Improving drainage and wastewater management plans and enhance Ofwat compliance under AMP-8
Drainage and Wastewater Management Plans (DWMPs) are central to how water companies plan long-term investment, manage network risk and demonstrate that decisions are based on clear evidence. Under AMP-8, DWMP activity sits alongside major delivery pressure around CSO spill reduction, WINEP, growth, resilience and capital programme delivery.
That creates a practical challenge. Many of these programmes rely on the same baseline models, the same modelling teams and the same experienced reviewers. A change made for one programme can affect another. A baseline update can trigger further checks. A revised growth assumption can alter predicted performance. A new intervention option can change the case for investment elsewhere in the catchment.
Improving DWMPs under AMP-8 therefore depends on more than producing a plan. Water companies need repeatable modelling methods, stronger evidence trails and a clearer way to compare the cost and performance of different options.
Keep baseline models fit for purpose
A DWMP is only as strong as the evidence behind it. If baseline models are outdated, inconsistently maintained or poorly aligned with observed performance, the decisions that follow become harder to defend.
This is especially important for CSO spill prediction. Many historic models were not built with long-duration spill assessment as their main purpose. They may need further work to improve how they represent infiltration, sediment, runoff response, population growth, creep and other factors that affect long-term network behaviour.
Model verification remains an engineering judgement-led process, but auto-calibration can assist and accelerate part of that process. By tuning defined parameters within engineer-approved limits, optimisation-led calibration can help modelling teams reach a fit-for-purpose model faster, without force-fitting results to observed data.
That matters for DWMPs because the same baseline model may need to support multiple planning decisions. Improving model confidence early reduces the risk of repeated rework later.
Standardise model update procedures
DWMPs require models to be updated across multiple catchments, planning horizons and scenario types. These updates may include:
- New development and population growth
- Completed schemes and planned upgrades
- Climate assumptions and future rainfall scenarios
- Asset changes, network constraints and intervention options
When these procedures are handled manually, consistency becomes difficult to maintain. Different teams may use different spreadsheets, scripts, naming conventions or assumptions. Even when each step is technically reasonable, the combined process can become hard to audit and hard to reproduce.
A stronger approach is to standardise repeatable model update procedures. This can include defined methods for applying growth, creep and upgrades, supported by process automation where appropriate. The goal is to reduce repeated manual handling while preserving engineering control over assumptions, limits and review points.
For Ofwat-facing evidence, this consistency is valuable. It helps water companies show how inputs were applied, which assumptions were used, what changed between scenarios and how results were generated.
Test scenarios across the full planning context
DWMPs need to account for uncertainty. Climate change, urban development, asset deterioration and changing rainfall patterns all affect future network performance.
Manual methods often restrict the number of scenarios that can be tested in detail. Long-duration simulations take time to run, and the outputs can be too large for practical human review across every asset and event. As a result, teams may focus on selected events.
That can leave important patterns hidden. A change that improves performance in one rainfall period may perform less well across another. An intervention that solves one issue may create or worsen another downstream. A scheme that appears cost-effective in isolation may perform poorly when tested as part of the wider catchment.
DWMP improvement should therefore include broader scenario testing, supported by automation and structured output analysis. This gives planners a clearer view of how options perform across different futures and helps reduce dependence on narrow evidence.
Compare cost and performance before committing to schemes
One of the biggest risks in DWMP delivery is design churn: A modelling team develops an intervention, the capital or asset planning team reviews the cost, and the option is then passed back for further modelling because the performance gain does not justify the spend.
This is inefficient, especially when teams are already operating under capacity constraints. Optimisation helps by testing costed combinations of interventions systematically. Instead of producing one candidate scheme at a time, teams can compare a range of high-performing options and understand the trade-offs between:
- Capital cost
- Spill reduction
- Flood reduction
- Resilience
- Level of service
- Long-term adaptability
This supports better capital decisions. Decision-makers can see where extra investment produces meaningful benefit, where diminishing returns begin and which options appear consistently in strong designs. That creates a clearer basis for selecting preferred programmes and explaining why a chosen option represents good value.
Improve auditability for regulatory confidence
Ofwat compliance depends on more than the final answer. Water companies need to show how decisions were reached, what evidence was used and why investment choices are defensible.
This is where fragmented modelling approaches create risk. If assumptions sit in spreadsheets, review notes sit in separate documents, model runs sit in different systems and outputs are manually transferred between tools, the audit trail becomes harder to maintain.
A better DWMP process should create structured records of inputs, processes, simulations, outputs and review decisions. This does not remove the need for engineering judgement. It gives that judgement a clearer evidence base.
For AMP-8, this is especially important because water companies are under pressure to demonstrate that planned investment will deliver measurable outcomes. Stronger traceability helps teams respond to internal governance, regulatory scrutiny and future programme review.
Creating stronger DWMP evidence under AMP-8
Improving DWMPs under AMP-8 means building a more reliable, repeatable and evidence-led planning process. Baseline models need to remain fit for purpose. Updates need to be applied consistently. Scenarios need to be tested across realistic planning conditions. Options need to be compared on cost and performance before schemes move forward.
HEEDS supports this approach by helping water companies and consultancies automate repeatable modelling procedures, apply optimisation to calibration and solution development, and produce structured outputs that support auditability. Implemented with STRIDE’s water-sector expertise and API integration, it helps teams improve DWMP evidence, reduce design churn and make stronger investment decisions.
For water companies working under AMP-8, the outcome is a clearer route from model evidence to capital planning: more options assessed, stronger trade-offs understood and greater confidence that selected interventions can be defended.
