GCRI · Water Nexus

Hydrology for water risk

Hydrology is the core science of this platform: it turns rainfall, snow, soil, vegetation and geology into the river flows, groundwater levels and storage that every water decision depends on.

01Register

Hydrology for water risk on the record.

Method recordSTEM method
  1. MethodHydrology and water science
  2. StewardWater Nexus
  3. Applied inWater Nexus
  4. 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 water, climate and hazards and critical infrastructure, 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.

Rainfall-runoff modelling of a basin

A conceptual or process-based model is built for each sub-catchment, calibrated on gauged flows and run with observed and forecast rainfall to give flow at the points where decisions are taken.

Water balance accounting

Inflows, abstractions, evaporation, returns and storage change are reconciled by season and by user, so allocation decisions start from a closed account rather than competing estimates.

Flood frequency and design flows

Annual maxima and peaks over threshold are fitted to give return-period flows for levee, culvert and reservoir design, with confidence bands that widen as the record shortens.

03Geography

Water now in the Nile Basin.

Flood water, rain and root-zone soil moisture over the Nile Basin, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Water now, for this page

About this layer
Evidence
Observed (clouds leave gaps)
Source
VIIRS Global Flood Product (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite detection; clouds leave gaps
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Estimated
Source
GPM IMERG (NASA GES DISC)
Provider
NASA GES DISC
Licence
NASA open data
Vintage
daily; Satellite estimate, near real time
Newest at build
2026-10-12
Resolution
about 2 km grid
Uncertainty
Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Modelled
Source
SMAP Level 4 (NASA NSIDC DAAC)
Provider
NASA NSIDC DAAC
Licence
NASA open data
Vintage
daily; Model with satellite data assimilated
Newest at build
2026-10-11
Resolution
about 2 km grid
Uncertainty
Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS NOAA-20 corrected reflectance (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite image
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Modelled (latest year GIBS publishes)
Source
MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
Provider
NASA LP DAAC
Licence
NASA open data
Vintage
yearly; Satellite classification; latest year GIBS publishes
Newest at build
2024-01-01
Resolution
about 500 m grid
Uncertainty
Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (latest month GIBS publishes)
Source
GRACE-FO JPL mascons (NASA JPL PO.DAAC)
Provider
NASA JPL PO.DAAC
Licence
NASA open data
Vintage
monthly; Satellite gravimetry; latest month GIBS publishes
Newest at build
2022-07-01
Resolution
about 2 km grid
Uncertainty
Satellite gravimetry; latest month GIBS publishes. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

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 Nile Basin.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 GPM IMERG Early · NASA JPL OPERA DIST-ALERTSources 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.
  • Live: NASA GPM IMERG Early. 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).
  • Observed (EO): NASA JPL OPERA DIST-ALERT (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.

DataRoleLabel
Stream gauge recordsCalibration and validation of flowOBSERVED
Gauge and radar rainfallModel forcingOBSERVED
Reservoir operation recordsStorage and release terms of the balancePublished statistics
Digital elevation modelCatchment delineation and routingMODELLED
Land cover and soil mapsRunoff and infiltration parametersESTIMATED
05Simulator

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 Nile Basin; 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
HourOutlet (m³/s)Unrouted (m³/s)Upper reach (m³/s)
2.40.0678.90.0
12.0308.31,151.4181.8
21.6452.435.173.7
31.2363.31.111.1
40.8198.80.01.5
50.479.30.00.2
60.025.40.00.0
69.66.90.00.0
79.21.70.00.0
88.80.40.00.0
98.40.10.00.0
108.00.00.00.0
117.60.00.00.0
127.20.00.00.0
136.80.00.00.0

Methods

  1. Curve number runoff USDA Soil Conservation Service (1972), National Engineering Handbook, Section 4: Hydrology
  2. Linear reservoir Nash (1957), The form of the instantaneous unit hydrograph, IAHS 45
  3. Muskingum channel routing McCarthy (1938); Cunge (1969), Journal of Hydraulic Research 7
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. 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
Route a storm through a river network. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).

Across the six pillars, led by Nexus Consortium

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.

CheckHow it is done
Split-sample calibrationFit on one period of flows, test on an independent period or a neighbouring catchment, report skill on the test period only
Mass balance closureEvery run reconciles inflow, outflow and storage change; an open balance is a finding, not a tolerance
Flood extent comparisonModelled inundation is compared with surveyed or satellite extents of past events
Stationarity checkTrends in the record are tested before a return period is quoted from it

Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.

07Twin and cascade simulator

Model hydrology for water risk as a system. Run the cascade.

Assembled from the Nexus Standards area profile Water risk 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

  • Flash flood
  • Meteorological drought
  • Groundwater depletion
  • Dam failure
  • Debris flow (set off downstream)
  • Agricultural drought (set off downstream)
  • Hydrological drought (set off downstream)
  • Wildfire (set off downstream)
  • Peat and underground fire (set off downstream)
  • Forest dieback (set off downstream)
  • Water supply shortfall (set off downstream)
  • Acute food crisis (set off downstream)
  • Dust storm (set off downstream)
  • Land subsidence (set off downstream)
  • Saltwater intrusion (set off downstream)
  • River flood (set off downstream)
  • Mass displacement (set off downstream)
  • Amazon rainforest dieback (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-1 The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-3b Dataset and product metadata profile (STAC, DCAT, PROV-O)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NRS-U28 Universe platform profile: Water Nexus
  • NRS-H61 Hazard family: Cascading failure (NHS-1 sys.cascade)
  • NRS-K42 Domain pack profile: Cross border basins (DOMAIN-042)
  • NRS-N1 Water risk and resilience
  • NRS-N7 Water-energy-food-health-biodiversity-infrastructure nexus coupling
  • NRS-H62 Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)

Observation

  • USGS Water Data APIs (listed for reference)
  • GloFAS global flood forecasts and reanalysis (listed for reference)
  • GRACE/GRACE-FO terrestrial water storage mascons (listed for reference)
  • Aqueduct 4.0 Water Risk Atlas (listed for reference)
  • CHIRPS quasi-global gridded precipitation (listed for reference)
  • Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
  • Proportion of transboundary basin area with an operational arrangement for water cooperation
  • Proportion of population using safely managed drinking water services
  • Number of disruptions to basic services attributed to disasters

Example geography

  • The Ganges and Brahmaputra Basin: 178 hexagonal cells
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Surface soil moisture (SMAP), NASA GIBS
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
09Sector signature

The sector signature of hydrology for water risk.

The sectors hydrology for water risk 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
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
10Stewardship

Who maintains the method.

Water Nexus stewards hydrology and water science for the Consortium and reviews how it is applied across platforms.

11Methods and evidence

How hydrology for water risk 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.

System model

The couplings between the systems in hydrology for water risk: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−++WaterClimateCritical infrastructureCommunitiesEconomyR reinforcingB balancing
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
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
12Questions

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.
ReportsContribute

Contribute evidence from this page.

Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIWater NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceObservatory Signal
Water Nexus

Bring a method, a dataset or a question.

Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.

The Global Centre for Risk and Innovation

Science · evidence · measurement

Not for profit

Our articles, maps and twins are free to read and free to republish. Readers and partners keep it that way.

Partner with us

Who funds us

GCRI is a not-for-profit institution. To ask who funds a piece of work, use the funding questions form.

Independence

Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.