Monitoring spatiotemporal variation of groundwater level and salinity under land use change using integrated field measurements, GIS, geostatistical, and remote-sensing approach: case study of the Feija aquifer, Middle Draa watershed, Moroccan Sahara

Moumane, Adil; Ghazali, Fatima Ezzahra El; Karkouri, Jamal Al; Delorme, Jonathan; Batchi, Mouhcine; Chafiki, Driss; Karmaoui, Ahmed

doi:10.1007/s10661-021-09581-2

Cited by 27 publications

(8 citation statements)

References 55 publications

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“…In fact, from the Hassan Addakhil Dam (the big dam of the province) in upstream (North)-downstream (South) direction a considerable change in groundwater quality was found, mainly an increase in salinity from upstream with TDS −460 mg/L to downstream with a TDS −16,000 mg/L (Lgourna et al, 2015). This increasing rate in groundwater salinity was observed in similar oases regions of Morocco and also in the golf from the United Arab Emirates (Moumane et al, 2021;Gómez-Alday et al, 2022). The use of solar energy in the context of groundwater desalination is a promising strategy to adapt to CC and drought effects.…”

Section: Discussionmentioning

confidence: 88%

“…A variety of methods and techniques were developed and tested to assess the state of these natural resources and the impact of CC, extreme events, and anthropogenic pressure including geospatial techniques (GSTs) such as remote sensing (RS). GSTs are used to assess groundwater (Moumane et al, 2021), water erosion (soil elimination by water) (Ben Salem et al, 2019;Ougougdal et al, 2020), seasonal crop estimates (Gonzalez-Gonzalez and Guertin, 2021), and drought vulnerability (Hoque et al, 2020). From a methodological point of view, the techniques used to explore the impact of CC and drought are numerous.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the land use and land cover change in the period 2005–2020 in the province of Errachidia, the pre-sahara of Morocco

et al. 2022

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The study investigates the land use and land cover (LULC) in Errachidia province (Pre-Saharan of Morocco) in the period 2005–2020. To this end, remote sensing (RS) tools such as LULC, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), the Enhanced Vegetation Index (EVI), Gravity Recovery and Climate Experiment (GRACE) satellite data, and precipitations were processed and combined. The monitoring of LULC over this period reveals different changes in this area. Generally, for water bodies, two trends can be observed, an increasing trend since 2006 and a decreasing trend from 2011 to the present. However, an increasing trend was recorded for the urban-and-built-up-lands and the grasslands categories. NDVI, NDWI, and EVI showed three major peaks recorded in the same periods (in 2007, 2009, and 2015). In terms of water storage, three phases were found. The first depicted the lowest groundwater quantities with a decreasing trend, which corresponds to a period of drought and/or human pressure. The second phase, the most important that recorded the greatest storage of water while the third phase corresponds to low storage with a decreasing trend. LULC of croplands reveals a slightly increasing trend from 2012 to 2020, which shows an orientation to an extension of crops mainly of date palm encouraged by government programs. A strong correlation between the seasonal NDVI and water storage using GRACE-Data for the period was found. In the end, recommendations on the adaptation to CC were proposed. The findings demonstrate that RS techniques are useful tools to detect hydrological droughts, observe, and conserve land resources. In arid areas such as Errachidia, the solution lies in rationalizing the use of water resources protecting them from uncontrolled anthropogenic events, which may alleviate the pressure. To support local sustainable development, environmental scientists and decision-makers may use the outputs of this study.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Exploring the land use and land cover change in the period 2005–2020 in the province of Errachidia, the pre-sahara of Morocco

et al. 2022

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“…Moreover, NDVI values provide insights into the relationship between groundwater and plant growth. In cases where a high NDVI area is experiencing drought, it indicates that vegetation is consuming more groundwater, which potentially impact groundwater levels variation (Hassan et al, 2019;Hadri et al, 2021;Lebrini et al, 2021;Moumane et al, 2021;Sajjad et al, 2022;Zhang Z. et al, 2022). However, given the positioning of well P3 in the floodplain, it is plausible that the influence of the nearby wadi contributes significantly to the observed decline in model performance between the training and test periods.…”

Section: Model Performance Assessment In Irrigated and Floodplain Areasmentioning

confidence: 99%

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

Bouramtane,

Leblanc,

Kacimi

et al. 2023

Front. Water

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The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.

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“…In recent years, the methods employed to accurately and quantitatively determine rock Fe content are unable to combine laboratory measurements with in situ field measurements (Moumane et al, 2021). In this research, two datasets comprising laboratory measurements and field measurements were comprehensively utilized, and the correlations between bare rock Fe content and spectral indices were accurately and extensively described and tested.…”

Section: Reflectance Of the Weathered And Fresh Rock Surfaces Measure...mentioning

confidence: 99%

Estimation of rock Fe content based on hyperspectral indices

Wang

Cheng

et al. 2021

J. Arid Land

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Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines. However, the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies. The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area. Specifically, we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements (Dataset I) and field measurements (Dataset II). In situ field measurements were acquired to verify the laboratory measurements. Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples. In addition, we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples. The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces. It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content. Based on sample data analysis, the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content, with R 2 values of 0.45 and 0.40 corresponding to datasets I and II, respectively. Hence, the modified normalized difference index (the wavelengths of 2220, 2290, and 2370 nm) identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas. The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.

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Monitoring spatiotemporal variation of groundwater level and salinity under land use change using integrated field measurements, GIS, geostatistical, and remote-sensing approach: case study of the Feija aquifer, Middle Draa watershed, Moroccan Sahara

Cited by 27 publications

References 55 publications

Exploring the land use and land cover change in the period 2005–2020 in the province of Errachidia, the pre-sahara of Morocco

Exploring the land use and land cover change in the period 2005–2020 in the province of Errachidia, the pre-sahara of Morocco

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

Estimation of rock Fe content based on hyperspectral indices

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