Flood risk mapping for planning
Hydrological and hydraulic models produce flood extents and depths for several return periods to inform land use and evacuation plans.
GCRI · Disaster Resilience and Recovery
Hydrology for disaster resilience
Hydrology connects rainfall to the floods and droughts that drive many disasters. In this field it supports flood risk mapping, forecast-based triggers and the design of drainage and defences in recovery.
01Register
Hydrology for disaster resilience on the record.
Method recordSTEM method
- MethodHydrology and water science
- StewardWater Nexus
- Applied inDisaster Resilience and Recovery
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What hydrology and water science does here.
Hydrology studies the movement, distribution and quality of water across the land surface, subsurface and atmosphere. It underpins the assessment of floods, droughts and water supply, which affect food, energy, health and infrastructure together. In disaster and crisis and climate and hazards, hydrological estimates set design standards for dams, levees and drainage and inform water allocation between competing users. They link climate hazards to downstream effects on power generation, crops and public health. The canonical description is on the STEM Commons.
Thresholds for anticipatory action
River levels and discharges associated with past damaging floods are identified to set impact-based triggers.
Drainage in reconstruction
Rebuilt neighbourhoods are checked for drainage capacity against design storms so that reconstruction does not recreate flooding.
03Geography
Water now in the Philippines.
Flood water, rain, root-zone soil moisture and true-colour imagery over the Philippines, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Water now, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Water now, framed on the Philippines.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GRACE and GRACE-FO JPL mascon solution, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Water storage anomaly, monthly: GRACE-FO JPL mascons, NASA JPL PO.DAAC · July 2022
04Data
What the method consumes.
Datasets and records used by hydrology and water science on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| River gauging records | Thresholds and calibration | OBSERVED |
| Flood hazard maps | Planning inputs | MODELLED |
| Rainfall records | Design storms | OBSERVED |
| Flood impact records | Damage thresholds | REPORTED |
05Monitor
Forecast verification.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Forecast verification
Disaster resilience · Hydrology for disaster resilience · Run R-5639
Skill above referenceGCRI · Disaster Resilience and Recovery
Forecast with range
Method card
- Method
- Hydrology and water science
- Reference
- climatology
- Lead times
- 1 to 7 steps
- Label
- MODELLED
Verification by lead time
held-out period
| T+2 | 0.44 | 0.08 | 0.22 | Skilful |
| T+4 | 0.18 | 0.13 | 0.14 | Skilful |
| T+6 | 0.37 | 0.13 | 0.40 | Marginal |
| T+8 | 0.26 | 0.24 | 0.15 | Marginal |
06Validation
How results are checked.
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
| Check | How it is done |
|---|---|
| Model calibration | Models are calibrated and validated against recorded floods |
| Threshold-impact match | Trigger thresholds are checked against documented impacts |
| Local verification | Maps are reviewed with local engineers and communities |
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
07Simulator
Route a storm through a river network.
Rain becomes runoff by the curve number method, drains each sub-catchment through a linear reservoir and travels down the main stem by Muskingum routing; play it to watch the flood wave move and flatten. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Philippines; the model itself is not tied to a place.
Controls
- Storm rainfall depth
- 120 mm
- Storm duration
- 12 h
- Curve number (soil and land cover)
- 78
- Catchment area
- 800 km²
- Reaches on the main stem
- 5
- Travel time per reach (Muskingum K)
- 6.0 h
- Muskingum X (storage weighting)
- 0.20
- Channel capacity per unit area
- 300 L/s per km²
Readouts
- Peak discharge at the outlet
- 452 m³/s
- Time to peak
- 21.6 h
- Attenuation by the channel
- 61%
- Hours over capacity
- 26.4 h
- Runoff coefficient
- 52%
Numbers, methods and sources
| Hour | Outlet (m³/s) | Unrouted (m³/s) | Upper reach (m³/s) |
|---|---|---|---|
| 2.4 | 0.0 | 678.9 | 0.0 |
| 12.0 | 308.3 | 1,151.4 | 181.8 |
| 21.6 | 452.4 | 35.1 | 73.7 |
| 31.2 | 363.3 | 1.1 | 11.1 |
| 40.8 | 198.8 | 0.0 | 1.5 |
| 50.4 | 79.3 | 0.0 | 0.2 |
| 60.0 | 25.4 | 0.0 | 0.0 |
| 69.6 | 6.9 | 0.0 | 0.0 |
| 79.2 | 1.7 | 0.0 | 0.0 |
| 88.8 | 0.4 | 0.0 | 0.0 |
| 98.4 | 0.1 | 0.0 | 0.0 |
| 108.0 | 0.0 | 0.0 | 0.0 |
| 117.6 | 0.0 | 0.0 | 0.0 |
| 127.2 | 0.0 | 0.0 | 0.0 |
| 136.8 | 0.0 | 0.0 | 0.0 |
Methods
- Curve number runoff USDA Soil Conservation Service (1972), National Engineering Handbook, Section 4: Hydrology
- Linear reservoir Nash (1957), The form of the instantaneous unit hydrograph, IAHS 45
- Muskingum channel routing McCarthy (1938); Cunge (1969), Journal of Hydraulic Research 7
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Rivers Natural Earth
- Lakes and reservoirs Natural Earth
- Cities Natural Earth
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Surface soil moisture (SMAP) NASA SMAP · register nasa.gibs.context-live-layers
- Surface water, 30 m (OPERA DSWx-HLS) NASA JPL OPERA DSWx-HLS (contains modified Copernicus Sentinel data 2026) · register nasa.gibs.context-live-layers
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar) NASA JPL OPERA DSWx-S1 (contains modified Copernicus Sentinel-1 data 2026) · register nasa.gibs.context-live-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Resilience and Recovery in Foundry
- Tested at Disaster Resilience and Recovery in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Resilience and Recovery in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Model hydrology for disaster resilience as a system. Run the cascade.
Assembled from the Nexus Standards area profile Compound and cascading hazard standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Natech event
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- Copernicus EMS rapid mapping activations (listed for reference)
- OpenStreetMap planet and replication (listed for reference)
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Number of disruptions to basic services attributed to disasters
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
10Stewardship
Who maintains the method.
Water Nexus stewards hydrology and water science for the Consortium and reviews how it is applied across platforms.
11Sector signature
The sector signature of hydrology for disaster resilience.
The sectors hydrology for disaster resilience touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Water and its partners
Water gives irrigation to food (0.8), cooling water and hydropower to energy (0.6) and environmental flows to biodiversity (0.6).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.43 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
12Connected
Where this page leads across the Consortium.
13Methods and evidence
How hydrology for disaster resilience is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for hydrology for disaster resilience could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
14Questions
Questions and answers.
What are the limits of hydrology and water science in this field?
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
How are results checked?
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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