Rainfall and groundwater interpolation
Kriging and related methods estimate fields between stations, with an error surface that shows where the estimate is weakest.
The Global Centre for Risk and Innovation (GCRI)
Spatial statistics fills the space between gauges: interpolating rainfall and groundwater levels, estimating what an ungauged catchment would show, and stating how uncertain the map is where data are thin.
Water NexusWireframe of the working environment, not a live screen. Names and values are placeholders.
Spatial statistics and geostatistics analyse data that vary across space, accounting for spatial dependence. They estimate values at unsampled locations and test for spatial patterns. In water, climate and hazards and critical infrastructure, spatial statistics produce continuous hazard and exposure surfaces with uncertainty. They reveal geographic clustering of risks and vulnerabilities across sectors. The canonical description is on the STEM Commons.
Kriging and related methods estimate fields between stations, with an error surface that shows where the estimate is weakest.
Model parameters and frequency curves are transferred from gauged to ungauged catchments by similarity, with the transfer error stated.
Water-quality exceedances and outbreak reports are tested for spatial clustering before a cause is suggested.
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 spatial statistics and geostatistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Station networks | Interpolation inputs | OBSERVED |
| Catchment descriptors | Similarity measures | ESTIMATED |
| Satellite fields | Covariates for interpolation | MODELLED |
Predictions are tested using spatial cross-validation that respects dependence. Uncertainty maps are compared with independent observations.
| Check | How it is done |
|---|---|
| Cross-validation | Stations are withheld in turn and predicted; the error is mapped |
| Variogram review | The spatial model is inspected, not assumed |
| Edge effects | Estimates near borders and coasts carry their larger uncertainty |
Results depend on the scale and zoning of analysis. Sparse or clustered sampling limits prediction in unsampled areas.
Space and Earth Observation stewards spatial statistics and geostatistics for the Consortium and reviews how it is applied across platforms.
| Link | Relationship | Why |
|---|---|---|
| Spatial statistics and geostatistics | Canonical method page | Platform application links to the canonical method page. |
| Spatial statistics and geostatistics in space and earth observation | Steward platform | Space and Earth Observation stewards this method. |
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