Impact assessment of upstream flooding on extreme flood frequency analysis by incorporating a flood-inundation model for flood risk assessment

Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs using different frequency definitions (copula “Or” and “And” scenario), and how these definitions affect flood risks. The approach was applied to the Meishan reservoir in central China. A set of flood hydrographs with 0.01 frequency under copula “Or” and “And” definitions were constructed, respectively. Upstream and downstream flood risks incorporating reservoir operation were calculated for each scenario. Comparisons between flood risks from univariate and bivariate flood frequency analysis showed that bivariate flood frequency analysis produced less diversity in the results, and thus the results are more reliable in risk assessment. More importantly, the peak-volume combinations in a bivariate approach can be adjusted according to certain prediction accuracy, providing a flexible estimation of real-time flood risk under different prediction accuracies and safety requirements.

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Flood risk assessment for Ecuador

10.5194/egusphere-egu21-14853 ◽

2021 ◽

Author(s):

Melisa Mena-Benavides ◽

Manuel Urrutia ◽

Konstantin Scheffczyk ◽

Angel A. Valdiviezo-Ajila ◽

Jhoyzett Mendoza ◽

...

Keyword(s):

Risk Assessment ◽

Risk Reduction ◽

Flood Risk ◽

Disaster Risk Reduction ◽

Disaster Risk ◽

Flood Frequency ◽

Flood Risk Assessment ◽

Essential Information ◽

Synthetic Aperture Radar Data ◽

Ecological Regions

Understanding disaster risk is the first priority for action of the Sendai Framework for Disaster Risk Reduction (SFDRR) and is the essential information needed to guide disaster governance and achieve disaster risk reduction. Flooding is a natural hazard that causes the highest number of affected people due to disasters. In Ecuador from 1970 to 2019 flooding caused the highest amount of loss and damage to housing, and from 2016 to 2019 there were 1263 flood events reported. However, the differentiated impacts in flood exposed areas and what can be done to reduce risk and its impacts are still not well understood. In this research, we explored the different dimensions of flood risk, namely hazard, exposure, and vulnerability, and investigated the drivers of risk in different ecological regions of Ecuador. The assessment was conducted at the parish level, the smallest administrative scale, for three selected provinces of Bolivar, Los R&#237;os, and Napo, representing not only the country&#8217;s three main ecological regions but also commonly affected territories due to flooding. Using an automated flood detection procedure based on Sentinel-1 synthetic aperture radar data, flood hazard information was derived from flood frequency and flood depth for the years 2017, 2018, and 2019. The drivers of exposure and vulnerability were derived from scientific literature and further evaluated and complemented during a participatory workshop with over 50 local experts from the different regions. Centered on this exercise, an indicator library was created to inform the data selection from various sources and provides the basis for deriving a spatially explicit flood risk assessment using an indicator-based approach. Impact data are available to validate the risk assessment at the parish level and with this reveal key drivers of flood risk in different ecological regions of Ecuador. This information will provide the basis to derive targeted measures for disaster risk reduction.

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Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall–snowmelt flood regimes

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1283-2014 ◽

2014 ◽

Vol 14 (5) ◽

pp. 1283-1298 ◽

Cited By ~ 20

Author(s):

D. Lawrence ◽

E. Paquet ◽

J. Gailhard ◽

A. K. Fleig

Keyword(s):

Frequency Analysis ◽

Probabilistic Model ◽

Extreme Precipitation ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Continuous Simulation ◽

Weather Patterns ◽

Extreme Flood ◽

Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom–up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

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Flood Frequency Analysis and Statistical Estimation of Flood Risk

Inland Flood Hazards ◽

10.1017/cbo9780511529412.013 ◽

2000 ◽

pp. 334-358 ◽

Cited By ~ 11

Author(s):

Jery R. Stedinger

Keyword(s):

Frequency Analysis ◽

Flood Risk ◽

Statistical Estimation ◽

Flood Frequency ◽

Flood Frequency Analysis

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May long-term historical hydrological data be misleading for flood frequency analysis in current conditions of climate change?

10.5194/egusphere-egu2020-1140 ◽

2020 ◽

Author(s):

Alexandra Fedorova ◽

Nataliia Nesterova ◽

Olga Makarieva ◽

Andrey Shikhov

Keyword(s):

Climate Change ◽

Water Level ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Economic Damage ◽

Irkutsk Region ◽

Extreme Flood ◽

The Town

In June 2019, the extreme flash flood was formed on the rivers of the Irkutsk region originating from the East Sayan mountains. This flood became the most hazardous one in the region in 80 years history of observations.The greatest rise in water level was recorded at the Iya River in the town of Tulun (more than 9 m in three days). The recorded water level was more than 5 m above the dangerous mark of 850 cm and more than 2.5 m above the historical maximum water level which was observed in 1984.The flood led to the catastrophic inundation of the town of Tulun, 25 people died and 8 went missing. According to preliminary assessment, economic damage from the flood in 2019 amounted up to half a billion Euro.Among the reasons for the extreme flood in June 2019 that are discussed are heavy rains as a result of climate change, melting of snow and glaciers in the mountains of the East Sayan, deforestation of river basins due to clearings and fires, etc.The aim of the study was to analyze the factors that led to the formation of a catastrophic flood in June 2019, as well as estimate the maximum discharge of at the Iya River. For calculations, the deterministic distributed hydrological model Hydrograph was applied. We used the observed data of meteorological stations and the forecast values &#226;&#128;&#139;&#226;&#128;&#139;of the global weather forecast model ICON. The estimated discharge has exceeded previously observed one by about 50%.The results of the study have shown that recent flood damage was caused mainly by unprepared infrastructure. The safety dam which was built in the town of Tulun just ten years ago was 2 meters lower than maximum observed water level in 2019. This case and many other cases in Russia suggest that the flood frequency analysis of even long-term historical data may mislead design engineers to significantly underestimate the probability and magnitude of flash floods. There are the evidences of observed precipitation regime transformations which directly contribute to the formation of dangerous hydrological phenomena. The details of the study for the Irkutsk region will be presented.

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Rapid Global Exposure Assessment for Extreme River Flood Risk Under Climate Change

Journal of Disaster Research ◽

10.20965/jdr.2016.p1128 ◽

2016 ◽

Vol 11 (6) ◽

pp. 1128-1136 ◽

Cited By ~ 3

Author(s):

Youngjoo Kwak ◽

◽

Yoichi Iwami ◽

Keyword(s):

Climate Change ◽

Risk Assessment ◽

Flood Risk ◽

Large Scale ◽

Flood Hazard ◽

Data Availability ◽

Flood Risk Assessment ◽

Daily Data ◽

Extreme Flood ◽

River Flood

Globally, large-scale floods are one of the most serious disasters, considering increased frequency and intensity of heavy rainfall. This is not only a domestic problem but also an international water issue related to transboundary rivers in terms of global river flood risk assessment. The purpose of this study is to propose a rapid flood hazard model as a methodological possibility to be used on a global scale, which uses flood inundation depth and works reasonably despite low data availability. The method is designed to effectively simplify complexities involving hydrological and topographical variables in a flood risk-prone area when applied in an integrated global flood risk assessment framework. The model was used to evaluate flood hazard and exposure through pixel-based comparison in the case of extreme flood events caused by an annual maximum daily river discharge of 1/50 probability of occurrence under the condition of climate change between two periods, Present (daily data from 1980 to 2004) and Future (daily data from 2075 to 2099). As preliminary results, the maximum potential extent of inundation area and the maximum number of affected people show an upward trend in Present and Future.

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Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall-snowmelt flood regimes

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-6785-2013 ◽

2013 ◽

Vol 1 (6) ◽

pp. 6785-6828 ◽

Cited By ~ 1

Author(s):

D. Lawrence ◽

E. Paquet ◽

J. Gailhard ◽

A. K. Fleig

Keyword(s):

Frequency Analysis ◽

Probabilistic Model ◽

Extreme Precipitation ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Continuous Simulation ◽

Weather Patterns ◽

Extreme Flood ◽

Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses are simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a "bottom-up" classification procedure was used for defining a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000 yr (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, with statistical flood frequency analysis based on the annual maximum series, and with the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

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FLOOD FREQUENCY ANALYSIS AND IMPACT ASSESSMENT FOR CLIMATE CHANGE IN THE NAGARA RIVER BASIN

Journal of JSCE ◽

10.2208/journalofjsce.8.1_79 ◽

2020 ◽

Vol 8 (1) ◽

pp. 79-86

Author(s):

Morihiro HARADA ◽

Yasuyuki MARUYA ◽

Toshiharu KOJIMA ◽

Daisuke MATSUOKA ◽

Yujin NAKAGAWA ◽

...

Keyword(s):

Climate Change ◽

Impact Assessment ◽

River Basin ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis

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Modeling the spatial dependence of floods using the Fisher copula

Hydrology and Earth System Sciences ◽

10.5194/hess-23-107-2019 ◽

2019 ◽

Vol 23 (1) ◽

pp. 107-124 ◽

Cited By ~ 9

Author(s):

Manuela I. Brunner ◽

Reinhard Furrer ◽

Anne-Catherine Favre

Keyword(s):

Frequency Analysis ◽

Spatial Dependence ◽

Regional Scale ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Flood Event ◽

Dependence Structure ◽

Suitable Model ◽

Flood Events ◽

Extreme Flood

Abstract. Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

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TL-Moments Approach: Application of non-Stationary GEV Model in Flood Frequency Analysis

10.21203/rs.3.rs-673867/v1 ◽

2021 ◽

Author(s):

Nur Amalina Mat Jan ◽

Ani Shabri ◽

Muhammad Fadhil Marsani ◽

Basri Badyalina

Keyword(s):

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Trend Test ◽

Stationary Model ◽

Area Of Interest ◽

Extreme Flood ◽

Consistent Performance ◽

Adf Test ◽

Stationary Behaviour

Abstract The non-stationarity in hydrological records is a significant concerning area of interest within the field of flood risk management. Ignoring the non-stationary behaviour in flood series will result in a substantial bias in floods quantile. Hence, the non-stationary flood frequency analysis appeared to be an appropriate option to maintain the independent and identically distributed (IID) assumptions in sample observation. This paper utilized the Generalized Extreme Value (GEV) distribution to analyze extreme flood series. The time-varying moment technique, namely the L-moment and TL-moment methods are employed to estimate the non-stationary model (GEV 1, GEV 2, and GEV 3) in the flood series. The ADF test, Mann-Kendall trend test, and Spearman’s Rho test showed that two out of ten streamflow stations in Johor, Malaysia demonstrated a non-stationary behaviour in the annual maximum streamflow. Results from the simulation study demonstrate a consistent performance on the non-stationary model. Furthermore, the TL-moments method could efficiently predict the flood event estimated at quantiles of the higher return periods.

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