Drought directly impacts the living organisms and environment, and thereby, its assessment is essential. Different drought indices require different data, which can be obtained based on models or in-situ measurements, demanding a significant amount of effort. Using remotely sensed (RS) data from satellites can facilitate this data acquisition. Nowadays, more and more satellite techniques are rising, highlighting the need to assess the accuracy of their data and the reliability of the results obtained by employing them. The Wet-environment Evapotranspiration Precipitation Standardized Index (WEPSI) has shown good performance in drought monitoring and assessment, especially for agricultural purposes. This chapter employs the Global Land Evaporation Amsterdam Model (GLEAM) data to investigate its applicability in the Lempa River basin drought assessment using WEPSI. In this order, evaluated data obtained from the Water Evaluation and Planning system (WEAP) were used as the basis for comparison. Precisely, a comparison was made with GLEAM and WEAP-based data as well as WEPSI time series based on these two datasets. The results show a relatively high similarity between these two datasets and calculated WEPSI drought indices. This validates the good performance of GLEAM-based data in drought monitoring and assessment based on WEPSI.@en

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.

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