River flows | UK Hydrological Outlook

There are several different forecasting approaches used for river flow forecasting in the Hydrological Outlook:

Current conditions

The current conditions are not forecasts but indicate the hydrological status at the end of the preceding month. They are based on using weather observations up to the forecast start date as inputs to the high-resolution Grid-to-Grid hydrological model (G2G).

There are several products presented as part of these current conditions.

The mean flow product shows the mean river flow for every pixel (or averaged over regions) over the preceding month.
The subsurface wetness and soil wetness products indicate the relative wetness of subsurface water stores (soil and groundwater) and soil water stores relative to their historic extremes.
The storage anomaly product indicates the deficit (or excess) subsurface storage depth relative to the historical monthly mean.
The deficit recovery maps show the return period of accumulated rainfall required to overcome any regional subsurface water storage deficits over the next few months.

This model is not currently used in Northern Ireland.

Bell, V A, Davies, H N, Kay, A L, Marsh, T J, Brookshaw, A, Jenkins, A. 2013. Developing a large-scale water-balance approach to seasonal forecasting: application to the 2012 drought in Britain. Hydrological Processes, 27(20), 3003–3012. doi:10.1002/hyp.9863

Historic Based Forecast Methods

Persistence and Hydrological Analogues method

This approach is not based on hydrological models; it is a statistical approach based on past river flow data.

Persistence forecasts are based on the assumption that the observed river flow will carry on into the next month. Because flows tend to be higher and more variable in winter than in summer, the flow is compared with the average flow for that particular month, and it is the anomaly compared with this average (and variability) that is used as the Persistence forecast for the coming months. Persistence forecasts are particularly useful for catchments on aquifer outcrops, where the river flow varies slowly from one month to the next due to the high groundwater contribution.

The hydrological analogues forecasts are based on selecting previously observed sequences of flows that are the most similar to the recently observed past, and using these to project forwards. The outlook derived by averaging the flows in the following months (the forecast duration) in these analogue years, giving a higher weight to years with more similar analogues. This is the Weighted Mean forecast. A variant of the Analogues method called the Shifted Weighted Mean forecast involves shifting the Outlook up or down so that the flow in the most recently observed month matches the weighted mean from the analogues in the corresponding month. The historical analogues are six months long for the one-month forecast, and nine months long for the three-month forecast.

This approach is applied to approximately 90 locations in the UK in the National Hydrological Monitoring Programme, these stations have at least 40 years of data in the period from January 1883 onwards. The forecast from the method with the higher confidence (best performance in the past) is presented. The confidence of the forecast is represented by the size of the dot. To be included, a forecast must have a hindcast correlation (correlation between past forecasts and observed data) of at least 0.23 and be significant at the 95% level for a one-sided t-test. The outline of the dot indicates which method has been used at each location: a solid line for Persistence and a dashed line for one of the Analogue methods.

Download comprehensive description of Persistence and Hydrological Analogues method

Further information is provided in the scientific paper: Svensson, C. 2016. Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues. Hydrological Sciences Journal, 61(1), 19–35 doi:10.1080/02626667.2014.992788.

Ensemble Streamflow Prediction (ESP) method

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 (one to twelve months ahead), at each location. The simulations are generated by the GR6J conceptual rainfall-runoff model, calibrated on observed flows.

These forecasts are available for a set of 316 catchments around the UK. On this portal, catchment-scale forecasts are available over the next one to twelve months at each site.

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.

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.

Note that the hydrological model used to generate the Outlook by historical climate was GR4J up to September 2023. From October 2023 onwards, GR6J has been used instead. For more details, please see below.

The model generally performs well across the UK, but there are important variations in skill. In addition, there are some biases in modelled river flows relative to observations, which should be taken into account when exploring raw streamflow forecast values (given in cumecs or mL/d).

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.

Meteorological Based Forecast Methods

Two hydrology models (GR6J and G2G-WBM) are run with the Met Office historical weather analogues data. The historical weather analogues method uses Met Office predictions of average weather 1 and 3 months ahead as input to an ensemble of hydrological forecasts. High resolution weather observations from HadUKGrid are used to provide finer spatial detail than directly produced by the Met Office forecast model. This is achieved by seeking 'analogues' — periods in UK weather records with similar weather patterns to those predicted. The forecast period is split into thirds and observed data for each third is taken from different past years, having accounted for non-weather-related trends. The pool of all possible combinations creates the set of weather scenarios available for use as analogues to the forecast weather. The benefit of this 'shuffling' approach is that it gives a much larger selection for matching the forecast, meaning the method is not constrained by the limited number of observed years. For each member of the Met Office forecast ensemble, the 10 analogues that best match the predicted average weather pattern (surface pressure map) over the whole forecast period are selected. Precipitation sequences constructed from the selected analogue scenarios are then used as inputs to hydrological models, creating the ensemble of hydrological forecasts.

GR6J

In this method the GR6J model uses the same modelling set up as used for the Ensemble Streamflow Prediction Method, as described in Harrigan et al (2018). The model is run using observed precipitation, and temperature-based Potential Evapotranspiration to achieve initial conditions. It is then run forward using historic climate data, extracted for non-sequential dates according to the ensemble of historical weather analogues provided by the Met Office. This provides ensemble river flow forecasts that are conditioned on the Met Office seasonal weather predictions.

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.

Research and testing for this forecasting method was funded under the HydroJULES programme.

Water Balance Model (G2G-WBM)

The Water Balance model (WBM) combines an estimate of the total subsurface water storage at the start of the month, estimated by the G2G, with an ensemble of rainfall forecasts to provide 1km gridded estimates of river flows up to a few months ahead for Great Britain (GB).

First, subsurface water storage at the start of the month is estimated using the Grid-to-Grid (G2G) hydrological model, driven by observed rainfall and evaporation over the preceding month. This GB-wide estimate is shown relative to historical extremes on the Current Conditions – Relative subsurface wetness and Current Conditions – Relative soil wetness pages.

The WBM is then run at monthly resolution for the next one- and three-months across GB using historical weather forecasts provided by the Met Office.

The WBM runs at a 1km spatial resolution across Great Britain and forecasts are presented on this Portal at both this 1km resolution and aggregated to regional scales. The WBM cannot currently be used in Northern Ireland.

For further technical details and an assessment of the performance of this method, please see:

Bell, V A, Kay, A L, Jones, R G et al. 2009. Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK. Journal of Hydrology. doi:10.1016/j.jhydrol.2009.08.031.

Bell, V A, Davies, H N, Kay, A L et al. 2017. A national-scale seasonal hydrological forecast system: development and evaluation over Britain. HESS. doi:10.5194/hess-21-4681-2017

Download comprehensive description of Water Balance Model method