Using UKCP09 probabilistic climate information for UK water resource planning

Abstract

Water companies in the United Kingdom have considered climate change in their water resources plans for more than a decade through studies funded by UK Water Industry Research (UKWIR). This paper presents an initial assessment of the impact of the UK Climate Projections 2009 (UKCP09) on river flows at a national scale for the 2020s under the A1B scenario and the implications for water resource planning. A daily hydrological modelling framework based on two conceptual model structures and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology has been applied to 70 catchments across the UK. A Latin Hypercube Sampling approach was adopted to handle the probabilistic nature of UKCP09. Results show a decrease in mean annual flow over most of the UK, with negative median values of all monthly changes except in winter over the western and northern mountainous areas. Furthermore the results indicate a high likelihood of a significant decline in summer flows. An analysis of variance shows that the main uncertainty in river flow changes comes from the spread in climate projections. Finally results are found to be consistent with a previous UKWIR assessment based on individual projections from 6 Global Climate Models (GCMs) under the A2 scenario. The reduction in summer low-flows, critical for water resources, appears however more limited with UKCP09. Although most expected changes are within natural variability, the drier conditions overall and the greater spread of results with respect to previous assessments indicates a need for testing the robustness of water resource plans.

Using the UKCP09 probabilistic scenarios to model the amplified impact of climate change on drainage basin sediment yield

“…Burton et al (2010) take a 1000 member random sample of UKCP09 projections which is then sub-sampled to identify 12 weighted samples for subsequent application in a high resolution urban flood model. Christierson et al (2012) use latin hypercube sampling to obtain 20 samples for use with hydrological models to assess changes in river flows at the UK national scale within the context of water resource planning. Alternatively, UKCP09 guidance recommends that a minimum of 100 random samples of the 10 000 sets of change factors are required to maintain the representativeness of the sampled dataset.…”

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

“…This model was implemented as WGEN (Weather GENerator) by Richardson and Wright (1984) [1], which used a simple Markov Chain for precipitation occurrence, a gamma distribution for simulation of rainfall amounts, and an autoregressive model for the remaining variables. A number of subsequent WGs, such as WXGEN [8], CLIGEN [9,10], LARS-WG [11][12][13], ClimGen [14], WeaGETS [15,16], Met and Roll [17], MOFRBC [18,19], WeatherMan [20], MarkSim [21], AAFC-WG [22,23], WM2 [24], KnnCAD [25][26][27], and the WG used by the UK Met Office (UKCP09) [28,29], all share the basic principles of stochastic simulation presented in WGEN. These WGs are station-scale generators, with time scales that range from daily (or even hourly in the case of rainfall) to annual, daily resolution being the most common.…”

Characterizing Uncertainty of the Hydrologic Impacts of Climate Change

“…In the water resources sector, this requires defining a range of different climate change 33 scenarios in order to evaluate the vulnerability of infrastructure systems and the effectiveness of 34 different adaptation strategies in managing climate--related stresses [ computational resources permit more sources to be combined, such that model ensemble sizes 50 have grown from a handful of experiments a few decades ago to hundreds of projections now. This 51 plethora of available projections and methodological options is outpacing the ability of the 52 applications community to handle large ensembles and thereby comprehensively characterize 53 uncertainty [Christierson et al 2012]. Furthermore, it is critical to keep the application community 54 engaged and informed to ensure that this plethora of science information can be translated into 55 actionable water resources planning and operational decisions.…”