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Atkins/UKWIR

Objectives: The objective of the project is to provide guidance to UK sewerage modellers on how to take account of climate change impacts when designing sewers or assessing the performance of sewer networks. The work was commissioned by UKWIR and Atkins Limited was appointed as the lead research consultants for the project.

  • How were UKCP09 products used?
The Weather Generator was used to provide climate change inputs to two different methods of modelling sewer flooding.
First method (Calculating uplift to design storm):

In this method, the Weather Generator output is used to find an uplift to apply to the design storm. This uplift is specific to a location, emissions scenario and percentile, and is found through either an Annual Maximum approach or a Peaks over Threshold approach.

The Annual Maximum approach uses the annual maximum hourly rainfall in each of the 30 year seasonal series (resulting in 30 values for each of the 100 runs). Return periods can then be calculated for each run in that 30-year period. The 100 calculated return periods can then be used to generate a frequency distribution across the 100 runs, giving an assessment of the uncertainty across the 100 runs. These can then be used to calculate the percentage change in uplift between the control and future.

The Peaks of Threshold approach uses the 30 hours of highest rainfall in each 30 year series (regardless of the year in which they occupy).  Using the return periods analysis, in the same way, these can then be used to calculate the percentage change in uplift between the control and future.

Second method (Generate a new timeseries consistent with future climate):
This approach uses two user-defined metrics to identify a selection of WG runs. For sewer modelling, these metrics are likely to be defined by a general identifier of higher rainfall and of the number of events over a certain value. With two metrics identified for each of the 100 runs, particular runs can be identified to be run through the sewer model using a least squares method.
  • Difficulties & limitations
  • The main problem encountered was running the Weather Generator in the User Interface. The project has 12 case studies and thus required numerous runs of the Weather Generator. This was very time consuming to achieve as only one Weather Generator run can be active on an account at a time, and runs cannot be queued up on a User Interface account.
  • With the number of hourly Weather Generator runs required for the project, this produces a substantial quantity of data. To speed the processing of data, it was necessary to produce a tool that would extract the data from the Weather Generator output zip files for analysis. The other consideration is the necessary server space to save the data to.
  • Lessons learned
  • An important part of the study was understanding how the Weather Generator works, and the sensitivity of the Weather Generator to the different variables that are selected by the user. As part of the study a sensitivity analysis was undertaken to understand the relative importance of the different variables.

  • Using the probabilistic information, and the Weather Generator, will allow users to understand the breadth of the uncertainties considered. This presents challenges in terms of the divergence of the outcomes, but should ultimately better inform investment decisions.
 
  • How will the results be communicated to the target audience?
  • The final report is available to purchase through the UKWIR website. In addition to the guidance and case study results, the final report includes the sensitivity analysis of the different Weather Generator variables and an analysis of the limitations of Weather Generator performance in terms of return period estimation and at different rainfall durations.

  • It is also intended to publish an academic paper on the methodology and results in due course.
 
  • Contact details: Graydon Jeal, Atkins Limited, Surrey & Carol Ham, UKWIR – contact for purchasing the full report.