CANadian Watershed Evaluation Tool (CANWET™)

CANadian Watershed Evaluation Tool (CANWET™)

CANWET™ is an open data, urban and rural, platform (and developed by GREENLAND® since 2003) for cumulative effects analysis and watershed management. CANWET™ is also “Powered by SWAT” and includes open-source GIS-software designed to inform decision making for watershed management; water supply and wastewater treatment infrastructure planning; urban drainage strategies (quantity and quality); and, climate change adaptation. The platform’s ability to accurately calculate hourly water balance, nutrients, erosion sediment, bacteria, water temperatures, dissolved oxygen and other parameters from GIS data, enables CANWET™ to serve as a powerful decision support system. It also includes science-based climate change impact and mitigative Best Management Practices & Low Impact Development analytics, as well as “automated” modelling data calibration and verification capabilities.

Initial proprietary versions (in the mid 2000’s) were completed by GREENLAND® and with support from the Government of Ontario (Canada), including its use for the award-winning “Lake Simcoe Protection Plan”. Subsequent updates have also been used solely by GREENLAND™ for its client base, which includes Wastewater Infrastructure Environmental Assessment Approval and Detailed Design Projects; Watershed Planning; Master Drainage Planning; and, Source Water Protection programs.

Since 2015, GREENLAND® and the University of Guelph (Canada) have collaborated to assist the Governments of Canada and Ontario to undertake Lake Erie watershed-wide phosphorus and other nutrient reduction investigations, and affecting all Canadian sources to Lake Erie. In 2018, the first Government of Canada publications had regard for (& confidential) GREENLAND® analytics (and report) about establishing an extensive list of viable policy options. Later on, other confidential evaluations with CANWET™ were undertaken by GREENLAND® and later (in 2024) considered for the next publication update by the Government of Canada. Our background analytics at that time also examined the effectiveness of the lake basin-policy options on the basis of achieving nutrient load reduction targets; sustainable cost effectiveness; potential impacts to the economy; social acceptance; and, efficiency of implementation.

Other additional Lake Erie projects considered what initiatives were already in place and recommended how gaps might be filled. The main objective was to determine what “best suite of policy actions” could achieve the greatest nutrient load reductions, while also being the most effective in terms of cost, time and social acceptance. Finally, a unique analytical and stakeholder engagement approach used CANWET™ and as a means of quantifying and understanding the origin / timing of nutrient loads from the Canadian lands draining to Lake Erie. A platform video snapshot is shown below.

After the pandemic, GREENLAND® then initiated a longer-term collaboration with the College of Engineering at the University of Guelph. The next project focused on the development of CANWET™ with Artificial Intelligence (machine learning) features, and maintaining current capabilities, while adding new / proven-science predictive modelling functions available for the “SWAT” analytical engine. “The system can now be accessed by everyone and from government to urban planners and researchers working with the GREENLAND® team,” said Professor Prasad Daggupati at the College of Engineering. “Users can also see spatially what is happening and take appropriate actions.”  The first project involving new A.I. functionality also enabled participating government agencies to reduce the harmful effects that algal blooms have on water quality, fish, and wildlife populations within the Lake Erie Basin.
 
The latest “Big Data Version” of CANWET™ advanced earlier (desktop) versions by also utilizing high performance parallel (cloud) computing functionality. The current version includes a fully functional web-based platform with SWAT modelling tools that can allow greater access by decision makers and stakeholders. Therefore, this version advances the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.
 
The evolution of CANWET^TM (since 2015) has taken advantage of high-performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. Therefore, the platform operates now with “dramatic reductions” in simulation runtimes. The reduced runtimes also facilitated the use of new automatic calibration and verification routines for SWAT model setups – thereby, reducing project labour costs. It can now enable faster analytics for “What-If” simulations and if a re-run is requested through the web-based user interface. 
 
Looking forward, the CANWET™ (machine learning) web-based platform (“Powered by SWAT) can be used by more decision and policy makers in Canada and to understand better the sources of pollution (and related climate change factors). CANWET™ can be used to also develop sustainable policies in supporting a wide variety of basin planning Best Management Practices. For example, in January 2024, a complimentary project was completed by GREENLAND® for the Government of Ontario. It used CANWET™ to complete a Stormwater Green Infrastructure: Benefit / Cost Analysis for Lake Erie Municipalities. The project approach & technology used were also intended (from the outset by the Government of Ontario) to be transferable to other Ontario municipalities.



Finally, as of December 2025, consultations are underway involving GREENLAND®, Federal Government and Canadian Provinces about further utilizing CANWET™ (and with THREATS™) for more public-private partnership collaborations of mutual interest.

Updated: January 7, 2026
Greenland International Consulting Ltd.
 

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