Author(s): Ronnie J. Araneda-Cabrera; Maria Bermudez; Jeronimo Puertas

Linked Author(s): María Bermúdez

Keywords: Drought index; Drought affected area; Mozambique; Drought monitoring

Abstract: Drought is one of the phenomena that causes the greatest socioeconomic and environmental losses. Climate change and human activity could increase its negative effects, so its management and prediction are of utmost importance. In this work we present the results and discussion of a methodology to evaluate and compare 7 standardized drought indices and explore their applicability to monitoring agricultural yields of rainfed cereals on a national scale. The methodology followed four steps: a) computation of the national level aggregated indicators (DIs) and the percentage of area affected by drought (PAA) for several temporal aggregations (-1 to -12 months); b) validation of the Dis and PAA through an analysis of the probability of detection (POD) or the hit rate, and the probability of false detection (POFD) or the false alarm rate; c) analysis of cross-correlations between DIs; d) application of a statistical model of crop yield using the DIs (each month) and PAA (annual) as candidate predictors. The case study was Mozambique, a country with poor hydrometeorological monitoring and scarce subnational agricultural data, so we used global climate databases (satellite and measured) for the computation of DIs and PAA. The cereals chosen for the analysis of yield variability were sorghum and maize, since they are the most important in the country and their production is mostly rainfed. The results showed that the Standardized Precipitation and Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI) and the Standardized Soil Moisture Index (SSI) were the ones that best detected the historical droughts registered, using the DIs as PAA. Following the physical development of the phenomenon, it was found that the SPEI, based on meteorological variables, presents drought events between 1 and 4 months earlier than those based on soil moisture (SSI and PDSI), with higher correlation values for 12-month temporal aggregations (0.55>R2>0.87). The SPEI (at the end of dry season and with short aggregations) best explained the agricultural yields of both crops, however, the PAA was consistently better than the DI for all aggregations evaluated. Through an empirical spatial analysis of the dry years, we found that the calculation of DI smooths the extreme values of the spatially distributed indices, while the PAA reveals all the spatial characteristics, which would explain the better performance of the PAA against the DI, despite having high correlations between them (R>0.80). The proposed methodology could be useful for countries or areas with limited subnational agricultural data, while the results could be useful as a tool for drought managers in Mozambique.

DOI: https://doi.org/10.3850/IAHR-39WC2521711920221613

Year: 2022