The Upper Tail of Flood Peaks over China: Hydrology, Hydrometeorology, and Hydroclimatology

2021 ◽  
Author(s):  
Long Yang ◽  
Yixin Yang ◽  
James Smith
Keyword(s):  
Flood Peaks

Maximum annual flood peaks distribution in non-stationary conditions

2012 ◽  
Author(s):  
E Zenoni ◽  
S Pecora ◽  
C De Michele ◽  
R Vezzoli
Keyword(s):  
Stationary Conditions ◽  
Flood Peaks

scholarly journals ỨNG DỤNG DỮ LIỆU MƯA CHIRPS VÀ MÔ HÌNH THỦY VĂN HEC-HMS MÔ PHỎNG DÒNG CHẢY LŨ Ở LƯU VỰC SÔNG LẠI GIANG

2021 ◽  
Vol 5 (1) ◽  
pp. 2252-2261
Author(s):  
Ngô Anh Tú ◽  
Phan Thái Lê ◽  
Nguyễn Hữu Xuân ◽  
Trần Văn Bình
Keyword(s):  
Economic Development ◽  
River Basin ◽  
Hydrological Model ◽  
Flow Data ◽  
Model Combination ◽  
The North ◽  
Socio Economic Development ◽  
Flood Peaks ◽  
Simulation Results ◽  
Flood Flow

Bài báo xác định lưu lượng dòng chảy theo thời đoạn dựa vào mô hình HEC-HMS, số liệu mưa từ ảnh vệ tinh CHIRPS của NASA và Hệ thống thông tin địa lý (GIS) trong mô phỏng dòng chảy lũ tháng 12 năm 2016 tại lưu vực sông Lại Giang, lưu vực lớn thứ hai của tỉnh Bình Định (sau lưu vực sông Kôn) và có vai trò quan trọng về phát triển kinh tế-xã hội ở phía Bắc của tỉnh. Kết quả mô phỏng dòng chảy lũ rất đáng tin cậy, lưu lượng dòng chảy lũ đạt đỉnh 2542,6 m3/s tương ứng với với tần suất lũ 5%. Chỉ số kiểm định mô hình NSE với giá trị là 0,93; hệ số R2 đạt 0,78 sai số PBIAS khoảng 24% và sai số đỉnh lũ PEC = 52,01.  ABSTRACT The paper aimed to introduce the application of the HEC-HMS hydrological model combination with the CHIRPS (Climate Hazards Group Infrared Precipitation with Station) and GIS to restore flood flow data in the Lai Giang river basin in 2016. The Lai Giang river basin is the second largest basin of Binh Dinh province (after the Kon river basin), it plays an important role in socio-economic development in the North of Binh Dinh province. The simulation results of flood peaks reached 2542,6 m3.s-1 (P=5%). Model test indices such as NSE = 0.93, the correlation coefficient reached 0,78; the percentage of PBIAS error was about 24%, and peak error (PEC) was 52,01.

scholarly journals Do small and large floods have the same drivers of change? A regional attribution analysis in Europe

2020 ◽  
Author(s):  
Miriam Bertola ◽  
Alberto Viglione ◽  
Sergiy Vorogushyn ◽  
David Lun ◽  
Bruno Merz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Return Period ◽  
Extreme Precipitation ◽  
Gumbel Distribution ◽  
Secondary Importance ◽  
Antecedent Soil Moisture ◽  
Covariate Data ◽  
Study Results ◽  
Flood Quantiles ◽  
Flood Peaks

Abstract. Recent studies have shown evidence of increasing and decreasing trends in mean annual floods and flood quantiles across Europe. Studies attributing observed changes in flood peaks to their drivers have mostly focused on mean annual floods. This paper proposes a new framework for attributing flood changes to potential drivers, as a function of return period (T), in a regional context. We assume flood peaks to follow a non-stationary regional Gumbel distribution, where the median flood and the 100-year growth factor are used as parameters. They are allowed to vary in time and between catchments as a function of the drivers quantified by covariates. The elasticities of floods with respect to the drivers and the contributions of the drivers to flood changes are estimated by Bayesian inference. The prior distributions of the elasticities of flood quantiles to the drivers are estimated by hydrological reasoning and from the literature. The attribution model is applied to European flood and covariate data and aims at attributing the observed flood trend patterns to specific drivers for different return periods. We analyse flood discharge records from 2370 hydrometric stations in Europe over the period 1960–2010. Extreme precipitation, antecedent soil moisture and snowmelt are the potential drivers of flood change considered in this study. Results show that, in northwestern Europe, extreme precipitation mainly contributes to changes in both the median (q2) and 100-year flood (q100), while the contributions of antecedent soil moisture are of secondary importance. In southern Europe, both antecedent soil moisture and extreme precipitation contribute to flood changes, and their relative importance depends on the return period. Antecedent soil moisture is the main contributor to changes in q2, while the contributions of the two drivers to changes in larger floods (T > 10 years) are comparable. In eastern Europe, snowmelt drives changes in both q2 and q100.

scholarly journals Anatomy of simultaneous flood peaks at a lowland confluence

2018 ◽  
Vol 22 (10) ◽  
pp. 5599-5613 ◽  
Author(s):  
Tjitske J. Geertsema ◽  
Adriaan J. Teuling ◽  
Remko Uijlenhoet ◽  
Paul J. J. F. Torfs ◽  
Antonius J. F. Hoitink
Keyword(s):  
Economic Value ◽  
Water Levels ◽  
Simultaneous Occurrence ◽  
Main Stream ◽  
Time Lags ◽  
Main River ◽  
Long Duration ◽  
Independent Observations ◽  
Flood Peaks ◽  
The Impact

Abstract. Lowlands are vulnerable to flooding due to their mild topography in often densely populated areas with high social and economic value. Moreover, multiple physical processes coincide in lowland areas, such as those involved in river–sea interactions and in merging rivers at confluences. Simultaneous occurrence of such processes can result in amplifying or attenuating effects on water levels. Our aim is to understand the mechanisms behind simultaneous occurrence of discharge waves in a river and its lowland tributaries. Here, we introduce a new way of analyzing lowland discharge and water level dynamics, by tracing individual flood waves based on dynamic time warping. We take the confluence of the Meuse River (∼33 000 km2) with the joining tributaries of the Dommel and Aa rivers as an example, especially because the January 1995 flood at this confluence was the result of the simultaneous occurrence of discharge peaks in the main stream and the tributaries and because independent observations of water levels and discharge are available for a longer period. The analysis shows that the exact timing of the arrival of discharge peaks is of little relevance because of the long duration of the average discharge wave compared to typical time lags between peaks. The discharge waves last on average 9 days, whereas the lag time between discharge peaks in the main river and the tributaries is typically 3 days. This results in backwaters that can rise up to 1.5 m over a distance of 4 km from the confluence. Thus, local measures to reduce the impact of flooding around the confluence should account for the long duration of flood peaks in the main system.

scholarly journals Case Study: Comparative Analysis of Hydrologic Simulations with Areal-Averaging of Moving Rainfall

Hydrology ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 12 ◽  
Author(s):  
Zheng N. Fang ◽  
Michael J. Shultz ◽  
Kevin J. Wienhold ◽  
Jiaqi Zhang ◽  
Shang Gao
Keyword(s):  
Comparative Analysis ◽  
Peak Discharge ◽  
Distributed Model ◽  
Lumped Model ◽  
Modeling Approaches ◽  
Flood Peaks ◽  
The Difference ◽  
Hydrologic Responses ◽  
The Impact

The goal of this investigation is to compare the hydrologic simulations caused by the areal-averaging of dynamic moving rainfall. Two types of synthetic rainfall are developed: spatially varied rainfall (SVR) is the typical input to a distributed model while temporally varied rainfall (TVR) emulates SVR but is spread uniformly over the entire watershed as in the case of a lumped model. This study demonstrates a direct comparison of peak discharge and peak timing generated by synthetic moving storms over idealized rectangular basins and a real watershed. It is found that the difference between the hydrologic responses from SVR and TVR reflects the impact from the areal-averaging of rainfall; the areal-averaging of rainfall for the movement from upstream to downstream over a lumped model can result in underestimated and delayed peak values in comparison to those from a distributed model; the flood peaks from SVR and TVR are found similar when the storm moves from downstream to upstream. The findings of the study suggest that extra cautions are needed for practitioners when evaluating simulated results from distributed and lumped modeling approaches even using the same rainfall information.

scholarly journals The role of spatial dependence for large-scale flood risk estimation

2020 ◽  
Vol 20 (4) ◽  
pp. 967-979 ◽  
Author(s):  
Ayse Duha Metin ◽  
Nguyen Viet Dung ◽  
Kai Schröter ◽  
Sergiy Vorogushyn ◽  
Björn Guse ◽  
...  
Keyword(s):  
Flood Risk ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Cumulative Distribution ◽  
Risk Assessments ◽  
Return Periods ◽  
Risk Estimates ◽  
Flood Peaks ◽  
Complete Dependence

Abstract. Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100 % for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.

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