Gauge and well telemetry
Level, flow and groundwater sensors report at fixed intervals over cellular or satellite links; gaps and drift are flagged automatically.
GCRI · Water Nexus
Sensors and telemetry for water networks
Sensing and telemetry bring the gauge, the well and the pipe network online: continuous levels, flows, pressures and quality readings that forecasting and operations depend on, each with its calibration record.
01Contents
Sensors and telemetry for water networks in five parts.
02Application
What sensors, IoT and observation networks does here.
Sensors, IoT and observation networks collect continuous measurements of physical, environmental and operational conditions. They provide the real-time inputs that early warning and asset monitoring depend upon. In water, climate and hazards and critical infrastructure, observation networks enable early warning and condition-based maintenance. They reveal how stresses in one system appear in connected systems in near real time. The canonical description is on the STEM Commons.
Utility network monitoring
Pressure, flow and quality sensors across a distribution network locate bursts, contamination and intrusion events.
Low-cost sensing with stated uncertainty
Community and low-cost sensors extend coverage where networks are thin, with calibration against reference instruments and their uncertainty carried into every use.
03Geography
Water now in the Nile Basin.
Rain, flood water 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
- 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
- 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
- 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
- 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
- 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 Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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 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
- 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
- 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
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS 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
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
04Data
What the method consumes.
Datasets and records used by sensors, IoT and observation networks on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Telemetered gauge networks | Real-time flows and levels | OBSERVED |
| Utility SCADA | Pressure, flow and quality | OBSERVED |
| Low-cost sensor networks | Coverage extension | ESTIMATED |
05Monitor
Telemetry network.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Telemetry network
Water · Sensors and telemetry for water · Run R-4107
- Channels reporting92of 100Normal
- Median latency363ms
- Records failing checks0.3per 1,000Watch
- Backlog6min
Gaps on 2 channelsGCRI · Water Nexus
Latency, p95
Events
- 06:50WarnChannel C-45 stale
- 07:28InfoSchema v4 accepted
- 08:54ErrorChecksum mismatch, batch quarantined
- 08:34DoneReplay complete
06Validation
How results are checked.
Sensors are calibrated against reference instruments and checked through co-location and redundancy. Data quality flags and automated range checks catch faults.
| Check | How it is done |
|---|---|
| Calibration schedule | Every sensor has a calibration record; readings outside the calibrated range are labelled |
| Quality flags | Drift, spikes and gaps are flagged in the data stream, never silently filled |
| Security review | Telemetry and control networks are assessed for unauthorised access |
Sensor drift, power loss and connectivity gaps degrade data during the events of most interest. Dense networks raise security and privacy concerns.
07Twin and cascade simulator
Model sensors and telemetry for water networks 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)
Systems and couplings
- Water risk standards
- Energy risk standards Common driver and cascade
- Food and agriculture risk standards Cascade
- Health risk standards Cascade
- Hydrometeorological hazard standards Shared hazard records
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-U28Universe platform profile: Water NexusNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-K42Domain pack profile: Cross border basins (DOMAIN-042)NRS-N1Water risk and resilienceNRS-N7Water-energy-food-health-biodiversity-infrastructure nexus couplingNRS-H62Hazard 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
08Connections
Topics that rely on this method.
09Sector signature
The sector signature of sensors and telemetry for water networks.
The sectors sensors and telemetry for water networks 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
10Stewardship
Who maintains the method.
Critical Infrastructure stewards sensors, IoT and observation networks for the Consortium and reviews how it is applied across platforms.
11Methods and evidence
How sensors and telemetry for water networks 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 sensors and telemetry for water networks: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
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
12Connected
Where this page leads across the Consortium.
13Process
How the process runs.
Process
Sensors and telemetry for water networks: Source to evidence record
A source is registered and assessed, its record labelled and classified, and a Digital Evidence Passport carries provenance; a correction reaches every dependent record.
01 Intake
- 01Identify the sourceSource · The source stewardDiscovery is not ingestion. Source identification records the source class, steward, institutional role and jurisdiction.
- 03Register the sourceStepRegistration makes a discovered source a governed participant in the evidence lifecycle. It implies no endorsement or certification.
02 Assessment
- 04Jurisdiction and rights classificationGateRights classification identifies licence, consent, confidentiality and reuse limits. The result determines access and routing.
- 05Evidence quality assessmentStepQuality assessment decides whether a record is fit for a defined use. Quality is multidimensional and never a single score.
- 06Evidence labelStepEvery finding carries one of nine evidence labels. Missing data is shown as missing, never as zero.
03 Record
- 07Evidence classificationRecordClassification gives records lifecycle states, such as source-verified, public-safe or restricted. States can coexist and set evidence use.
- 08Source release and identifierRecord · NXS-GRX (canonical data, provenance and risk)A source release records rights, temporal coverage and source lineage. A UNI is allocated through a catalogue and never typed by hand.
- 09Digital Evidence PassportRecordThe passport records provenance, quality, rights, jurisdiction, permitted use and history, so evidence keeps its context when it travels.
04 Use and correction
- 02Correction reaches dependent recordsStep · NXS-GRX (canonical data, provenance and risk)A source correction propagates to dependent cuts and products without rewriting old results.
- 10Public-safe reviewGatePublic-safe review decides whether a record can be shared publicly. It is a distinct stage and is not validation.
Data protocols: evidence lifecycle · Data protocols: core data objects and evidence records · The Global Risks Index (NXS-GRX)Designed programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The data-protocol pages describe a design, not a running system.
14Questions
Questions and answers.
What are the limits of sensors, IoT and observation networks in this field?
Sensor drift, power loss and connectivity gaps degrade data during the events of most interest. Dense networks raise security and privacy concerns.
How are results checked?
Sensors are calibrated against reference instruments and checked through co-location and redundancy. Data quality flags and automated range checks catch faults.
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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GCRIWater NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceObservatory Signal
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