Peter Chalk Centre

University of Exeter

Stocker Road

Exeter

EX4 4QD

Tel: +44 (0)1392 263637

E-mail: CCWI2019@exeter.ac.uk 

17th International Computing & Control for the Water Industry Conference

1st - 4th September 2019
University of Exeter, UK
  • Facebook Social Icon
  • Twitter Social Icon
  • Instagram

9B Flood modelling & management

Please note the time shown on this page is automatically adjusted by the server according to the time zone set in your computer.

Green

Ian Barker

Chair:

to

Achieving Regional Scale Surface Water Management using Synthetic Stream Networks

James Webber

Presenter:

Authors:

James Webber, Zoran Kapelan and Guangtao Fu

Surface water flooding causes significant damage, disruption and loss of life, both in the UK and globally. The hazard is predicted to worsen in response to the emerging pressures of climate change, urban growth and aging drainage infrastructure, and is consequently prioritised as an area for future research across international strategic planning. Past flood management has been achieved through application of conventional drainage interventions, typically developed using site based design standards. A significant challenge is developing the current site scale implementation of strategies through to integrated regional surface water management, accommodating a range of measures such as conventional strategies, natural flood management (NFM), sustainable drainage systems (SuDS) and flood resilience opportunities, to name a few. This research responds to a need for regional scale surface water management through developing a novel surface water catchment data-set based on a new concept of ‘synthetic stream networks’ (SSN). These are computationally derived flow distributions which map likely overland runoff to delimit the spatial relationships and connections between surface water catchments. This abstract outlines the key methodological advances, applications and limitations of this approach through presenting a case study application across the entire South West UK. The study finds that SSN enables researchers and practitioners to screen the spatial dynamics of regional surface water flooding at an early stage of management and can assist stakeholders in collaborating to design and implement integrated and novel surface water management strategies which advance on the current paradigm of site based management. Particular attention is drawn to the possibility of applying SSN to evaluate catchment hierarchy and design regional management strategies which link urban and rural flood management interventions, developing opportunities for offsetting flooding which may not be apparent when measures are considered independently at the site level.

to

An integrated machine learning approach for visualizing inundation information

Syed Kabir

Presenter:

Authors:

Syed Kabir, Sandhya Patidar and Gareth Pender

This research presents a novel data driven modelling framework that uses rainfall data from meteorological stations to forecast probabilistic flood inundation maps. The research activity focused on the town of Upton-Upon-Severn situated on the west bank of the River Severn, Worcestershire, England. The analysis time frame covers the flooding event of October-November 2000. A Random Forest (RF) model forecasted the upstream boundary conditions which were systematically fed into Multi-layer Perceptron (MLP) classifiers. The classifiers detect states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging (RK) method to produce high resolution probabilistic inundation (e.g. 3m) maps. Separate MLP models were trained for each of the sampled locations using identical model parameters. The RF rainfall-discharge model was trained using observed rainfall-flow data and the MLP classifiers were trained on outputs from a 2D hydrodynamic model (e.g. Flood Modeller). Four synthetic inflow hydrographs were used to run the 2D model with 30m grid resolution in order to generate substantial training data for the classifiers. We then tested the performance of the forecasting model using an observed flood event. Results show that the proposed data centric modelling engine can efficiently simulate the outcomes of the hydrodynamic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. Mean arrival time error was found to be 1h48min while classification accuracies were 90.9%, 99.45%, and 95.8% for flood growing, flood peak and receding period. Compared to current methods applied to real time flood forecasting the proposed model can potentially reduce computational costs while allowing further external information to be added/assimilated.

to

Assessment of a GIS-MCDM approach to hazard, vulnerability and exposure mapping in a city-scale

Priscila Barros Ramalho Alves

Presenter:

Authors:

Priscila Barros Ramalho Alves, Slobodan Djordjevic and Akbar Javadi

More than half of the world population currently lives in urban areas and over 500 cities shelter more than one million people worldwide. While the population growth can be observed in many countries, the degree of problems related to management varies greatly across the world. Often more, people experience issues related to urban growth, changes in climate and deficiencies in water management. This complex context asks for a better cooperation in decision-making process, with an integration of all sectors and parameters, in order to effectively analyse and suggest changes in environments. The decision-making is considered a process that combines different interactions of water resources, with city development activities and climate change. In recent years, there is a trend in applying spatial analysis (Geographic Information System - GIS) and multiple datasets (multi-criteria decision making - MCDM) to better understand those interactions and support decisions in a realistic view. Those tools are applied in both pre and post-phases of management with a range of objectives, that goes beyond the identification of most efficient strategies for mitigation but also to gain more concrete and truthful insights about interrelationships between datasets. This research applies the multi-criteria analysis in a GIS environment for flood hazard, vulnerability and exposure mapping. The study city, Campina Grande – Brazil, is located in a semi-arid region with dry climate and long water scarcity periods but also with a recurrence of floods cases. The research proposes a mixed-method approach with a combination of physical, social and urban indicators, in ArcMap software, to model flooding and vulnerability prone areas and also to assess exposure in different urban development configurations. Results indicate that GIS-MCDM can be extremely useful for decision-making in real water-related applications. This research expects to support the mixed-method approach for spatial integrated analysis of extremes events in urban environments.

to

Functional Properties of StormWater Systems Based on Graph Theory

Sina Hesarkazzazi

Presenter:

Authors:

Sina Hesarkazzazi, Mohsen Hajibabaei and Robert Sitzenfrei

Urban drainage infrastructures are intricately interconnected systems, the deep understanding of which enhances our knowledge towards more efficient functionality under critical conditions. The stormwater networks play a crucial role in collecting and conveying the storm runoff to the outlets (e.g. discharging into receiving water body) in particular and urban flood management in general. One of the influencing factors which can boost the functionality of these systems is to have redundancies (i.e. alternative flow paths) which is in the area of responsibility of the planning engineer and/or design guidelines. In this context, this work aims to systematically analyse the impact of stormwater structures (based on meshedness coefficient) under various scenarios of storm rain events on the functional behaviour of the systems (performance indicators). In order to identify the optimal level of redundancy, different performance indicators using graph measure, describing the topology of the networks, are applied. Reaching to an optimal level of redundancy, results demonstrate that introducing further loops into the system, cannot efficiently mitigate the negative impacts of heavy rainfall events.

to

Building urban flood resilience with rainwater management

Sangaralingam Ahilan

Presenter:

Authors:

Sangaralingam Ahilan, James Webber, Peter Melville-Shreeve and David Butler

Urban stormwater is a significant hazard and a promising resource. Recent studies have highlighted that effective and smart rainwater management provides both flood and drought mitigation benefits through capturing extreme rainfall and contributing to water demands at the property scale, indicating opportunities to upscale benefits across urban areas. However, for stormwater management to reach this potential, planners must move away from ad-hoc and localised application towards integrated catchment-wide strategies, capable of delivering catchment-wide benefits. New planning methodologies are required to achieve this shift and key questions remain regarding how strategies could be applied to maximise flood resilience, supply augmentation and cost-effectiveness across urban scales. This study responds to these emerging challenges through assessing the potential benefits of catchment-scale rainwater management across the Pandon Dene surface water catchment in Newcastle-upon Tyne, NE England.

to