Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco

Milewski, A.; Seyoum, Wondwosen M.; Elkadiri, R.; Durham, Michael

doi:10.3390/geosciences10010013

Cited by 27 publications

(13 citation statements)

References 91 publications

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“…In the semi-arid Oum Er-Rbia catchment located in the central part of Morocco, water resources are threatened by climate change. As a result, the economy and population requirements will be increased in the future [16][17][18]. Groundwater resources are derived from two system types: (1) the multi-layered system of the Tadla plains; and (2) the karst aquifer of the Atlas Mountains.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Namous

Hssaisoune

Pradhan

et al. 2021

Water

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The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

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Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Namous

Hssaisoune

Pradhan

et al. 2021

Water

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“…Projections indicate that by 2050, the climate in Northwest Africa is likely to get warmer and dryer with a decrease in precipitation of about −10.6% and an increase in temperature of +1.2% • C [1]. Future projections for the same time horizon reveal that climate change will account for 22% of future water shortages in North Africa, while 78% of increased future water shortages could be attributed to socioeconomic factors [2,3].…”

Section: Introductionmentioning

confidence: 99%

Global Climate Pattern Impacts on Long-Term Olive Yields in Northwestern Africa: Case from Souss-Massa Region

2021

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In arid to semi-arid regions, vulnerability to climate change combined with the overexploitation of water resources is jeopardizing food security. In the Souss-Massa region in central Morocco, the rural population relies on growing olives for a living. The management of these orchards is mostly traditional under rainfed irrigation, which induces a high level of dependence on climate variability. In the present study, we investigate the long-term trends of the relationship between the observed olive yields and global climate patterns during the period 1973–2014. We apply lagged Spearman’s correlations and cross-wavelet analysis to detect the potential influence of El Niño-southern oscillation (ENSO), the Indian Ocean Dipole (IOD), North Atlantic oscillation (NAO) and Pacific decadal oscillation (PDO) on the yield variability of olive orchards. The results of a Mann-Kendall test show a statistically significant decreasing trend in olive yields during the studied period. Statistically significant negative correlations were observed for (lag = −1) with spring and summer NINO 3.4 and with summer and autumn PDO. No statistically significant correlations between olive yields and NAO and IOD were observed. The results of wavelet coherence between annual olive yields and PDO and ENSO revealed that the highest values of power spectrum coherence occurred during the (lag = 0) spring PDO and (lag = −1) spring ENSO, both with an antiphase relationship. During the studied period, the extreme events of El Niña and El Niño years corresponded to below average yields.

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“…CC BY 4.0 License. (Palomino-Ángel, et al, 2019), Cyprus (Retalis et al, 2018), Egypt (Saber et al, 2015), and Morocco (Milewski et al, 2015;Milewski et al,2020). However, most of these studies indicate that IMERG had greater performance in the characterization of precipitation variability and precipitation detection aptitude, with the only slight improvement compared to TMPA products.…”

Section: Introductionmentioning

confidence: 99%

Multiscale assessment of TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products over a Mediterranean mountainous watershed with sparse rain gauges in the Moroccan High Atlas (case study of Zat basin)

Benkirane

Laftouhi

Khabba

et al. 2021

Preprint

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Abstract. The performance of Tropical Precipitation Measurement Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), has provided hydrologists with a source of critical precipitation data for hydrological applications in basins where ground-based observations of precipitation are sparse, or spatially undistributed. The very high temporal and spatial resolution satellite precipitation products have therefore become a reliable alternative that researchers are increasingly using in various hydro-meteorological and hydro-climatological applications. This study aims to evaluate statistically and hydrologically the TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitations products (SPPs), at multiple temporal scales from 2010 to 2017, in a mountainous watershed characterized by the Mediterranean climate. The results show that TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products have a significant capacity for detecting precipitation at different time steps. However, the statistical analysis of SPPs against ground observation shows good results for both statistical metrics and contingency statistics with notable values (CC > 0.8), and representative values relatively close to 0 for the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR). Moreover, the sorting of the events implemented on the hydrological model was performed seasonally, at daily time steps. The calibrated episodes showed very good results with Nash values ranging from 53.2 % to 95.5 %. Nevertheless, the (IMERG V5) product detects more efficiently precipitation events at short time steps (daily), while (3B42 V7) has a strong ability to detect precipitation events at large time steps (monthly and yearly). Furthermore, the modeling results illustrate that both satellite precipitation products tend to underestimate precipitation during wet seasons and overestimate them during dry seasons, while they have a better spatial distribution of precipitation measurements performance, which shows the importance of their use for basin modeling and potentially for flood forecasting in Mediterranean catchment areas.

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Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco

Cited by 27 publications

References 91 publications

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Global Climate Pattern Impacts on Long-Term Olive Yields in Northwestern Africa: Case from Souss-Massa Region

Multiscale assessment of TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products over a Mediterranean mountainous watershed with sparse rain gauges in the Moroccan High Atlas (case study of Zat basin)

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