干旱区生态环境知识资源中心(Arid): Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone

Arid

DOI	10.3390/w15111997
	Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone
	Rachidi, Said; El Mazoudi, E. L. Houssine; El Alami, Jamila; Jadoud, Mourad; Er-Raki, Salah
通讯作者	Er-Raki, S
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:11
英文摘要	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 km(2)) 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 (R-2 = 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: R-2 = 0.88, validation phase: R-2 = 0.69 and Nash = 92%). The results offer interesting prospects for modeling the water resources of the coastal zone watersheds with this data.
英文关键词	ANN satellites precipitation Atlantic area of Morocco rainfall-runoff modeling and remote sensing
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001005025900001
WOS关键词	NEURAL-NETWORK ; WATER-LEVEL ; PRECIPITATION ; BASIN
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399032
推荐引用方式 GB/T 7714	Rachidi, Said,El Mazoudi, E. L. Houssine,El Alami, Jamila,et al. Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone[J],2023,15(11).
APA	Rachidi, Said,El Mazoudi, E. L. Houssine,El Alami, Jamila,Jadoud, Mourad,&Er-Raki, Salah.(2023).Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone.WATER,15(11).
MLA	Rachidi, Said,et al."Assessment and Comparison of Satellite-Based Rainfall Products: Validation by Hydrological Modeling Using ANN in a Semi-Arid Zone".WATER 15.11(2023).