Estimation des réserves en eau souterraine régulatrices dans la partie moyen-atlasique du fleuve Sebou (Maroc)

L'estimation de la ressource en eau souterraine dans le Moyen-Atlas est investiguée à partir des débits mesurés dans l'oued Sebou à la station d'Azzaba, sur la période 1959–2015. Pour cela presqu'une centaine de phases de tarissement a été individualisé pour construire des courbes maîtresses de tarissement à partir desquelles le calcul des volumes des nappes est possible. Il est calculé mensuellement à partir du débit modal dont nous posons l'hypothèse qu'il représente la limite entre ruissellement et alimentation uniquement par les nappes. Les calculs donnent un volume de la réserve régulatrice moyen de 54 hm3, mais extrêmement variable à l'échelle interannuelle et intra-annuelle. Ces résultats démontrent la faible inertie des aquifères du Moyen Atlas et soulignent la vulnérabilité du secteur agricole vis-à-vis de cette ressource même lors de courte période de sécheresse. The estimate of the groundwater resource in the Middle Atlas is investigated from the runoff measured in the Sebou wadi at the Azzaba station, over the period 1959–2015. Almost a hundred recession curves have been individualized to build the master recession curves from which the calculation of the volumes of groundwater is possible. It was calculated at a monthly scale from the modal flow, which we assume is the limit between runoff and flow supply only by the aquifers. The results indicated a volume of the average regulatory reserve of 54 hm3, but extremely variable on an inter-annual and intra-annual scale. These results demonstrate the low inertia of the Middle Atlas aquifers and underline the vulnerability of the agricultural sector to this resource even during short periods of drought.

Groundwater Resources Management Modelling: A Review

Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often changes as a result of land usage, abstraction as well as variation in climate. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity, and minimising the effects of drought has become a major challenge for researchers as well as water managers. Furthermore, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater resource model have been revealed in the management of groundwater resources. To solve these challenges, many conventional solutions such as numerical techniques have been proffered for groundwater modelling. Also, the use of data-driven techniques such as machine learning is gaining more attraction to solve these aforementioned challenges. Thus, this has made efficient data gathering essential to maintain da-ta-driven groundwater resources management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data At the moment, some pieces of the literature revealed the groundwater managers lack an efficient, scalable and real-time groundwater management system to gather the required data. Also, the literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management methods, nonetheless, this paper seeks to highlight the current groundwater management models as well as the IoT contributions