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Water resource planning and management

Objectives: The University of Newcastle developed this mock case study in order to demonstrate how probabilistic projections of water resources, derived using the uncertainties in the climate projections, can be used to inform water resource planning and management. The study uses dummy data.

How they used UKCP09 dummy data

1. The Weather Generator was run for a Thames aggregated river basin area to derive a 999-year daily time series of rainfall and potential evapotranspiration (PET) for each of the three time periods and emission scenarios.

2. These outputs were then used to drive the Environment Agency's CATCHMOD hydrologic model to generate runoff time series representative of these projected future climates.

3. Observed historical (5 km gridded) climate data sets covering the study area were used to assess the capabilities of the Weather Generator to reproduce the observed daily climate (precipitation and PET). This validation was done by comparing the statistics of the observed historical data with that of the Weather Generator outputs.

4. This historical data, including the gauge flow data of the Thames River at Kingston, were used to verify and calibrate the runoff from the hydrological model.

Next steps

The resulting information could be used to simulate the statistics for the projected number of days on which the gauged flows equals or exceeds a particular threshold and this information used to examine implications for abstraction licensing. This could be done by examining the nature of the risk of exceedance based on specific flow percentiles.

The results could be used to demonstrate the implications of uncertainties on the viability of proposed adaptation measures and strategies within the context of water resource planning and management.

What they learnt about UKCP09

  • The lack of PET projections in the probabilistic projections is a limiting factor, but can be overcome by using the Weather Generator outputs. The Weather Generator uses the change factors from the probabilistic projections.
  • The lack of PET observed information could prove limiting in terms of validating the Weather Generator. In this case, daily  MORECS PET data (external website) is also available for the Thames catchment.
  • The availability of historical climate data for spatially aggregated areas requires the derivation of this information from the historical gridded climate data. It would be useful to have a standard set of this information.
  • One 5 km x 5 km grid square or even 40 such grid squares (1000 square km) is realistically too small to represent the whole catchment.

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