scholarly journals Generalized Skew Coefficients of Annual Peak Flows for Rural, Unregulated Streams in West Virginia

2009 ◽  
Author(s):  
John T. Atkins ◽  
Jeffrey B. Wiley ◽  
Katherine S. Paybins
Keyword(s):  
West Virginia ◽  
Peak Flows ◽  
Annual Peak

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

scholarly journals Verification of empirical formulas for calculating annual peak flows with specific return period in the upper Vistula basin

2018 ◽  
Vol 2 ◽  
pp. 145-154 ◽  
Author(s):  
Dariusz Młyński ◽  
◽  
Andrzej Wałęga ◽  
Andrea Petroselli ◽  
◽  
...  
Keyword(s):  
Return Period ◽  
Empirical Formulas ◽  
Peak Flows ◽  
Annual Peak

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

2018 ◽  
Author(s):  
Naoki Mizukami ◽  
Oldrich Rakovec ◽  
Andrew Newman ◽  
Martyn Clark ◽  
Andrew Wood ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Peak Flow ◽  
Hydrologic Model ◽  
High Flow ◽  
Hydrologic Models ◽  
Peak Flows ◽  
Annual Peak ◽  
Performance Metric ◽  
Squared Error ◽  
Application Specific

Abstract. Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models, and a key modeler decision is the selection of the performance metric to be optimized. It has been common to used 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 estimation of high flows. However, we find 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, the Variable Infiltration Capacity model (VIC) 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 variants, and (3) Annual Peak Flow Bias (APFB), where the latter is an application-specific hydrologic signature metric that focuses on annual peak flows. As expected, the application specific 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 result in underestimation of observed flow variability. Meanwhile, the use of KGE results in annual peak flow estimates that are better than from NSE, 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. Overall this work highlights the need for a fuller understanding of performance metric behavior and design in relation to the desired goals of model calibration.

scholarly journals Regional Frequency Analysis of Annual Peak Flows in Pakistan Using Linear Combination of Order Statistics

2016 ◽  
Vol 25 (6) ◽  
pp. 2255-2264 ◽  
Author(s):  
Ishfaq Ahmad ◽  
Muhammad Fawad ◽  
Muhammad Akbar ◽  
Aamar Abbass ◽  
Hafiz Zafar
Keyword(s):  
Order Statistics ◽  
Linear Combination ◽  
Frequency Analysis ◽  
Regional Frequency Analysis ◽  
Peak Flows ◽  
Annual Peak ◽  
Regional Frequency
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