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 now “Powered by SWAT” and includes open source GIS-software designed to inform decision making around watershed management; integrated water supply and wastewater treatment infrastructure; urban drainage control; 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.

The initial versions were completed with support from the Province of Ontario (Canada) to develop the award-winning “Lake Simcoe Protection Plan”. Further tool updates were then used for similar Assimilative Capacity; Watershed Planning; Master Drainage Planning; Water and Wastewater Infrastructure Planning; and, Source Water Protection projects in Canada.
 
From 2015 - Present, GREENLAND® and the University of Guelph (Canada) were retained by the Government of Canada to undertake an “Evaluation of Policy Options to Achieve Phosphorus and Nutrient Reductions from Canadian Sources to Lake Erie”. The initial project established an extensive list of the most viable policy options. Subsequent evaluations then examined the effectiveness of all policy options on the basis of achieving nutrient load reduction targets; sustainable cost effectiveness; potential impact to the economy; social acceptance; and, efficiency of implementation. Later projects then considered what initiatives were 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. A unique analytical and stakeholder engagement approach was undertaken using CANWET™ (v.4) and as a means of quantifying and better understanding the origin / timing of phosphorus loads from the Canadian watershed lands draining to Lake Erie.

In 2018, GREENLAND® initiated a 4-year software collaboration with the University of Guelph (Canada). The first project included further developing CANWET™ with Artificial Intelligence (machine learning) features, as well as maintaining current capabilities and adding new / proven-science predictive modelling functions available for the “SWAT” analytical engine. “The system will make this information accessible to everyone from government to urban planners and researchers,” says Professor Prasad Daggupati from the University of Guelph. “Users will be able to see spatially what is happening and take appropriate actions.”  The project will enable regulatory agencies to reduce the harmful effects that algal blooms have on water quality, fish, and wildlife populations in and surrounding the Great Lakes Basin.

Grand River Watershed (Ontario) - Monthly Time Series of Surface Water Runoff (2002-2013):

CANWET™ Spatial (HRU Scale) Distribution of Annual Average Phosphorus Forms (Organic, Mineral, Total at the “Site Level”) in a Subwatershed of the Grand River Watershed (Lake Erie Basin – Canada)
 
The latest “Big Data Version” of CANWET™ (developed by GREENLAND® and University of Guelph) has advanced earlier (desktop) versions by utilizing high performance parallel (cloud) computing functionality. The latest update is a fully functional web-based platform with SWAT modelling tools that can allow greater access by decision makers and stakeholders. Therefore, the new 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 CANWET^TM evolution (since 2018) 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 also enable faster analytics for “What-If” watershed simulations and if a re-run is requested through the web-based user interface. In 2021 (and beyond), it is anticipated the CANWET™ (machine learning) web-based platform (“Powered by SWAT) will be used more by decision and policy makers in Canada and to understand better the sources of pollution (and related climate change factors). For example, this includes phosphorus which is a major contributor to Lake Erie eutrophication problems. Therefore, CANWET™ can be used to also develop sustainable policies in supporting a wide variety of watershed planning Best Management Practices and ultimately help achieve the Canadian Government’s commitments to reduce 40% phosphorus entering Lake Erie by 2025.

In January 2020, another CANWET™ public – private partnership involving the City of Waterloo (Canada) was initiated by the GREENLAND® and University of Guelph team. This collaboration will develop an integrated model of surface/groundwater interactions using CANWET™ and with available “MODFLOW” databases. The platform will be used by the project team to investigate sodium chloride (salt) transport affecting the Laurel Creek Watershed. The integrated surface / groundwater platform will also take advantage of in-house high performance computing resources to calibrate and validate the CANWET™ model. This second-to-none platform will enable the Municipality to:
 

• Identify contributing areas to stormwater management infrastructure (Ponds and LIDs);
• Estimate seasonal sodium chloride loads associated with rock salt applications to contributing area roads;
• Estimate infiltrated volumes of stormwater at Ponds and LID facility locations;
• Estimate infiltrated volumes of stormwater from watercourses and pervious surfaces;
• Estimate groundwater elevations, flows and directions receiving stormwater infiltration;
• Conduct a mass balance analysis on sodium chloride and assess its transport and fate;
• Assess potential for sodium and chloride concentration increases resulting from induced stormwater infiltration practices over time; and,
• Evaluate cumulative effects from upstream drainage areas on each stormwater facility.

 
“What-If” scenario analyses will also be undertaken using CANWET™ to compare the outcomes of multiple policy options. The options will include (but not be limited to):
 

• Selecting between stormwater conveyance options that either enhance or avoid infiltration in specific geographic areas. Infiltration might be used in headwater areas with lower urban density or where alternatives to spring - winter period rock salts are applied;
• Directing future urban growth away from sensitive drinking water recharge areas that sustain groundwater sources (and via regulated groundwater wellhead protection areas);
• Identifying areas that should only receive road sanding or other alternatives to rock salt (during spring –winter periods) due to elevated risks; and,
• Identifying facilities that should be prioritized for retrofit to prevent infiltration losses.

 
The resulting CANWET™ (“Powered by SWAT and MODFLOW”) platform will allow the City of Waterloo (and neighbouring municipalities) to make better informed decisions that impact the protection of vulnerable source water in the Waterloo Region. The new tool will also have added benefits in assisting with the management of other water quality and water balance issues too.

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