A comprehensive analysis of projected changes of extreme precipitation indices in West Rapti River basin, Nepal under changing climate

2020 ◽  
Author(s):  
Rocky Talchabhadel ◽  
Anil Aryal ◽  
Kenji Kawaike ◽  
Kazuki Yamanoi ◽  
Hajime Nakagawa
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Comprehensive Analysis ◽  
Changing Climate ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Projected Changes

scholarly journals Bias correction of gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo-Brahmaputra River Basin

2019 ◽  
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Spatial Coverage ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data are challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB) using two linear and two nonlinear methods, and assessed their influence on extreme precipitation indices. The results showed that the original APHRODITE data tended to underestimate precipitation during the summer monsoon season, especially in the topographically complex Himalayan belt. Bias correction using complementary rainfall observations to add spatial coverage in data-sparse regions greatly improved the performance of extreme precipitation analysis. Although all methods could correct mean precipitation, their ability to correct the wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Generally, higher-skill bias-corrected APHRODITE data are expected to perform better than those corrected by lower-skill approaches. This study would provide reference for using gridded precipitation data in extreme precipitation analysis and selecting bias-corrected method for rainfall products in data-sparse regions.

scholarly journals Spatiotemporal variation characteristics of extreme precipitation in the upper reaches of the Hongshui River Basin during 1959–2016

2021 ◽  
Author(s):  
Ya Huang ◽  
Ling Yi ◽  
Weihua Xiao ◽  
Guibing Hou ◽  
Yuyan Zhou
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Water Resource Management ◽  
Flood Control ◽  
Southern Oscillation ◽  
Disaster Mitigation ◽  
Eastern Region ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Precipitation Events

Abstract Understanding changes in the intensity and frequency of extreme precipitation is vital for flood control, disaster mitigation, and water resource management. In this study, 12 extreme precipitation indices and the best-fitting extreme value distribution were used to analyze the spatiotemporal characteristics of extreme precipitation in the upper reaches of the Hongshui River Basin (UHRB). The possible links between extreme precipitation and large-scale circulation were also investigated. Most extreme precipitation indices increased from west to east in the UHRB, indicating that the eastern region is a humid area with abundant precipitation. The indices for consecutive wet days (CWD) and precipitation events (R0.1) decreased significantly, indicating that the UHRB tends to be dry, with few precipitation events. The probability distribution functions of most extreme precipitation indices, especially that of R0.1, shifted significantly to the left in 1988–2016 compared with 1959–1987, further indicating that the UHRB has experienced a significant drying trend in recent decades. The East Asian summer monsoon and the El Niño–Southern Oscillation/Pacific Decadal Oscillation were confirmed to influence extreme precipitation in the UHRB. These findings are helpful for understanding extreme precipitation variation trends in the UHRB and provide references for further research.

scholarly journals Bias correction of a gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo–Brahmaputra River basin

2020 ◽  
Vol 20 (8) ◽  
pp. 2243-2254
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo ◽  
Local Intensity

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data is challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates in the Yarlung Tsangpo–Brahmaputra River basin (YBRB) using two linear and two nonlinear methods, and their influence on extreme precipitation indices was assessed by cross-validation. Bias correction greatly improved the performance of extreme precipitation analysis. The ability of four methods to correct wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Local intensity scaling (LOCI) and quantile–quantile mapping (QM) performed better than linear scaling (LS) and power transformation (PT). This study would provide a reference for using gridded precipitation data in extreme precipitation analysis and selecting a bias-corrected method for rainfall products in data-sparse regions.

Projected changes in mean and extreme precipitation indices over India using PRECIS

2014 ◽  
Vol 113 ◽  
pp. 77-90 ◽  
Author(s):  
K. Koteswara Rao ◽  
S.K. Patwardhan ◽  
Ashwini Kulkarni ◽  
K. Kamala ◽  
S.S. Sabade ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Projected Changes

scholarly journals Insights on the Impacts of Hydroclimatic Extremes and Anthropogenic Activities on Sediment Yield of a River Basin

Earth ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 32-50
Author(s):  
Rocky Talchabhadel ◽  
Jeeban Panthi ◽  
Sanjib Sharma ◽  
Ganesh R. Ghimire ◽  
Rupesh Baniya ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Extreme Precipitation ◽  
Suspended Sediment Concentration ◽  
Anthropogenic Activities ◽  
Sediment Concentration ◽  
Sediment Flux ◽  
Mountain River ◽  
Ecological Processes ◽  
Precipitation Indices

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability.

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