation from eddy correlation towers over selected field sites. Subsequently, a simple approach is developed to directly estimate evaporation from SMAP soil moisture data. This approach enables the investigation of dynamics in evaporation during the dry-down after storms. The timing of the transition between the different stages of evaporation is assessed for different climates especially the transition from stage 1 to stage 2 evaporation; atmosphere limited evaporation to soil limited evaporation respectively. Investigations into the dynamics of unstressed evaporation and transpiration and the transition from stage 1 to stage 2 evaporation increases our understanding of water stress and soil desiccation. It also provides insights into how the water, energy and carbon cycles link together over land.

Salvucci, G.D., 2001. Estimating the moisture dependence of root zone water loss using conditionally averaged precipitation. Water Resour. Res. 37, 1357–1365.