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

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

Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco

In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission). In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER) basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual) between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r) of 0.2 and average Root Mean Square Error (RMSE) of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm) or annual time scale (r of 0.71 and RMSE of 157 mm). Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions

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

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 R 2 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

When climate variability partly compensates for groundwater depletion: An analysis of the GRACE signal in Morocco

International audienceSince April 2002, the Gravity Recovery and Climate Experiment (GRACE) mission have opened new pathways for hydrologists to monitor the changes in terrestrial total water storage (TWS). Here, the Center for Space Research (CSR), Goddard Space Flight Center (GSFC), Jet Propulsion Laboratory (JPL), and the average (AVG) GRACE mascon solutions were used to examine the changes in TWS and groundwater storages (GWS) in Morocco, with an emphasis on natural replenishment events and their link to snow cover area (SCA) and rainfall variability. New hydrological insights for the region: The results showed that GRACE TWS from AVG (TWS AVG) and GSFC (TWS GSFC) can fairly describe the temporal patterns of the groundwater level (GWL). Moreover, during 2002-2020, the TWS underwent a strong depletion relatively masked by natural recharge events. This was revealed as we identified two intermittent depletion episodes with statistically significant rates (− 1.03 ± 0.11 to − 0.31 ± 0.1 cm yr − 1) higher than those obtained for the long-term trend lines (− 0.28 ± 0.11 to − 0.15 ± 0.07 cm yr − 1). The TWS appeared to be strongly linked with the SCA metrics and rainfall indices with 1-3 months of lag. Our findings suggest that the rainfall distribution can be more insightful about changes in groundwater levels compared to the rainfall monthly totals

Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco

Daily hydrological modelling is among the most challenging tasks in water resource management, particularly in terms of streamflow prediction in semi-arid areas. Various methods were applied in order to deal with this complex phenomenon, but recently data-driven models have taken a better space, given their ability to solve prediction problems in time series. In this study, we have employed the Long Short-Term Memory (LSTM) network to simulate the daily streamflow over the Ait Ouchene watershed (AIO) in the Oum Er-Rbia river basin in Morocco, based on a temporal sequence of in situ and remotely sensed hydroclimatic data ranging from 2001 to 2010. The analysis adopted in this work is based on three-dimension input required by the LSTM model (1); the input samples used three splitting approaches: 70% of the dataset as training, splitting the data considering the hydrological year and the cross-validation method; (2) the sequence length; (3) and the input features using two different scenarios. The prediction results demonstrate that the LSTM performs poorly using the default data input scenario, whereas the best results during the testing were found in a sequence length of 30 days using approach 3 (R2 = 0.58). In addition, the LSTM fed with the lagged data input scenario using the Forward Feature Selection (FFS) method provides high performance accuracy using approach 2 (R2 = 0.84) in a sequence length of 20 days. Eventually, in applications related to water resources management where data are limited, the use of the deep learning technique is able to create high predictive accuracy, which can be enhanced with the right combination subset of features by using FFS

Sensitivity and Interdependency Analysis of the HBV Conceptual Model Parameters in a Semi-Arid Mountainous Watershed

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters’ behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byråns Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model’s reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover

RECOURS AUX SATELLITES POUR APPUYER LE MANAGEMENT DE L’EAU D’IRRIGATION: ESTIMATION DES BESOINS EN EAU DES AGRUMES PAR TÉLÉDÉTECTION DANS LA PLAINE DE TRIFFA-BERKANE (MAROC)

Citrus represent the main irrigated crop in Trifa plain (Berkane, Morocco) and their area are in continuous extension, which increases the pressures on the limited water resources of the region. In this context, effective and rational governance of agricultural water is highly needed. In this study, we propose a satellite based approach to support irrigation water management of citrus in Trifa plain. This approach was developed in two steps: citrus crop coefficient maps were firstly developed from an age-stratified citrus map, derived from Sentinel optical and radar satellite images. Then, a validated implementation of Hargreaves model was used to estimate potential evapotranspiration from Landsat 8 TIRS thermal images. Compared to Penman-Montheith reference model, Hargreaves method estimates accurately potential evapotranspiration, with an error (RMSE) lower than 0.5mm / day. Finlay, the combination of crop coefficient and potential evapotranspiration maps allowed to spatialize the maximum citrus evapotranspiration at the pixel scale (one hectare). This information could be used to assist irrigation services to increase agricultural water productivity in irrigated areas

High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data

Snowpack and groundwater recharge in the Atlas mountains: New evidence and key drivers

Study region: The Atlas Mountains of Morocco, specifically the High Oum Er-rbiaa (HOER) and Ourika catchments. Study focus: to identify the recharge processes within the semi-arid watersheds, in the Atlas Mountains, through monthly monitoring of snow, rainfall, surface water, and groundwater isotope signal, but also the usage of remote sensing data. New hydrological insights for the region: The spatial-temporal analysis of groundwater and precipitation isotopes reveals significant spatial heterogeneity, primarily influenced by the geological variations in each aquifer. Temporal variations indicate that direct recharge occurs in response to winter precipitation, whereas a delayed response is observed during the summer when snow replenishes groundwater towards the end of the melting season. The findings are further supported by the ''Gravity Recovery and Climate Experiment'' (GRACE) dataset, which demonstrates that high values of Total Water Storage (TWS) align with groundwater isotopes. This highlights the substantial groundwater abstraction rate between March and June to compensate for the lack of precipitation during this period. The analysis of isotope data indicates that 50% of groundwater recharge in the upstream Jurassic aquifer and 80% in the downstream Triassic-Paleozoic aquifers in the HOER catchment is sourced from snowmelt. Similarly, in the Ourika catchment, snowmelt contributes 30% and 50% of groundwater recharge in the upstream and downstream portions of the catchments respectively. This disparity is due to different melting rates across altitudinal ranges and variations in the lithology of each catchment