Biomedical subjects

R L Wilby

Publications and source records attributed to R L Wilby.

4 recordsLinked to original sources

PubMed2006

Risks posed by climate change to the delivery of Water Framework Directive objectives in the UK.

The EU Water Framework Directive (WFD) is novel because it integrates water quality, water resources, physical habitat and, to some extent, flooding for all surface and groundwaters and takes forward river basin management. However, the WFD does not explicitly mention risks posed by climate change to the achievement of its environmental objectives. This is despite the fact that the time scale for the implementation process and achieving particular objectives extends into the 2020s, when climate models project changes in average temperature and precipitation. This paper begins by reviewing the latest UK climate change scenarios and the wider policy and science context of the WFD. We then examine the potential risks of climate change to key phases of the River Basin Management Process that underpin the WFD (such as characterisation of river basins and their water bodies, risk assessments to identify pressures and impacts, programmes of measures (POMs) options appraisal, monitoring and modelling, policy and management activities). Despite these risks the WFD could link new policy and participative mechanisms (being established for the River Basin Management Plans) to the emerging framework of national and regional climate change adaptation policy. The risks are identified with a view to informing policy opportunities, objective setting, adaptation strategies and the research agenda. Key knowledge gaps have already been identified during the implementation of the WFD, such as the links between hydromorphology and ecosystem status, but the overarching importance of linking climate change to these considerations needs to be highlighted. The next generation of (probabilistic) climate change scenarios will present new opportunities and challenges for risk analysis and policy-making.

Chemical Precipitation↗

PubMed2006

Impacts of climate change on in-stream nitrogen in a lowland chalk stream: an appraisal of adaptation strategies.

The impacts of climate change on nitrogen (N) in a lowland chalk stream are investigated using a dynamic modelling approach. The INCA-N model is used to simulate transient daily hydrology and water quality in the River Kennet using temperature and precipitation scenarios downscaled from the General Circulation Model (GCM) output for the period 1961-2100. The three GCMs (CGCM2, CSIRO and HadCM3) yield very different river flow regimes with the latter projecting significant periods of drought in the second half of the 21st century. Stream-water N concentrations increase over time as higher temperatures enhance N release from the soil, and lower river flows reduce the dilution capacity of the river. Particular problems are shown to occur following severe droughts when N mineralization is high and the subsequent breaking of the drought releases high nitrate loads into the river system. Possible strategies for reducing climate-driven N loads are explored using INCA-N. The measures include land use change or fertiliser reduction, reduction in atmospheric nitrate and ammonium deposition, and the introduction of water meadows or connected wetlands adjacent to the river. The most effective strategy is to change land use or reduce fertiliser use, followed by water meadow creation, and atmospheric pollution controls. Finally, a combined approach involving all three strategies is investigated and shown to reduce in-stream nitrate concentrations to those pre-1950s even under climate change.

Agriculture↗

PubMed2000

Inductive learning approaches to rainfall-runoff modelling.

Trying to model the rainfall-runoff process is a complex activity as it is influenced by a number of implicit and explicit factors--for example, precipitation distribution, evaporation, transpiration, abstraction, watershed topography, and soil types. However, this kind of forecasting is particularly important as it is used to predict serious flooding, estimate erosion and identify problems associated with low flow. Inductive learning approaches (e.g. decision trees and artificial neural networks) are particularly well suited to problems of this nature as they can often interpret underlying factors (such as seasonal variations) which cannot be modelled by other techniques. In addition, these approaches can easily be trained on the explicit factors (e.g. rainfall) and the inexplicit factors (e.g. abstraction) that affect river flow. Inductive learning approaches can also be extended to account for new factors that emerge over a period of time. This paper evaluates the application of decision trees and two artificial neural network models (the multilayer perceptron and the radial basis function network) to river flow forecasting in two flood prone UK catchments using real hydrometric data. Comparisons are made between the performance of these approaches and conventional flood forecasting systems.

Algorithms↗

PubMed1994

Exceptional weather in the midlands, UK, during 1988-1990 results in the rapid acidification of an upland stream.

Numerous catchment studies have identified the control exerted by hydrological processes on short-term (within a year) fluctuations in surface water acidity. Because discharge is, in turn, a function of broad climate parameters, there has been growing interest in the potential impact of changing precipitation and temperature regimes on water chemistry. The exceptionally warm and arid period 1988-1990 provided an opportunity to investigate the response of an acidic catchment in the East Midlands to an extreme climate scenario. The results obtained from three years of intensive monitoring indicated that between 1988 and 1990 there was a fourfold increase of the surface-water acidity at several observation sites within the Beacon catchment, Charnwood Forest, Leicestershire. As well as providing an indication of currently extreme hydrochemical conditions which in the near future may become the norm, these observations also have a bearing on the validity of long-term predictions derived from process-orientated models.

Journal Article↗