Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.
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