article · Journal of Water and Climate Change
Satellite-based rainfall datasets offering high spatial and temporal resolutions are required to evaluate the impacts of climate change. This research assesses the performance of three precipitation products, PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT, at a monthly scale for rainfall and drought monitoring within a semi-arid watershed in Morocco. Using records from 1983 to 2017, the study calculated the Standardized Precipitation Index to simulate drought conditions via a Gamma distribution. Both PERSIANN-CCS-CDR and ERA5 demonstrated strong correlation coefficients for monthly rainfall at the basin scale, although both slightly overestimated observed precipitation. ERA5 achieved a higher Nash-Sutcliffe efficiency score of 0.72 compared to 0.41 for PERSIANN-CCS-CDR, whereas SM2RAIN-ASCAT showed an overestimation. However, when evaluating three-month drought indices, PERSIANN-CCS-CDR outperformed ERA5, showing correlation coefficients above 0.67 and a root mean square error close to zero relative to ground station observations.
Reliable rainfall monitoring is vital for managing water scarcity and assessing drought risk in semi-arid environments. Ground-based weather stations can be sparse, making satellite and reanalysis data essential alternatives. Identifying which remote-sensing datasets accurately replicate ground rainfall and drought conditions ensures that regional planners, hydrologists, and environmental managers base their water management decisions on dependable climate indicators.
This work represents applied and tested benchmarking research that informs regional climate and water management tools. Water basin authorities, agricultural planners, and climate risk assessors could incorporate the higher-performing datasets into operational drought monitoring software. However, the abstract does not indicate a direct commercial product, proprietary technology, or immediate commercialisation pathway.
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Abstract Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercussions. Previous research used datasets neglecting either good temporal or good spatial resolution, PERSIANN-CCSCDR, ERA5, and SM2RAIN-ASCAT are some of the projects aiming to remedy these limitations. This study's goal is to evaluate the accuracy of the PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT at a monthly scale and their suitability for drought assessment in a Moroccan semiarid watershed. Several statistical indices were computed, the drought SPI was calculated using PERSIANN-CCS-CDR estimates, ERA5 products, and observed records as an input in the SPI formula using Gamma distribution to simulate drought from 1983 to 2017. The preliminary comparison and evaluation results of PERSIANN-CCS-CDR estimates and ERA5 datasets showed good CC on a basin scale for monthly precipitation, with a slight overestimation of the observed precipitation shown by the PBIAS. The NSE scored 0.41 for PERSIANN-CCS-CDR and 0.72 for ERA5. The results for SM2RAIN-ASCAT showed an overestimation of the observed precipitation data. At the basin scale, the SPI3 correlation coefficients between the PERSIANN-CCS-CDR monthly estimates and observed gauge rainfall data were greater than 0.67, and the RMSE was closer to 0, outperforming ERA5 in the SPI3 evaluation.
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DOI: 10.2166/wcc.2023.461
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