Robust and transparent planning and operation of water resource infrastructure
Lead Research Organisation:
University of Bristol
Department Name: Civil Engineering
Abstract
Ensuring a reliable and safe supply of water is essential for the socioeconomic and environmental sustainability of our society. In the UK, several water companies are responsible for supplying clean water to industrial and domestic users in different parts of the country. Water companies need to estimate what the water demand and the available resource will be in the future (typically over a 25-years ahead period) so to be able to plan infrastructure development (for example, building a new reservoir) or changes in their management (for example, reducing or increasing river abstractions that feed into an existing reservoir) wherever they anticipate a gap between demand and supply.
Making decisions is becoming increasingly complex in the fast-changing world we live in. On the supply side, extreme events such as floods and droughts are becoming more frequent and unpredictable under the combined effect of climate and land-use change. On the demand side, water demand is also becoming more variable due to changes in population density and distribution, changing life-style and socioeconomic conditions, and technological developments (for example, the introduction of smart water meters), which all together may affect water consumption in different ways in different places.
To tackle all these complexities, the water industry needs to adopt innovative, flexible and adaptive planning and management solutions, which will increase the efficiency and resilience of water systems while avoiding raising costs. Mathematical models can provide a vital contribution to this end. By reproducing the behavior of the main components of a water resource system (such as reservoirs, pumping stations, treatment plants, etc.) and their connections among each other and with the natural environment, mathematical models enable water practitioners to predict the key system variables (for example, the future storage levels in a reservoir, the amount of energy consumed for pumping, the supply rate of clean water to a group of domestic users) and to simulate the system response under different infrastructural/management scenarios.
The use of mathematical models in the water industry has increased in recent years, however their adoption is still relatively limited with respect to their potential. A key challenge water resource practitioners face is in recognising the uncertainty and errors that unavoidably affect all model predictions while still extracting useful information from them. A great opportunity that they are offered today, is to extract more and more useful information from fast growing sensing and computing technology, for example satellite data, smart sensors and high-performance computers. In this research project, I aim to tackle the uncertainty challenge and take the IT opportunity to develop the next-generation modelling tools that will support more sustainable water resource management in the UK.
This project will develop mathematical methods and software tools to assist water system managers in their day-to-day decisions (for example, how much water to abstract from a river or a reservoir, how much water to pump to a treatment plant, etc.) as well as long-term decisions (for example, whether to build a new reservoir or connect existing ones) by finding "low-regret" solutions that would prove effective across a range of possible futures. All methods will be developed and tested on case study applications provided by water companies, so to ensure that they are actually valuable to address the most urgent issues they face, and they will be implemented in open-source software packages so that also other water practitioners besides those directly involved in the project will benefit from its findings and outputs.
Making decisions is becoming increasingly complex in the fast-changing world we live in. On the supply side, extreme events such as floods and droughts are becoming more frequent and unpredictable under the combined effect of climate and land-use change. On the demand side, water demand is also becoming more variable due to changes in population density and distribution, changing life-style and socioeconomic conditions, and technological developments (for example, the introduction of smart water meters), which all together may affect water consumption in different ways in different places.
To tackle all these complexities, the water industry needs to adopt innovative, flexible and adaptive planning and management solutions, which will increase the efficiency and resilience of water systems while avoiding raising costs. Mathematical models can provide a vital contribution to this end. By reproducing the behavior of the main components of a water resource system (such as reservoirs, pumping stations, treatment plants, etc.) and their connections among each other and with the natural environment, mathematical models enable water practitioners to predict the key system variables (for example, the future storage levels in a reservoir, the amount of energy consumed for pumping, the supply rate of clean water to a group of domestic users) and to simulate the system response under different infrastructural/management scenarios.
The use of mathematical models in the water industry has increased in recent years, however their adoption is still relatively limited with respect to their potential. A key challenge water resource practitioners face is in recognising the uncertainty and errors that unavoidably affect all model predictions while still extracting useful information from them. A great opportunity that they are offered today, is to extract more and more useful information from fast growing sensing and computing technology, for example satellite data, smart sensors and high-performance computers. In this research project, I aim to tackle the uncertainty challenge and take the IT opportunity to develop the next-generation modelling tools that will support more sustainable water resource management in the UK.
This project will develop mathematical methods and software tools to assist water system managers in their day-to-day decisions (for example, how much water to abstract from a river or a reservoir, how much water to pump to a treatment plant, etc.) as well as long-term decisions (for example, whether to build a new reservoir or connect existing ones) by finding "low-regret" solutions that would prove effective across a range of possible futures. All methods will be developed and tested on case study applications provided by water companies, so to ensure that they are actually valuable to address the most urgent issues they face, and they will be implemented in open-source software packages so that also other water practitioners besides those directly involved in the project will benefit from its findings and outputs.
Planned Impact
The project will benefit all the organisations responsible for, and contributing to, sustainable water management in the UK:
- Water companies will gain new methods and software tools to improve their operational and planning practice. This will bring reduction in operation costs (e.g. from reduced pumping) and more cost-effective investment decisions (e.g. finding cheaper alternatives to building new reservoirs).
- Consultancy companies and organisations (such as project partners HR Wallingford and JBA Trust). They will gain a range of up-to-date research methods that will enhance their capacities. Consultancies will play a dual role of both beneficiaries and indirect contributors to delivering impacts. In fact, by picking up the project's research outputs (knowledge and methods) and embedding them in their practice, they will also contribute to accelerate their uptake by industry.
- Governmental and intergovernmental organization that act as providers of models, data or forecast product to the water industry, such as the UK MetOffice or the European Centre for Medium-Range Weather Forecasts (ECMWF). By developing operational tools that integrate their forecast products, the project will help these organisations to demonstrate the value of their models for supporting operational objectives and to set priorities for their improvement.
- Regulators (Environment Agency and OFWAT). They will benefit from the project since it will ensure that the most advanced scientific knowledge and methods are exploited to help achieving the national targets for the protection of the natural environment, while avoiding raising costs by help identifying cost-effective and "low-regret" solutions.
All the above advances will, in turn, benefit the UK society by increasing the resiliency of water supply systems, reducing adverse impacts on the natural environment and reducing costs for water companies' costumers.
- Water companies will gain new methods and software tools to improve their operational and planning practice. This will bring reduction in operation costs (e.g. from reduced pumping) and more cost-effective investment decisions (e.g. finding cheaper alternatives to building new reservoirs).
- Consultancy companies and organisations (such as project partners HR Wallingford and JBA Trust). They will gain a range of up-to-date research methods that will enhance their capacities. Consultancies will play a dual role of both beneficiaries and indirect contributors to delivering impacts. In fact, by picking up the project's research outputs (knowledge and methods) and embedding them in their practice, they will also contribute to accelerate their uptake by industry.
- Governmental and intergovernmental organization that act as providers of models, data or forecast product to the water industry, such as the UK MetOffice or the European Centre for Medium-Range Weather Forecasts (ECMWF). By developing operational tools that integrate their forecast products, the project will help these organisations to demonstrate the value of their models for supporting operational objectives and to set priorities for their improvement.
- Regulators (Environment Agency and OFWAT). They will benefit from the project since it will ensure that the most advanced scientific knowledge and methods are exploited to help achieving the national targets for the protection of the natural environment, while avoiding raising costs by help identifying cost-effective and "low-regret" solutions.
All the above advances will, in turn, benefit the UK society by increasing the resiliency of water supply systems, reducing adverse impacts on the natural environment and reducing costs for water companies' costumers.
People |
ORCID iD |
Francesca Pianosi (Principal Investigator / Fellow) |
Publications
Bozzolan E
(2023)
A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.
in The Science of the total environment
Bozzolan E
(2020)
Including informal housing in slope stability analysis - an application to a data-scarce location in the humid tropics
in Natural Hazards and Earth System Sciences
Dobson B
(2019)
How Important Are Model Structural and Contextual Uncertainties when Estimating the Optimized Performance of Water Resource Systems?
in Water Resources Research
Dobson B
(2019)
An argument-driven classification and comparison of reservoir operation optimization methods
in Advances in Water Resources
Kupzig J
(2023)
Towards parameter estimation in global hydrological models
in Environmental Research Letters
Lee Y
(2023)
Catchment-scale skill assessment of seasonal precipitation forecasts across South Korea
in International Journal of Climatology
Noacco V
(2019)
Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox.
in MethodsX
Ozturk U
(2022)
How climate change and unplanned urban sprawl bring more landslides.
in Nature
Peñuela A
(2021)
An open-source package with interactive Jupyter Notebooks to enhance the accessibility of reservoir operations simulation and optimisation
in Environmental Modelling & Software
Peñuela A
(2020)
Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK
in Hydrology and Earth System Sciences
Pianosi F
(2018)
Distribution-based sensitivity analysis from a generic input-output sample
in Environmental Modelling & Software
Pianosi F
(2020)
How successfully is open-source research software adopted? Results and implications of surveying the users of a sensitivity analysis toolbox
in Environmental Modelling & Software
Pianosi F
(2020)
Use of Reservoir Operation Optimization Methods in Practice: Insights from a Survey of Water Resource Managers
in Journal of Water Resources Planning and Management
Rougé C
(2023)
Forecast Families: A New Method to Systematically Evaluate the Benefits of Improving the Skill of an Existing Forecast
in Journal of Water Resources Planning and Management
Salwey S
(2023)
National-Scale Detection of Reservoir Impacts Through Hydrological Signatures
in Water Resources Research
Sarailidis G
(2023)
Integrating scientific knowledge into machine learning using interactive decision trees
in Computers & Geosciences
Sarrazin F
(2018)
V2Karst V1.1: a parsimonious large-scale integrated vegetation-recharge model to simulate the impact of climate and land cover change in karst regions
in Geoscientific Model Development
Wagener T
(2022)
On the evaluation of climate change impact models
in WIREs Climate Change
Wagener T
(2019)
What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling
in Earth-Science Reviews
Wagener T
(2021)
On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation
in WIREs Water
Wang A
(2020)
Technical Report-Methods: A Diagnostic Approach to Analyze the Direction of Change in Model Outputs Based on Global Variations in the Model Inputs
in Water Resources Research
Description | We carried out a survey of water managers across the UK and identified barriers to the uptake of mathematical optimisation models for more effective water management. We found a key issue is the lack of transparency of many models used in the sector and the difficulty in understanding and implementing new methods in practice. We developed an open-source software for reservoir simulation and optimisation (iRONS) using advances in literature programming tools to improve model documentation and foster knowledge transfer. We demonstrated the value of using mathematical optimisation models (and the iRONS software) to make best use of increasingly available numerical weather forecasts and improve water systems operations for drought management. We co-developed the application with project partner Wessex Water on their pumped-storage reservoir system for water supply. We found that using hydro-meteorological forecasts in a model optimisation framework can improve system resilience while reducing operations costs, however these benefits can only be achieved if uncertainty in forecasts is explicitly quantified and accounted for. Future research should aim at scaling up these findings by investigating how the relationship between forecast skill (that is, its ability to anticipate future hydro-meteorological conditions) and forecast value (the usefulness of forecasts to make more effective decisions) change across different places and management problems. This will further help water managers to understand when and where investing on more sophisticate modelling capability will deliver the greatest benefits for improved management of drought events. We further developed the open-source SAFE toolbox for better handling of uncertainty in mathematical models and contributed new statistical methods to quantify uncertainty in model outputs and attribute it to key uncertainty sources. We demonstrated how these new methods can be used to identify long-term drivers of system vulnerabilities (such as climate or land use change) for the assessment of current and future natural hazards such as droughts, landslides, and floods. We trained hundreds of early-career scientists and practitioners on the use of the SAFE toolbox and underpinning methodologies through seminars/workshops for specific companies in the water and environment sector (incl. Atkins, HR Wallingford, JBA-Risk Management) and insurance (incl. Aon, Axa XL, Munich Re), and national and international summer schools and short-courses (incl. for the European Geosciences Union, the Young Hydrology Society, and the University of Bristol, Southampton, Exeter, Freiburg, Dresden, Cordoba). As of February 2023, the SAFE toolbox has been downloaded by over 4000 researchers and practitioners across the world. |
Exploitation Route | Our software package for reservoir simulation and optimisation (iRONS) is publicly available for others to use and adapt to their needs. Beyond the water resource systems modelling community for which the software and underpinning methods were originally proposed, iRONS will benefit the hydrological modelling community in its efforts to improve the representation of human interventions on the hydrological cycle, by providing a suite of well documented functions that can be easily integrated into hydrological models to better represent the effects of water infrastructure on river basins. Our work on advancing methods and tools (SAFE software) for handling uncertainty in mathematical models is and will be beneficial for a range of modelling communities to enhance model construction and use for decision-making under uncertainty. One particularly interesting direction for future research and application is to use SAFE and the underpinning methodology to scrutinise model behaviour and therefore improve model validation beyond the 'fit-to-data' criterion. This will be particularly beneficial for climate change impacts assessment, where evaluating models on their ability to reproduce historical data is insufficient given that past records may not be representative of conditions that will be experienced in the future. |
Sectors | Digital/Communication/Information Technologies (including Software) Environment |
Description | The methodology delivered in this project has helped model developers and users in the environmental sectors to improve the way they construct, evaluate, and use models under uncertainty. The SAFE toolbox implementing this methodology is used at many public and private research and consultancy organisations (including the British Geological Survey, the UK Centre for Ecology and Hydrology, HR Wallingford, Atkins, the US Geologic Survey, the Environmental Protection Agency, NASA) for faster and cheaper model development and more rigorous and defensible model validation. The knowledge generated through our own research and systematic literature review has informed the Environment Agency to identify priorities for future research and development for floods and droughts management. Specifically, we have helped to identify key sources of uncertainty in the economic assessment of future flood and coastal erosion risk management, and opportunities to improve future analysis; to review the management of uncertainty in flood risk models as part of the Environment Agency's Flood hydrology roadmap; to review data and models in use in the UK water sector for drought management and identify key gaps in knowledge and capability. |
First Year Of Impact | 2018 |
Sector | Environment |
Impact Types | Societal Economic Policy & public services |
Description | National Review on current scientific knowledge about drought in the UK |
Geographic Reach | National |
Policy Influence Type | Contribution to a national consultation/review |
URL | https://www.gov.uk/government/publications/review-of-the-research-and-scientific-understanding-of-dr... |
Description | Knowledge Transfer Partnership |
Amount | £113,263 (GBP) |
Funding ID | 13266 |
Organisation | Innovate UK |
Sector | Public |
Country | United Kingdom |
Start | 06/2022 |
End | 01/2025 |
Description | Uncertainty quantification and sensitivity analysis for resilient infrastructure systems |
Amount | £139,486 (GBP) |
Funding ID | ST/Y003713/1 |
Organisation | Science and Technologies Facilities Council (STFC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2023 |
End | 03/2025 |
Description | Water Management and Adaption based on Watershed Digital Twins |
Amount | £95,193 (GBP) |
Funding ID | EP/Y036999/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2024 |
End | 02/2027 |
Title | Towards Parameter Estimation in Global Hydrological Models |
Description | The provided elementary effects are used in the publication J. Kupzig, R. Reinecke, F. Pianosi, M.Flörke and T. Wagener: Towards Parameter Estimation in Global Hydrological Models (submitted to Environmental Research Letters in Feb 2023). In a large sample study, the Morris Method (Morris 1991) application produces the provided elementary effects using a new lightweight version of the global hydrological model WaterGAP3: WaterGAPLite. elementary_effects.zip : elementary effects for all 50 trajectories and all basins (each trajectory is the result of 18 model runs; used bounds of parameters can be found in the Supplement of the manuscript) results_overview.xlsx: parameter ranks for each basin and different evaluation criteria based on the elementary effects. MC_Sample.csv: normalized parameter samples of the additional Monte-Carlo Simulation (used bounds of parameters are the same as for the Morris method) MC_NSE.csv: resulting NSE values of the Monte-Carlo simulation better_performing_basins.csv: list of basins (using GRDC no.) where minimal NSE is greater than -1 within all Monte-Carlo runs standard_calib.csv: calibrated gamma value for each basin and corresponding evaluation criteria, using the standard calibration for WaterGAP3 (fit to mean discharge) standard_calib_mod.csv: calibrated gamma value for each basin and corresponding evaluation criteria, using a modified version of the standard calibration for WaterGAP3 (maximizing the NSE) |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://zenodo.org/record/7585481 |
Title | SAFE Toolbox - python version with interactive Jupyter Notebooks |
Description | The Sensitivity Analysis For Everybody (SAFE) toolbox is a software that supports the robust and efficient construction and application of mathematical models. It provides a range of statistical tools and visual analytics to characterise and manage model uncertainties. It implements a range of Global Sensitivity Analysis methods for the quantification and attribution of uncertainty in mathematical models. The Toolbox was first developed in 2015 in Matlab and R. Besides continuously maintaining and improving these two versions, we have also developed a python version, which enables easier integration with python-based modelling software used in the water sector. We have also developed a set of interactive Jupyter Notebooks that provide accessible examples of how sensitivity analysis can enhance the construction, evaluation and use of mathematical models for decision-making. |
Type Of Technology | Software |
Year Produced | 2020 |
Open Source License? | Yes |
Impact | SAFE has been downloaded by over 3000 researchers across different fields of engineering and science, who use SAFE for more efficient model development and more rigorous and defensible model validation. PI Pianosi has trained hundreds of early-career researchers on the use of SAFE and underpinning methodology through short courses at EPSRC Centre for Doctoral Training, international conferences such as the European Geoscience Union (EGU) General Assembly, and international Summer Schools. The python version has been licensed to several environmental consultants and water companies in the UK for testing its use in support of the implementation of risk-based approaches to water resource management. |
URL | https://safetoolbox.github.io/ |
Title | iRONS (interactive Reservoir Operation Notebooks and Software) |
Description | iRONS is a Python package that enables the simulation, forecasting and optimisation of reservoir systems. iRONS includes a set of Python functions implementing typical reservoir modelling tasks, such as: estimating inflows to a reservoir, simulating operator decisions, closing the reservoir mass balance equation - in the context of both short-term forecasting and long-term predictions. The package also includes a set of interactive Jupyter Notebooks that demonstrate its key functionalities through practical examples, and that can be run in the Jupyter environment either locally or remotely via a web browser. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | iRONS has been co-developed with water resource managers at Wessex Water and tested for use in support of the management of their reservoir pumped-storage system. It is currently being tested for use in support of reservoir management decisions in South Korea through a collaboration with the national water agency KWater. The software is meant to be equally accessible for practice and research purposes. The paper introducing the software in the scientific literature has only been published in November 2021 but we expect iRONS to be also used for research and training purposes. |
URL | https://ironstoolbox.github.io/ |