Ensemble Streamflow Prediction

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The graphics above demonstrate the proportions of ensemble members within each of the five Outlook categories. Note: the map and the info boxes above combine the "below normal" with "low" and "above normal" with "high" to create three categories. The ensembles are presented in two ways:

  1. as the percentage of ensemble members within each category
  2. as a relative number, indicating how many more, or less ensemble members are in each outlook category compared to the number we would expect

This distinction is important, as the expected probabilities of the five categories are not of equal size. You can adjust the colouring of the points on the map to represent either of these options.

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This boxplot shows the distribution of the accumulated flows predicted by the ESP ensemble members. The forecast for the first month is a one-month average forecast; the forecast for the second month is a two-month average forecast, and so on. The last three months of the simulated observations are shown as a line graph at the start of the timeseries. The background colours show the bandings of the historic flows for reference; these also represent accumulated flow bandings, with the exception of the first three "status" months on the plot.

Continuous ranked probability skill scores (CRPSS) are shown at the bottom of this graph. This skill score measures the skill of the ESP method over the hindcast period, and compares it with a simple climatology forecast (a simple forecast based just on the distribution of historic flows). A skill score of 1 represents a perfect forecast, a score of 0 indicates the forecast is no better than a climatology forecast. It is worth noting that when there is low skill, the ESP forecast defaults to a climatology forecast, so despite the low skill, it can still be used as a basic forecast. These scores are calculated on the accumulated forecast (e.g. the score for 6 months is for the flow forecast for the 6-month averaged flows).

Years with the lowest daily flow:

Thresholds:

The spaghetti plot shows daily flows from each ensemble member in more detail. This plot simulates what would happen in the river in question if we received the weather conditions of each of those years today. You can choose to highlight five years for reference (from 1962–present). The default highlighted years are the five historic years with the lowest average flow from the month preceding the start of the forecast. The dashed lines set three thresholds of flow, which you can adjust (according to particular flow thresholds of interest to you, e.g. percentiles relevant to management decisions). The default thresholds are the flows exceeded 80, 90 and 99 percent of the time in the simulated observational record. The stacked plot underneath then represents the percentage of ensemble members that fall below each threshold.

Important note: while this plotting functionality allows the user to examine the probability of reaching particular flows within the forecast horizon, it is important to take account of the uncertainty in modelling which means the model outputs can be biased relative to observations.

Historical Analogue and Persistence

In each of the time series graphs the bold black line represents the observed flow during the past months. The grey band indicates the normal flow range (the normal band includes 44% of observed flows in each month). The selected historical analogues are shown as thin lines and the trajectories that flows took in the following are also shown. The year each analogue started in is shown in the legend. The forecast is shown as the dashed red line, and in each plot it states whether this has come from the analogues or has been generated on the basis of persistence.

Regional river flow forecast range using rainfall ensemble data

1-month ahead
Exceptionally high
Notably high
Above normal
Normal range
Below normal
Notably low
Exceptionally low

Full region names can be found by hovering over the letters in the table.

The tables show the range of river flow forecasts using all members of the seasonal rainfall forecast ensemble. The numbers in the tables are the percentage of ensemble members falling in each of the flow categories as generated by the monthly-resolution water balance model.

The regional river flow forecasts are derived from the average of 1km river flow estimates within each region and ranked in terms of 54 years of historical flow estimates (1963–2016).

Expected probability

River flow forecasts using selected flow quantiles

These monthly-mean river flow forecasts are produced using the hydrological initial condition, from the G2G hydrological model, and five quantiles of the Met Office seasonal rainfall forecast ensemble as input to a monthly-resolution water balance hydrological model.

The river flow forecasts are derived from the 1km river flow estimates for each pixel the average of 1km river flow estimates within each region and ranked in terms of 54 years of historical flow estimates (1963–2016).

The five maps illustrate the wide range of possible river flows and while there is a 50% chance of flows between the 1st and 3rd quartiles, actual flows may be more extreme than the flows derived using the highest or lowest rainfall forecasts.

Note: 3-month gridded forecast option to follow in a later version of this portal.

Will average rainfall overcome any dry conditions?

The deficit recovery maps show the return period of accumulated rainfall required to overcome the estimated current subsurface water storage deficit (simulated using the G2G hydrological model) over the next few months.

These maps do not provide a drought forecast. Instead they indicate the return period of rainfall required to overcome the dry conditions for the following 6 months based on current conditions.

Note that these maps are typically white for the winter forecast months because we do not usually have a subsurface water storage deficit in the wintertime.

Modelled sub-surface moisture anomaly

Each month the 1km gridded Grid-to-Grid (G2G) hydrological model is run, with observed inputs of precipitation and potential evaporation, to provide the hydrological initial conditions for a monthly-resolution water balance hydrological model.

The storage anomaly maps show the G2G simulated regional mean subsurface water storage, expressed as an anomaly from the historical monthly mean (1981–2010). Negative anomalies (pink/red) represent subsurface storage deficits, while positive anomalies (blue) represent a subsurface water surplus.

Subsurface water storage deficits (mm) can be interpreted as an estimate of the additional rainfall that would be required in future months to overcome dry conditions (i.e. rainfall in addition to what is expected on average).

Monthly mean river flows simulated by the Grid-to-Grid hydrological model

This map shows the simulated monthly mean flow across Great Britain for last month, ranked in terms of 54 years of historical flow estimates (1963–2016).

These flows are produced by the 1km resolution Grid-to-Grid (G2G) hydrological model (Bell et al., 2009), which is run up to the end of each calendar month using observed rainfall (Met Office NCIC) and MORECS (Hough and Jones, 1997) potential evaporation as input.

Note that the G2G model provides estimates of natural flows.

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. J. Hydrol., 377(3–4), 335–350.

Hough M, Jones R J A. 1997. The United Kingdom Meteorological Office rainfall and evaporation calculation system: MORECS version 2.0 – an overview. Hydrol. Earth Syst. Sci., 1(2), 227–239.

Current daily simulated subsurface water storage conditions

These maps are based on Grid-to-Grid (G2G) hydrological model simulated subsurface water storage, expressed as an anomaly from the historical monthly mean. To highlight areas that are particularly wet or dry, the storage anomaly is presented here using a colour scale highlighting water storage relative to historical extremes. The maps below show the “relative wetness” which combines maps previously shown separately as the “relative wetness” and “relative dryness”.

These maps do not provide a forecast and are not maps of soil moisture. Instead they indicate areas which are particularly wet or dry. Rainfall in areas with high positive relative wetness could result in flooding in the coming days/weeks. Areas of negative relative wetness provide an indication of locations which are particularly dry, and little or no rain in these areas could potentially lead to (or prolong) a drought.