WREW Water Resource System Model of England and Wales

• Model code: nismod/water_supply (not public)
• Key reference: Dobson, B., Coxon, G., Freer, J., Gavin, H., Mortazavi‐Naeini, M., & Hall, J. W. (2020). The spatial dynamics of droughts and water scarcity in England and Wales. Water Resources Research, 56, e2020WR027187. https://doi.org/10.1029/2020WR027187

Details of model inputs, parameters and outputs:

• Water supply
• Water demand

Notes on data sources:

• Dataset metadata, below

Description

A water resource system model of England and Wales (WREW hereafter) has been developed. [1] It includes all major water supply assets (reservoirs, boreholes, transfers, water treatment works, pumped storage, desalination plants and river abstraction points) that are connected into England and Wales’s wider water network via any river or transfer of significance (i.e. > 2Ml/d). This includes more than 90% of England and Wales’s population and water demand, and more than 80% of their combined land area.

WREW is the product of an extensive collaboration led by the University of Oxford between a range of stakeholders: England and Wales’s environmental agencies, UK-based water consultancies, the Water UK council, and all of England and Wales’s water supply companies. The water system formulation in the model is based on communications with, and datasets provided by, the above stakeholders. This formulation includes: pipe capacities, treatment works capacities, reservoir capacities, abstraction and operational licence conditions, operational preferences, control curves, system connectivity, and asset locations where necessary (e.g. for river abstractions or boreholes). Beyond its use for this research, WREW will become a key tool for England’s Environment Agency (EA) that can provide them with a model-based national perspective on droughts, policy reform and infrastructure planning.

WREW is simulated at a daily time-step using the WATHNET water resource simulation software. [2] Every time-step, WATHNET solves a mass balance optimization problem that allocates water between model nodes, via arcs, under both constraints inherent to mass balance (e.g. nonzero flows and storages) and constraints set out by the water system’s formulation (e.g. pipe capacities and minimum required river flows). The solver minimizes a set costs associated with each model arc, performed by Network Linear Programming. These costs do not represent literal economic costs but are instead used to direct the model’s behaviour according to operator preferences. For example, if one source is preferable to another its cost is set lower than the other, if one is preferable during summer and one during winter the arc costs are updated to reflect this. Arcs and nodes have their own scripts for which custom rules can be set, allowing incredibly detailed implementation of operator preferences and complex licences. To enable the solver to cope with this high level of customisation, which may introduce non-linearity or discontinuity, it is run repeatedly every time-step to enable navigation of the decision space. WATHNET is also highly efficient in its simulation; WREW contains 1252 nodes and 1756 arcs yet one year of simulation at daily timestep takes around only 2 minutes on a 3.6GHz processor. For context, the similar sized CALVIN water resource simulation model runs at around 10 minutes per year at a monthly timestep on a 2 GHz PC [3] (we note this is for context and not comparison since CALVIN’s simulation philosophy is inherently different; it is a perfect foresight optimization model that represents operation as a release sequence as defined in Dobson et al. (2019). [4]

As one would expect from any national scale water resource simulation model, a range of assumptions (beyond those described in the following sections) have gone into its creation. These can be separated into modelling assumptions that have been informed by water company instruction/practice, and assumptions that are primarily the result of data/information availability or the scope of work.

Company informed assumptions include:

• Aggregation of some reservoirs that supply a single treatment works,
• Representing redistribution of water in the unmodelled distribution network by allowing multiple sources/transfers to deliver water to the same demand node,
• The omission of small sources, particularly those with <1Ml/d – which is WATHNET’s solver accuracy.

Instead, assumptions that are the result of limited data/scope are:

• Instantaneous travel time along arcs (with the exception of a few large aqueducts whose flow travel times are known),
• Reservoirs have zero evaporation (with the exception of a few large surface area reservoirs for which an evaporation relationship is well described), we note here that the UK experiences low rates of evaporation and that most reservoirs have evaporation lower than WATHNET’s solver accuracy of 1Ml/d,
• Water quality is not modelled but instead assumed that it will always be acceptable provided that volumetric licence conditions and minimum required flow volumes are met,
• The decision making of water companies during a drought is highly complex due to a range of pressures that cannot be modelled, we have worked with water companies to represent these in an acceptable way but the full range of options available under drought conditions was considered outside the scope of this work.

References

1. Dobson, B., Coxon, G., Freer, J., Gavin, H., Mortazavi‐Naeini, M., & Hall, J. W. (2020). The spatial dynamics of droughts and water scarcity in England and Wales. Water Resources Research, 56, e2020WR027187. https://doi.org/10.1029/2020WR027187
2. Kuczera, G. (1992). Water supply headworks simulation using network linear programming. Advances in Engineering Software, 14(1), 55–60.
3. Harou, J. J., Medellín-Azuara, J., Zhu, T., Tanaka, S. K., Lund, J. R., Stine, S., … Jenkins, M. W. (2010). Economic consequences of optimized water management for a prolonged, severe drought in California. Water Resources Research, 46(5).
4. Dobson, B., Wagener, T., & Pianosi, F. (2019). An argument-driven classification and comparison of reservoir operation optimization methods. Advances in Water Resources, 128(October 2018), 74–86.

NISMOD Water Data

River flows, output from DECIPHeR hydrological model.

Irrigation water demand, output from Wasim Irrigation model.

Maximum borehole abstraction and parameters for sensitivity to antecedent abstractions, from limiting abstraction borehole groundwater model.

Public water demand from Water Resource Management plans.

Historic non-public water demand (excluding irrigation) from National Abstraction Licensing Database.