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This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades. • Significant decreases will occur in the intensity and amount of heavy precipitation for upcoming decades. • Change rates of precipitation indices combinations illustrate obvious spatial heterogeneity. • The frequency of simultaneous floods and droughts in a year and that of extreme heavy precipitation events will augment. [ABSTRACT FROM AUTHOR]

The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS. • Four precipitation products (TRMM, CDR, CMADS and CFSR) were selected for evaluation and corrected against OBS. • The hydrological simulation results show that satellite-based precipitation performs better than reanalysis precipitation. • TRMM performs best during the wet season, while CDR performes best during the dry season. [ABSTRACT FROM AUTHOR]