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Integration of hillslope-hydrology and hydraulics for modelling natural flood management

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Integration of hillslope-hydrology and hydraulics for modelling natural flood management. / Hankin, Barry; Metcalfe, Peter; Beven, Keith; Chappell, Nick.

In: Hydrology Research, 17.07.2019.

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@article{1b91793c00ea41f78a741c571b3cc78d,
title = "Integration of hillslope-hydrology and hydraulics for modelling natural flood management",
abstract = "Natural Flood Management (NFM) has recently invigorated the hydrological community into redeploying its process-understanding of hydrology and hydraulics to try to quantify the impacts of many distributed, ‘nature-based’ measures on the whole-catchment response. Advances in spatial data analysis, distributed hydrological modelling and fast numerical flow equation solvers mean that whole catchment modelling including computationally intensive uncertainty analyses are now possible, although perhaps the community has not yet converged on the best overall parsimonious framework. To model the effects of tree-planting we need to understand changes to wet canopy evaporation, surface roughness and infiltration rates; to model inline storage created by ‘leaky-barriers’ or offline storage, we need accurate channel hydraulics to understand the changes to attenuation; to model the complex behaviour of the whole network of NFM measures, and the possibility of flood peak synchronisation effects, we need efficient realistic routing models, linked to key flow pathways that take into account the main physical processes in soils and the antecedent moisture conditions for a range of different rainfall events. This paper presents a new framework to achieve this, based on a cascade of the Dynamic Topmodel runoff generation model and the JFlow or HEC-RAS 2d hydraulic models, with an application to the Swindale Catchment in Cumbria, UK. We demonstrate the approach to quantify both the effectiveness of a relatively large ‘runoff attenuation feature’ in the landscape, and the uncertainty in the calculation given model parameter uncertainty.",
keywords = "hydrology, hydraulics, NFM, Topmodel uncertainty",
author = "Barry Hankin and Peter Metcalfe and Keith Beven and Nick Chappell",
note = "Manuscript ID: Hydrology-D-18-00150",
year = "2019",
month = "7",
day = "17",
doi = "10.2166/nh.2019.150",
language = "English",
journal = "Hydrology Research",
issn = "0029-1277",
publisher = "Nordic Association for Hydrology",

}

RIS

TY - JOUR

T1 - Integration of hillslope-hydrology and hydraulics for modelling natural flood management

AU - Hankin, Barry

AU - Metcalfe, Peter

AU - Beven, Keith

AU - Chappell, Nick

N1 - Manuscript ID: Hydrology-D-18-00150

PY - 2019/7/17

Y1 - 2019/7/17

N2 - Natural Flood Management (NFM) has recently invigorated the hydrological community into redeploying its process-understanding of hydrology and hydraulics to try to quantify the impacts of many distributed, ‘nature-based’ measures on the whole-catchment response. Advances in spatial data analysis, distributed hydrological modelling and fast numerical flow equation solvers mean that whole catchment modelling including computationally intensive uncertainty analyses are now possible, although perhaps the community has not yet converged on the best overall parsimonious framework. To model the effects of tree-planting we need to understand changes to wet canopy evaporation, surface roughness and infiltration rates; to model inline storage created by ‘leaky-barriers’ or offline storage, we need accurate channel hydraulics to understand the changes to attenuation; to model the complex behaviour of the whole network of NFM measures, and the possibility of flood peak synchronisation effects, we need efficient realistic routing models, linked to key flow pathways that take into account the main physical processes in soils and the antecedent moisture conditions for a range of different rainfall events. This paper presents a new framework to achieve this, based on a cascade of the Dynamic Topmodel runoff generation model and the JFlow or HEC-RAS 2d hydraulic models, with an application to the Swindale Catchment in Cumbria, UK. We demonstrate the approach to quantify both the effectiveness of a relatively large ‘runoff attenuation feature’ in the landscape, and the uncertainty in the calculation given model parameter uncertainty.

AB - Natural Flood Management (NFM) has recently invigorated the hydrological community into redeploying its process-understanding of hydrology and hydraulics to try to quantify the impacts of many distributed, ‘nature-based’ measures on the whole-catchment response. Advances in spatial data analysis, distributed hydrological modelling and fast numerical flow equation solvers mean that whole catchment modelling including computationally intensive uncertainty analyses are now possible, although perhaps the community has not yet converged on the best overall parsimonious framework. To model the effects of tree-planting we need to understand changes to wet canopy evaporation, surface roughness and infiltration rates; to model inline storage created by ‘leaky-barriers’ or offline storage, we need accurate channel hydraulics to understand the changes to attenuation; to model the complex behaviour of the whole network of NFM measures, and the possibility of flood peak synchronisation effects, we need efficient realistic routing models, linked to key flow pathways that take into account the main physical processes in soils and the antecedent moisture conditions for a range of different rainfall events. This paper presents a new framework to achieve this, based on a cascade of the Dynamic Topmodel runoff generation model and the JFlow or HEC-RAS 2d hydraulic models, with an application to the Swindale Catchment in Cumbria, UK. We demonstrate the approach to quantify both the effectiveness of a relatively large ‘runoff attenuation feature’ in the landscape, and the uncertainty in the calculation given model parameter uncertainty.

KW - hydrology

KW - hydraulics

KW - NFM

KW - Topmodel uncertainty

U2 - 10.2166/nh.2019.150

DO - 10.2166/nh.2019.150

M3 - Journal article

JO - Hydrology Research

JF - Hydrology Research

SN - 0029-1277

ER -