Estimating the impact of rural land management changes on catchment runoff generation in England and Wales

Hess, Tim; Holman, Ian P.; Rose, S. C.; Rosolova, Z.; Parrott, Amy

doi:10.1002/hyp.7598

Cited by 44 publications

(42 citation statements)

References 32 publications

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“…Soils with BFI Ã HOST close to the cutoffs are classified as either of two USDA classes, e.g., HOST type 15 is classified as the USDA soil type C or D. Although this mapping contains significant uncertainty, it is presumed to be useful in that it provides information about land use and management effects that would otherwise not be available for UK applications. The proposed HOST to USDA soil group mapping is similar to the mapping hypothesized by Hess et al [2010].…”

Section: Regionalized Indices As Sources Of Informationmentioning

confidence: 62%

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bulygina¹,

McIntyre²,

Ballard³

et al. 2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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[1] A novel method is presented for conditioning rainfall-runoff models for ungauged catchment and land use impact applications. The method conditions the model on information from multiple regionalized response indices using a formal Bayesian approach. Two indices that hold information about soil type and land use effects are the base flow index from the Hydrology of Soil Type (HOST) classification and curve number from the U.S. Department of Agriculture's Soil Conservation Service soil and land use classification. These indices are used to constrain a five-parameter probability distributed moisture model for subcatchments of the Wye (grazed grassland) and Severn (mainly afforested) catchments in the United Kingdom. The base flow index and curve number constrain only two of the five model parameters, indicating that ideally, other sources of information would be sought. Nevertheless, the procedure significantly reduces the prior uncertainty in runoff prediction and gives predictions close to those of the calibrated models. For the case study, the introduction of the curve number in addition to the base flow index has only a small effect on model performance and uncertainty; however, it allows a distinction between the effects of soil type and land management for the purpose of scenario analysis. The principal assumptions used in the method are the applicability of the curve number classification system and its mapping to UK soil types and the likelihood function used for Bayesian conditioning.

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Section: Regionalized Indices As Sources Of Informationmentioning

confidence: 62%

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bulygina¹,

McIntyre²,

Ballard³

et al. 2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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“…There are many overlapping and related ways in which both environmental management and flood risk are framed, with related uncertainties. For example natural flood management (NFM) is sometimes characterised as uncertain because it lacks a 'sufficient' scientific evidence base (O'Connell et al 2007;Marshall et al 2009;Hess et al 2010). The majority of scientific analyses of NFM explore the question of whether upland land management can affect river flow in order to attenuate flood peaks (Lane et al 2006;McCormick et al 2009).…”

Section: Approaches To Flood Risk Management (Frm)mentioning

confidence: 99%

Flood risk management, an approach to managing cross-border hazards

et al. 2016

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River flooding is a serious hazard in the UK with interest driven by recent widespread events. This paper reviews different approaches to flood risk management and the borders (physical, conceptual and organisational) that are involved. The paper showcases a multi-method approach to negotiating flood risk management interventions. We address three fundamental issues around flood risk management: differences and similarities between a variety of approaches; how different approaches work across borders between professionals, lay people, organisations and between different planning regimes; and, whether the science evidence base is adequate to support different types of flood risk management. We explore these issues through a case study on the River Tweed using Q methodology, community mapping and focus groups, participatory GIS, and interviews, which enabled co-production of knowledge around possible interventions to manage flooding. Our research demonstrated that excellent networks of practice exist to make 123Nat Hazards (2016) 82:S217-S240 DOI 10.1007/s11069-016-2284-2 decisions about flood risk management in the Scottish-English borders. Physical and organisational borders were continually traversed in practice. There was an overwhelming desire from professional flood managers and local communities for an alternative to simply structural methods of flood management. People were keen to make use of the ability of catchments to store water, even if land needed to be sacrificed to do so. There was no difference in the desire to embrace natural flood management approaches between people with different roles in flood management, expertise, training or based in different locations. Thus conceptual borders were also crossed effectively in practice.

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“…Although originally developed as a teaching and learning tool [16], its value in hydrological research has been demonstrated in several applications including estimation of irrigation requirements [17], runoff estimation [18], drainage performance [19] and groundwater recharge potential [20]. Full details of the modelling approach are given in Hess et al [21].…”

Section: Wasim Daily Water Balance Methodsmentioning

confidence: 99%

Estimating Green Water Footprints in a Temperate Environment

Hess

2010

Water

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Abstract:The "green" water footprint (GWF) of a product is often considered less important than the "blue" water footprint (BWF) as "green" water generally has a low, or even negligible, opportunity cost. However, when considering food, fibre and tree products, is not only a useful indicator of the total appropriation of a natural resource, but from a methodological perspective, blue water footprints are frequently estimated as the residual after green water is subtracted from total crop water use. In most published studies, green water use (ET green ) has been estimated from the FAO CROPWAT model using the USDA method for effective rainfall. In this study, four methods for the estimation of the ET green of pasture were compared. Two were based on effective rainfall estimated from monthly rainfall and potential evapotranspiration, and two were based on a simulated water balance using long-term daily, or average monthly, weather data from 11 stations in England. The results show that the effective rainfall methods significantly underestimate the annual ET green in all cases, as they do not adequately account for the depletion of stored soil water during the summer. A simplified model, based on annual rainfall and reference evapotranspiration (ETo) has been tested and used to map the average annual ET green of pasture in England.

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Estimating the impact of rural land management changes on catchment runoff generation in England and Wales

Cited by 44 publications

References 32 publications

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

Flood risk management, an approach to managing cross-border hazards

Estimating Green Water Footprints in a Temperate Environment

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