Flood forecasting by reach
Ensemble weather forecasts drive hydrological and hydraulic models to give the probability of exceeding warning thresholds at each gauge, by lead time.
The Global Centre for Risk and Innovation (GCRI)
Forecasting turns observations into lead time: hours for flash floods, days for river floods, weeks to a season for drought. The value is in the lead time and the stated skill, not in a single number.
Water NexusWireframe of the working environment, not a live screen. Names and values are placeholders.
Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support early warning and planning across hazards, markets and health. In water, climate and hazards and critical infrastructure, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected systems. The canonical description is on the STEM Commons.
Ensemble weather forecasts drive hydrological and hydraulic models to give the probability of exceeding warning thresholds at each gauge, by lead time.
Sub-seasonal and seasonal climate forecasts are combined with current storage and soil moisture to state the odds of deficit categories for the coming months.
Operators receive inflow distributions, not point forecasts, so release rules can be set against the risk of both spill and shortage.
Where this page’s subject sits in the world: boundaries and context now, the field layers when the interactive map loads.
MapWorld map in the Equal Earth projection, land and boundaries from Natural Earth (public domain). The interactive map loads through the Nexus Foundry plugin (globe or flat view).
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method’s use, not a record of a run.
Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Ensemble numerical weather prediction | Forcing | FORECAST |
| Seasonal climate forecasts | Outlook forcing | FORECAST |
| Real-time gauge telemetry | Data assimilation | OBSERVED |
| Snow and soil-moisture state | Initial conditions | MODELLED |
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
| Check | How it is done |
|---|---|
| Hindcast verification | Skill is measured on past forecasts against what happened, by lead time and threshold |
| Reliability diagrams | A forecast of thirty percent must come true about thirty percent of the time |
| Warning performance | Hits, misses and false alarms are reported for every threshold |
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
Climate and Hazards stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.
| Link | Relationship | Why |
|---|---|---|
| Forecasting and time-series analysis | Canonical method page | Platform application links to the canonical method page. |
| Forecasting and time-series analysis in climate and hazards | Steward platform | Climate and Hazards stewards this method. |
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