scholarly journals Analysis of Peak Flow Distribution for Bridge Collapse Sites

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 52 ◽  
Author(s):  
Fahmidah U. Ashraf ◽  
Madeleine M. Flint
Keyword(s):  
Peak Flow ◽  
Flow Distribution ◽  
Heavy Tail ◽  
Flood Frequency ◽  
Return Periods ◽  
Peak Flows ◽  
Tail Distribution ◽  
Bridge Collapse ◽  
Flood Magnitude ◽  
Distribution Parameters

Bridge collapse risk can be evaluated more rigorously if the hydrologic characteristics of bridge collapse sites are demystified, particularly for peak flows. In this study, forty-two bridge collapse sites were analyzed to find any trend in the peak flows. Flood frequency and other statistical analyses were used to derive peak flow distribution parameters, identify trends linked to flood magnitude and flood behavior (how extreme), quantify the return periods of peak flows, and compare different approaches of flood frequency in deriving the return periods. The results indicate that most of the bridge collapse sites exhibit heavy tail distribution and flood magnitudes that are well consistent when regressed over the drainage area. A comparison of different flood frequency analyses reveals that there is no single approach that is best generally for the dataset studied. These results indicate a commonality in flood behavior (outliers are expected, not random; heavy-tail property) for the collapse dataset studied and provides some basis for extending the findings obtained for the 42 collapsed bridges to other sites to assess the risk of future collapses.

scholarly journals Climate and hydrological variability: the catchment filtering role

2015 ◽  
Vol 19 (1) ◽  
pp. 379-387 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Annual Maximum ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Maximum Peak ◽  
Hydrological Variability ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

scholarly journals Climate and hydrological variability: the catchment filtering role

2014 ◽  
Vol 11 (9) ◽  
pp. 10411-10430 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Correct Identification ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Hydrological Variability ◽  
Runoff Series ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and in fact, this may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? The research herein presented aims to analytically derive the flood frequency distribution basing on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The peak flow probability distribution is analytically derived to quantify the filtering effect operated by the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that in regard to changes in the annual number of rainfall events, the catchment filtering role is particularly significant when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly impacted by the climatic input, while for lower return periods, infiltration processes smooth out the effects of climate change.

scholarly journals The unit peak discharge as a tool for flood magnitude comparison and analysis

2021 ◽  
Author(s):  
Josep Carles Balasch ◽  
Jordi Tuset ◽  
Mariano Barriendos ◽  
Xavier Castelltort ◽  
David Pino
Keyword(s):  
Peak Flow ◽  
Temporal Distribution ◽  
Peak Discharge ◽  
Coastal System ◽  
Peak Flows ◽  
Magnitude Comparison ◽  
River Flooding ◽  
Flood Plains ◽  
Flow Generation ◽  
Flood Magnitude

<p>To analyze the river floods dynamics, it is common to fix the observations of the flow at a characteristic checkpoint of the basin, showing its evolution over time:  the hydrograph. A less common way of studying this hydrological phenomenon is the analysis of the unit peak discharge of the flood (i.e., the peak flow divided by the contributory area of the basin) along different checkpoints of the drainage axis.</p><p>If this second methodology is chosen for the analysis of the river flooding, the circulation of flows through the river network generally shows that as the contributory area of the basin increases the unit peak discharge decreases. This is due to the reduction in the amount of precipitation and the slope of the riverbed with the increase of the basin area as it moves away from the headwaters. However, this simple scheme can have very different behavior depending on factors such as the spatial and temporal distribution of precipitation, the presence of snow, the soil moisture, the geological substrate, land uses, or human activities.</p><p>This study compares the hydrological data of several historical and recent floods in NE basins of the Iberian Peninsula from the perspective of observing the unit peak flows depending on the size of the drained basin (i.e., the spatial evolution of the specific maximum discharge). These basins are small in size (usually below 500 km<sup>2</sup>) and drain regions such as the central Pyrenees (Garonne, Noguera Pallaresa), the Ebro Depression (rivers Ribera Salada, Sió, Ondara, Corb) and the Catalan Coastal System (Francolí), that is, they belong to very diverse geographical environments.</p><p>The results allow to compare the magnitude of the unit peak flows in the headwaters and the decreasing of this variable when moving downstream. The unit peak discharges of the tributaries of the Ebro Depression, near the Catalan Coastal System are much higher when comparing with the flow of the Pyrenean rivers. For many floods of the Ebro basin of medium magnitude, the unit peak flow is reduced by the runoff infiltration in the flood plains favored by agricultural activities. In the Pyrenean rivers the spatial decrease of the unit peak discharge is gentle than in those of the Ebro Depression. The results show different patterns of flow generation and propagation that have implications for managing the dangerousness of flood risk, especially in very small basins (< 10 km<sup>2</sup>), where peak flows can be unexpectedly large and devastating.</p>

scholarly journals Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

2017 ◽  
Vol 473 ◽  
pp. 60-71 ◽  
Author(s):  
Timothy Graves ◽  
Christian L.E. Franzke ◽  
Nicholas W. Watkins ◽  
Robert B. Gramacy ◽  
Elizabeth Tindale
Keyword(s):  
Long Range ◽  
Heavy Tail ◽  
Long Range Dependence ◽  
Arfima Models ◽  
Tail Distribution ◽  
Distribution Parameters

scholarly journals On the choice of calibration metrics for “high-flow” estimation using hydrologic models

2019 ◽  
Vol 23 (6) ◽  
pp. 2601-2614 ◽  
Author(s):  
Naoki Mizukami ◽  
Oldrich Rakovec ◽  
Andrew J. Newman ◽  
Martyn P. Clark ◽  
Andrew W. Wood ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Peak Flow ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
High Flow ◽  
Hydrologic Models ◽  
Peak Flows ◽  
Annual Peak ◽  
Performance Metric ◽  
Squared Error

Abstract. Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.

Rainfall Events as Paths of a Stochastic Process: Problems of Design Storm Analysis

1984 ◽  
Vol 16 (8-9) ◽  
pp. 131-138 ◽  
Author(s):  
Johannes Brummer
Keyword(s):  
Stochastic Process ◽  
Peak Flow ◽  
Rainfall Events ◽  
Peak Flows ◽  
Design Storm ◽  
Adequate Evaluation ◽  
Rainfall Patterns

Problems in the construction of design storms are expressed in mathematical terms. Introduced here is a concept for approximating natural peak flow values by means of the distribution of typical rainfall patterns. A comparison demonstrates the quality of this concept and the competency of some well-known design storms for the adequate evaluation of peak flows.

Analysis of Magnitude and Frequency of Floods in the Damanganga Basin: Western India

2021 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Vitthal Anwat ◽  
Pramodkumar Hire ◽  
Uttam Pawar ◽  
Rajendra Gunjal
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Western India ◽  
Type I ◽  
Return Periods ◽  
Pearson Type ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

Flood Frequency Analysis (FFA) method was introduced by Fuller in 1914 to understand the magnitude and frequency of floods. The present study is carried out using the two most widely accepted probability distributions for FFA in the world namely, Gumbel Extreme Value type I (GEVI) and Log Pearson type III (LP-III). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) methods were used to select the most suitable probability distribution at sites in the Damanganga Basin. Moreover, discharges were estimated for various return periods using GEVI and LP-III. The recurrence interval of the largest peak flood on record (Qmax) is 107 years (at Nanipalsan) and 146 years (at Ozarkhed) as per LP-III. Flood Frequency Curves (FFC) specifies that LP-III is the best-fitted probability distribution for FFA of the Damanganga Basin. Therefore, estimated discharges and return periods by LP-III probability distribution are more reliable and can be used for designing hydraulic structures.

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals Bivariate Hydrologic Risk Assessment of Flood Episodes using the Notation of Failure Probability

2020 ◽  
Vol 6 (10) ◽  
pp. 2002-2023
Author(s):  
Shahid Latif ◽  
Firuza Mustafa
Keyword(s):  
Failure Probability ◽  
Goodness Of Fit ◽  
Peak Flow ◽  
Gaussian Copula ◽  
Dependence Structure ◽  
Distribution Analysis ◽  
Return Periods ◽  
Goodness Of Fit Test ◽  
Flood Peak ◽  
Flood Volume

Floods are becoming the most severe and challenging hydrologic issue at the Kelantan River basin in Malaysia. Flood episodes are usually thoroughly characterized by flood peak discharge flow, volume and duration series. This study incorporated the copula-based methodology in deriving the joint distribution analysis of the annual flood characteristics and the failure probability for assessing the bivariate hydrologic risk. Both the Archimedean and Gaussian copula family were introduced and tested as possible candidate functions. The copula dependence parameters are estimated using the method-of-moment estimation procedure. The Gaussian copula was recognized as the best-fitted distribution for capturing the dependence structure of the flood peak-volume and peak-duration pairs based on goodness-of-fit test statistics and was further employed to derive the joint return periods. The bivariate hydrologic risks of flood peak flow and volume pair, and flood peak flow and duration pair in different return periods (i.e., 5, 10, 20, 50 and 100 years) were estimated and revealed that the risk statistics incrementally increase in the service lifetime and, at the same instant, incrementally decrease in return periods. In addition, we found that ignoring the mutual dependency can underestimate the failure probabilities where the univariate events produced a lower failure probability than the bivariate events. Similarly, the variations in bivariate hydrologic risk with the changes of flood peak in the different synthetic flood volume and duration series (i.e., 5, 10, 20, 50 and 100 years return periods) under different service lifetimes are demonstrated. Investigation revealed that the value of bivariate hydrologic risk statistics incrementally increases over the project lifetime (i.e., 30, 50, and 100 years) service time, and at the same time, it incrementally decreases in the return period of flood volume and duration. Overall, this study could provide a basis for making an appropriate flood defence plan and long-lasting infrastructure designs. Doi: 10.28991/cej-2020-03091599 Full Text: PDF

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