Abstract
Reliable long-term precipitation estimation data holds immense hydrometeorological value due to its extensive spatial coverage and extended temporal records. Its availability is indispensable for managing critical aspects such as drinking water supply, agriculture, and various socioeconomic activities. However, in mountainous and arid regions, where the sustainable use of water resources is pivotal due to freshwater scarcity and erratic rainfall patterns, the suitability and performance of available precipitation datasets remain a pertinent question. The arid region of Balochistan, Pakistan, exemplifies this challenge, where more than 85% of the population resides in rural areas and relies heavily on farming and livestock for income. This study endeavors to assess the spatiotemporal characteristics of rainfall in Balochistan from 1980 to 2016 using multi-source data. Employing point-to-pixel techniques and statistical indicators at various temporal scales (daily, monthly, seasonal, and annual), we evaluated the performance of satellite-based datasets. Furthermore, categorical statistical indices, including Probability of Detection (POD), False-Alarm Ratio (FAR), and Critical Success Index (CSI), were employed to gauge each dataset’s precipitation detection capabilities. Results of the study reveal that Aphro and Multi-Source Weighted-Ensemble Precipitation (MSWEP) datasets exhibit the highest correlation coefficients (0.96 and 0.92, respectively), while the Climate Prediction Center (CPC) dataset yields the lowest correlation (0.77). Notably, the maximum precipitation intensity was observed in Barkhan, whereas Nokkundi recorded the lowest. Spatially, the monsoon influence led to a shift in rainfall distribution from the southeast to the northeast. Balochistan experiences precipitation primarily during two distinct seasons: the summer monsoon (July to August) and the winter western disturbance (November to January). The monthly rainfall volume is predominantly contributed by rainfall events with an intensity exceeding 10 mm. This research underscores the critical significance of judiciously selecting precipitation data sources for informed water management policies in arid regions, addressing the pressing need for reliable water resource allocation and sustainability planning in areas highly vulnerable to climate variations.
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Acknowledgements
This study is sponsored by the Taishan Young Scholar Program (No. tsqn202103070) funded by the Taishan Scholar Foundation of Shandong Province (CN).
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The research is finically supported by the Taishan Young Scholar Program (tsqn202103070), and the Taishan Scholar Foundation of Shandong Province, China.
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Ehsan Elahi and Mohammad Ilyas Abro contributed to developing a conceptual framework, data curation, and writing an original draft of the article. Ehsan Elahi received a financial grant. Murad Ali Khaskheli, Ghulam Abbas Kandhro, Tasneem Zehra, Sikandar Ali, and Muhammad Najam Shaikh investigated the activities of research and conducted the formal analysis. Other parts of the research such as setting methodology, collection of resources, application of software, validation, and visualization were completed by Barkat Ali Laghari, Mahdi Hassan, and Mushtaque Ahmed Memon. All authors reviewed and approved the manuscript.
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Elahi, E., Abro, M.I., Khaskheli, M.A. et al. Long-term evaluation of rainfall in the arid region of Pakistan using multi-source data. Theor Appl Climatol 155, 2819–2840 (2024). https://doi.org/10.1007/s00704-023-04797-3
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DOI: https://doi.org/10.1007/s00704-023-04797-3