Ain Sefra is one of the Algerian cities that had been experienced several devastating floods during the past 100 years. The purpose of this study is to simulate runoff in the semi-arid region of Ain Sefra watershed through the employing of the Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). In this paper, the frequency storm is used for the meteorological model, the Soil Conservation Service – curve number (SCS-CN) is selected to calculate the loss rate and Soil Conservation Service unit hydrograph method have been applied to simulate the runoff rate. After calibration and validation, the simulated peak discharges were very close with observed values. The Nash–Sutcliffe efficiency coefficient was 0.95, indicates that the hydrological modeling results are satisfactory and accepted for simulation of rainfall-runoff. The peak discharges obtained for the 10, 50, 100 and 1000 year storms are respectively 425.8, 750.5, 904.3 and 1328.3 m³∙s⁻¹.

Soil erosion by water is a major problem that the Northern part of Algeria witnesses nowadays; it reduces: the productivity of agricultural areas due to the loss of lands, and leads to the loss of storage capacity in reservoirs, the deterioration of water quality etc. The aim of this study is to evaluate the soil losses due to water erosion, and to identify the sectors which are potentially sensitive to water erosion in the Bouhamdane watershed, that is located in the northeastern part of Algeria. To this end, the Revised Universal Soil Loss Equation (RUSLE) was used. The application of this equation takes into account five parameters, namely the rainfall erosivity, topography, soil erodibility, vegetative cover and erosion control practices. The product of these parameters under GIS using the RUSLE mathematical equation has enabled evaluating an annual average erosion rate for the Bouhamdane watershed of 11.18 t·ha^-1·y^-1. Based on the estimates of soil loss in each grid cell, a soil erosion risk map with five risk classes was elaborated. The spatial distribution of risk classes was 16% very low, 41% low, 28% moderate, 12% high and 3% very high. Most areas showing high and very high erosion risk occurred in the lower Bouhamdane watershed around Hammam Debagh dam. These areas require adequate erosion control practices to be implemented on a priority basis in order to conserve soil resources and reduce siltation in the reservoir.

Water demand has been increasing considerably around the world, mostly since the start of the COVID-19 pandemic. It has caused many problems for water supply, especially in arid areas. Consequently, there is a need to assimilate lessons learned to ensure water security. In arid climates, evaluating the groundwater potential is critical, particularly because the only source of drinking water and irrigation for the community is groundwater. The objective of this report is to locate and identify probable groundwater basins in the upper Wadi Namous basin’s Ain Sefra area. GIS and RS were used to evaluate the parameters of morphometry and to demarcate groundwater potential zones by using eight different influencing factors, viz., geology, rainfall, height, slope, land cover, land use, and lineaments density are all factors to consider. The analytical hierarchical process (AHP) was used to give weightages to the factors, and definitions within each attribute were sorted in order of priority for groundwater potentiality. The major findings of the research were the creation of groundwater-potential zones in the watershed. The hydrogeological zone of the basin was assessed as follows: very poor (0.56%), poor (26.41%), moderate (44.72%), good (25.22%), and very good (3.1%). The groundwater recharge potential zones are concentrated in low cretaceous locations, according to analytical data. The groundwater potential regions were checked to field inventory data from 45 water locations to corroborate the findings. The qualitative findings and the groundwater inventory data agreed 77.78%, according to the cross-validation study. The produced groundwater potential map might substantially assist in the development of long-term management plans by enabling water planners and decision-makers to identify zones appropriate for the placement of productive wells and reducing investment losses caused by well drilling failures. The results of the study will also serve as a benchmark for further research and studies, such as hydrogeological modeling.

The adverse impact of climate change on different regionally important sectors such as agriculture and hydropower is a serious concern and is currently at the epicentre of global interest. Despite the extensive efforts to project the future climate and assess its potential impact, it is surrounded by uncertainties. This study aimed to assess climate models’ performance and associated uncertainties in rainfall projection over the eastern Nile basin, Ethiopia. Seventeen climate models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and their four ensemble models were evaluated in terms of their historical prediction performance (1986–2005) and future simulation skill (2006–2016) at rainfall station (point location), grid-scale (0.44° × 0.44°) and basin scale. Station-based and spatially interpolated observed rainfall data were used as a reference during climate model performance evaluation. In addition, CRU data was used as an alternative reference data to check the effect of the reference data source on the climate models evaluation process. As the results showed, climate models have a large discrepancy in their projected rainfall and hence prior evaluation of their performance is necessary. For instance, the bias in historical mean annual rainfall averaged over the basin ranges from +760 mm (wet bias) to −582 mm (dry bias). The spatial pattern correlation (r) of climate models output and observed rainfall ranges from −0.1 to 0.7. The ensemble formed with selected (performance-based) member models outperforms the widely used multi-model ensemble in most of the evaluation metrics. This showed the need for reconsidering the widely used multi-model approach in most climate model-based studies. The use of CRU data as a reference resulted in a change in the magnitude of climate model bias. To conclude, each climate model has a certain degree of uncertainty in the rainfall projection, which potentially affects the studies on climate change and its impact (e.g., on water resources). Therefore, climate-related studies have to consider uncertainties in climate projections, which will help end-users (decision-makers) at least to be aware of the potential range of deviation in the future projected outcomes of interest.

Drought is one of the important phenomena resulting from variability and climate change. It has negative effects on all economic, agricultural and social sectors. The objective of this study is to rapidly detect climate dryness situations on an annual scale at the Mellah catchment (Northeast Algeria) for periods ranging from 31 years through the calculation of: the standardized precipitation index (SPI), the standardized Streamflow index (SSFI), the standardized temperature index (STI). Calculations made it possible to locate periods of drought more precisely by their intensity, duration and frequency, and detect years of breaks using the tests of Pettitt, rang, Lee and Heghinian, Hubert and Buishand. The use of the statistical tests for the rainfall series analyzed show all breaks, the majority of which are in 1996/1997 and 2001/2002. For the temperatures the breaks are situated in 1980/1981.