bwi

BWI’s hydrological model for the La Mayenne and Le Vicoin catchment in western France achieves NNSE 0.75 on the testing dataset (April 2024 – April 2026), consistent with training performance (NNSE 0.76). Across 16 validated stations, 15 exceed the deployment threshold of NNSE 0.65. The model reliably simulates baseflow dynamics and seasonal flow variability on a slow-response, dam-regulated river system where oscillation control and low-flow accuracy are the primary operational challenges.

Water security is no longer just about supply. In the Mediterranean and beyond, the real challenge is the water trilemma: too little water, too much water, and too much pollution. The answer lies in combining governance, finance, technology, and local participation into one connected system.

Water scarcity threatens 45 percent of global GDP by 2050. Forty-one utilities across 24 countries reveal their top struggles: non-revenue water, service reliability, and climate vulnerability. This article exposes the urgent water crisis and why operational decision support-not just data-is the gap utilities desperately need filled.

BWI’s hydrological model for the La Dyle catchment in central Belgium achieves a catchment-area-weighted mean NNSE of 0.76 on the testing dataset (January 2025 – March 2026), with 9 of 10 validated stations exceeding the deployment threshold of NNSE 0.65. The model demonstrates reliable performance across a mixed agricultural and urban catchment with no dam regulation, where seasonal variability and land-use influence on runoff are the primary hydrological challenges.

On 4-5 June 2026, BWI attended Forum by Aerospace Valley 2026 in Biarritz to discuss Earth observation, hydrological forecasting and the future of water intelligence.

BWI’s hydrological model for the La Dordogne catchment at Domme achieves a catchment-area-weighted mean NNSE of 0.88 on the testing dataset (May 2025 – May 2026) — the highest area-weighted score across BWI’s published performance reports to date. Of 27 validated stations, 25 exceed the deployment threshold of NNSE 0.65, and 19 exceed NNSE 0.80. Performance at station level is highly variable however, with a standard deviation of 0.17 and one station recording a minimum NNSE of 0.07 — a localised outlier discussed below.

At GeoField 2026 in Rome, BWI explores how river basin digitization and river flow forecasting can improve irrigation dispatch, strengthen water efficiency, and help basin managers balance agriculture with energy, industry, drinking water, and ecosystem needs.

Climate change is intensifying 9 major flood hazards worldwide. From river flooding to GLOF, discover which threats impact your region and why early warning saves lives.

Water is now a strategic variable that shapes performance, resilience, and investment decisions. BWI helps organizations turn fragmented hydrological data into actionable river-flow forecasts and basin-wide intelligence, even in ungauged or data-sparse basins.

BWI is not replacing weather forecasts with kriging, but correcting and densifying them at the basin scale using connected ground sensors. That makes the hydrological model more sensitive to the rainfall that actually falls on the catchment, which is the main reason kriging is valuable for small basins with high spatial variability.