Robert L. Wilby

SDSM - a decision support tool for the assessment of regional climate change impacts

General Circulation Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called ‘downscaling’ techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are currently able to provide and what impact assessors require. This paper describes a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. Statistical DownScaling Model (sdsm) facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. Additionally, the software performs ancillary tasks of predictor variable pre-screening, model calibration, basic diagnostic testing, statistical analyses and graphing of climate data. The application of sdsm is demonstrated with respect to the generation of daily temperature and precipitation scenarios for Toronto, Canada by 2040–2069.  2002 Elsevier Science Ltd. All rights reserved.

Statistical downscaling of general circulation model output: A comparison of methods

A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centres coupled ocean/atmosphere model (HadCM2) forced by combined CO2 and sulfate aerosol changes. Climate model results for 1980–1999 (present) and 2080–2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard “WGEN” method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980–1999 and 2080–2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.

A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK

A probabilistic framework is presented for combining information from an ensemble of four general circulation models (GCMs), two greenhouse gas emission scenarios, two statistical downscaling techniques, two hydrological model structures, and two sets of hydrological model parameters. GCMs were weighted according to an index of reliability for downscaled effective rainfall, a key determinant of low flows in the River Thames. Hydrological model structures were weighted by performance at reproducing annual low-flow series. Weights were also assigned to sets of water resource model (CATCHMOD) parameters using the Nash-Sutcliffe efficiency criterion. Emission scenarios and downscaling methods were unweighted. A Monte Carlo approach was then used to explore components of uncertainty affecting projections for the River Thames by the 2080s. The resulting cumulative distribution functions (CDFs) of low flows were most sensitive to uncertainty in the climate change scenarios and downscaling of different GCMs. Uncertainties due to the hydrological model parameters and emission scenario increase with time but were less important. Abrupt changes in low-flow CDFs were attributed to uncertainties in statistically downscaled summer rainfall. This was linked to different behavior of atmospheric moisture among the chosen GCMs.

The weather generation game: a review of stochastic weather models

This article reviews the historical development of statistical weather models, from simple analyses of runs of consecutive rainy and dry days at single sites, through to multisite models of daily precipitation. Weather generators have been used extensively in water engineering design and in agricultural, ecosystem and hydrological impact studies as a means of in-filling missing data or for producing indefinitely long synthetic weather series from finite station records. We begin by describing the statistical properties of the rainfall occurrence and amount processes which are necessary precursors to the simulation of other (dependent) meteorological variables. The relationship between these daily weather models and lower-frequency variations in climate statistics is considered next, noting that conventional weather generator techniques often fail to capture wholly interannual variability. Possible solutions to this deficiency - such as the use of mixtures of slowly and rapidly varying conditioning variables - are discussed. Common applications of weather generators are then described. These include the modelling of climate-sensitive systems, the simulation of missing weather data and statistical downscaling of regional climate change scenarios. Finally, we conclude by considering ongoing advances in the simulation of spatially correlated weather series at multiple sites, the downscaling of interannual climate variability and the scope for using nonparametric techniques to synthesize weather series.

Hydrological modelling using artificial neural networks

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

A review of the potential impacts of climate change on surface water quality

Abstract It is now accepted that some human-induced climate change is unavoidable. Potential impacts on water supply have received much attention, but relatively little is known about the concomitant changes in water quality. Projected changes in air temperature and rainfall could affect river flows and, hence, the mobility and dilution of contaminants. Increased water temperatures will affect chemical reaction kinetics and, combined with deteriorations in quality, freshwater ecological status. With increased flows there will be changes in stream power and, hence, sediment loads with the potential to alter the morphology of rivers and the transfer of sediments to lakes, thereby impacting freshwater habitats in both lake and stream systems. This paper reviews such impacts through the lens of UK surface water quality. Widely accepted climate change scenarios suggest more frequent droughts in summer, as well as flash-flooding, leading to uncontrolled discharges from urban areas to receiving water courses and estuaries. Invasion by alien species is highly likely, as is migration of species within the UK adapting to changing temperatures and flow regimes. Lower flows, reduced velocities and, hence, higher water residence times in rivers and lakes will enhance the potential for toxic algal blooms and reduce dissolved oxygen levels. Upland streams could experience increased dissolved organic carbon and colour levels, requiring action at water treatment plants to prevent toxic by-products entering public water supplies. Storms that terminate drought periods will flush nutrients from urban and rural areas or generate acid pulses in acidified upland catchments. Policy responses to climate change, such as the growth of bio-fuels or emission controls, will further impact freshwater quality.

An artificial neural network approach to rainfall- runoff modelling

Abstract This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in two flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.

A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado

Abstract The fundamental rationale for statistical downscaling is that the raw outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub-grid box scales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling has been proposed as a practical means of bridging this spatial difference. This study compared three sets of current and future rainfall-runoff scenarios. The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM output; and (3) raw GCM output corrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output of the UK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in order to downscale daily precipitation and temperature series for the Animas River in the San Juan River basin, Colorado. Significant differences arose between the modelled snowpack and flow regimes of the three future climate scenarios. Overall, the raw GCM output suggests larger reductions in winter/spring snowpack and summer runoff than the downscaling, relative to current conditions. Further research is required to determine the generality of the water resource implications for other regions, GCM outputs and downscaled scenarios.

Precipitation predictors for downscaling: observed and general circulation model relationships

Because of the coarse resolution of general circulation models (GCM), ‘downscaling’ techniques have emerged as a means of relating meso-scale atmospheric variables to grid- and sub-grid-scale surface variables. This study investigates these relationships. As a precursor, inter-variable correlations were investigated within a suite of 15 potential downscaling predictor variables on a daily time-scale for six regions in the conterminous USA, and observed correlations were compared with those based on the HadCM2 coupled ocean/atmosphere GCM. A comparison was then made of observed and model correlations between daily precipitation occurrence (a time series of zeroes and ones) and wet-day amounts and the 15 predictors. These two analyses provided new insights into model performance and provide results that are central to the choice of predictor variables in downscaling of daily precipitation. Also determined were the spatial character of relationships between observed daily precipitation and both mean sea-level pressure (mslp) and atmospheric moisture and daily precipitation for selected regions. The question of whether the same relationships are replicated by HadCM2 was also examined. This allowed the assessment of the spatial consistency of key predictor–predictand relationships in observed and HadCM2 data. Finally, the temporal stability of these relationships in the GCM was examined. Little difference between results for 1980–1999 and 2080–2099 was observed. For correlations between predictor variables, observed and model results were generally similar, providing strong evidence of the overall physical realism of the model. For correlations with precipitation, the results are less satisfactory. For example, model precipitation is more strongly dependent on surface divergence and specific humidity than observed precipitation, while the latter has a stronger link to 500 hPa divergence than is evident in the model. These results suggest possible deficiencies in the model precipitation process, and may indicate that the model overestimates future changes in precipitation. Correlation field patterns for mslp versus precipitation are remarkably similar for observed data and HadCM2 output. Differences in the correlation fields for specific humidity are more noticeable, especially in summer. In many cases, maximum correlations between precipitation and mslp occurred away from the grid box; whereas correlations with specific humidity were largest when the data were propinquitous. This suggests that the choice of predictor variable and the corresponding predictor domain, in terms of location and spatial extent, are critical factors affecting the realism and stability of downscaled precipitation scenarios. Copyright

Hydrological responses to dynamically and statistically downscaled climate model output

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic simulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.