Monitoring of water resources and a better understanding of the eco-hydrological processes governing their dynamics are necessary to anticipate and develop measures to adapt to climate and water-use changes. Focusing on this aim, a research project carried out within the framework of French–Moroccan cooperation demonstrated how remote sensing can help improve the monitoring and modelling of water resources in semi-arid Mediterranean regions. The study area is the Tensift Basin located near Marrakech (Morocco) – a typical Southern Mediterranean catchment with water production in the mountains and downstream consumption mainly driven by agriculture. Following a description of the institutional context and the experimental network, the main recent research results are presented: (1) methodological development for the retrieval of key components of the water cycle in a snow-covered area from remote-sensing imagery (disaggregated soil moisture from soil moisture and ocean salinity) at the kilometre scale, based on the Moderate Resolution Imaging Spectroradiometer (MODIS); (2) the use of remote-sensing products together with land-surface modelling for the monitoring of evapotranspiration; and (3) phenomenological modelling based only on time series of remote-sensing data with application to forecasting of cereal yields. Finally, the issue of transfer of research results is also addressed through two remote sensing-based tools developed together with the project partners involved in water management and irrigation planning.

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R²) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET₀) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R² = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco.

In several areas, many social, economic, and physical subsystems interact around water resources. Integrated water management is applied to maximize economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems, mainly in hydrologic-stressed areas. The Souss-Massa basin, with its semi-arid climate, has a significant demand for agricultural, industrial, tourism, and domestic water. It constitutes a complex system where the lack of knowledge of all the interacting subsystems has led to a shortage of water in quantity and quality. The objective of this study is to investigate the interactions between supply and demand at different stages using a System Dynamics (SD) approach. The model developed promotes a holistic understanding of the interactions between the different problem indicators that operate in water resources management in order to support decision-making action and successfully manage water resources at the Souss-Massa basin scale. The chosen performance indicator is based on the achievement of a baseline sustainability index (SI) defined as the ratio of available water to supply water that should be higher than 20% to avoid a water stress situation. The multisource data were gathered from different government agencies for the period spanning between 2007 and 2020. The results showed that the current policies do not lead to sustainable water management. Groundwater withdrawals have increased considerably, from 747 Mm³ in 2007 to 4884 Mm³ in 2020. The balance between water supply and demand is only reached for three years, 2010, 2015, and 2018, without ever reaching an SI of 20%. The sensitivity analysis showed that the sustainability of water resources in the Souss-Massa basin is mainly impacted by the availability of surface water, irrigated areas, and irrigation efficiency. This study will be of great interest to policymakers to provide optimal and sustainable water management strategies based on improved water use efficiency, and to contribute to the sustainable development agenda in arid and semi-arid regions.

This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt.