A new precipitation dataset is provided since 2014 by the Global Precipitation Measurement (GPM) satellite constellation measurements combined in the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm. This recent GPM-IMERG dataset provides potentially useful precipitation data for regions with a low density of rain gauges. The main objective of this study is to evaluate the accuracy of the near real-time product (IMERG-E) compared to observed rainfall and its suitability for hydrological modeling over a mountainous watershed in Morocco, the Ghdat located upstream the city of Marrakech. Several statistical indices have been computed and a hydrological model has been driven with IMERG-E rainfall to estimate its suitability to simulate floods during the period from 2011 to 2018. The following results were obtained: (1) Compared to the rain gauge data, satellite precipitation data overestimates rainfall amounts with a relative bias of +35.61% (2) In terms of the precipitation detection capability, the IMERG-E performs better at reproducing the different precipitation statistics at the catchment scale, rather than at the pixel scale (3) The flood events can be simulated with the hydrological model using both the observed and the IMERG-E satellite precipitation data with a Nash–Sutcliffe efficiency coefficient of 0.58 and 0.71, respectively. The results of this study indicate that the GPM-IMERG-E precipitation estimates can be used for flood modeling in semi-arid regions such as Morocco and provide a valuable alternative to ground-based precipitation measurements.

Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercussions. Previous research used datasets neglecting either good temporal or good spatial resolution, PERSIANN-CCSCDR, ERA5, and SM2RAIN-ASCAT are some of the projects aiming to remedy these limitations. This study's goal is to evaluate the accuracy of the PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT at a monthly scale and their suitability for drought assessment in a Moroccan semiarid watershed. Several statistical indices were computed, the drought SPI was calculated using PERSIANN-CCS-CDR estimates, ERA5 products, and observed records as an input in the SPI formula using Gamma distribution to simulate drought from 1983 to 2017. The preliminary comparison and evaluation results of PERSIANN-CCS-CDR estimates and ERA5 datasets showed good CC on a basin scale for monthly precipitation, with a slight overestimation of the observed precipitation shown by the PBIAS. The NSE scored 0.41 for PERSIANN-CCS-CDR and 0.72 for ERA5. The results for SM2RAIN-ASCAT showed an overestimation of the observed precipitation data. At the basin scale, the SPI3 correlation coefficients between the PERSIANN-CCS-CDR monthly estimates and observed gauge rainfall data were greater than 0.67, and the RMSE was closer to 0, outperforming ERA5 in the SPI3 evaluation.

In most parts of the world, groundwater is the main source of their water supply, particularly during periods of drought when surface water is scarce. As a result, groundwater drought is among the most worrying problems of our time. In order to shed light on the diversity of scientific productions related to this theme, this analysis was conducted on 151 publications, 76 sources, and 469 authors using version 4.3.1 of Rstudio’s Bibliometrix tool. The clusters and keyword occurrence analysis reveals a research trend towards the use of advanced technologies and a more holistic approach that takes into account the complexity of hydrological systems. The use of drought indices to characterize and monitor groundwater drought, as well as satellite products and their assimilation into Land Surface Models are among the adopted solutions. This was endorsed through a summary of the five most cited publications in this field. The results also highlighted the performance of Chinese institutions in analyzing the various aspects related to this topic, as well as a lack of international collaboration between research structures. In conclusion, this study has enabled us to present the evolution and trends in scientific research and helped to specify the main emerging themes and future areas of research related to groundwater drought.

The Haouz aquifer is undergoing climatic aridity and anthropic pressure largely related to the agricultural sector. In this study, special attention was given to the main factors that have a direct impact on the fluctuations of the piezometric level (PL). Different statistical analyses (cross-correlations, PCA, cascading analysis) of the relationship between these factors were applied here. The results identify three distinct groundwater operating systems. The first is manifested in areas dominated by groundwater irrigation. The correlation is insignificant between the PL and surface water (R ≤ ±0.3). The natural balance of the water cycle is then disturbed causing a pronounced deficit in the PL. The second system is perceptible in areas dominated by irrigation from surface water, while the third system is noticeable in Bour areas, cultivated in rainfed mode. For both systems, the hydrological cycle is preserved, and the contribution of surface water to groundwater recharge is noticeable (±0.4 ≤ R ≤ ±1). Drought transfer between the water cycle components occurs in a cascading process for both systems. These results can help decision-makers to identify the risks related to groundwater vulnerability to climatic variability and overexploitation in the Haouz region, allowing for the promotion of efficient groundwater management.

Recently, the Global Precipitation Measurement (GPM) satellite constellation measurements combined in the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm is provided. This GPM-IMERG dataset provides potentially useful precipitation data for regions with a low density of rain gauges. This study is aimed to evaluate the accuracy of the near real-time product (IMERG-E) compared to observed rainfall and its suitability for hydrological modeling over the Ghdat watershed located upstream the city of Marrakech. Several statistical indices have been computed and a hydrological model has been driven with IMERG-E rainfall to estimate its suitability to simulate floods during the period from 2011 to 2018. The following results were obtained: (1) In terms of the precipitation detection capability, the IMERG-E performs better at reproducing the different precipitation statistics at the catchment scale rather than at the pixel scale (2) compared to the rain gauge data, satellite precipitation data overestimates rainfall amounts with a relative Bias of +35.61% (3) The flood events can be simulated with the hydrological model using both the observed and the IMERG-E satellite precipitation data with a Nash–Sutcliffe efficiency coefficient of 0.58 and 0.71, respectively. The results of this study indicate that the GPM-IMERG-E precipitation estimates can be used for flood modeling in semi-arid regions such as Morocco and provide a valuable alternative to ground-based precipitation measurements.