The importance of using modelling optimisation technology in CSO spill reduction planning
Using modelling optimisation technology in Combined Sewer Overflow (CSO) spill reduction planning helps water companies identify stronger intervention strategies before time, budget and design assumptions become fixed.
Optimisation gives modelling, asset planning and capital delivery teams a clearer view of which intervention combinations are likely to reduce spills, control cost, protect wider network performance and support auditable AMP-8 decisions.
Why optimisation is important in CSO planning?
Optimisation is valuable during CSO planning because CSO spill reduction is a catchment-level design challenge. Decisions made during the planning stage shape which assets, rainfall periods, constraints, costs and future scenarios are considered. Applying optimisation at this stage enables a more strategic approach to option development, helping teams evaluate interventions across the catchment before progressing into detailed design.
CSO pressure is already visible in national performance data. The Environment Agency’s 2025 monitoring data showed a 35% reduction in monitored spill events and a 48% reduction in total spill duration compared with 2024, but the agency still described the level of sewage entering waterways as unacceptable. Since 1 January 2025, water and sewerage companies in England have also been required to publish storm overflow data in near real time, increasing transparency around spill performance.
Optimisation helps teams move from reactive scheme development to structured option comparison. It enables water companies to take a more strategic view of catchment performance, understand what is achievable before preferred schemes become embedded in delivery plans, and build a stronger evidence base for investment decisions. As projects progress into detailed design, optimisation can then be used to refine shortlisted solutions against technical, operational and cost objectives.
What problem does optimisation solve?
Optimisation reduces the risk of developing individual local solution without understanding the wider catchment impact. A pipe upgrade, storage asset, flow control, SuDS scheme or surface water separation project may improve one CSO while increasing pressure elsewhere or overlooking a more effective intervention elsewhere in the network.
Manual modelling methods are still valuable, but they naturally limit how much of the design space can be explored within available time and resource. In practice, teams may develop one candidate solution, pass it for costing or capital review, and then repeat the process if the benefit does not justify the spend.
Optimisation helps identify the right direction before projects become committed to a particular solution. It supports more strategic decision-making by comparing different intervention themes across the catchment, giving teams greater confidence that they are progressing the most effective approach before refining it through detailed design.
Using optimisation during the planning phase gives teams a stronger view of:
| Planning question | Why it matters early |
| Which CSOs should be improved, monitored or deprioritised? | Prevents efforts being spent on low-impact locations |
| Which interventions are feasible across the catchment? | Sets realistic boundaries before design work expands |
| Which combinations perform well together? | Reduces the risk of isolated fixes creating downstream issues |
| Where do cost-performance trade-offs sit? | Supports earlier conversations with asset planning and capital teams |
| Which options remain useful under future scenarios? | Strengthens DWMP, AMP-8 and longer-term planning evidence |
How does optimisation improve CSO intervention planning?
Optimisation supports more strategic CSO intervention planning by testing intervention combinations against defined objectives and constraints, helping identify the most promising solution before detailed refinement. The goal is to identify strong cost-performance options without relying on repeated manual trial and error. For CSO reduction, relevant intervention types may include:
- Pipe upgrades
- Pump upgrades
- Online or offline storage
- Flow controls
- Real-time control strategies
- SuDS
- Surface water separation
- Strategic diversions
- Operational changes
Each option affects the catchment differently, and its value often depends on what else is changed at the same time. Optimisation-led modelling helps teams search the design space systematically. Efficient algorithms can focus simulation effort on promising areas, helping modellers compare combinations without attempting to run every possible case. This supports earlier identification of no-regrets options, weaker options and areas where a larger bespoke intervention may be required.
What should teams define before optimisation begins?
Teams should define the CSO problem in engineering terms before optimisation begins. Optimisation software can only search within the problem it has been given, so senior modellers and engineers remain essential.
Before running an optimisation study, teams should agree the timespan, target CSOs, spill metrics, wider performance constraints and future scenarios. They should also define feasible intervention types, buildability constraints, cost assumptions, hydraulic dependencies and any assets that need physical survey or further review.
This protects the quality of the results. A poorly defined optimisation study may produce outputs that look precise but do not reflect the practical engineering choices available in the catchment.
Where does model confidence fit into early planning?
Model confidence should be established before CSO intervention options are treated as reliable. A fit-for-purpose baseline model needs to reproduce observed spill frequencies and represent the factors driving those spills, so that proposed interventions can be evaluated with confidence.
Many historic models were developed to satisfy earlier regulatory requirements, where the accuracy required for spill assessment was lower than it is today. Modern CSO assessment places greater emphasis on seasonal variation, annual spill frequency and confidence in root cause. Runoff, rainfall-dependent infiltration and groundwater infiltration can all influence predicted spill behaviour, especially where models have been calibrated against short monitoring periods.
Auto-calibration can support this process, but it should be described carefully. It helps accelerate the calibration part of verification by tuning agreed parameters within engineer-approved limits. It does not replace verification, remove engineering judgement or decide whether a model is fit for purpose.
Building better CSO plans from the start
CSO spill reduction planning benefits from early optimisation because the strongest solutions are rarely single-asset fixes. They are usually combinations of interventions, assessed across cost, performance, risk and future uncertainty.
HEEDS, implemented by STRIDE for the water sector, supports this approach by applying optimisation to CSO intervention planning, automating repeatable modelling procedures and turning large simulation datasets into clear cost-performance evidence. Used early, it helps water companies and consultancies identify stronger options, reduce design churn and build more defensible CSO reduction plans.
