Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality
-
Published:2019-05-07
Issue:5
Volume:23
Page:2225-2243
-
ISSN:1607-7938
-
Container-title:Hydrology and Earth System Sciences
-
language:en
-
Short-container-title:Hydrol. Earth Syst. Sci.
Author:
Yu GuoORCID, Wright Daniel B., Zhu Zhihua, Smith Cassia, Holman Kathleen D.ORCID
Abstract
Abstract. Floods are the product of complex interactions among processes including
precipitation, soil moisture, and watershed morphology. Conventional flood
frequency analysis (FFA) methods such as design storms and discharge-based
statistical methods offer few insights into these process interactions and
how they “shape” the probability distributions of floods. Understanding and
projecting flood frequency in conditions of nonstationary hydroclimate and
land use require deeper understanding of these processes, some or all of
which may be changing in ways that will be undersampled in observational
records. This study presents an alternative “process-based” FFA approach
that uses stochastic storm transposition to generate large numbers of
realistic rainstorm “scenarios” based on relatively short rainfall remote
sensing records. Long-term continuous hydrologic model simulations are used
to derive seasonally varying distributions of watershed antecedent
conditions. We couple rainstorm scenarios with seasonally appropriate
antecedent conditions to simulate flood frequency. The methodology is applied
to the 4002 km2 Turkey River watershed in the Midwestern United States,
which is undergoing significant climatic and hydrologic change. We show that,
using only 15 years of rainfall records, our methodology can produce accurate
estimates of “present-day” flood frequency. We found that shifts in the
seasonality of soil moisture, snow, and extreme rainfall in the Turkey River
exert important controls on flood frequency. We also demonstrate that
process-based techniques may be prone to errors due to inadequate
representation of specific seasonal processes within hydrologic models. If
such mistakes are avoided, however, process-based approaches can provide a
useful pathway toward understanding current and future flood frequency in
nonstationary conditions and thus be valuable for supplementing existing FFA
practices.
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference103 articles.
1. Alexander, G. N.: Using the probability of storm transposition for estimating
the frequency of rare floods, J. Hydrol., 1, 46–57, 1963. 2. Ayalew, T., Krajewski, W., and Mantilla, R.: Exploring the Effect of
Reservoir Storage on Peak Discharge Frequency, J. Hydrol. Eng., 18,
1697–1708, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000721, 2013. 3. Ayalew, T. B. and Krajewski, W. F.: Effect of River Network Geometry on Flood
Frequency: A Tale of Two Watersheds in Iowa, J. Hydrol. Eng., 22, 06017004,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001544, 2017. 4. Ball, J., Babister, M., Nathan, R., Weeks, W., Weinmann, E., and Retallick,
M.: Australian Rainfall and Runoff: A Guide to Flood Estimation, edited by:
Testoni, I., Commonwealth of Australia (Geoscience Australia), 2016. 5. Bartles, M., Brunner, G., Fleming, M., Faber, B., and Slaughter, J.: HEC-SSP
Statistical Software Package Version 2.1, Computer Program Documentation, US
Army Corps of Engineers, Institute for Water Resources Hydrologic Engineering
Center (HEC), 609 Second Street Davis, CA 95616-4687, 2016.
Cited by
36 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|