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Since the early 1980s, several regions of Morocco have experienced alternating series of wet and dry periods related to climate change, with significant consequences for the country’s economy. This study aims to map flood hazards by using a hydraulic modeling approach, with a specific focus on the Oued (river) Zaouiat Sidi Hamza passing through Zaouiat Sidi Hamza township belonging to the High Ziz watershed (province of Midelt, region of Drâa-Tafilalet, Morocco). Hydraulic modeling and flood simulation were carried out followed by mapping of the flood hazard. The Oued Zaouiat Sidi Hamza River can cause torrential flooding issues, particularly during periods of heavy storms, impacting agricultural land and certain buildings on both river banks. All the cultivated areas developed alongside the river are at risk of flooding. Moreover, undermining and erosion of the river’s banks lead to rising high-water levels and flooding agricultural terraces. The use of geographic information systems and hydraulic modeling tools is essential to gain a deeper understanding of this situation. Also, the mapping of lineaments and fractures in the study area, carried out by satellite image processing, helped to quantify the directional influence of these lineaments on the flow of the hydrographic network, on the orientation of the circulation of surface water, and on the storage of groundwater. According to this study, the most vulnerable areas to flooding and erosion are those that receive the most water in the basin and have steep slopes. Decision-makers can use the results to produce development plans tailored to combat these risks and raise awareness among the population concerned, improving water resource evaluation and forecasting, as well as improving agricultural production.

The future of groundwater is one of the key challenges for sustainable water management, hence the need to monitor its overall quality. The objective of this work is to assess the overall quality and determine the spatiotemporal evolution of the Angads aquifer in northeastern Morocco in 2014 and 2020, based on the parameters NH4+, NO3−, EC, Cl−, and FC, as well as the Geographic Information System (GIS). The results of the comparison of these five parameters between 2014 and 2020 show a general increase in NH4+ and a decrease in NO3− and FC at most sampling points. These changes could be attributed to a shift in pollution sources or biological processes affecting water quality. On the other hand, the stability of EC and Cl− levels suggests a consistency in the inputs of salts or minerals. The quality percentages show a decrease in good, poor, and very poor quality, following an increase in average quality, from 10.52% (in 2014) to 5.26% (in 2020), 31.57% (in 2014) to 21.05% (in 2020), 31.57% (in 2014) to 26.31% (in 2020), and 26.31% (in 2014) to 47.36% (in 2020), respectively. Spatial and temporal mapping of the quality over these 2 years shows that the deterioration continues toward the east, southeast, and southwest. This is justified by very high measurements of the parameters NO3−, EC, and Cl− at sampling points 2, 3, 4, 5, 7, 8, and 15 for 2014 and 2020, reaching 156 mg/L, 10,570 µS/cm, and 3790 mg/L in 2014 and 134 mg/L, 10,355 µS/cm, and 3597 mg/L in 2020, respectively, due to effluents from pollution points such as the Oujda public landfill, the wastewater treatment plant, and the former Sidi Yahya landfill to the west. On the other hand, in the north, northeast, and northwest, there has been an improvement in quality due to the remoteness of these pollution points. In order to protect this vital resource, recommendations need to be put in place, in particular by treating leachates so as to ensure that the quality of the water is not discharged directly into the aquifer or used for other purposes, and to avoid discharging effluent from the wastewater treatment plant into the natural environment. [ABSTRACT FROM AUTHOR]

Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km2), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset's total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed. [ABSTRACT FROM AUTHOR]

This study aims to contribute to a better estimation of the density of fractures likely to favour the drainage of surface water in the High Ziz River (HZR) basin. The method adopted consists of the use of different airborne image processing techniques (filtering, enhancement and slope analysis) which allow a good discrimination of image discontinuities (lineaments) that can then be used for producing lineament maps. The influence of these lineaments on the underground flow is then studied. Indeed, it was possible to extract the lineament network, following the exploitation of remote sensing and an objectively completed analog analysis using the PCI Geomatica LINE module and its directional filters (Sobel directional filters by the ACP method), and to extract the hydrographic network of the HZR basin, using the ArcHydro tool. After superimposing these data, their thematic maps (of the lineament and hydrographic network) were developed. In addition, this comparison of the two hydrographic networks and lineament maps showed us very varied flow directions influenced by geological structures, particularly fracturing. The topography of the region also has a secondary impact on flows through the presence of slopes generally oriented towards the SE of the basin. The correlation of the results with the different directions of the orders of the hydrographic network allowed us to identify the potential effects of lineaments on the orientations of surface water circulation. In addition to these results, the realization of the Rose diagram indicated that the dominant trend in lineaments is N90–45N° (NE–SW).

One of the adverse impacts of climate change is drought, and the complex nature of droughts makes them one of the most important climate hazards. Drought indices are generally used as a tool for monitoring changes in meteorological, hydrological, agricultural and economic conditions. In this study, we focused on meteorological drought events in the High Ziz river Basin, central High Atlas, Morocco. The application of drought index analysis is useful for drought assessment and to consider methods of adaptation and mitigation to deal with climate change. In order to analyze drought in the study area, we used two different approaches for addressing the change in climate and particularly in precipitation, i) to assess the climate variability and change over the year, and ii) to assess the change within the year timescale (monthly, seasonally and annually) from 1971 to 2017. In first approach, precipitation data were used in a long time scale e.g. annual and more than one-year period. For this purpose, the Standardized Precipitation Index (SPI) was considered to quantify the rainfall deficit for multiple timescales. For the second approach, trend analysis (using the Mann-Kendall (M-K) test) was applied to precipitation in different time scales within the year. The results showed that the study area has no significant trend in annual rainfall, but in terms of seasonal rainfall, the magnitude of rainfall during summer revealed a positive significant trend in three stations. A significant negative and positive trend in monthly rainfall was observed only in April and August, respectively.

In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d'Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies. [ABSTRACT FROM AUTHOR]

In Morocco, the scarcity of rainfall is linked to global warming and to the overexploitation of groundwater due to demographic and socio-economic pressures, which generates a major problem, namely a significant decline of the piezometric level. The goal of this study is to assess the piezometric and rainfall levels of the groundwater in the Angads aquifer, which is one of the most important in the Eastern part of Morocco. To do so, graphs were drawn from the piezometric data of three piezometers, namely AP1, AP2, and AP3 located respectively in the Eastern, Central, and Western part of the country, and from the rainfall data of the Oujda station during the period from 1985 to 2015. The main results revealed a fluctuation in rainfall over time, with an increase recorded in 2009. Indeed, the results of the variation of piezometric levels over the years generally showed a downward trend, especially in the area between the aquifer outlet and the Maghnia plain in Algeria, while the results of the assessment of the piezometric land rainfall levels over time generally show a downward trend. In the face of this decline, effective strategies need to be considered to protect these resources from depletion, including artificial recharge of the aquifer, wastewater reuse which aims to restore an aquifer overexploited by excessive pumping, whose drawdown is detrimental, and multi-purpose water storage. [ABSTRACT FROM AUTHOR]

Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensionality reduction, the PCA reduces datasets to representations consisting of principal components. Kernel PCA (KPCA) is the nonlinear form of PCA, which efficiently presents a complicated data in a lower dimensional space. In this work the KPCA techniques was applied to measure and map flood resilience across a local level. Therefore, it aims to improve the performance achieved by non-linear PCA application, compared to standard PCA. Twenty-one resilience indicators were gathered, including social, economic, physical, and natural components into a composite index (Flood resilience Index). The experimental results demonstrate the KPCA performance to get a better Flood Resilience Index, guiding q decision making to strengthen the flood resilience in our case of study of M’diq-Fnideq and martil municipalities in Northern of Morocco.