Estimating Hydrological Regimes from Observational Soil Moisture, Evapotranspiration, and Air Temperature Data

Abstract

Abstract Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability. Significance Statement When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.