MONITORNG PROCESS
The goal of the Elk River Watershed Monitoring Collaborative is to design and implement a monitoring program by engaging partners with diverse knowledge bases but a mutual goal to understand the health of the Elk River. To ensure the monitoring efforts continue to represent the goals of partners and the community, we apply the Adaptive Monitoring Framework.
Monitoring Priorities
In 2022 the Monitoring Working Group narrowed down three key areas of focus for monitoring the watershed. These initial priorities will guide monitoring work during the early years of the Collaborative.
Climate change driven flooding & drought
Provide information to increase our understanding of time trends and spatial patterns of extreme flows to support effective planning, mitigation, and management decisions.
Indicators:
water flow, water turbidity, and water temperature
Fish Habitat
Provide fish habitat quality/quantity information to understand and help safeguard fish habitat throughout the watershed.
Indicators:
water flow, water temperature, water column turbidity, water chemistry, and benthic invertebrates (CABIN method).
Ktunaxa Land and Water Uses
Yaq̓it ʔa·knuqⱡi’it to lead and govern monitoring results coming out of this program, to support the recognition, protection, and restoration of Ktunaxa land and water uses in the Qukin ʔamakʔis.
Indicators:
traditional Ktunaxa land and water uses and current Ktunaxa land and water use.
The Adaptive Monitoring Framework
By using this framework, we allow for continuous re-evaluation of monitoring practices to ensure they serve the purpose of assessing the health of the watershed.
Existing Data Assessment
To develop an effective monitoring program, it’s essential to understand what knowledge already exists and what the gaps in understanding are. Many organisations collect and release open-access data, and the Monitoring Collaborative is working to consolidate available data to get a big-picture understanding of what data says about the state of the watershed.
Data Consolidation
Data from different sources needs to be collected and aligned into a single structure. Data quality needs to be assessed by consistent communication with data owners, and poor quality data will be removed.
Types of Data Collected
Water quality, benthic invertebrates, precipitation, temperature, water flows, landuse data
Data Visualisation and Hypothesis Building
Data needs to be visualised to observe potential trends visually and build testable hypotheses.
Visualisation Types
Site distribution, timeseries, seasonality, simple spatial variation, landuse concentration
Evaluation
Relationships among sampling locations, sampling season and year, weather and climate, and land uses will be explored using applicable statistical tools and other data exploration approaches.
Statistical Tests
Statistical tests will be based on hypotheses developed