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Digital Surface Flood Warning System with Multi-Sensor Network

Solution Overview

Manchester City Council has committed to investing in remote monitoring systems at known flooding sites to enable a more effective response.[5] The Digital Surface Flood Warning System (DSFWS) will integrate data from existing river gauges, new low-cost sensors, weather radar, and social media to provide more accurate, timely, and localised flood warnings.[1] This approach addresses the critical need for enhanced flood prediction and response capabilities in areas vulnerable to surface water flooding.

Technical Components

The system architecture includes procurement and installation of a network of new, low-cost sensors for monitoring river levels, soil moisture, and surface water.[1] The project will focus on surface water level monitoring using new bollard sensors and rainfall, soil moisture and gully sensors.[3] These compact, battery-powered sensors collect data continuously, transmitting it via a mobile data network to a cloud-based platform.[6] The networks are low energy and low-frequency allowing the transfer of data from remote, battery-powered sensors to user-friendly dashboards.[3] The network of sensors would transmit real-time surface water levels to a central hub and create an early warning system of potential flooding.[3] Data from the Environment Agency and the Met Office will be accessed through existing data sharing agreements.[1] The system will use machine learning algorithms to continuously improve its predictive capabilities, learning from each new event.[1] Development of a user-friendly interface (dashboard) for flood incident managers will provide real-time data visualisation and decision support tools.[1] The scope also includes development of a public-facing web portal and mobile app for disseminating flood warnings and advice.[1] The core data platform and forecasting models will be developed by a blended team of LCC staff and specialists from the University of Leeds, building on the research and development work already undertaken through the iCASP project.[1]

Implementation Details

The total estimated cost for the development and implementation of the DSFWS is £500,000 over two years.[1] The DSFWS capital costs include hardware (sensors, servers) at £150,000, software development at £250,000, and project management and contingency at £100,000.[1] Ongoing operational costs are estimated at £25,000 per annum.[1] The ongoing operational cost of £25,000 per year includes staff time for system maintenance and operation at £15,000, data hosting and communication costs at £5,000, and sensor maintenance and replacement at £5,000.[1] The scope includes a pilot implementation of the system in a sub-catchment of the River Aire to test and refine its performance.[1] The DSFWS will provide improved warnings for an area containing 5,000 residential properties and 1,000 commercial properties at risk of flooding.[1] A post-project evaluation will be carried out 12 months after the system becomes fully operational to assess the extent to which the project has achieved its objectives and delivered its intended benefits.[1] A further evaluation will be conducted after five years to assess the long-term impacts and benefits of the system.[1]

Comparable projects demonstrate the feasibility of large-scale sensor deployment. United Utilities is planning to install over 20,000 sensors at key points on the sewer pipes across the North West region, providing live information back to teams so they can monitor, identify and resolve any issues quickly.[2] The plan is to have all 20,000 sensors installed by the end of 2022.[2] The sensor installation normally takes just a few hours with no digging or noisy machinery required.[2] Ongoing maintenance of the sensors involves simple straightforward checks inside the manhole once a year or less in some cases to ensure the sensor is working as it should.[2] Plans are underway to create a network of sensors across West Yorkshire to give people earlier warnings about the risk of surface water flooding following a successful bid for £97,000.[3]

Technical implementations in other contexts provide additional insights. The Community Flood Telemetry project developed a low-cost, open-source telemetry system that could be built, installed, and maintained by community flood action groups to monitor local water levels, with a total cost of approximately £115 per monitoring station.[4] The Community Flood Telemetry system is based on a Raspberry Pi single-board computer, connected to an ultrasonic sensor to measure water level and a camera to provide visual verification, with a 3G dongle used to transmit data to a central server.[4] The Community Flood Telemetry system has an ongoing cost for a data-only SIM card for the 3G dongle, which is typically around £5-£10 per month depending on the network provider and data allowance.[4] A public-facing website (www.communityfloodtelemetry.co.uk) was developed to display the data in near real-time, showing the latest water level and image from each monitoring station, as well as interactive graphs of historical data.[4] The main challenges encountered with the Community Flood Telemetry system were related to power supply, with the system currently requiring a mains power source, and intermittent mobile signal in some locations.[4] Recommendations for future work include exploring alternative power sources such as solar, investigating more robust sensor technologies, developing a mobile app with alert functionalities, and expanding the network to more at-risk communities.[4]

Metasphere's Sense Level technology, a radar-based level sensor, was selected to monitor water levels within key components of the Mansfield SuDS scheme including downpipe planters, raingardens, bioswales, detention basins and specific points within the sewer network.[6] The real-time water level data captured by Metasphere's monitoring solutions enables accurate calculation of drainage volumes and supports sophisticated hydrological analysis and modelling, providing a deeper understanding of the system's behaviour under varying conditions.[6]

Benefits and Impacts

The project aims to increase the lead time for flood warnings from 2 hours to 6 hours within 2 years of implementation for a 1-in-100-year flood event.[1] The project aims to achieve a 25% reduction in the number of false alarms and missed warnings within 3 years of implementation.[1] The project aims to reduce annual average damages from flooding in the target area by 10% over the 10-year appraisal period.[1] The improved warning system is expected to lead to a 10% reduction in damages, based on evidence from previous studies.[1] The annual average benefit is calculated as £775,000 per year based on avoided damages to residential and commercial properties.[1]

Operational efficiency improvements include a 20% reduction in the time taken to deploy temporary flood barriers within 2 years of implementation.[1] The project aims to increase public awareness by raising the percentage of residents in at-risk areas signed up to receive flood warnings from 50% to 75% within 2 years of implementation.[1] The sensors help United Utilities reduce the risk of flooding or pollution from the pipework by discovering potential issues in the sewers before they become a problem.[2]

User experience data from comparable systems indicates significant value in visual monitoring capabilities. Community members reported that the camera image was consistently highlighted as the most useful feature of the system, providing a richer and more intuitive understanding of the situation than numerical data alone and allowing them to spot debris and potential blockages.[4] The most requested feature from community users was the ability to set custom alert thresholds to receive email or text message notifications when water levels reach pre-defined points.[4]

Climate Adaptation Relevance

This monitoring infrastructure directly addresses the increasing frequency and intensity of surface water flooding events driven by climate change. Enhanced early warning capabilities enable communities and emergency responders to take protective actions before flood conditions develop, reducing both property damage and risks to public safety. The system's integration of multiple data sources and machine learning capabilities allows for adaptive improvement as precipitation patterns continue to shift under changing climate conditions.

Business Analysis

The Benefit-Cost Ratio (BCR) for the project is estimated at 4.1, with a Net Present Value (NPV) of £1.55 million over a 10-year period.[1] The payback period for the initial investment is within 3 years of the system becoming operational.[1] Sensitivity analysis demonstrates project robustness across varying scenarios. A 20% increase in project costs would reduce the BCR to 3.4.[1] A 20% decrease in project benefits would reduce the BCR to 3.3.[1] If the damage reduction is only 5% instead of 10%, the BCR falls to 2.05.[1] These financial metrics indicate strong economic viability even under conservative assumptions, supporting the case for municipal investment in flood monitoring infrastructure as a cost-effective climate adaptation measure.

[1]: DSFWS Project Documentation [2]: United Utilities Sensor Installation Programme [3]: West Yorkshire Surface Water Flooding Sensor Network [4]: Community Flood Telemetry Project Report [5]: Manchester City Council Climate Adaptation Strategy [6]: Metasphere Mansfield SuDS Monitoring Case Study

Sources