Full description
At WaterITech, we have many years of expertise in application of state-of-the-art, open-source hydrological and water quality models, including the SWAT+ model for watershed hydrology, and the GOTM-WET model for water quality in lakes and reservoirs. We can apply the models for any giving watershed or reservoir in the world based on globally available data sources, or specific data sources requested by our clients. We also offer advanced data analysis including time series trend analysis and a wide range of machine learning approaches for regression and classification analysis.
Value proposition
We are world leading model experts with many years of experience. We apply mainly free open-source model solutions that you may share at your own discretion. We enable an array of climate change and environmental impact scenario options based on highly recognized, science-based and very well documented solutions. We also offer complete onboarding of the models to the WaterWebTools forecasting platform - giving you easy access to operational forecasts (9 days into the future) for watershed hydrology, reservoir storage, water quality and more.
External links
Product pricing
- Pricing depending on specific customer demands
Solution implementation
A digital twin of Europe's largest saltwater lagoon
The SMARTLAGOON project is a good example of how several of our services can be utilized and integrated. The main purpose of the project is to develop a holistic digital twin of the Mar Menor lagoon and its catchment - which can provide insights into both the shortterm (days) and longterm (decades) future of the lagoon's health. A SWAT+ model is applied for the catchment, and the GOTM-WET model for the lagoon itself. In addition, an IoT based sensor system is installed in the center of the lagoon. Models and sensor data are implemented and coupled within the WaterWebTools Platform, which effectively comprise an integrated digital twin for the lagoon and its catchment and enable 9-day forecasting of its water quality. The ability to assimilate sensor data in real-time into the model forecast was developed in the project, thereby improving the skill of the forecasts.
