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Due to its unique structure and excellent purification efficiency (e.g., 98% for organic matter and between 94 and 100% for nutrients), multi-soil-layering (MSL) has emerged as an efficient eco-friendly solution for wastewater treatment and environmental protection. Through infiltration-percolation, this soil-based technology allows pollutants to move from the MSL upper layers to the outlet while maintaining direct contact with its media, which helps in their removal via a variety of physical and biochemical mechanisms. This paper attempts to comprehensively evaluate the application of MSL technology and investigate its progress and efficacy since its emergence. Thus, it will attempt via a bibliometric analysis using the Web of Science database (from 1993 to 01/06/2022) related to MSL technology, to give a clear picture of the number of publications (70 studies), the most active academics, and countries (China with 27 studies), as well as collaborations and related topics. Furthermore, through hybrid combinations, pollutant removal processes, MSL effective media, and the key efficiency parameters, this paper review will seek to provide an overview of research that has developed and examined MSL since its inception. On the other hand, the current review will evaluate the modeling approaches used to explore MSL behavior in terms of pollutant removal and simulation of its performance (R2 > 90%). However, despite the increase in MSL publications in the past years (e.g., 13 studies in 2021), many studies are still needed to fill the knowledge gaps and urging challenges regarding this emerging technology. Thus, recommendations on improving the stability and sustainability of MSLs are highlighted.

Although weather stations provide accurate measurements of ground climate conditions in close proximity to the station, they are expensive and require periodic recording of measurements. In addition, weather stations are therefore distributed sparsely, especially in developing countries. Conversely, satellites see entire landscapes and are therefore able to offer precise measurements at each location. This paper compares the performance of satellite products with weather station observations at three sites characterized by different climates Ounagha, Chichawa, and R3 in Morocco.Precipitation and temperature data over the period of 2 years (2018 - 2019) at the three sites were collected. Data based on satellite imagery were collected for two satellite products, namely ERA5 and POWER over a similar period. The data were compared and analyzed through inferential statistics such as the root-mean-square error (RMSE), and the coefficient of determination (R2 ). The results showed that the temperature minimum daily simulated using the ERA5 satellite reached the highest coefficient of determination R2 = 0.92, with RMSE=1.34 (daily for Ounagha), R2 = 0.94, and with RMSE=1.27 (daily for Chichawa), R2 = 0.96, and with RMSE=1.13 (daily for R3). The temperature maximum daily simulated through the POWER satellite showed the best coefficient of determination R2 =0.924 with RMSE=2.174 (Ounagha daily). In contrast, the ERA5 satellite presents a better coefficient of determination R2 =0.97 in Chichawa and R3 stations. The results of comparing the observed weather stations and the satellite data in terms of precipitation show that the acceptable performance was attributed to the ERA5 data for cumulative, decadaire, and monthly precipitation in the three sites.The use of satellite products is a good way to solve the lack of weather stations and to make data available to the scientific community for further investigation. Furthermore, since our interest in monitoring drought in the Smimou region and our need for climate data in this area, which lacks a meteorological station, the results of this study encourage us to use the ERA5 satellite to collect climate data for the region.

Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

Morocco often faces significant intense rainfall periods that can generate flash floods and raging torrents, causing serious damage in a very short period of time. This study aims to monitor wetland areas after a flash-flood event in an arid region, Saquia El hamra Saharan of Morocco, using a technique that combines hydraulic modeling and remote sensing technology, namely satellite images. The hydrological parameters of the watershed were determined by the WMS software. Flood flow was modeled and simulated using HEC HMS and HEC-RAS software. To map the flooded areas, two satellite images (Sentinel-2 optical images) taken before and after the event were used. Three classifications were carried out using two powerful classifiers: support vector machines and decision tree. The first classifier was applied on both dates’ images, and the resulting maps were used as input for a constructed decision tree model as a post-classification change detection process.

Excess phosphorus (P) in wastewater can produce eutrophication, posing a serious risk to the safety of water resources and ecosystems. Therefore, effective pollutant removal including P from wastewater is the key strategy to save the environment and public health. Multi-soil-layering (MSL) is a promising nature-based technology that mainly relies on a soil mixture containing iron to remove P-pollution from wastewater. In the MSL influent, fourteen water quality indicators were measured, including pH, dissolved oxygen, total suspended solids, electrical conductivity, organic matter, nutrients, and coliform bacteria, to determine which ones have the strongest relationship with total phosphorus (TP) removal. The influence of hydraulic loading rate (HLR) and climatic variables (air temperature, rainfall, and evaporation) on the removal of TP was investigated. Four data-driven methods including multiple linear regression (MLR), k-nearest neighbors (KNN), random forest (RF), and neural network (NN) were conducted to predict TP removal at the MSL system outlet. In contrast to climatic variables, the results reveal that the HLR has a significant impact (p 43− and TP were suggested as the relevant input variables affecting TP removal in the MSL system, while an examination of accuracy shows that the RF model achieves good prediction accuracy (R2 = 0.93) and can help to understand MSL behavior for pollutants.

Sunlight and water are factors that affect seedling development. However, the effects of acclimatization of seedlings to sunlight and water stress remain poorly understood. This study aimed to examine the interactive effects of acclimatization to light and water stress on argan tree (Argania spinosa) seedlings in nurseries. An experiment was conducted with 504 seedlings using two sunlight treatments (L1 and L2, i.e. 100% and 60% of full sunlight, respectively), and three watering treatments (well-watered, moderate stress and severe stress, i.e. 100%, 50% and 25% of field capacity, respectively). According to our results, water stress treatments caused a reduction in total biomass accumulation, nitrogen and phosphorus uptake. Water stress significantly increased other macroelements, H2O2 and MDA levels and antioxidant enzyme activities compared to well-watered seedlings. Seedlings grown under moderate shade (L2) showed higher macroelement uptake, which probably contributed to the increase in total biomass in all water treatments. The highest membrane stability index (MSI) values, H2O2 and MDA levels and lowest antioxidant enzyme activities were recorded in acclimatized argan seedlings under moderate shading (L2). These results suggest that moderate shade can effectively prevent stress caused by light excess and can also mitigate the harmful effects of water stress on A. spinosa seedlings.

The hydraulic basin of Tensift is a concrete example of diversification and increase of pollutants discharged without treatment into the natural environment. This issue strongly threatens the water resources of this basin and makes it extremely sensitive to pollution, including groundwater that is a strategic resource in this area. The purpose of this study is to simulate two phenomena, which are hydrodynamic operation and leaching of solute in the conditions of the Haouz region.The study was conducted on the R3 perimeter. It is an irrigated agricultural sector in the region of Sidi Rahal, about 40 km east of Marrakech city, Morocco. To carry out this work, the VS2DI model was chosen for the following reasons: accessibility, reliability, and free of charge. This model popularly uses Cartesian or radial coordinates and allows solving the Richards equation to model the water transfer and the convection-dispersion equation to model the transport of solutes and heat in a porous and variably saturated medium. Our research team collects the data needed for the VS2DI model during the agricultural season of 2002/2003. The collected data is related to climatic conditions, soil, plant, and cultivation practice. The results obtained showed that for the scenarios studied the moisture of the upper layers increases and tends towards saturation depending on the value of the flux imposed on the surface. However, the deep layers remain unsaturated for a long time because of drainage. Thus, after one day and for a flux of 12 cm/d, the first 40cm of the soil is saturated. For the 4 cm/d flow, the saturation, during 24h, did not exceed the 30 cm depth. Knowing that these upper layers are subject to strong thermal gradients and root extractions. On the other hand, in the simulations of solute transport, we try to describe the evolution of the degree of contamination of a layer after a period of one day as a function of the imposed water flow and the concentration of solute on the surface.The results obtained by these simulations show that at 20cm depth the solute concentration starts to change only after a period of 4h and that the rate of change of the concentration is almost linear with time for each given water flux. Beyond 4h, the rate of change of the solute at this depth decreases in a non-linear way with the increase of the water flux imposed on the surface. From these first tests, we can say that this model performs water balances in an acceptable way. It has also been proved that the saturation rate of the soil increases with the increase of the imposed flux and of the moisture of this layer. Finally, it was found that the rate of change of the solute at a given depth decreases non-linearly with the imposed water flux at the surface.

Due to the water shortage and poor management of limited water resources in semi-arid region, the improvement of irrigation efficiency became crucial. In this context, the objective of this work is to estimate water content and evapotranspiration using the HYDRUS-1D model. The study was carried out over wheat fields of the R3 perimeter (located in the Haouz plain of Marrakesh-Safi region, Morocco) for the growing season of 2003/2004. Four types of data have been used, namely: data related to the soil, data related to the plant, data related climatic parameters (recorded in a station close to the study site), and data related to the local agricultural practice.We firstly used the inverse method, available in HYDRUS-1D, in order to estimate the hydrodynamic parameters of cultivated soil. The analysis of the convergence and consistency of this method showed that for correct calibration of the model, it is necessary to take into account the vertical heterogeneity of the soil. Therefore, we proceeded to the manual calibration of the model by testing different choices of initial parameters and taking into account the soil vertical heterogeneity. The calibration carried out concerned mainly the water content of the soil and the evapotranspiration of the surface.The simulations of the real evapotranspiration (ET) were carried out using the inputs obtained for the manual calibration. The results obtained by the HYDRUS-1D model gave an overestimation of the soil water content. This underestimation can be explained either by an underestimation of the LAI inputs or by the root extraction module that needs to be readjusted.In the present work, the HYDRUS-1D model was tested, for the first time, over an area of Haouz plain in central Morocco. The obtained results are not yet final. The model needs to be tested sufficiently over a wide spatio-temporal range. The HYDRUS-1D model is widely used in different regions of the world and the extensive literature reports a good score on the consistency and robustness of the model. Thus we are convinced that, overall, HYDRUS-1D seems to be an adequate model to estimate with acceptable accuracy the water content and the real ET under semi-arid conditions. It is also a promising tool for planning and decision support in the field of water and agro-environmental research.

Excess phosphorus (P) in wastewater can produce eutrophication, posing a serious risk to the safety of water resources and ecosystems. Therefore, effective pollutant removal including P from wastewater is the key strategy to save the environment and public health. Multi-soil-layering (MSL) is a promising nature-based technology that mainly relies on a soil mixture containing iron to remove P-pollution from wastewater. Fifteen water quality parameters were monitored in the MSL influent to determine which ones have the strongest relationship with total phosphorus (TP) removal. The influence of hydraulic loading rate (HLR) and climatic variables on the removal of TP was investigated. Three data-driven methods including multiple linear regression (MLR), k-nearest neighbors (KNN), and random forest (RF) were conducted to predict TP removal at the MSL system outlet. In contrast to climatic variables, the results reveal that the HLR has a significant impact (p 2 = 0.94).

Çalışmanın amacı: Bu çalışmanın amacı, basit, etkili ve daha ucuz bir prosedür oluşturmak için ısı şoku ile farklı fiziksel ön işlemlerden sonra A. spinosa tohumlarının çimlenmesini optimize etmektir.Çalışma alanı: Çalışma Fas'ın dört bölgesinden (Aoulouz, Essaouira, Sidi Bou Othmane ve Boulaouane) tohumlar üzerinde laboratuvarda yürütülmüştür.Materyal ve yöntem: Tohumlar kurutuldu ve saklandı. Deney protokolünün başlatılmasından önce, kabukları çıkarıldı ve dört orijin için her birine ön muamele uygulandı. Kullanılan ön işlem aşağıdaki gibidir. C termal şok olmadan kontrol, HC1: donma, HC2: sıcak su, HC3: donma + sıcak su.Temel sonuçlar: Sonuçlar, HC3 ön işlemine tabi tutulan argan tohumlarının çimlenme başlangıcının, ön işleme (C) tabi tutulan tohumlara kıyasla ortalama 2 gün azaldığını gösterdi. Soğuk veya sıcak ön işlemin uygulanması, nihai çimlenme yüzdesinin %82'sine kadar ulaşmamızı sağladı. Bununla birlikte, iki ön işlemin kombinasyonu, nihai çimlenme yüzdesinin %100'üne kadar ulaşmamızı sağladı.Araştırma vurguları: Sıcak su ile birlikte yapılan dondurma işlemi, A. spinosa tohumlarının çimlenmesini önemli ölçüde iyileştirmiştir. Bu ön tedavi, fidanlikta uygulayıcılar için önerilebilir, Aim of study: The objective of this study was to optimize the germination of A. spinosa seeds after different physical pretreatments by thermal shock to establish a simple, effective, and less expensive procedure.Area of study: The study was conducted in the laboratory on seeds from four regions of Morocco (Aoulouz, Essaouira, Sidi Bou Othmane, and Boulaouane).Material and methods: The seeds were dried and stored. Before the launching of the experimental protocol, they were dehulled and applied each their pre-treatment for the four provenances. The pre-treatment used are the following. C: control without thermal shock, HC1: freezing, HC2: hot water, HC3: freezing + hot water.Main results: The results showed that the onset of germination of argan seeds subjected to pretreatment HC3 was reduced by 2 days on mean compared to seeds that were subjected to pretreatment (C). The application of the cold or hot pretreatment allowed us to reach up to 82% of the final percentage of germination. However, the combination of two pretreatments allowed us to reach up to 100% of the final germination percentage. Highlights: Freezing combined with hot water significantly improved the germination of A. spinosa seeds. This pretreatment could be recommended in the nursery for practitioners