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Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows

Gao, Hongkai ; Cai, Huayang and Duan, Zheng LU (2018) In Hydrology Research 49(1). p.90-106
Abstract

In various conceptual models, the shape parameter (β) of the storage capacity curve, representing the non-linear relationship between relative soil moisture and runoff, determines runoff yield given certain circumstances of rainfall and antecedent soil moisture. In practice, β is typically calibrated for individual catchments and for different purposes, which limits more systematic understanding and also prediction in ungauged basins. Moreover, its regionalization and linkage to catchment characteristics is also not well understood, especially in relation to large-sample datasets. In this study, we used 404 catchments in the USA to explore β regionalization and attributes in relation to key catchment characteristics: elevation, slope,... (More)

In various conceptual models, the shape parameter (β) of the storage capacity curve, representing the non-linear relationship between relative soil moisture and runoff, determines runoff yield given certain circumstances of rainfall and antecedent soil moisture. In practice, β is typically calibrated for individual catchments and for different purposes, which limits more systematic understanding and also prediction in ungauged basins. Moreover, its regionalization and linkage to catchment characteristics is also not well understood, especially in relation to large-sample datasets. In this study, we used 404 catchments in the USA to explore β regionalization and attributes in relation to key catchment characteristics: elevation, slope, depth-to-bedrock, soil erodibility, forest cover, urban area, aridity index, catchment area, and stream density. We found a clear regionalized pattern of β, coherent with topography. Comparisons between β and various features demonstrated that slope has the largest impact. Land-cover, soil, geology, and climate also have an impact, but with lower correlation coefficients. This finding not only reveals spatial variation in β, but also deepens our understanding of its linkage to catchment features and flood flows. Moreover, the results provide a useful reference for decision-makers for flood prevention and mitigation.

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Catchment characteristics, FLEX model, Flooding, MOPEX, Peak flow, Storage capacity curve
in
Hydrology Research
volume
49
issue
1
pages
17 pages
publisher
IWA Publishing
external identifiers
  • scopus:85042130695
ISSN
1998-9563
DOI
10.2166/nh.2017.245
language
English
LU publication?
no
id
0ede6f9a-22c4-4566-b516-c8377c68f3cb
date added to LUP
2019-12-22 20:21:02
date last changed
2020-10-07 06:49:52
@article{0ede6f9a-22c4-4566-b516-c8377c68f3cb,
  abstract     = {<p>In various conceptual models, the shape parameter (β) of the storage capacity curve, representing the non-linear relationship between relative soil moisture and runoff, determines runoff yield given certain circumstances of rainfall and antecedent soil moisture. In practice, β is typically calibrated for individual catchments and for different purposes, which limits more systematic understanding and also prediction in ungauged basins. Moreover, its regionalization and linkage to catchment characteristics is also not well understood, especially in relation to large-sample datasets. In this study, we used 404 catchments in the USA to explore β regionalization and attributes in relation to key catchment characteristics: elevation, slope, depth-to-bedrock, soil erodibility, forest cover, urban area, aridity index, catchment area, and stream density. We found a clear regionalized pattern of β, coherent with topography. Comparisons between β and various features demonstrated that slope has the largest impact. Land-cover, soil, geology, and climate also have an impact, but with lower correlation coefficients. This finding not only reveals spatial variation in β, but also deepens our understanding of its linkage to catchment features and flood flows. Moreover, the results provide a useful reference for decision-makers for flood prevention and mitigation.</p>},
  author       = {Gao, Hongkai and Cai, Huayang and Duan, Zheng},
  issn         = {1998-9563},
  language     = {eng},
  month        = {02},
  number       = {1},
  pages        = {90--106},
  publisher    = {IWA Publishing},
  series       = {Hydrology Research},
  title        = {Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows},
  url          = {http://dx.doi.org/10.2166/nh.2017.245},
  doi          = {10.2166/nh.2017.245},
  volume       = {49},
  year         = {2018},
}