Abstract
The civil war, harsh climate, tough topography, and lack of accurate meteorological stations have limited observed data across Afghanistan. To fulfill the gap, this study analyzed the trend in precipitation and its extremes using Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) daily dataset between 1951 and 2010 at the spatial resolution of 0.25° × 0.25°. Non-parametric modified Mann-Kendall test and Sen’s slope estimator were employed to detect the trend and quantify it at the significance level of 5%. Significant decreasing trends were observed only in small clusters of southwestern regions ranging between 0 and −1.5mm/year and the northeastern region between −1.5 and −6 mm/year for the annual time series. A similar trend pattern was observed in the spring season decreasing at the rate of −0.15 and 0.54 mm/year in the northeastern and 0 to −0.15 mm/year southwestern region. A decrease in spring precipitation is expected to affect crop production especially in the northeastern region which hosts 22% of the arable area. An increasing trend in the eastern region at a maximum rate of 0.16 mm/year was observed which could intensify the flooding events. Trend analysis of extreme precipitation indices indicated similar spatial distribution to the mean precipitation, concentrated around southwestern, northeastern, and eastern regions. The increasing frequency of consecutive dry days in the western region and very heavy precipitation (R10mm) and extremely heavy precipitation (R20mm) in the eastern region could be fueling the occurrence of droughts and floods respectively. Taking these findings of the erratic nature of rainfall and extreme events into consideration for sustainable management of water resources would be fruitful.
Highlights
Precipitation is one of the most important variables in hydrology that directly influences the hydrologic processes
In order to achieve the objective, this study evaluates publicly available and differently scaled gauge based interpolated gridded precipitation dataset at daily scale: Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) and Climate Prediction Center (CPC)
During the period of 2006-2011, APHRODITE product outperforms CPC dataset for about 70 % of stations in terms of bias, root mean square error (RMSE) and correlation coefficient
Summary
Precipitation is one of the most important variables in hydrology that directly influences the hydrologic processes. Change in precipitation has direct effect on water resources, agriculture, forestry, ecosystem, natural resources, plant cover and drinking water (Cannarozzo et al 2006). Because of this, it has garnered wider attention of scientific communities in light of climate change. Understanding historical trend of extreme precipitation events provide insightful to understanding change in regional climate dynamics across time and space. These understandings help governments to cope up with the hazards and adopt necessary strategies for sustainable water management
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