An Enhanced SPEI Drought Monitoring Method Integrating Land Surface Characteristics

Abstract. Atmospheric evaporative demand is a key metric for monitoring agricultural drought. The existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints of land surface characteristics and become less accurate over non-uniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic and physiological parameters, into existing potential evapotranspiration (PET) methods. This approach is implemented over the Continental United States (CONUS) for the period of 1981–2017 and is tested in a recently developed drought index the Standardized Precipitation Evapotranspiration Index (SPEI). We show that activating realistic maximum surface and aerodynamic conductance could improve prediction of soil moisture dynamics and drought impacts by 29 % on average compared to the widely used simple methods, especially effective in the forests and humid regions. Surface characteristics that have a strong influence on the performance of the SPEI are mainly driven by leaf area index (LAI). Our approach only requires the minimum amount of ancillary data, while permitting both historical reconstruction and real-time forecast of drought. This offers a physically meaningful, yet easy-to-implement way to account for the vegetation control in drought indices.