Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

Abstract

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation.Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records

“…We report surface soil drying rates as mm day −1 water equivalent, by multiplying the rate of volumetric soil moisture drying (mm 3 mm −3 day −1 ) by the sensing depth (50 mm). Drying rates are almost always less than 2 mm day −1 and tend to decrease through time after rainfall (e.g., Figure 5d), consistent with that reported by Shellito et al (2018) [32] and McColl et al (2017) [31]. Negative drying rates (i.e., soil is wetting) between 0 and 0.5 mm day −1 are not uncommon (Figure 5d), even during intervals when the soil is expected to dry from day to day (e.g., see early October observation in Figure 5d).…”

Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

“…Therefore, the fine‐resolution SMAP product will lead to a lower soil moisture, similar to the one observed by SMAP satellite, if it is averaged to a coarser spatial scale. Another plausible reason behind this, is the fact that SMAP is measuring a large‐scale average, and the averaging process dissipates anomalies more rapidly compared to the point (in situ) scale, leading to a faster drydown time scale (Shellito & Small, ; Shellito et al, ). Here the downscaled SMAP soil moistures are obtained using the model that is calibrated by in situ observations.…”

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

“…Figure 1b illustrates the general linear consistency between RR CVS and RR ASC . This apparent bias could also be associated with the violation of specific assumptions underlying our interpretation of RR CVS and RR ASC , for example, systematic differences in the representative depth of the surface soil moisture values derived from different sources (Shellito et al, 2018). This apparent bias could also be associated with the violation of specific assumptions underlying our interpretation of RR CVS and RR ASC , for example, systematic differences in the representative depth of the surface soil moisture values derived from different sources (Shellito et al, 2018).…”