Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone

نویسندگان

چکیده

Several satellite precipitation estimates are becoming available globally, offering new possibilities for modeling water resources, especially in regions where data scarce. This work provides the first validation of four products, CHIRPS v2, Tamsat, Persiann CDR and TerraClimate data, a semi-arid region Essaouira city (Morocco). The from different satellites compared with ground observations 4 rain gauges measurement stations using comparison methods, namely: Pearson correlation coefficient (r), Bias, mean square error (RMSE), Nash-Sutcliffe efficiency absolute (MAE). Secondly, rainfall-runoff basin study area (Ksob Basin S = 1483 km2) was carried out based on artificial neural networks type MLP (Multi Layers Perceptron). model -then used to evaluate best products estimating discharge. results indicate that is most appropriate product (R2 0.77 0.62 training phase, respectively). By this combination hydrological ANN (Artificial Neural Network) approach, simulations monthly flow watershed were not very satisfactory. However, clear improvement estimations occurred when ESA-CCI (European Space Agency’s (ESA) Climate Change Initiative (CCI)) soil moisture added (training phase: R2 0.88, 0.69 Nash ≥ 92%). offer interesting prospects resources coastal zone watersheds data.

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ژورنال

عنوان ژورنال: Water

سال: 2023

ISSN: ['2073-4441']

DOI: https://doi.org/10.3390/w15111997