Detection, attribution and frequency analysis of non-stationary flood peaks in 32 big rivers worldwide  

2021 ◽  
Author(s):  
Yanlai Zhou ◽  
Chong-Yu Xu ◽  
Cosmo Ngongondo ◽  
Lu Li
Keyword(s):  
Frequency Analysis ◽  
Flood Risk ◽  
Kendall Test ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Stationary Condition ◽  
Reservoir Storage ◽  
Reservoir Regulation ◽  
Mann Kendall Test ◽  
Flood Peaks

<p>Due to climate variability and reservoir regulation worldwide, it is fundamentally challenging to implement holistic assessments of detection, attribution and frequency analysis on non-stationary flood peaks. In this study, we proposed an integrated approach that combines the prewhitening Mann-Kendall test technique, Partial Mutual Information-Partial Weights (PMI-PW) method and Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS) method to achieve reliable non-stationary flood frequency analysis. Firstly, the prewhitening Mann-Kendall test was employed to detect the trend change of flood peaks. Secondly, the PMI-PW was employed to attribute the contribution of climate change and reservoir regulation to non-stationarity of flood peaks. Lastly, the GAMLSS method was employed to quantify the change in flood risks under the non-stationary condition. The applicability of the proposed approach was investigated by long-term (1931-2017) flood series collected from 32 big river catchments globally. The results suggested that global flood trends varied from increasing +19.3%/decade to decreasing −31.6%/decade. Taking the stationary flood frequency analysis as the benchmark, the comparative results revealed that the flood risk in 5 rivers under the non-stationary condition in response to warming climate significantly increased over the historical period whereas the flood risk in 7 rivers in response to increasing reservoir storage largely reduced. Despite the spatiotemporal heterogeneity of observations, the changes in flood peaks evaluated here were explicitly associated with the changing climate and reservoir storage, supporting the demand for considering the non-stationarity of flood peaks in the best interest of social sustainability.</p><p><strong>Keywords:</strong> Flood peaks; Large catchments; Non-stationarity; Frequency analysis</p><p>*This work was supported by the Research Council of Norway (FRINATEK Project 274310).</p><p> </p><p> </p><p> </p>

Flood frequency from maximum daily vs. instantaneous peak flows

2021 ◽  
Author(s):  
Anne Fangmann ◽  
Uwe Haberlandt
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Error Assessment ◽  
Annual Maximum ◽  
High Resolution Data ◽  
Flood Peaks ◽  
Design Floods ◽  
Resolution Data

<p>Flood frequency analysis (FFA) has long been the standard procedure for obtaining design floods for all kinds of purposes. Ideally, the data at the basis of the statistical operations have a high temporal resolution, in order to facilitate a full account of the observed flood peaks and hence a precise model fitting and flood quantile estimation.</p><p>Unfortunately, high-resolution flows are rarely disposable. Often, average daily flows pose the only available/sufficiently long base for flood frequency analysis. This averaging naturally causes a significant smoothing of the flood wave, such that the “instantaneous” peak can no longer be observed. As a possible consequence, design floods derived from these data may be severely underrated.</p><p>How strongly the original peaks are flattened and how this influences the design flood estimation depends on a variety of factors and varies from gauge to gauge. In this study we are looking at a range of errors arising from the use of daily instead of instantaneous flow data. These include differences in the observed individual flood peaks and mean annual maximum floods, as well as the estimated distribution parameters and flood quantiles. The aim is to identify catchment specific factors that influence the magnitude of these errors, and ultimately to provide a means for error assessment on the mere basis of local hydrological conditions, specifically where no high-resolution data is available.</p><p>The analyses are carried out on an all-German dataset of discharge gauges, for which high-resolution data is available for at least 30 years. The classical FFA approach of fitting distributions to annual maximum series is utilized for error assessment. For identification of influencing factors, both the discharge series themselves and a catalogue of climatic and physiographic catchment descriptors are screened.</p>

scholarly journals A stochastic event-based approach for flood estimation in catchments with mixed rainfall and snowmelt flood regimes

2019 ◽  
Vol 19 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Valeriya Filipova ◽  
Deborah Lawrence ◽  
Thomas Skaugen
Keyword(s):  
Frequency Analysis ◽  
Initial Conditions ◽  
Flood Frequency ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Design Storm ◽  
Snowmelt Flood ◽  
Flood Estimation ◽  
Flood Peaks ◽  
Event Based

Abstract. The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, statistical flood frequency analysis and an event-based model (PQRUT) using a single design storm have been applied in Norway. We here propose a stochastic PQRUT model, as an extension of the standard application of the event-based PQRUT model, by considering different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small- and medium-sized catchments in Norway and the results give good fits to observed peak-over-threshold (POT) series. A sensitivity analysis of the method indicates (a) that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years and (b) that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100- and 1000-year return level based on the stochastic PQRUT model are compared with results for (a) statistical frequency analysis and (b) a standard implementation of the event-based PQRUT method. The differences in flood estimates between the stochastic PQRUT and the statistical flood frequency analysis are within 50 % in most catchments. However, the differences between the stochastic PQRUT and the standard implementation of the PQRUT model are much higher, especially in catchments with a snowmelt flood regime.

scholarly journals Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 475 ◽  
Author(s):  
Ting Zhou ◽  
Zhiyong Liu ◽  
Juliang Jin ◽  
Hongxiang Hu
Keyword(s):  
Risk Assessment ◽  
Frequency Analysis ◽  
Flood Risk ◽  
Reservoir Operation ◽  
Integrated Approach ◽  
Flood Frequency ◽  
Central China ◽  
Flood Frequency Analysis ◽  
Flood Peak ◽  
Flood Risks

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.

scholarly journals Assessing the impacts of reservoirs on the downstream flood frequency by coupling the effect of the scheduling-related multivariate rainfall into an indicator of reservoir effects

2019 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jun Xia ◽  
Chong-Yu Xu ◽  
Cong Jiang ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Risk ◽  
Composite Index ◽  
Estimation Method ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Cumulative Probability ◽  
Reservoir Capacity ◽  
Vine Copula

Abstract. Many studies have shown that the downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally carried out by comparing the pre-dam and post-dam frequencies of some streamflow indicators such as floods and droughts. In this paper, a rainfall-reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under the framework of covariate-based flood frequency analysis. The RRCI is derived from both the reservoir index (RI) of the previous study and the joint cumulative probability (JCP) of multiple rainfall variables (i.e., the maximum, intensity, volume and timing) of multiday rainfall input (MRI), and is calculated by a c-vine copula model. Then, using RI or RRCI as covariate, a nonstationary generalized extreme value (NGEV) distribution model with time-varying location and/or scale parameters is developed and used to analyze the annual maximum daily flow (AMDF) of Ankang, Huangjiagang and Huangzhuang gauging stations of the Hanjiang River, China with the Bayesian estimation method. The results show that regardless of using RRCI or RI, nonstationary flood frequency analysis demonstrates that the overall flood risk of the basin has been significantly reduced by reservoirs, and the reduction increases with the reservoir capacity. What’s more, compared with RI, RRCI through incorporating the effect of the scheduling-related multivariate MRI can better explain the alteration of AMDF. And for a given reservoir capacity (i.e., a specific RI), the flood risk (e.g., the Huangzhuang station) increases with the JCP of rainfall variables and gradually approaches the risk of no reservoir (i.e., RI = 0). This analysis, combining the reservoir index with the scheduling-related multivariate MRI to account for the alteration in flood frequency, provides a comprehensive approach and knowledge for downstream flood risk management under the impacts of reservoirs.

scholarly journals Evaluating nature-based solutions in a non-stationary climate with changing risk of flooding

2021 ◽  
Author(s):  
Sisay Debele ◽  
Jeetendra Sahani ◽  
Silvia Maria Alfieri ◽  
Paul Bowyer ◽  
Nikos Charizopoulos ◽  
...  
Keyword(s):  
Climate Change ◽  
Frequency Analysis ◽  
Flood Risk ◽  
Flood Frequency ◽  
Time Dependent ◽  
Flood Frequency Analysis ◽  
Inundation Depth ◽  
Rcp 8.5 ◽  
Flooded Area ◽  
Open Air Laboratories

<p><strong>Abstract</strong></p><p>Under climate change scenarios, it is important to evaluate the changes in recent behavior of heavy precipitation events, the resulting flood risk, and the detrimental impacts of the peak flow of water on human well-being, properties, infrastructure, and the natural environment. Normally, flood risk is estimated using the stationary flood frequency analysis technique. However, a site’s hydroclimate can shift beyond the range of historical observations considering continuing global warming. Therefore, flood-like distributions capable of accounting for changes in the parameters over time should be considered. The main objective of this study is to apply non-stationary flood frequency models using the generalized extreme value (GEV) distribution to model the changes in flood risk under two scenarios: (1) without nature-based solutions (NBS) in place and; (2) with NBS i.e. wetlands, retention ponds and weir/low head dam implemented. In the GEV model, the first two moments i.e. location and scale parameters of the distribution were allowed to change as a function of time-variable covariates, estimated by maximum likelihood. The methodology is applied to OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks, which is in Europe. The time-dependent 100-year design quantiles were estimated for both the scenarios. We obtained daily precipitation data of climate models from the EURO-CORDEX project dataset for 1951–2020 and 2022–2100 representing historical and future simulations, respectively. The hydrologic model, HEC-HMS was used to simulate discharges/flood hydrograph without and with NBS in place for these two periods: historical (1951-2020) and future (2022-2100). The results showed that the corresponding time-dependent 100-year floods were remarkably high for the without NBS scenario in both the periods. Particularly, the high emission scenario (RCP 8.5) resulted in dramatically increased flood risks in the future. The simulation without NBS also showed that flooded area is projected to increase by 25% and 40% for inundation depth between 1.5 and 3.5 m under RCP 4.5 and RCP 8.5 scenarios, respectively. For inundation depth above 3.5 m, the flooded area is anticipated to rise by 30% and 55% in both periods respectively. With the implementation of NBS, the flood risk was projected to decrease by 20% (2022–2050) and 45% (2071–2100) with a significant decrease under RCP 4.5 and RCP 8.5 scenarios. This study can help improve existing methods to adapt to the uncertainties in a changing environment, which is critical to develop climate-proof NBS and improve NBS planning, implementation, and effectiveness assessment.</p><p><strong>Keywords</strong>: Nature-based solutions; flood frequency analysis; climate change; wetlands; GEV model</p><p><strong>Acknowledgments</strong></p><p>This work has been carried out under the framework of OPERANDUM (OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks) project, which is funded by the European Union's Horizon 2020 research and innovation programme under the Grant Agreement No: 776848.</p>

scholarly journals Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects

2019 ◽  
Vol 23 (11) ◽  
pp. 4453-4470 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jun Xia ◽  
Chong-Yu Xu ◽  
Cong Jiang ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Frequency Analysis ◽  
Reservoir Operation ◽  
Storage Capacity ◽  
Flood Frequency ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Exceedance Probability ◽  
Antecedent Rainfall ◽  
Reservoir Storage

Abstract. Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

scholarly journals A stochastic event-based approach for flood estimation in catchments with mixed rainfall/snowmelt flood regimes

2018 ◽  
Author(s):  
Valeriya Filipova ◽  
Deborah Lawrence ◽  
Thomas Skaugen
Keyword(s):  
Frequency Analysis ◽  
Initial Conditions ◽  
Flood Frequency ◽  
Return Level ◽  
Flood Frequency Analysis ◽  
Design Storm ◽  
Flood Estimation ◽  
Flood Peaks ◽  
Event Based ◽  
Stochastic Event

Abstract. The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, flood frequency analysis or event-based model using a single design storm have been applied. We propose here an alternative, stochastic event-based modelling approach. The stochastic PQRUT method involves Monte Carlo procedure to simulate different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small and medium-sized catchments in Norway and the results show good fit to the observations. A sensitivity analysis of the method indicates that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years, and that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100- and 1000-year return level based on the stochastic PQRUT model are compared with results for a) statistical frequency analysis, and b) a standard implementation of the event-based PQRUT method. The differences between the estimates can be up to 200 % for some catchments, which highlights the uncertainty in these methods.

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