Complementary-Relationship-Derived Actual Evapotranspiration for Operational Drought Monitoring
Journal Article
Overview
abstract
Abstract; ; To develop a dataset of actual evapotranspiration (ET; ; a; ; ) for operational drought monitoring, we used a model of the advection–aridity approach to the complementary relationship between ET; ; a; ; and evaporative demand and North American Land Data Assimilation System phase 2 (NLDAS-2) atmospheric forcing variables. We first ran the model on an uncalibrated basis, daily from 1980 to 2014 across the contiguous United States (CONUS). We observed strong seasonal differences (overestimation) relative to ET; ; a; ; from the fourth generation of Global Land Evaporation Amsterdam Model (GLEAM4), particularly in the peak summer season. This motivated calibrating model parameters to maintain a long-term water balance using streamflow from the Hydro-Climatic Data Network in 651 minimally disturbed catchments from the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset. Though calibrated ET; ; a; ; still exceeded GLEAM4 over most of the CONUS, absolute differences were reduced to <1 mm day; −1; , with regional variations. On a mean annual basis, its spatial distribution aligned with expectations, exhibiting inverse relationships with elevation and latitude, a minimum in the desert Southwest, and maxima in the upper Midwest and along the Gulf Coast and Atlantic coast in the summer. The response of calibrated ET; ; a; ; to past droughts defined in the U.S. Drought Monitor shows spatial similarity with GLEAM4 and other land surface models driven by NLDAS-2 but with higher skill across the southeastern and western United States and Great Plains and lower skill in the Intermountain West. The development of the 2011 Texas drought was well represented by ET; ; a; ; anomalies. These results suggest that, although technically simple, our complementary-relationship-derived ET; ; a; ; has utility in operational drought monitoring.; ; ; Significance Statement; ; Reliable estimates of actual evapotranspiration (ET; ; a; ; ) are critical for monitoring and understanding drought, yet existing datasets often require complex modeling frameworks that do not permit decomposition of the meteorological drivers of ET; ; a; ; and drought. We develop a long-term, contiguous United States (CONUS)-wide ET; ; a; ; dataset based on a physically grounded, computationally efficient model using the complementary relationship approach. After regional and seasonal calibration using streamflow data from minimally disturbed catchments and independent estimates of potential evaporation, our ET; ; a; ; closely aligns with independent benchmarks. The dataset demonstrates particular strength in capturing regional drought signals, especially in the southeastern and western United States and Great Plains. These findings highlight the value of a parsimonious ET; ; a; ; modeling approach for improving operational drought monitoring capabilities.; ;
