Ungauged basin hydropower potential assessment from satellite observations
Satellite-derived slope, width, and modelled discharge can estimate gross hydropower potential in basins with no ground records. Uncertainties run 30–50 %, making the method a triage tool, not a bankable study.
Sensors
- ICESat-2 ATL13 (inland water surface elevation): Photon-counting lidar at 532 nm. Along-track water-surface elevation accurate to roughly 10 cm vertically over calm water reaches of at least 100 m width. Ground-track spacing ~3.3 km across-track; repeat cycle 91 days. Provides the river-slope signal needed to estimate head at candidate sites.
- Sentinel-2 MSI: 10 m multispectral imagery at 5-day revisit (two-satellite constellation). Used for channel-width extraction via water-index methods (MNDWI, AWEInsh). Reliable width retrieval requires channels wider than roughly 20–30 m; narrower rivers are underestimated. Free and openly archived from 2015.
- PlanetScope SuperDove: 3 m, 8-band daily imagery. Improves channel-width retrieval for rivers 10–30 m wide where Sentinel-2 pixels straddle the bank. Commercial licence required; tasking on client agreement. Does not add elevation data.
- GRACE-FO (terrestrial water storage anomaly): Twin-satellite microwave ranging at ~300 km spatial resolution, monthly cadence. Used to calibrate or constrain large-scale hydrological models (VIC, mHM, PCR-GLOBWB) that produce discharge estimates at ungauged locations. Not a direct discharge sensor; the spatial smearing is severe at sub-basin scale.
The physics is simple. The data is not.
Gross hydropower potential at any site reduces to P = ρgQH, where P is power in watts, ρ is water density (1000 kg/m³), g is gravitational acceleration (9.81 m/s²), Q is discharge in cubic metres per second, and H is the available hydraulic head in metres. A site with 50 m³/s and 20 m of head yields roughly 9.8 MW gross before any civil or electromechanical losses. The relationship is exact. The difficulty is that Q and H both carry substantial uncertainty when derived from orbit rather than a gauging station and a surveyed weir.
Traditional desk studies for ungauged basins rely on regional flow-duration curves, area-ratio scaling from the nearest analogue catchment, or rainfall-runoff models forced by gridded precipitation. All of those methods are imprecise. Satellite inputs are imprecise differently: they have global coverage, consistent methodology, and a growing archive, which makes them well-suited to comparing many candidate sites against each other even when absolute values are uncertain.
What ICESat-2 gives you, and where it goes quiet
ICESat-2's ATL13 product reports along-track water-surface elevation for inland water bodies, including rivers, at roughly 10 cm vertical accuracy over reaches wide enough for the 17 m diameter laser footprint to land cleanly on water. Published validation studies on large rivers show root-mean-square errors of 0.08–0.20 m against in-situ gauges. For a 10 km reach with 5 m of total fall, that translates to a slope uncertainty of perhaps 0.01–0.02 %, which is acceptable for first-pass ranking.
The caveats are real. ICESat-2 ground tracks are fixed; a candidate site may sit kilometres from the nearest track crossing. The 91-day repeat means the elevation snapshot may not correspond to the flow season of interest. Steep, narrow gorges, precisely the reaches with the most head, are often shadowed or turbulent, degrading photon returns. In practice, useful ATL13 retrievals require channel widths above roughly 60–100 m for consistent quality, though narrower reaches occasionally produce clean returns. Analysts should inspect the ATL13 quality flags and segment-level confidence scores rather than accepting all retrievals uncritically.
Channel width as a discharge proxy, and its ceiling
River hydraulic geometry theory, established by Leopold and Maddock in 1953 and extended by many subsequent workers, shows that channel width scales with discharge as a power law: W = aQ^b, with b typically 0.4–0.6 for alluvial rivers. Measuring width from Sentinel-2 or PlanetScope imagery and inverting that relationship gives a discharge estimate without any in-situ data. The approach is the basis of several published global discharge datasets, including the HydroSAT and SWORD-derived products.
The honest ceiling: power-law coefficients vary by river type, bed material, and confinement. Bedrock gorges, which dominate high-head hydropower sites, often violate alluvial geometry assumptions. Width-to-discharge conversion errors of a factor of two are plausible in confined reaches. Sentinel-2 at 10 m also systematically underestimates width for channels narrower than two to three pixels, introducing a bias that PlanetScope at 3 m partially corrects but does not eliminate. Width-derived discharge is best treated as an order-of-magnitude constraint rather than a calibrated estimate.
GRACE-FO and the hydrological model layer
GRACE-FO measures month-to-month changes in terrestrial water storage by detecting tiny variations in the distance between two satellites flying 220 km apart. At basin scales above roughly 100,000 km², those storage changes constrain the water balance well enough to calibrate global hydrological models. Several operational models, including PCR-GLOBWB 2 and the W3RA framework, assimilate GRACE or GRACE-FO data to produce gridded runoff and discharge estimates at 0.5 degree resolution.
For hydropower prospecting, the model output provides seasonal discharge distributions: mean annual flow, Q90 (exceeded 90 % of the time, relevant to firm power), and flood quantiles. At sub-basin scale, the 300 km GRACE footprint means the storage signal is an average over a large area that may include very different geology and land cover. Downscaling to a specific tributary introduces additional uncertainty. The combined effect of model structure, GRACE resolution, and precipitation forcing error typically produces discharge estimates with uncertainties of 30–50 % at ungauged sites, consistent with the range cited for this method overall.
Putting the chain together: what you get and what you do not
A satellite-based hydropower desk study produces a ranked list of candidate sites characterised by estimated gross power (in MW), seasonal flow variability, approximate head, and channel width. It can cover an entire river network in a roadless basin within weeks, at a fraction of the cost of helicopter-supported field reconnaissance. For governments or developers screening dozens of sub-basins, that triage function is genuinely valuable.
What the method cannot do: it cannot replace a rated-current survey, a bathymetric cross-section, a sediment load assessment, or a geotechnical investigation of dam abutments. It cannot reliably detect sites with head below 5 m where slope uncertainty dominates. It gives no information on diurnal or inter-annual flow variability beyond what the hydrological model provides. Sites that rank highly in a satellite study should be visited; sites that rank poorly may be safely deprioritised. That asymmetry is the appropriate use of a 30–50 % uncertain estimate.
Satellize structures this workflow as a prioritisation report: satellite inputs processed against a client-defined river network, candidate sites ranked by estimated gross potential and data confidence, with uncertainty bands explicit at each step. The Tonga crop-estimation programme demonstrated a similar principle of using open satellite data to guide ground-truth investment rather than replace it.
Honest comparison with traditional desk studies
A conventional ungauged-basin desk study using regional flood-frequency analysis and 1:50,000 topographic maps typically achieves discharge uncertainty of 40–60 % and head uncertainty that depends on contour interval, often 5–10 m for sites where the contours are sparse. Satellite methods are competitive on discharge uncertainty and substantially better on head, given ICESat-2's decimetre vertical accuracy where tracks intersect the reach of interest.
The satellite approach adds global consistency: the same method applied in Laos, the Democratic Republic of Congo, and Peru uses identical physics and sensor characteristics, removing the analyst-to-analyst variability that plagues regional curve comparisons. The trade-off is coverage gaps where ICESat-2 tracks miss the site and where cloud cover prevents optical width retrieval. In persistently cloudy tropical basins, Sentinel-2 may yield only a handful of usable cloud-free composites per year, making seasonal width variation hard to characterise. SAR-based width retrieval from Sentinel-1 can partially fill that gap, though it is addressed in a separate page in this library.
Typical figures
| Water-surface elevation accuracy (ICESat-2 ATL13) | ~0.08–0.20 m RMSE on rivers wider than ~60–100 m (published validation range) |
| Spatial resolution, optical width retrieval | 10 m (Sentinel-2); 3 m (PlanetScope SuperDove). Minimum reliable channel width ~20–30 m (Sentinel-2), ~10 m (PlanetScope) |
| ICESat-2 repeat cycle | 91 days; fixed ground tracks, ~3.3 km across-track spacing |
| Sentinel-2 revisit | 5 days at equator (two-satellite constellation); cloud-free compositing may extend effective revisit in tropical basins |
| GRACE-FO spatial resolution | ~300 km (effective); monthly cadence; usable for basins >~100,000 km² |
| Discharge estimate uncertainty at ungauged sites | 30–50 % (combined model, width-inversion and GRACE downscaling error) |
| Gross power estimate uncertainty | 30–50 % (propagated from Q and H uncertainties; not suitable for bankable feasibility) |
| ICESat-2 archive depth | From October 2018 (science operations); Sentinel-2 from June 2015 |
| Deliverable formats | GeoPackage / Shapefile of candidate sites, CSV ranked site table, PDF prioritisation report with uncertainty bands |
Analytics Satellize can run
| River-slope profile from ICESat-2 ATL13 | Along-track water-surface elevation extraction from ATL13 product; quality-flag filtering; linear regression over candidate reach | GeoJSON polyline with reach-averaged slope, elevation uncertainty, and ATL13 quality tier per segment |
| Channel-width time series | MNDWI or AWEInsh thresholding on Sentinel-2 cloud-free composites; cross-section width at candidate site centrelines; PlanetScope refinement for channels <30 m | CSV of monthly median width per site with interquartile range; GIS layer of delineated water mask |
| Modelled discharge distribution | GRACE-FO-calibrated hydrological model (PCR-GLOBWB 2 or equivalent public model) downscaled to sub-basin using drainage-area ratio; width-hydraulic-geometry inversion as independent cross-check | Flow-duration curve (Q10–Q90) per candidate site, mean annual discharge estimate with stated uncertainty band |
| Gross hydropower potential ranking | P = ρgQH applied with Monte Carlo uncertainty propagation across Q and H distributions; sites ranked by P50 estimate and by P90 lower-bound | Ranked site table (PDF and CSV) with gross MW estimate, uncertainty range, and recommended field-investigation priority tier |
| Data-confidence scoring per site | Rule-based scoring on ICESat-2 track proximity, number of usable Sentinel-2 scenes, GRACE basin-area coverage fraction, and channel-type classification | Per-site confidence flag (high / medium / low) appended to ranked table; flags drive field-survey sequencing |
Who does the work
We can get this done for you. Satellize runs its own analyst desk and a strong science team. You do not buy a data feed and work out what it means; our people source the imagery, run the analysis described on this page, and hand you the answer with its confidence limits stated. Discuss this requirement.