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.

Eastern Algeria, the wettest region of the country, has experienced a vast program of dam construction, distributed across basins marked by strong physio-climatic disparities. The hydrological functioning studied through the use of data from the regulation balance sheet for the period from September 1990 to August 2013, concerned a sample of 10 dams (capacity varying from 3 to 200 hm³) whose year of impoundment varies from 1939 to 1987. The results of the statistical and spectral analysis applied to two main terms of the water balance (inputs and rainfalls) indicate a strong relationship between the studied parameters, reflecting a net increase in dry years from September 1990 to August 2002, with a return of wet years from the year 2003, however, interrupted by other very dry years like that of 2007/2008. The outflows (outputs) of the dams reflect considerable water losses in the form of evaporation and also in the form of leaks on karstic sites. However, it is the flood phenomena that are at the origin of remarkable overflows and bottom outlets. In addition to these losses, there are significant variations in the regulated volume of dams, particularly in semi-arid zones and during periods of drought, particularly.

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.