article · Water
Satellite precipitation datasets provide alternative options for monitoring water resources in regions with limited ground measurements. A validation study in the semi-arid Ksob Basin near Essaouira, Morocco, evaluated four satellite rainfall products against records from four ground rain gauges: CHIRPS v2, Tamsat, Persiann CDR, and TerraClimate. Statistical metrics identified TerraClimate as the most accurate product for estimating local precipitation. To model river discharge, an artificial neural network based on a multi-layer perceptron was developed. While running the hydrological model with TerraClimate data alone yielded unsatisfactory monthly streamflow simulations, integrating soil moisture data from the European Space Agency Climate Change Initiative substantially enhanced accuracy. The combined data inputs achieved strong flow estimation metrics during both training and validation phases, showing that pairing satellite rainfall estimates with satellite soil moisture offers a workable method for hydrological modelling in data-scarce coastal catchments.
Accurate water resource planning is difficult in semi-arid areas where ground monitoring networks are sparse. Demonstrating that satellite rainfall and soil moisture records can be integrated through machine learning gives catchment managers an accessible way to estimate river flows. This helps regional authorities better forecast water availability and prepare for climate variability without relying exclusively on dense ground-based sensor infrastructure.
The method is relevant to water basin authorities, environmental consultancies, and flood management agencies operating in data-scarce catchments. The work represents applied research tested on a single watershed, meaning commercialisation remains at an early stage. Developing this into an operational decision-support tool would require integrating the neural network workflow into accessible software and validating performance across broader geographic regions and operational conditions.
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Several satellite precipitation estimates are becoming available globally, offering new possibilities for modeling water resources, especially in regions where data are scarce. This work provides the first validation of four satellite precipitation products, CHIRPS v2, Tamsat, Persiann CDR and TerraClimate data, in a semi-arid region of Essaouira city (Morocco). The precipitation data from different satellites are first compared with the ground observations from 4 rain gauges measurement stations using the different comparison methods, namely: Pearson correlation coefficient (r), Bias, mean square error (RMSE), Nash-Sutcliffe efficiency coefficient and mean absolute error (MAE). Secondly, a rainfall-runoff modeling for a basin of the study area (Ksob Basin S = 1483 km2) was carried out based on artificial neural networks type MLP (Multi Layers Perceptron). This model was -then used to evaluate the best satellite products for estimating the discharge. The results indicate that TerraClimate is the most appropriate product for estimating precipitation (R2 = 0.77 and 0.62 for the training and validation phase, respectively). By using this product in combination with hydrological modeling based on ANN (Artificial Neural Network) approach, the simulations of the monthly flow in the watershed were not very satisfactory. However, a clear improvement of the flow estimations occurred when the ESA-CCI (European Space Agency’s (ESA) Climate Change Initiative (CCI)) soil moisture was added (training phase: R2 = 0.88, validation phase: R2 = 0.69 and Nash ≥ 92%). The results offer interesting prospects for modeling the water resources of the coastal zone watersheds with this data.
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DOI: 10.3390/w15111997
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