Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.

Lately, drought is more intense and much more severe around the globe, causing more deaths than other hazards in the past century. Drought can be characterized quantitatively for its spatial extent, intensity and duration by using drought indicators. Several indicators have been developed in order to characterize drought, being the most widespread the Standardized Precipitation Index (SPI). Nevertheless, due to its known limitations, other indicators have been proposed. In this paper, evaporation and runoff simulations of a basin were used to evaluate the variation and performance of different meteorological and hydrological drought indicators in identifying drought. Daily simulations of evaporation and runoff were computed by using a distributed hydrological model of a catchment located in the southeast of Mexico. After calibration of the hydrological model, we calculated at different time steps the drought indicators: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Standardized Evapotranspiration Deficit Index (SEDI) and Standardized Runoff Index (SRI). Furthermore, the so-called Non-Contiguous Drought Analysis (NCDA) was carried out to compare the skill of each indicator to identify drought. Results show that meteorological drought indicators do not identify all drought events for the time steps of 1 and 3 months. For 3-, 6- and 9-month time steps, meteorological drought indicators tend to identify the onset with a lag. For long-time steps of 12 and 24, the use of agricultural and hydrological droughts indicators is recommended, since these indicators can identify prolonged drought periods. The results suggest that for a better monitoring of drought in a catchment, it is important the joint evaluation and the use of not only meteorological drought indicators but also hydrological and agricultural ones, in order to identify drought events and their spatio-temporal evolution

This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R²). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R² > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.