Meet Mohamed Fadel Thiam, Finance & Accounting Intern at BWI. Based in Paris and currently studying accounting, control and audit, Fadel supports the company’s day-to-day financial activities — from financial reporting and bank reconciliation to treasury operations. Discover his journey and what drew him to BWI.

We’re pleased to welcome Issam El Kadiri, Research & Development Engineer at BWI. With a strong background in data science and applied mathematics, Issam contributes to improving BWI’s hydrological forecasting models and developing new approaches – a key element for delivering reliable river flow forecasts to our clients.

We’re pleased to announce the release of BWI service v2.16.00, bringing meaningful improvements to forecast horizon and data transparency.

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.

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.

On April 8, 2026, the Indo-French Climate Resilience Seminar at IIT Delhi brought together public institutions, researchers and industry leaders to address the growing climate challenges in the Himalayan region. Through three panel discussions, the event highlighted the urgent need to strengthen hydrological forecasting, early warning systems and integrated water–energy–food resilience approaches.

With the release of v2.14, Blue Water Intelligence continues to strengthen the performance and reach of its hydrological forecasting service. This update focuses on a key improvement: significantly faster access to forecasts, alongside expanded geographic coverage and ongoing model enhancements.

We’re pleased to welcome Hanbing Peng, Precise Orbit Determination Specialist at BWI. With a strong background in geoscience and satellite navigation, Hanbing contributes to the REVALTO satellite mission by ensuring highly accurate orbit determination – a key element for reliable Earth observation data.