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The consequences of global warming will aggravate the impacts of droughts. Future drought patterns are important for planning and policy formulation to mitigate the adverse effect of climate change. Consequently, this study aims at examining the projected the drought characteristics in seven meteorological stations in the Upper Draa Catchment (UDC) during the period from 1980 to 2016 using standardized precipitation index. The future climate scenarios were predicted by the model CNRM-ALADIN63 for three periods (2025–2049, 2050–2074, and 2075–2099). The changes were examined based on two Representative Concentration Pathways scenarios, namely: RCP4.5and RCP8.5. The findings indicated that increasingly extreme droughts are anticipated to occur during (2050-2074) followed by (2025-2049) than (2075-2099) under both scenarios. The results reveal a contrast in drought event frequency between historical data and projections with a noticeable variation of patterns of droughts characteristics across stations and time periods. This accentuates how urgent it is for the Upper Draa Catchment to implement proactive water resource management and adaptive strategies.

Drought forecasting has implications for managing water and irrigation. Currently, with improved technology like artificial intelligence, forecasting can be more accurate. In this research, we chose standardized potential evapotranspiration index (SPEI) to characterize drought pattern. To achieve this, the data used was acquired from five meteorological stations in an irrigated Moroccan perimeter from 1976 to 2015. Besides, we predict SPEI at two scales (SPEI-3 and SPEI-6) with two inputs combination by exploring the capabilities of M5 pruned (M5P) and Light Gradient Boosting Machine (LightGBM), along with their hybrid model (LightGBM-M5P). To assess their effectiveness, we employed three statistical metrics (R2, MAE and RMSE). The findings indicated that using a larger time scale for analysis enhances the ability to forecast SPEI values more accurately. Moreover, the forecasting analysis revealed that M5P model demonstrated superior performance compared to the other studied models.

Monitoring changes in water areas within arid regions is vital for directing water resource development and ensuring efficient use, and addressing the impacts of drought and climate change. Thus, in this research, we examined the annual changes in surface water at the El Mansour Eddahbi (EME) dam (southeastern Morocco) from 1984 to 2023. To achieve this, we exploited the Google Earth Engine’s potential and applied four water indices – MNDWI, EWI, NWI, and AWEI – to Landsat satellite images. Subsequently, for each year, a supervised classification utilizing random forest was implemented to accurately extract and identify water areas. The findings revealed that the EME dam’s water surface area fluctuates substantially on an inter-annual basis. Besides, Pearson’s analysis demonstrates that the EME dam’s water surface area has a strong positive correlation with drought indices such as SPI-12 and SPEI-12. This highlights the link between changing surface water, drought, and the need for adaptive water management under climate change.

Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M'semrir and Agouim (>218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (<19%) across all the timescales considered for both SPI and SPEI, with Mansour Eddahbi Dam and Assaka recording the highest frequencies for SPI-3 and SPEI-3, respectively. [ABSTRACT FROM AUTHOR]