Transformative Sensor Technologies And Smart Watersheds

Monitoring is a powerful tool that can reveal changes in water health, what’s causing the change, and how best to respond. Canada’s vast, complex, and often remote water systems are extremely difficult to regularly monitor. Understanding and adapting to short and long-term water threats in the face of climate change requires transformation and innovation in the way environmental data are collected and communicated.  

The Transformative Technologies and Smart Watersheds project (TTSW) developed new tools and ways of integrating data to advance water research in Canada. A team of 52 researchers collaborated with industry and government to develop, test, and implement innovative technologies that improve the measurement and modeling of water quantity and water quality parameters.

TTSW developed or refined new sensors (for the field, drones, and deployable aircraft) that can measure variables such as soil moisture, snow water equivalent (i.e. the amount of water stored in snowpacks), the extent and thickness of lake, river and sea ice, and chlorophyll-a and cyanobacteria in lakes and reservoirs. The team advanced Artificial Intelligence (AI) techniques through the development of machine learning/deep learning and physics-based algorithms. They are being considered for satellite mission concepts, including the Canadian Space Agency’s (CSA) Terrestrial Snow Mass Mission and the WaterSat hyperspectral mission.

TTSW researchers advanced monitoring systems to help predict when algal blooms will occur. Working alongside Environment and Climate Change Canada’s Canada Centre for Inland Waters, the National Research Council, and the U.S. National Oceanic and Atmospheric Administration, the team simultaneously collected data using surface, drone, and airborne hyperspectral sensors over Lake Erie. They compared results to obtain spectral signatures of non-toxic and potentially-toxic algae, leading to a critical step towards developing early warning systems.

TTSW is also providing guidance on the design of future microsatellite water missions. They are testing various sensors on aircraft and drones, such as GNSS Reflectometry, which exploits existing reflected radar signals off the land, ice and ocean, from Global Navigation Satellites, such as GPS. The team is working with space agencies and private industry to demonstrate the value of GNSS-R data acquired from current and upcoming missions such as HydroGNSS – a European Space Agency (ESA) Scout Mission – for the monitoring of lake ice phenology, soil moisture and frozen/thawed status, and inundated areas. There is enormous potential to use sensors to drastically improve our knowledge of water systems, especially in areas outside the reach of traditional monitoring techniques.  

Through integrated hydrologic monitoring of TTSW observatories in both southern Ontario and the Northwest Territories, the research team provided new insights into the factors controlling major components of the water balance, informing advance regional modeling tools. By combining satellite and helicopter-based remote sensing tools with novel terrestrial measurements, team members were able to characterize water circulation characteristics within rapidly changing discontinuous permafrost terrain. This is now supporting the next generation modeling platforms in northern landscapes. The team applied long-term time-series data sets, Artificial Intelligence (AI) techniques at local scales to predict shallow groundwater responses to event-based hydrologic events. These new data-driven models provide alternatives to complex physically-based models for the prediction of highly transient hydrologic phenomena.

Overall, TTSW has made available new game-changing technologies that not only function well in Canada’s cold climate but also provides measurements at a level of accuracy and scale that are meaningful to water managers and other researchers. This supports water research not only in Canada but globally.

Cutting-edge instruments advanced by TTSW to measure environmental parameters in cold regions include:

• The Chione device which sends acoustic signals into a snowpack to determine snow density, liquid water content, temperature, and importantly, snow water equivalent (SWE). With SWE, we can better predict streamflow and water infiltration to soils which is important when forecasting flood risk and freshwater availability.
• CryoSAR is a new airborne Synthetic Aperture Radar (SAR) system that can observe SWE and soil moisture over large and inaccessible areas of tundra, prairie, and forested landscapes, as well as marine and freshwater ice. This work is informing the development of snow and ice monitoring plans of CSA and ESA as they design upcoming satellite missions.
• Drones and drone-based sensors are being used to fill the critical data gap between ground-based field measurements and satellite imagery. Sensors on drones can collect incredibly high-resolution data including thermal, hyperspectral, and LiDAR. The data has been used to study water level change, snow depth, and algae blooms in water bodies.
• “Smart network” modems (under development by an industry partner) installed at field sites within the Alder Creek Watershed Observatory in southern Ontario connect researchers with their data in near-real-time. They will receive a notification when a specific event occurs, such as when a stream reaches a certain speed or water level or water quality changes within a range of interest. This technology will drastically improve monitoring in remote areas that are challenging and expensive to access.
• Portable sensor to detect lead in water – a microwave microfluidics-based sensing method can be used to detect concentrations of lead ions as low as one ppb in water systems. This technology could be very useful in water monitoring and disaster warning.