Drought is one of the most severe natural disasters worldwide, but with a particular emphasis in sub-humid and semi-arid climates. Several indices have been created to appropriately identify drought’s characteristics and variability. The main objectives of this study consisted of analyzing the behavior of different indices applied in northeast Algeria and comparing them across a long-term data set (1961–2014). The SPI and RDI at 9-month time scales were compared to the PDSI and MedPDSI based on 123 rainfall stations and gridded PET data interpolated to all the locations. A principal component analysis (PCA) in S-mode with varimax rotation (RPC) was applied to the monthly values of all indices to analyze the spatiotemporal patterns of droughts. Two principal components were retained, which identified two sub-regions with coherent differences related to their distance from the Mediterranean Sea and the UNEP aridity index. Trends in the RPC scores were assessed using the modified Mann–Kendall (MMK) test and Sen’s slope estimator, which showed a fundamental difference between the two sub-regions. The RPC of all drought indices showed trends of decreases in the frequency and severity of droughts in the northern sub-region, and trends of increases in the frequency and severity of droughts in the southern region, where the climate is mostly semi-arid and arid. Only a few cases were statistically significant, mostly when using the PDSI and MedPDSI for the southern sub-region. The spatial patterns of moderate, severe, and extreme drought occurrences were similar for the SPI and RDI pair of indices based on the probability of rainfall anomalies, and for the Sc-PDSI and MedPDSI pair based on water balance anomalies. The interpretation of the spatial variability of droughts, mainly of the extreme ones, was supported by an analysis of semi-variograms. The novel index MedPDSI compared well with the other indices and showed advantages of performing the soil water balance following the FAO56 dual K_c method with the actual olive evapotranspiration instead of PET, and of better explaining the spatial variability of extreme droughts; in addition, the trends detected were significant for both the northern and southern sub-regions.

This study investigates the spatio-temporal distribution of meteorological drought in the upper Cheliff basin, Algeria, downstream of the Boughzoul dam, between September 1982 and August 2021. This research use two drought indices—the Standardised Precipitation Index (SPI) and the Standardised Precipitation and Evapotranspiration Index (SPEI)—to evaluate drought trends, frequency, duration, severity, and number of events across various time scales (1 year, 1 month, 3 months, 6 months, 9 months, and 12 months). The results identify five major drought periods (1983/84, 1993/94, 1987/88, 1999/2000–2001/2002, and 2020/21). Both the SPI and the SPEI capture the monthly variability of drought on various time scales, with different intensities. The SPEI identifies a higher number of drought events than the SPI, particularly on shorter time scales (1 and 3 months). However, at longer timescales (6, 9, and 12 months), the number of drought events detected by both indices converges. The correlation between SPI and SPEI (R ranging from 0.73 to 0.93) across the same time scales is notably high, though the lowest correlation was found in the western part of the catchment area. This suggests that for accurate meteorological drought identification in this region, particularly in its intensively irrigated agricultural areas, SPI and SPEI should be considered.

In recent years, there has been a common use of automatic precipitation mapping based on various interpolation methods. Based on multiple linear regression, the study primarily focuses on the entire Cheliff basin (43 750 km²), to search a suitable model for mapping mean annual precipitation based on 89 rainfall stations of the observed series (1968/69–2001/2002). Then conducts on the upper part of Cheliff basin downstream the Boughzoul dam (4777 km²) using 11 rainfall stations of annual precipitation series relative to the period 1982/83–2020/2021. The same approach was used to map median annual precipitations and quantiles of rain associated with the return periods of 5 and 10 years, representing both wet and dry periods. Indeed, this work is based on the backward elimination method between the observed annual precipitation and four predictors: smoothed altitude (Zs), longitude (X), latitude (Y) and distance from the sea (D). The model’s results are satisfactory with a global correlation coefficient for both regional (R = 0,89) and local scale (R > 0,81). The final maps obtained are produced after interpolating by kriging the residuals. The median annual precipitation map was compared to that elaborated by the National Hydraulic Resources Agency (ANRH). The comparison results of 2296 nodes of the grid covering the Upper Cheliff basin showed negative deviations on average - 10% and reaching - 34% in northern area reflecting a dry trend in precipitation. The final grids of rainfall can be integrated into Geographical Information Systems related to many sectors including water management and climate change.