Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows
(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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- author
- Gao, Hongkai ; Cai, Huayang and Duan, Zheng LU
- publishing date
- 2018-02-01
- 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
- 2022-04-10 23:20:10
@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}},
keywords = {{Catchment characteristics; FLEX model; Flooding; MOPEX; Peak flow; Storage capacity curve}},
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}},
}