scholarly journals Historical changes in annual peak flows in Maine and implications for flood-frequency analyses

2010 ◽  
Author(s):  
Glenn A. Hodgkins
Keyword(s):  
Flood Frequency ◽  
Peak Flows ◽  
Historical Changes ◽  
Annual Peak

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.

scholarly journals Flood frequency analysis by an event-based rainfall-runoff model in selected catchments of southern Poland

2018 ◽  
Vol 13 (No. 3) ◽  
pp. 170-176 ◽  
Author(s):  
Młyński Dariusz ◽  
Petroselli Andrea ◽  
Walega Andrzej
Keyword(s):  
Statistical Method ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Southern Poland ◽  
Annual Peak ◽  
Pearson Type ◽  
Pearson Type Iii Distribution ◽  
Event Based

The study evaluated the applicability of Event-Based Approach for Small and Ungauged Basins (EBA4SUB) for calculating annual peak flows with a specific return period (Q<sub>T</sub>) in southern Poland. Data used in the calculations in a form of observation series of annual peak flows were derived from the Institute of Meteorology and Water Management in Warsaw and covered a multi-year period 1971–2015. The data were statistically verified for their homogeneity, significance of monotonic trends, outliers and equality of variances. Peak flows with a given return period were estimated by a statistical method of Pearson Type III distribution, and by EBA4SUB model. The analysis showed that Q<sub>T</sub> for the investigated catchments was the most accurately matching the values derived from the statistical method when EBA4SUB model was employed. This was evidenced by the values of average relative errors that reached 34% for EBA4SUB model (with beta hyetograph). The results of the study demonstrated usefulness of EBA4SUB model for the estimation of Q<sub>T</sub> quantiles in catchments of the upper Vistula water region.

scholarly journals On the tails of distributions of annual peak flow

2011 ◽  
Vol 42 (2-3) ◽  
pp. 171-192 ◽  
Author(s):  
Witold G. Strupczewski ◽  
Krzysztof Kochanek ◽  
Iwona Markiewicz ◽  
Ewa Bogdanowicz ◽  
Stanislaw Weglarczyk ◽  
...  
Keyword(s):  
Flood Frequency ◽  
Correct Selection ◽  
Data Sets ◽  
Frequency Model ◽  
Peak Flows ◽  
Annual Peak ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Log Normal ◽  
Selection Of

This study discusses an application of heavy-tailed distributions to modelling of annual peak flows in general and of Polish data sets in particular. One- and two-shape parameter heavy-tailed distributions are obtained by transformations of random variables. The correct selection of a flood frequency model with emphasis on heavy-tailed distribution discrimination is then discussed. If a distribution is wrongly assumed, the error, in the upper quantile, arising as a result, depends on the method of parameter estimation and is shown analytically for three methods. Asymptotic and sampling values (got by simulation) were assessed for the pair log-Gumbel (LG) as a false distribution and log-normal (LN) as a true distribution. Comparing the upper quantiles of various distributions with the same values of moments, it is found that heavy-tailed distributions do not consistently provide higher flood frequency estimates than do soft-tailed distributions. Based on L-moment ratio diagrams and the test of linearity on log–log plots, it is concluded that Polish datasets of annual peak flows should be modelled using soft-tailed distributions, such as the three-parameter Inverse Gaussian, rather than heavy-tailed distributions.

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 Using comparative analysis to teach about the nature of nonstationarity in future flood predictions

2012 ◽  
Vol 16 (5) ◽  
pp. 1269-1279 ◽  
Author(s):  
S. B. Shaw ◽  
M. T. Walter
Keyword(s):  
Comparative Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Atmospheric Rivers ◽  
Changing Climate ◽  
Annual Peak ◽  
The Us ◽  
Hydrologic System ◽  
Daily Flows

Abstract. Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students' insights into the degree and predictability of future non-stationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics.

Long-term dependence in annual peak flows of Canadian rivers

1994 ◽  
Vol 160 (1-4) ◽  
pp. 89-103 ◽  
Author(s):  
L.M. Lye ◽  
Yude Lin
Keyword(s):  
Peak Flows ◽  
Annual Peak

Using Artificial Neural Networks to Fill-in Missing Annual Peak Flows

2007 ◽  
Author(s):  
Steven K. Starrett ◽  
Shelli K. Starrett ◽  
Travis Heier ◽  
Yunsheng Su ◽  
Denny Tuan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Peak Flows ◽  
Annual Peak ◽  
Artificial Neural
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