Integrated assessment of extreme events and hydrological responses of Indo-Nepal Gandak River Basin

Author(s):  
Pawan K. Chaubey ◽  
Prashant K. Srivastava ◽  
Akhilesh Gupta ◽  
R. K. Mall
2013 ◽  
Vol 13 (12) ◽  
pp. 3145-3156 ◽  
Author(s):  
M. Velasco ◽  
P. A. Versini ◽  
A. Cabello ◽  
A. Barrera-Escoda

Abstract. Global change may imply important changes in the future occurrence and intensity of extreme events. Climate scenarios characterizing these plausible changes were previously obtained for the Llobregat River basin (NE Spain). This paper presents the implementation of these scenarios in the HBV (Hydrologiska Byråns Vattenbalansavdelning) hydrological model. Then, the expected changes in terms of flash flood occurrence and intensity are assessed for two different sub-basins: the Alt Llobregat and the Anoia (Llobregat River basin). The assessment of future flash floods has been done in terms of the intensity and occurrence of extreme events, using a peak over threshold (POT) analysis. For these two sub-basins, most of the simulated scenarios present an increase of the intensity of the peak discharge values. On the other hand, the future occurrence follows different trends in the two sub-basins: an increase is observed in Alt Llobregat but a decrease occurs in Anoia. Despite the uncertainties that appear in the whole process, the results obtained can shed some light on how future flash floods events may occur.


CATENA ◽  
2022 ◽  
Vol 209 ◽  
pp. 105859
Author(s):  
Sangam Shrestha ◽  
Binod Bhatta ◽  
Rocky Talchabhadel ◽  
Salvatore Gonario Pasquale Virdis

Urban Climate ◽  
2012 ◽  
Vol 1 ◽  
pp. 40-54 ◽  
Author(s):  
Goro Mouri ◽  
Seirou Shinoda ◽  
Valentin Golosov ◽  
Michiharu Shiiba ◽  
Tomoharu Hori ◽  
...  

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.


Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 130 ◽  
Author(s):  
Wenlong Hao ◽  
Zhenchun Hao ◽  
Feifei Yuan ◽  
Qin Ju ◽  
Jie Hao

Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.


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