Climate change and river flooding: part 1 classifying the sensitivity of British catchments

Effective national and regional policy guidance on climate change adaptation relies on robust scientific evidence. This two-part series of papers develops and implements a novel scenario-neutral framework enabling an assessment of the vulnerability of flood flows in British catchments to climatic change, to underpin the development of guidance for the flood management community. In this first part, the sensitivity of the 20-year return period flood peak (RP20) to changes in precipitation (P), temperature (T) and potential evapotranspiration (PE) is systematically assessed for 154 catchments. A sensitivity domain of 4,200 scenarios is applied combining 525 and 8 sets of P and T/PE mean monthly changes, respectively, with seasonality incorporated using a single-phase harmonic function. Using the change factor method, the percentage change in RP20 associated with each scenario of the sensitivity domain is calculated, giving flood response surfaces for each catchment. Using a clustering procedure on the response surfaces, the 154 catchments are divided into nine groups: flood sensitivity types. These sensitivity types show that some catchments are (very) sensitive to changes in P but others buffer the response, while the location of catchments of the same type does not show any strong geographical pattern. These results reflect the range of hydrological processes found in Britain, and demonstrate the potential importance of catchment properties (physical and climatic) in the propagation of change in climate to change in floods, and so in characterising the sensitivity types (covered in the companion paper)

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

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 sensitivity-exposure-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 tools to quantify the vulnerability of catchments to climate change without the need to undertake a full climate change impact study. © 2013 Springer Science+Business Media Dordrecht

Using a scenario-neutral framework to avoid potential maladaptation to future flood risk

This study develops a coherent framework to detect those catchment types associated with ahigh risk of maladaptation to futureflood risk. Using the“scenario‐neutral”approach to impactassessment the sensitivity of Irish catchments tofluvialflooding is examined in the context of nationalclimate change allowances. A predefined sensitivity domain is used to quantifyflood responses to +2 °Cmean annual temperature with incremental changes in the seasonality and mean of the annual precipitationcycle. The magnitude of the 20‐yearflood is simulated at each increment using two rainfall‐runoff models(GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchmentsensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributesare used to classify 215 ungauged/data‐sparse catchments. To address possible redundancies, the exposure ofdifferent catchment types to projected climate is established using an objectively selected subset of theCoupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown tosignificantly influence sensitivity and have a greater effect than ensemble bias. A nationalflood riskallowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of theuncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative ConcentrationPathway 8.5; 2070–2099), irrespective of hydrological model and catchment type. However, results indicatethat assuming a standard national or regional allowance could lead to local over/under adaptation. Herein,catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle ofprecipitation and warrant special attention

How well do large-scale models reproduce regional hydrological extremes in Europe?

This paper presents a new methodology for assessing the ability of gridded hydrological models to reproduce large-scale hydrological high and low flow events (as a proxy for hydrological extremes) as described by catalogues of historical droughts [using the regional deficiency index (RDI)] and high flows [regional flood index (RFI)] previously derived from river flow measurements across Europe. Using the same methods, total runoff simulated by three global hydrological models from the Water Model Intercomparison Project (WaterMIP) [Joint U.K. Land Environment Simulator (JULES), Water Global Assessment and Prognosis (WaterGAP), and Max Planck Institute Hydrological Model (MPI-HM)] run with the same meteorological input (watch forcing data) at the same spatial 0.58 grid was used to calculate simulated RDI and RFI for the period 1963-2001 in the same European regions, directly comparable with the observed catalogues. Observed and simulated RDI and RFI time series were compared using three performance measures: the relative mean error, the ratio between the standard deviation of simulated over observed series, and the Spearman correlation coefficient. Results show that all models can broadly reproduce the spatiotemporal evolution of hydrological extremes in Europe to varying degrees. JULES tends to produce prolonged, highly spatially coherent events for both high and low flows, with events developing more slowly and reaching and sustaining greater spatial coherence than observed-this could be due to runoff being dominated by slow-responding subsurface flow. In contrast, MPI-HM shows very high variability in the simulated RDI and RFI time series and a more rapid onset of extreme events than observed, in particular for regions with significant water storage capacity-this could be due to possible underrepresentation of infiltration and groundwater storage, with soil saturation reached too quickly. WaterGAP shares some of the issues of variability with MPIHM- also attributed to insufficient soil storage capacity and surplus effective precipitation being generated as surface runoff-and some strong spatial coherence of simulated events with JULES, but neither of these are dominant. Of the three global models considered here, WaterGAP is arguably best suited to reproduce most regional characteristics of large-scale high and low flow events in Europe. Some systematic weaknesses emerge in all models, in particular for high flows, which could be a product of poor spatial resolution of the input climate data (e.g., where extreme precipitation is driven by local convective storms) or topography. Overall, this study has demonstrated that RDI and RFI are powerful tools that can be used to assess how well large-scale hydrological models reproduce large-scale hydrological extremes-an exercise rarely undertaken in model intercomparisons. © 2011 American Meteorological Society

Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK)

Potential increases of phytoplankton concentrations in river systems due to global warming and changing climate could pose a serious threat to the anthropogenic use of surface waters. Nevertheless, the extent of the effect of climatic alterations on phytoplankton concentrations in river systems has not yet been analysed in detail. In this study, we assess the impact of a change in precipitation and temperature on river phytoplankton concentration by means of a physically-based model. A scenario-neutral methodology has been employed to evaluate the effects of climate alterations on flow, phosphorus concentration and phytoplankton concentration of the River Thames (southern England). In particular, five groups of phytoplankton are considered, representing a range of size classes and pigment phenotypes, under three different land-use/land-management scenarios to assess their impact on phytoplankton population levels. The model results are evaluated within the framework of future climate projections, using the UK Climate Projections 09 (UKCP09) for the 2030s. The results of the model demonstrate that an increase in average phytoplankton concentration due to climate change is highly likely to occur, with the magnitude varying depending on the location along the River Thames. Cyanobacteria show significant increases under future climate change and land use change. An expansion of intensive agriculture accentuates the growth in phytoplankton, especially in the upper reaches of the River Thames. However, an optimal phosphorus removal mitigation strategy, which combines reduction of fertiliser application and phosphorus removal from wastewater, can help to reduce this increas

The drying up of Britain? A national estimate of changes in seasonal river flows from 11 Regional Climate Model simulations

As climate change may modify the hydrological cycle significantly, understanding the impact on river flow is important because it affects long-term water resources planning. Here, we describe a high-resolution British assessment of changes in river flows in the 2050s under 11 different realisations of HadRM3. In winter, river flows may either increase or decrease, with a wide range of possible decreases in summer flow. These results should encourage adaptation that copes with a broad range of future hydrological conditions. © 2011 John Wiley & Sons, Ltd

Characterizing uncertainty of the hydrologic impacts of climate change

The high climate sensitivity of hydrologic systems, the importance of those systems to society, and the imprecise nature of future climate projections all motivate interest in characterizing uncertainty in the hydrologic impacts of climate change. We discuss recent research that exposes important sources of uncertainty that are commonly neglected by the water management community, especially, uncertainties associated with internal climate system variability, and hydrologic modeling. We also discuss research exposing several issues with widely used climate downscaling methods. We propose that progress can be made following parallel paths: first, by explicitly characterizing the uncertainties throughout the modeling process (rather than using an ad hoc “ensemble of opportunity”) and second, by reducing uncertainties through developing criteria for excluding poor methods/models, as well as with targeted research to improve modeling capabilities. We argue that such research to reveal, reduce, and represent uncertainties is essential to establish a defensible range of quantitative hydrologic storylines of climate change impacts

The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data

Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment

Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by biascorrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO2 and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty

Hydrology needed to manage droughts: the 2015 European case

Hydrology needed to manage droughts: the 2015 European cas