The data scarcity reflex
The default response to data scarcity in national early warning systems is to wait. The national meteorological office will eventually telemetry its stations. The transboundary basin organisation will eventually publish its discharge measurements through a machine-readable interface. The agricultural delegations will eventually centralise their rainfall observations. The waiting is reasonable. The waiting is also operationally unacceptable when populations are exposed to floods, droughts, heatwaves, and storms that are intensifying year on year.
The deeper problem with the waiting reflex is that it treats national data as a precondition for the system. The premise is wrong. National data is desirable, important, and central to long-term sovereignty over the system. It is not a precondition. The global scientific community has spent decades building the satellite missions, the reanalysis products, and the operational forecasting infrastructure that makes early warning possible without it. The discipline is to use what already exists, build the architecture so that national data slots in seamlessly when it matures, and protect lives in the meantime.
What the global sources actually offer
The global scientific infrastructure available for hydrometeorological forecasting in West Africa is substantial. Open-Meteo aggregates and redistributes weather forecasts from multiple operational centres at no cost for institutional use. The Copernicus Emergency Management Service produces fluvial discharge ensembles globally through GloFAS, providing thirty-day forecasts with quantified uncertainty. NASA POWER publishes weather archives validated through cross-comparison with global ground stations. The Copernicus Climate Change Service maintains the ERA5 reanalysis covering more than eighty years. Google Earth Engine hosts satellite-derived precipitation through CHIRPS, particularly accurate over West Africa and used by the United States Famine Early Warning Systems Network. Soil moisture is available through the NASA SMAP mission. Land cover comes from Sentinel-2. Topography comes from SRTM. The combination provides continuous, scientifically defensible operational coverage of the Senegal River Valley.
The discipline that makes these sources useful is engineering integration. Each source has its own update cadence, its own coordinate conventions, its own indicator definitions, and its own revision pattern. The ingestion layer reconciles them into a harmonised operating picture without flattening their distinctions. Pipelines run on configurable schedules from hourly forecasts to weekly satellite ingestion. Quality control flags missing data, marks outliers, and triggers recalibration signals when anomalies persist. The provenance of every data point is preserved through the audit chain. The harmonised picture the prediction engine consumes is honest about what each source contributes and how recent each contribution is.
What we built for HydroMet AI
PANEOTECH delivered the multi-source data architecture for HydroMet AI in joint venture with Effica SYS for UNDP Mauritania. Seven external sources feed the system: Open-Meteo for weather forecasts at four-times-daily cadence, Open-Meteo Flood and GloFAS via Copernicus for daily discharge ensembles, NASA POWER for daily weather archives, the ERA5 reanalysis for climatological context, CHIRPS via Google Earth Engine for weekly satellite precipitation, SMAP via Google Earth Engine for soil moisture, and the Copernicus Climate Data Store for historical flood reconstruction. Sentinel-2 and SRTM provide land cover and topographic data for the watershed model. The architecture is open: the system was designed to accept additional national sources as they mature, with no redevelopment required.
The architectural choice protects lives now while preserving the institutional path toward national data sovereignty. National Mauritanian sources will integrate as additional inputs the day they become available in machine-readable form. The system architecture, the prediction engine, the validation workflow, and the diffusion infrastructure stay constant through that transition. The work the institution invests in operating the system today compounds rather than gets discarded when the national infrastructure matures.
The institutional lesson
For national early warning systems the choice is not between waiting for national data and operating without it. It is between architecting for the data that exists today, with the global scientific infrastructure that already serves operational forecasting worldwide, and waiting indefinitely while populations remain unprotected. Architect openly, integrate honestly, and the system protects lives now while preserving the path toward national sovereignty over the data layer.
