Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates

Abstract. Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modelled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the flood distributions are modelled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach it is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies. The inclusion of external covariates permits the use of these models as predictive tools. Finally, the application of non-stationary analysis shows that the differences between the non-stationary quantiles and their stationary equivalents may be important over long periods of time.

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Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-3103-2013 ◽

2013 ◽

Vol 10 (3) ◽

pp. 3103-3142 ◽

Cited By ~ 19

Author(s):

J. López ◽

F. Francés

Keyword(s):

Frequency Analysis ◽

Western Mediterranean ◽

Flood Frequency ◽

Additive Models ◽

Stationary Time Series ◽

Flood Frequency Analysis ◽

Climate Indices ◽

Flood Regime ◽

Mediterranean Oscillation ◽

The Impact

Abstract. Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modeled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the distributions are modeled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies and to be used as predictive tools. Application of non-stationary analysis shows that the differences between the quantiles obtained and their stationary equivalents may be important over long periods of time.

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Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain

Water Resources Management ◽

10.1007/s11269-018-2041-2 ◽

2018 ◽

Vol 32 (13) ◽

pp. 4239-4252 ◽

Cited By ~ 9

Author(s):

Jianzhu Li ◽

Yuming Lei ◽

Senming Tan ◽

Colin D. Bell ◽

Bernard A. Engel ◽

...

Keyword(s):

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Flood Peak

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Flood frequency analysis using mean daily flows vs. instantaneous peak flows

10.5194/hess-2021-466 ◽

2021 ◽

Author(s):

Anne Bartens ◽

Uwe Haberlandt

Keyword(s):

Frequency Analysis ◽

Linear Models ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Estimation Methods ◽

Entire Study ◽

Flood Peak ◽

Flood Dynamics ◽

Event Based ◽

Available Information

Abstract. In many cases flood frequency analysis needs to be carried out on mean daily flow (MDF) series without any available information on the instantaneous peak flow (IPF). We analyze the error of using MDFs instead of IPFs for flood quantile estimation on a German dataset and assess spatial patterns and factors that influence the deviation of MDF floods from their IPF counterparts. The main dependence could be found for catchment area but also gauge elevation appeared to have some influence. Based on the findings we propose simple linear models to correct both MDF flood peaks of individual flood events and overall MDF flood statistics. Key predictor in the models is the event-based ratio of flood peak and flood volume obtained directly from the daily flow records. This correction approach requires a minimum of data input, is easily applied, valid for the entire study area and successfully estimates IPF peaks and flood statistics. The models perform particularly well in smaller catchments, where other IPF estimation methods fall short. Still, the limit of the approach is reached for catchment sizes below 100 km2, where the hydrograph information from the daily series is no longer capable of approximating instantaneous flood dynamics.

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Comparison Between Bivariate and Trivariate Flood Frequency Analysis Using the Archimedean Copula Functions, A Case Study of the Karun River in Iran

10.21203/rs.3.rs-524239/v1 ◽

2021 ◽

Author(s):

Mohamad Haytham Klaho ◽

Hamid R. Safavi ◽

Mohamad H. Golmohammadi ◽

Maamoun Alkntar

Keyword(s):

Frequency Analysis ◽

Return Period ◽

Goodness Of Fit ◽

Flood Frequency ◽

Archimedean Copula ◽

Flood Frequency Analysis ◽

Cumulative Probability ◽

Copula Functions ◽

Flood Peak ◽

Multiple Variables

Abstract Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak, volume and duration poses more motivation for hydrologists to study. In this paper, the bivariate and trivariate flood frequency analysis and modeling using Archimedean copula functions is focused. For this purpose, the annual flood data over a 55-year historical period recorded at the Dez Dam hydrometric station were used. The results showed that based on goodness of fit criteria, the Frank function built upon the couple of the flood peak-volume and the couple of the flood peak-duration as well as the Clayton function built upon the flood volume-duration were identified to be the best copula families to be adopted. The trivariate analysis was conducted and the Clayton family was chosen as the best copula function. Thereafter, the common and conditional cumulative probability distribution functions were built and analyzed to determine the periodic "and", "or" and "conditional" bivariate and trivariate flood return periods. The results suggest that the bivariate conditional return period obtained for short-term periods is more reliable than the trivariate conditional return period. Additionally, the trivariate conditional return period calculated for long-term periods is more reliable than the bivariate conditional return period.

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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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Evaluation dam overtopping risk based on univariate and bivariate flood frequency analysis

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-9757-2011 ◽

2011 ◽

Vol 8 (6) ◽

pp. 9757-9796 ◽

Cited By ~ 7

Author(s):

E. Goodarzi ◽

M. Mirzaei ◽

L. T. Shui ◽

M. Ziaei

Keyword(s):

Uncertainty Analysis ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Logistic Distribution ◽

Maximum Height ◽

Risk And Uncertainty ◽

Flood Peak ◽

Bivariate Frequency Analysis ◽

Dam Overtopping

Abstract. There is a growing tendency to assess the safety levels of existing dams based on risk and uncertainty analysis using mathematical and statistical methods. This research presents the application of risk and uncertainty analysis to dam overtopping based on univariate and bivariate flood frequency analyses by applying Gumbel logistic distribution for the Doroudzan earth-fill dam in south of Iran. The bivariate frequency analysis resulted in six inflow hydrographs with a joint return period of 100-yr. The overtopping risks were computed for all of those hydrographs considering quantile of flood peak discharge (in particular 100-yr), initial depth of water in the reservoir, and discharge coefficient of spillway as uncertain variables. The maximum height of the water, as most important factor in the overtopping analysis, was evaluated using reservoir routing and the Monte Carlo and Latin hypercube techniques were applied for uncertainty analysis. Finally, the achieved results using both univariate and bivariate frequency analysis have been compared to show the significance of bivariate analyses on dam overtopping.

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Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data

Hydrology Research ◽

10.2166/nh.2011.065 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 193-216 ◽

Cited By ~ 62

Author(s):

Hemant Chowdhary ◽

Luis A. Escobar ◽

Vijay P. Singh

Keyword(s):

Frequency Analysis ◽

Goodness Of Fit ◽

Peak Flow ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Dependence Structure ◽

Volume Data ◽

Data Set ◽

Flood Peak ◽

Goodness Of Fit Tests

Multivariate flood frequency analysis, involving flood peak flow, volume and duration, has been traditionally accomplished by employing available functional bivariate and multivariate frequency distributions that have a restriction on the marginals to be from the same family of distributions. The copula concept overcomes this restriction by allowing a combination of arbitrarily chosen marginal types. It also provides a wider choice of admissible dependence structure as compared to the conventional approach. The availability of a vast variety of copula types makes the selection of an appropriate copula family for different hydrological applications a non-trivial task. Graphical and analytic goodness-of-fit tests for testing the suitability of copulas are beginning to evolve and are being developed; there is limited experience of their usage at present, especially in the hydrological field. This paper provides a step-wise procedure for copula selection and illustrates its application to bivariate flood frequency analysis, involving flood peak flow and volume data. Several graphical procedures, tail dependence characteristics, and formal goodness-of-fit tests involving a parametric bootstrap-based technique are considered while investigating the relative applicability of six copula families. The Clayton copula has been identified as a valid model for the particular flood peak flow and volume data set considered in the study.

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Frequency Analysis of Snowmelt Flood Based on GAMLSS Model in Manas River Basin, China

Water ◽

10.3390/w13152007 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2007

Author(s):

Chaofei He ◽

Fulong Chen ◽

Aihua Long ◽

Chengyan Luo ◽

Changlu Qiao

Keyword(s):

Time Series ◽

Frequency Analysis ◽

Flood Frequency ◽

Additive Models ◽

Flood Frequency Analysis ◽

Climate Indices ◽

Design Flood ◽

Stationary Model ◽

Snowmelt Flood ◽

The Impact

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

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