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Decision Patterns

Proven strategies for solving water infrastructure challenges — what works, what it costs, and where it has been tried.

What is a decision pattern? A decision pattern is a proven approach to a common water infrastructure challenge. Think of it as a playbook entry: “when you face this problem, here is a strategy that has worked elsewhere, along with the infrastructure it requires, how you monitor it, and the trade-offs to consider.”

Each pattern is linked to use cases (the operational scenarios where it applies) and case studies (real-world examples of it in action). Together, these three layers form the Water Decisions knowledge framework.

How to read this page: Patterns are grouped into categories. Each card shows the problem it solves and a brief description of how it works. Click any card to see the full detail page with infrastructure, monitoring, strengths, trade-offs, and linked case studies.
17
Patterns
4
Categories
15
Use Cases
22
Case Studies

Flooding & Sewage

Strategies for managing floods and sewage overflows — from barriers and tunnels to natural floodplains.

Water Supply

Approaches to securing reliable water supply through diversification, storage, and alternative sources.

Demand & Conservation

Ways to reduce water demand and extend supply through behaviour change and community management.

Monitoring & Data

Sensor networks, data systems, and forecasting tools that underpin informed decision-making.

Continuous monitoring networks
Problem: Without continuous data, operators only discover problems after damage has occurred. Intermittent sampling misses short-lived events like pollution spills or pressure drops.
How it works: Networks of sensors installed across the water system transmit readings in real time to a central control room, enabling immediate detection and response.
Hydrological monitoring networks
Problem: River levels and flows change rapidly during storms. Without continuous measurement, flood warnings come too late and water allocation decisions are based on guesswork.
How it works: Gauging stations along rivers measure water levels and flow rates continuously, feeding data to forecasting models and warning systems.
Predictive modelling systems
Problem: Reacting to events after they happen costs lives and money. Decision-makers need advance warning of floods, droughts, and system failures.
How it works: Computer models combine real-time sensor data with weather forecasts and historical patterns to predict what will happen hours or days ahead, giving time to ...
Smart monitoring networks
Problem: Traditional manual inspections and periodic sampling miss problems between visits. Large networks with thousands of assets cannot be physically checked frequently enough.
How it works: Internet-connected sensors across the water network continuously measure pressure, flow, quality, and other parameters. Data is analysed centrally to detect ...
Water quality monitoring networks
Problem: Pollution from agriculture, industry, and sewage can make water unsafe for drinking, bathing, or ecosystems. Without monitoring, contamination goes undetected.
How it works: Networks of sampling points and automated sensors track parameters like dissolved oxygen, nutrients, heavy metals, and bacteria across rivers, lakes, and coa...

Governance & Planning

Frameworks for coordinating water management across regions, agencies, and competing interests.