An extreme flood caused by a heavy snowfall over the Indigirka River basin in Northeastern Siberia

2019 ◽  
Vol 34 (3) ◽  
pp. 522-537 ◽  
Author(s):  
Shunsuke Tei ◽  
Tomoki Morozumi ◽  
Shin Nagai ◽  
Shinya Takano ◽  
Atsuko Sugimoto ◽  
...  
Keyword(s):  
River Basin ◽  
Heavy Snowfall ◽  
Northeastern Siberia ◽  
Extreme Flood

Holocene extreme paleofloods and their climatological context, Upper Colorado River Basin, USA

2020 ◽  
Vol 44 (5) ◽  
pp. 727-745
Author(s):  
Tao Liu ◽  
Lin Ji ◽  
Victor R Baker ◽  
Tessa M Harden ◽  
Michael L Cline
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Meta Analysis ◽  
Flood Frequency ◽  
Colorado River Basin ◽  
Extreme Floods ◽  
Upper Colorado River Basin ◽  
Extreme Flood ◽  
Southwestern Usa ◽  
Extreme Flooding

Given its singular importance for water resources in the southwestern USA, the Upper Colorado River Basin (UCRB) is remarkable for the paucity of its conventional hydrological record of extreme flooding. Short-term record-based flood frequency analyses lead to very great aleatory uncertainties about infrequent extreme flood events and their climate-driven causal associations. This study uses paleoflood hydrology to examine a small portion of the underutilized, but very extensive natural record of Holocene extreme floods in the UCRB. We perform a meta-analysis of 82 extreme paleofloods from 18 slack water deposit sites in the UCRB to show linkages between Holocene climate patterns and extreme floods. The analysis demonstrates several clusters of extreme flood activity: 8040–7960, 4400–4300, 3600–3460, 2900–2740, 2390–1980, 1810–720, and 600–0 years BP. The extreme paleofloods were found to occur during both dry and wet periods in the paleoclimate record. When compared with independent paleoclimatic records across the Rocky Mountains and the southwestern USA, the observed temporal clustering pattern of UCRB extreme paleofloods shows associations with periods of abruptly intensified North Pacific-derived storms connected with enhanced variability of El Niño. This approach demonstrates the value of creating paleohydrological databases and comparing them with hydro-climatic proxies in order to identify natural patterns and to discover possible linkages to fundamental processes such as changes in climate.

scholarly journals Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

2019 ◽  
Vol 51 (1) ◽  
pp. 105-126 ◽  
Author(s):  
Eugene Zhen Xiang Soo ◽  
Wan Zurina Wan Jaafar ◽  
Sai Hin Lai ◽  
Faridah Othman ◽  
Ahmed Elshafie ◽  
...  
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Monsoon Season ◽  
Rainfall Threshold ◽  
Careful Consideration ◽  
Northeast Monsoon ◽  
Satellite Precipitation ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Langat River

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

scholarly journals Trends of Extreme Flood Events in the Pearl River Basin during 1951–2010

2013 ◽  
Vol 4 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Wu Zhi-Yong ◽  
Lu Gui-Hua ◽  
Liu Zhi-Yu ◽  
Wang Jin-Xing ◽  
Xiao Heng
Keyword(s):  
River Basin ◽  
Pearl River ◽  
Flood Events ◽  
Pearl River Basin ◽  
Extreme Flood ◽  
The Pearl River ◽  
The Pearl River Basin

scholarly journals Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 66
Author(s):  
Hang Zeng ◽  
Jiaqi Huang ◽  
Zhengzui Li ◽  
Weihou Yu ◽  
Hui Zhou
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Central China ◽  
Northwest Pacific ◽  
Design Flood ◽  
Xiangjiang River ◽  
Northwest Pacific Ocean ◽  
South Central ◽  
Extreme Flood ◽  
Xiangjiang River Basin

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

Development of a Flow Routing Model Including Inundation Effect for the Extreme Flood in the Chao Phraya River Basin, Thailand 2011

2013 ◽  
Vol 8 (3) ◽  
pp. 415-423 ◽  
Author(s):  
Supattana Wichakul ◽  
◽  
Yasuto Tachikawa ◽  
Michiharu Shiiba ◽  
Kazuaki Yorozu
Keyword(s):  
River Basin ◽  
Kinematic Wave ◽  
Flow Routing ◽  
Tank Model ◽  
Kinematic Wave Equation ◽  
Prediction Tools ◽  
Overbank Flow ◽  
Extreme Flood ◽  
Efficient Prediction ◽  
Chao Phraya River

In the second half of 2011, massive flooding in the lower part of the Yom and Nan River Basins and the Chao Phraya River Basin resulted in tremendous loss and adversely impacted on the livelihood, society, and economy of Thailand. The development of reliable and efficient prediction tools is important for prevent similar occurrences in the future. Consequently, this study thus aimed to develop an applicable distributed flow routing model including the inundation effect. A flow routing model using a kinematic wave equation was modified based on the concept of a diffusive tank model, which included the inundation effect. Overbank flow was estimated by using a broad crested weir equation. The inundation model had ten parameters that provided a good fit for observed and simulated discharge in 2011 at the C.2 Station with a Nash-Sutcliffe efficiency of 0.91.

scholarly journals Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

2008 ◽  
Vol 12 (1) ◽  
pp. 207-221 ◽  
Author(s):  
W. Wang ◽  
X. Chen ◽  
P. Shi ◽  
P. H. A. J. M. van Gelder
Keyword(s):  
River Basin ◽  
Gamma Distribution ◽  
Extreme Precipitation ◽  
Scale Parameter ◽  
Southern China ◽  
Low Flow ◽  
Small Data ◽  
Dongjiang River ◽  
Extreme Flood ◽  
Precipitation Processes

Abstract. Extreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated with several nonparametric methods, including one method (Mann-Kendall test) for detecting trend, and three methods (Kolmogorov–Smirnov test, Levene's test and quantile test) for detecting changes in probability distribution. It was shown that little change is observed in annual extreme precipitation in terms of various indices, but some significant changes are found in the precipitation processes on a monthly basis, which indicates that when detecting climate changes, besides annual indices, seasonal variations in extreme events should be considered as well. Despite of little change in annual extreme precipitation series, significant changes are detected in several annual extreme flood flow and low-flow series, mainly at the stations along the main channel of Dongjiang River, which are affected significantly by the operation of several major reservoirs. To assess the reliability of the results, the power of three non-parametric methods are assessed by Monte Carlo simulation. The simulation results show that, while all three methods work well for detecting changes in two groups of data with large sample size (e.g., over 200 points in each group) and large differences in distribution parameters (e.g., over 100% increase of scale parameter in Gamma distribution), none of them are powerful enough for small data sets (e.g., less than 100 points) and small distribution parameter difference (e.g., 50% increase of scale parameter in Gamma distribution). The result of the present study raises the concern of the robustness of statistical change-detection methods, shows the necessity of combined use of different methods including both exploratory and quantitative statistical methods, and emphasizes the need of physically sound explanation when applying statistical test methods for detecting changes.

scholarly journals Changing properties of extreme flood in Xiliugou River Basin, China

2019 ◽  
Vol 295 ◽  
pp. 042136
Author(s):  
Suzhen Dang ◽  
Manfei Yao ◽  
Huijuan Yin ◽  
Guotao Dong
Keyword(s):  
River Basin ◽  
Extreme Flood

scholarly journals Evaluation of High-Resolution Satellite-Based Real-Time and Post-Real-Time Precipitation Estimates during 2010 Extreme Flood Event in Swat River Basin, Hindukush Region

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Naveed Anjum ◽  
Yongjian Ding ◽  
Donghui Shangguan ◽  
Muhammad Wajid Ijaz ◽  
Shiqiang Zhang
Keyword(s):  
Real Time ◽  
Correlation Coefficient ◽  
Spatial Correlation ◽  
River Basin ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
Extreme Precipitation Events ◽  
Extreme Flood ◽  
Precipitation Estimates ◽  
Swat River

Satellite-based real-time and post-real-time precipitation estimates of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-3B42) were evaluated during an extreme heavy precipitation event (on 28–30 July 2010) over Swat River Basin and adjacent areas in Hindukush Region. Observations of 15 rain gauging stations were used for the evaluation of TMPA products. Results showed that the spatial pattern of precipitation in the event was generally captured by post-real-time product (3B42V7) but misplaced by real-time product (3B42RT), witnessed by a high spatial correlation coefficient for 3B42V7 (CC = 0.87) and low spatial correlation coefficient for 3B42RT (CC = 0.20). The temporal variation of the storm precipitation was not well captured by both TMPA products. 3B42V7 product underestimated the storm accumulated precipitation by 32.15%, while underestimation by 3B42RT was 66.73%. Based on the findings of this study, we suggest that the latest TMPA-based precipitation products, 3B42RT and 3B42V7, might not be able to perform well during extreme precipitation events, particularly in complex terrain regions like Hindukush Mountains. Therefore, cautions should be considered while using 3B42RT and 3B42V7 as input data source for the modelling, forecasting, and monitoring of floods and potential landslides in Hindukush Region.

Sign in / Sign up

Export Citation Format

Share Document