Deliver Catchment Results with Confidence.

We use HEEDS to coordinate the automation of model updates across creep, growth, and intervention layers. Each update process is modular, traceable, and designed for consistent application across 5-, 10-, and 25-year planning periods. Our system integrates and executes scripts written in Python, Ruby, and Excel, resolving interdependencies and eliminating repetitive tasks. This creates a standardised, auditable update process that can be rapidly deployed across catchments, regardless of existing tooling or data structure.

We provide consultancy support to implement and adapt auto-calibration methods to specific modelling contexts, helping clients structure their input data, define verification targets, and interpret optimisation results. Our engineers work alongside in-house teams to build confidence in the approach and tailor it to different project types, from dry weather verification to long-term spill alignment. Where needed, we assist in integrating these methods into existing modelling workflows, offering a pathway to standardise and scale verification activity across programmes.

We use automated sensitivity analysis to help clients prioritise SuDS, storage, pipe upgrades, or other interventions across catchments. The method systematically tests thousands of design combinations to reveal which locations consistently deliver flood or spill reduction, and which ones only perform well in specific contexts. This allows engineers to focus design effort on robust, cost-effective interventions and deprioritise those with only conditional benefit.

To support this, we use three complementary analysis perspectives:

• Initial impact – measures how effective an option is when implemented on its own.
• Remaining value – reveals whether an option still provides benefit after other interventions have been applied.
  Consistent contribution – identifies options that perform well across a wide range of combinations and scenarios.

These perspectives give planners the tools to make rapid, evidence-based decisions. Outputs are presented as ranked lists and spatial heatmaps, enabling clear communication of complex results. Although SuDS is a common use case, the method works equally well for any upgrade option and provides a scalable foundation for screening interventions across portfolios.

We support the high-level stages of DWMP delivery, specifically Option Development and Appraisal (ODA) and Investment Planning and Prioritisation (IPP), through automated multi-objective optimisation. This strategic approach explores large-scale upgrade combinations across entire catchments, using design storms and generic intervention sets to rapidly identify effective planning themes. Each option is tested under multiple climate, growth, and performance scenarios to uncover trade-offs between cost and outcome. The methodology highlights “no regrets” upgrades and reveals how strategic interventions perform across time horizons. Structured proformas and automated simulation workflows ensure consistency and repeatability, with outputs presented as ranked designs, upgrade heatmaps, and interactive visualisations. This helps planners construct adaptive investment pathways and justify their decisions with confidence. It supports transparent regulatory submissions and produces robust, scalable results suitable for long-term planning.

For near-term delivery, typically within the current or upcoming AMP period, we support mid-level optimisation of solution strategies with a focus on detailed, catchment-wide implementation. These studies often centre around CSO spill reduction and make use of Time Series Rainfall (TSR) simulations rather than design storms. Our approach incorporates bespoke interventions such as local SuDS, RTC schemes, or network reconfigurations and applies multi-objective optimisation to balance performance and cost across a realistic solution space. Outputs include ranked options, sensitivity analysis, and prioritised interventions, supporting investment decisions that are ready for internal business case approval or regulatory review. This fills the gap between high-level strategy and final design.

We support detailed optimisation to refine the solution into a deliverable intervention. This work often focuses on tuning existing infrastructure to extract maximum performance at low cost. We use optimisation to fine-tune settings such as pump control rules, weir levels, or flow splits, especially where real-time control (RTC) or underutilised storage assets exist. These smart interventions are often suitable for near-term implementation within the next 1 to 2 years. The process ensures that designs meet all performance constraints while balancing cost and operational feasibility. Rather than starting from scratch, we build on existing solutions and add detail and confidence, helping clients move from concept to construction with greater speed and certainty.

We offer a practical method for developing adaptive investment pathways that balance early action with long-term flexibility. Our process uses strategic optimisation across multiple time horizons and scenarios to identify robust upgrade themes, then constructs pathways by comparing which designs remain effective under different futures. Users can quickly see which interventions are beneficial across all cases, and which ones are only relevant under certain assumptions. Interactive planning tools make it easy to visualise risks, explore trade-offs, and test adaptive trigger points. This avoids stranded assets and supports long-term plans that are both efficient and resilient, as required by regulators and investors alike.