A hydrological–economic model for sustainable groundwater use in sparse-data drylands: Application to the Amtoudi Oasis in southern Morocco, northern Sahara

Alcalá, Francisco; Martínez‐Valderrama, Jaime; Robles-Marín, Pedro; Guerrera, Francesco; Martı́n-Martı́n, Manuél; Raffaelli, Giuliana; Leon, Julian Tejera de; Asebriy, L.

doi:10.1016/j.scitotenv.2015.07.062

Cited by 40 publications

(27 citation statements)

References 46 publications

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“…Eco-environmental crises caused by the unreasonable utilization of groundwater have been reported in Northwest China, particularly along the ancient Silk Road, which includes the Hexi Corridor [5,6], Tianshan Mountains [7], and the edge of the Tarim Basin [8]. The same problems have been observed in the desert oases of Australia, the United States, and Africa [9][10][11]. Groundwater level, which is an important indicator of groundwater balance, exhibits cyclical and random characteristics under the influences of climatic factors and human activities.…”

Section: Introductionmentioning

confidence: 81%

“…where a represents a natural number within [1,10], and m, n, and l represent the nodes of the hidden, input, and output layers, respectively. In the current study, the optimal numbers of nodes in the hidden layer were the range of [3,9] calculated using multi-trials algorithms in accordance with the aforementioned formula. The error of the simulated and measured values in the network training firstly gradually decreased and then increased with the number of hidden layer neurons increased in Table 1.…”

Section: Topology Of the Bp Neural Network With Double Hidden Layersmentioning

confidence: 99%

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Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Zheng

et al. 2019

Water

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Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m3/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m3/year to promote the sustainable development of groundwater resources in Yaoba Oasis.

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Section: Introductionmentioning

confidence: 81%

Section: Topology Of the Bp Neural Network With Double Hidden Layersmentioning

confidence: 99%

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Zheng

et al. 2019

Water

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“…The fraction of R produced in upstream contributing areas can be used as a reliable proxy for the additional baseflow fraction contributing to the CRB baseline [26,27]. To assess the IGF, R is the significant factor [4,34].…”

Section: Igf Series Generationmentioning

confidence: 99%

“…Hydrological models have become essential tools for water management issues due to their ability to simulate the hydrological cycle through integrated and multidisciplinary approaches, along with their skills to simulate climate change scenarios, land use, and water management [1]. Reliability of such models depends on the spatial and temporal scales covered, as well as the capacity to conceptualize the system functioning [2][3][4]. Those hydrological models operating at a basin scale are powerful decision support tools because they can provide insights into water resource management [5].…”

Section: Introductionmentioning

confidence: 99%

“…In such undisturbed hydrological functioning, net aquifer discharge equals the baseflow component of streamflow [28][29][30][31], and the problem shrinks to a matter of implementing suitable and viable techniques to determine R. Note that R is the infiltration amount that effectively contributes to the aquifer storage after some delay, smoothing out the variability inherent to precipitation events [32,33]. To assess renewable groundwater resources that finally reach streambeds, R is the governing variable [4,34].…”

Section: Introductionmentioning

confidence: 99%

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Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow

Senent‐Aparicio

Alcalá

Liu

et al. 2020

Water

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This paper couples the Soil and Water Assessment Tool (SWAT) model and the chloride mass balance (CMB) method to improve the modeling of streamflow in high-permeability bedrock basins receiving interbasin groundwater flow (IGF). IGF refers to the naturally occurring groundwater flow beneath a topographic divide, which indicates that baseflow simulated by standard hydrological models may be substantially less than its actual magnitude. Identification and quantification of IGF is so difficult that most hydrological models use convenient simplifications to ignore it, leaving us with minimal knowledge of strategies to quantify it. The Castril River basin (CRB) was chosen to show this problematic and to propose the CMB method to assess the magnitude of the IGF contribution to baseflow. In this headwater area, which has null groundwater exploitation, the CMB method shows that yearly IGF hardly varies and represents about 51% of mean yearly baseflow. Based on this external IGF appraisal, simulated streamflow was corrected to obtain a reduction in the percent bias of the SWAT model, from 52.29 to 22.40. Corrected simulated streamflow was used during the SWAT model calibration and validation phases. The Nash–Sutcliffe Efficiency (NSE) coefficient and the logarithmic values of NSE (lnNSE) were used for overall SWAT model performance. For calibration and validation, monthly NSE was 0.77 and 0.80, respectively, whereas daily lnNSE was 0.81 and 0.64, respectively. This methodological framework, which includes initial system conceptualization and a new formulation, provides a reproducible way to deal with similar basins, the baseflow component of which is strongly determined by IGF.

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Agronomy Suitability of Treated Wastewater with Microalgae as Alternative Sources for Irrigation

El-Moustaqim,

Mabrouki,

Hmouni

2024

World Sustainability Series

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A hydrological–economic model for sustainable groundwater use in sparse-data drylands: Application to the Amtoudi Oasis in southern Morocco, northern Sahara

Cited by 40 publications

References 46 publications

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow

Agronomy Suitability of Treated Wastewater with Microalgae as Alternative Sources for Irrigation

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