Probabilistic assessment of agricultural droughts using graphical models

Abstract

Agricultural droughts are often characterized by soil moisture in the root zone of the soil, but crop needs are rarely factored into the analysis. Since water needs vary with crops, agricultural drought incidences in a region can be characterized better if crop responses to soil water deficits are also accounted for in the drought index. This study investigates agricultural droughts driven by plant stress due to soil moisture deficits using crop stress functions available in the literature. Crop water stress is assumed to begin at the soil moisture level corresponding to incipient stomatal closure, and reaches its maximum at the crop’s wilting point. Using available location-specific crop acreage data, a weighted crop water stress function is computed. A new probabilistic agricultural drought index is then developed within a hidden Markov model (HMM) framework that provides model uncertainty in drought classification and accounts for time dependence between drought states. The proposed index allows probabilistic classification of the drought states and takes due cognizance of the stress experienced by the crop due to soil moisture deficit. The capabilities of HMM model formulations for assessing agricultural droughts are compared to those of current drought indices such as standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (SC-PDSI). The HMM model identified critical drought events and several drought occurrences that are not detected by either SPEI or SC-PDSI, and shows promise as a tool for agricultural drought studies.