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

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.

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

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-1-2019 ◽

2019 ◽

Vol 19 (1) ◽

pp. 1-18 ◽

Cited By ~ 1

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.

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

Water ◽

10.3390/w11030475 ◽

2019 ◽

Vol 11 (3) ◽

pp. 475 ◽

Cited By ~ 2

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.

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Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference

Water Resources Research ◽

10.1029/1999wr900012 ◽

1999 ◽

Vol 35 (5) ◽

pp. 1551-1557 ◽

Cited By ~ 119

Author(s):

George Kuczera

Keyword(s):

Monte Carlo ◽

Bayesian Inference ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis

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Flood frequency analysis of historical flood data under stationary and non-stationary modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-525-2015 ◽

2015 ◽

Vol 12 (1) ◽

pp. 525-568 ◽

Cited By ~ 12

Author(s):

M. J. Machado ◽

B. A. Botero ◽

J. López ◽

F. Francés ◽

A. Díez-Herrero ◽

...

Keyword(s):

Frequency Analysis ◽

Flood Frequency ◽

City Council ◽

Additive Models ◽

Water Levels ◽

Flood Frequency Analysis ◽

Environmental Drivers ◽

Exceedance Probability ◽

Nao Index ◽

Flood Magnitude

Abstract. Historical records are an important source of information about extreme and rare floods with a great value to establish a reliable flood return frequency. The use of long historic records for flood frequency analysis brings in the question of flood stationarity, since climatic and land-use conditions can affect the relevance of past flooding as a predictor of future flooding. In this paper, a detailed 400 year flood record from the Tagus River in Aranjuez (Central Spain) was analysed under stationary and non-stationary flood frequency approaches, to assess their implications on hazard studies. Historical flood records in Aranjuez were obtained from documents (Proceedings of the City Council, diaries, chronicles, memoirs, etc.), epigraphic marks, and indirect historical sources and reports. The water levels associated with different floods (derived from descriptions or epigraphic marks) were computed into discharge values using a one-dimensional hydraulic model. Secular variations on flood magnitude and frequency, found to respond to climate and environmental drivers, showed a good correlation between high values of historical flood discharges and a negative mode of the North Atlantic Oscillation index (NAO index). Over the systematic gauge record (1913–2008), an abrupt change on flood magnitude was produced in 1957 due to constructions of three major reservoirs in the Tagus headwaters (Bolarque, Entrepeñas and Buendia) controlling 80% of the watershed surface draining to Aranjuez. Two different models were used for the flood frequency analysis: (a) a stationary model estimating statistical distributions incorporating imprecise and categorical data based on maximum likelihood estimators; (b) a time–varying model based on "generalized additive models for location, scale and shape" (GAMLSS) modelling, that incorporates external covariates related to climate variability (NAO index) and catchment hydrology factors (in this paper a reservoir index; RI). Flood frequency analysis using documentary data (plus gauged record) improved the estimates of the probabilities of rare floods (return intervals of 100 year and higher). Under non-stationary modelling flood occurrence associated with an exceedance probability of 0.01 (i.e. return period of 100 year) has changed over the last 500 year due to decadal and multi-decadal variability of the NAO. Yet, frequency analysis under stationary models was successful on providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.

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

10.5194/nhess-2018-174 ◽

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

10.5194/egusphere-egu21-10635 ◽

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

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 &#8722;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.Keywords: Flood peaks; Large catchments; Non-stationarity; Frequency analysis*This work was supported by the Research Council of Norway (FRINATEK Project 274310).&#160;&#160;&#160;

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