Climate change and river flooding: Part 2 sensitivity characterisation for british catchments and example vulnerability assessments

Abstract

This paper is the second of a series describing a scenario-neutral methodology to assess the sensitivity and vulnerability of British catchments to changes in flooding due to climate change. In paper one, nine flood sensitivity types were identified from response surfaces generated for 154 catchments. The response surfaces describe changes in 20-year return period flood peaks (RP20) in response to a large set of changes in precipitation, temperature and potential evapotranspiration. In this paper, a recursive partitioning algorithm is used to link families of sensitivity types to catchment properties, via a decision tree. The tree shows 85% success characterising the four sensitivity families, using five properties and nine paths.Catchment annual average rainfall is the primary partitioning factor, with drier catchments having a more variable response to climate (precipitation) change than wetter catchments and higher catchment losses and permeability being aggravating factors. The full sensitivityexposure-vulnerability methodology is illustrated for two catchments: sensitivity is estimated by using the decision tree to identify the sensitivity family (and its associated average response surface); exposure is defined from a set of climate model projections and combined with the response surface to estimate the resulting impacts (changes in RP20);vulnerability under a range of adaptive capacity thresholds is estimated from the set of impacts. Even though they are geographically close, the two catchments show differing vulnerability to climate change, due to their differing properties. This demonstrates that generalised response surfaces characterised by catchment properties are useful screening 2 tools to quantify the vulnerability of catchments to climate change without the need to undertake a full climate change impact study.

Probabilistic impacts of climate change on flood frequency using response surfaces I: England and Wales

“…Here, for 20-year return period flood peaks, the probabilistic UKCP09 projections for 10 riverbasin regions across England and Wales have been used to produce probabilistic response-type impacts. Furthermore, the decision trees of Prudhomme et al (2013b) have been modified and applied, to estimate the response type of 1,120 NRFA catchments in the 10 regions. This information has been used to estimate regional impact ranges, by weighting response-type impact ranges.…”

“…Following Prudhomme et al (2013b), in order to minimise possible underestimation of impacts, the range for the Neutral response type is used with the number of Neutral/Damped catchments, and the range for the EnhancedHigh response type is used with the number of Enhanced catchments. The resulting regional average impact ranges could be considered to represent a central-estimate of the average impact range for a catchment in the region, taking account of the range of UKCP09 projections and the range of response types in the region.…”

“…Prudhomme et al (2013a) implemented this domain for 154 catchments across Britain and grouped the catchment response surfaces, leading to nine 'response types' each represented by composite (average) response surfaces. Prudhomme et al (2013b) characterised families of response types by catchment properties, enabling estimation of type for any catchment where the necessary properties are available. Thus the impacts of multiple projections can be easily estimated even for un-modelled catchments, by using composite response surfaces for the appropriate type.…”

“…This paper a. Develops the response type estimation method of Prudhomme et al (2013b), to allow robust application to a large set of catchments across England and Wales. b. Overlays the UKCP09 projections on the composite response surfaces of Prudhomme et al (2013a), to estimate the impacts (percentage changes in flood peaks) for each response type in regions across England and Wales.…”

“…The Neutral type has changes similar to those for P, while changes for Mixed and Sensitive types are more dependent on the specific seasonality and magnitude of P changes. Prudhomme et al (2013b) characterised families of response types by catchment properties, using a decision tree -a set of binary rules (based on catchment properties) which divide a sample (set of catchments) into a number of categories (response types/families). The families were formed by merging some of the response types, to enable more robust characterisation and take some account of variation of response surfaces with P harmonic phase: a Neutral/Damped family was formed by merging Damped-High, Damped-Low and Neutral catchments into one group, and an Enhanced family was formed by merging Enhanced-Low, Medium and High.…”