River flows | UK Hydrological Outlook

Three different approaches are used for producing river flows for the UK Hydrological Outlook.

River flow analogy

The historical flow analogues approach to seasonal river flow forecasting is based on selecting the previously observed sequences of flows that are the most similar to the recently observed past.

The assumption is that this similarity will carry on in the coming few months. New one-month and three-month forecasts are made each month using monthly river flow at around 100 stations in the National Hydrological Monitoring Programme. These stations have at least 30 years of data in the period from January 1883 onwards.

The bulk of the forecasts are persistence forecasts, which are made when these outperform the historical analogues approach. They are particularly useful for slowly responding catchments with large underground water storage in aquifers.

Download comprehensive description of analogy method

River flow from historical climate

The Ensemble Streamflow Prediction (ESP) outlook, or “Outlook by historical climate”, is based on monthly ensembles of historical sequences of observed climate (rainfall and potential evapotranspiration) that form input to a hydrological model. The outputs are probabilistic simulations of the average river flow over the forecast period (1 to 12 months ahead), at each location. The simulations are generated by the GR6J conceptual rainfall-runoff model from INRAE (France), calibrated on observed flows.

This outlook is based entirely on historical sequences and therefore does not contain any knowledge of the state of the atmosphere and ocean. It is hence possible that some of the historical sequences used might be inconsistent with current large-scale atmospheric conditions and would therefore be unlikely to occur in the next few months.

To help with the interpretation of the results, the outlook is compared with the expected (or reference) distribution for each location, time of the year and N-month accumulation period. To avoid possible bias in the hydrological modelling to influence the interpretation of the results, the reference distribution is derived from simulated (and not gauged) flows using observed historical climate as input data. This is done for each start date and N-month accumulation period.

Detail of the modelling set up, and the skill of this method have been published in:
Harrigan, S, Prudhomme, C, Parry, S, Smith, K, and Tanguy, M. 2018. Benchmarking ensemble streamflow prediction skill in the UK. Hydrology and Earth Systems Sciences

Please note that the hydrological model used to generate the Outlook by historical climate was switched from GR4J to GR6J from October 2023 onwards.

Read more about the switch from GR4J to GR6J

Switch from GR4J to GR6J lumped hydrological model to produce ESP forecasts for the UK Hydrological Outlook (UKHO)

The GR4J (Génie Rural à 4 paramètres Journalier) and GR6J (Génie Rural à 6 paramètres Journalier) models come from a suite of hydrological models provided in the airGR modelling suite (Coron et al., 2021) for the R software program. GR6J (Pushpalatha et al., 2011) is a six-parameter variant of the GR modelling suite that was developed to improve low-flow simulation and groundwater exchange compared to GR4J which has only four parameters. Notably, GR6J has gained increasing popularity in UK water resources applications. To align with industry norms in UK water resource management, we have chosen to shift from using the GR4J model, which was employed for ESP forecasting until September 2023, to using the GR6J model starting from October 2023.

Hannaford et al. (2023) showed that GR6J outperformed GR4J for a range of performance metrics for a set of 200 catchments across the UK. Employing the methodology outlined in Harrigan et al. (2018), we assessed how this model transition affected forecasting skill when generating river flow forecasts through the ESP method for the 314 catchments used in the UKHO. The assessment employed the Continuous Ranked Probability Skill Score (CRPSS) as the forecasting skill metric. Results indicate that, for most regions and lead times, the differences in skill are marginal, with mean differences of less than 0.03 (Figure 1). We therefore anticipate this transition will have a minimal impact on users' experience with these forecasts. However, four catchments (27032, 28039, 28060, and 42004, as indicated by darker red triangles on the maps in Figure 3) exhibit a deterioration in skill when switching to GR6J, and investigations are currently underway to address this issue with these specific catchments. We would remind users that the magnitude of skill is represented by the size of the dot on the forecast map displayed on the UKHO portal, reflecting our confidence in the issued forecasts.

Figure 1: Change in forecasting skill (CRPSS) at different lead time when transitioning from GR4J to GR6J to produce ESP forecasts in the different UK hydroclimate regions. Blue signifies improved forecast skill with GR6J compared to GR4J, while orange shades represent the reverse.

Figure 2: Boxplots showing the range of CRPSS for all catchments used in the UKHO at different leadtimes in ESP forecasts generated using GR4J (in red) and GR6J (in blue).

Figure 3: Map of difference in skill (CRPSS) between ESP forecasts generated using GR6J and GR4J at (left) 1-month lead time and (right) 3-month lead time. Blue shades signify improved forecast skill with GR6J compared to GR4J, red shades represent the reverse, while white signifies negligeable differences.

References:

Coron, L., Delaigue, O., Thirel, G., Dorchies, D., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling, R package version 1.6.12, Recherche Data Gouv [code], https://doi.org/10.15454/EX11NA, 2021

Hannaford, J., Mackay, J. D., Ascott, M., Bell, V. A., Chitson, T., Cole, S., Counsell, C., Durant, M., Jackson, C. R., Kay, A. L., Lane, R. A., Mansour, M., Moore, R., Parry, S., Rudd, A. C., Simpson, M., Facer-Childs, K., Turner, S., Wallbank, J. R., Wells, S., and Wilcox, A.: The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18, Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, 2023.

Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the UK, Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, 2018.

Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andréassian, V. A.: Downward structural sensitivity analysis of hydrological models to improve low-flow simulation, J. Hydrol., 411, 66–76, https://doi.org/10.1016/j.jhydrol.2011.09.034, 2011.

River flow from rainfall forecast

An experimental modelling tool for national hydrological outlooks has been developed which combines a hydrological model (Grid-to-Grid or “G2G”) estimate of total subsurface water storage (in both soil and groundwater) and across Britain with a range of seasonal rainfall forecasts provided by the Met Office to provide estimates of area-wide hydrological conditions up to a few months ahead.

For many areas, hydrological “forecasts” up to a few weeks or months ahead are dependent on accurate knowledge of the current storage of water in the landscape. This information provides the hydrological initial condition, or “initial state”, from which future simulations will depart following changes in boundary conditions, consisting primarily of the weather and water consumption. For the Hydrological Outlook UK, the G2G estimate of subsurface water storage across the UK can provide an initial condition of subsurface water in storage across the UK, derived using the most recent observations of rainfall and potential evaporation. This hydrological initial condition then provides a starting point from which estimates of water storage and river flows for one to three months ahead can be produced as perturbations from the initial state, driven by Met Office rainfall forecasts.

During periods of drought, the link can made between a deficit in subsurface water storage and a requirement for additional rainfall over subsequent months to enable subsurface water storage and river flow to return to mean monthly values. The methodology can also provide an indication of locations where sub-surface water storage is particularly high and may be prone to flooding in the coming days/weeks.

Download comprehensive description of rainfall forecast method