Alberta Innovates - Water Innovation Program ForumMay 24, 2018
Greg Piorkowski1, Madison Kobryn1, Suzanne Tank2, Rolf Vinebrooke2, Andy Jedrych1
1Water Quality Section, Alberta Agriculture and Forestry2Department of Biology, University of Alberta
Nutrient Objectives for Small Streams in Agricultural Watersheds of Alberta
Project Background
•Increasing agricultural intensity leads to impaired water quality
Understand the Problem
•Agricultural BMPsreduce nutrient transport
Identify Solutions •Watershed-scale BMP
adoption needed to improve streams
Implement Solutions
•What are suitable nutrient targets for small streams in Alberta?
Achieve Outcome
Project Objectives
1 Conduct algal bioassessments and stream function assessments across Central and Southern Alberta
2 Derive regionally-applicable nutrient objectives through weighted Multiple Lines of Evidence (MLoE) approach
3 Compare generalized nutrient objectives with site-specific objectives determined through in-stream water quality modelling
4 Assess the achievability of generalized and site-specific nutrient objectives through watershed-scale BMP modelling
Stressor-Response Study Design
“Stressor-response modeling estimates a relationship between N and P concentrations and a response measure that is directly or indirectly related to a designated use of the waterbody (e.g., a biological index or recreational use measure)” – USEPA
Study Sites
1
2
Natural region focus
• Parkland (26 sites)
• Grassland (30 sites)
Seasonal Sampling
• Spring (April – May)
• Summer (June – August)
• 2016 – 2018 field seasons
3 Scope
3rd Strahler Order 4th Strahler Order
Stressor: Site-Level Nutrient Concentrations
ResponsesAquatic Ecosystem
Responses
Structural Functional
Nutrient UptakeMetabolismSuspended AlgaeAttached Algae
DecompositionOxygen CyclingCommunity Composition
Pigments Biomass
http://microbiologyonline.org/about-microbiology/introducing-microbes/algae
Stressor-Response: Linear
0
5
10
15
20
25
30
35
40
0 0.2 0.4 0.6 0.8 1 1.2
Resp
onse
Nutrient (mg/L)
Stressor-Response: Threshold
0
5
10
15
20
25
30
35
40
45
0 0.2 0.4 0.6 0.8 1 1.2
Resp
onse
Nutrient (mg/L)
Stressor-Response: Threshold
0
5
10
15
20
25
30
35
40
45
0 0.2 0.4 0.6 0.8 1 1.2
Resp
onse
Nutrient (mg/L)
Inverse Weighting:
Higher statistical error = Lower weight toward nutrient objective
Parkland: Summer TP Objective
Phyto-plankton
Peri-phyton
Putting it Together: Multiple Lines of Evidence
Metabolism
Nutrient Uptake
Literature
Phyto. Index
Chl a
Species Response
Chl a
Biomass
Diversity
P uptake
Decomp.DO Cycling
N uptake
Field Studies
Lab Studies
Percentile
Metric:‘threshold’+ error
Component:Composite ‘threshold’+ composite error
Nutrient Objectives: Proposed Approach
Unlikely to be Impaired
All ecosystem components are likely be performing well
Lower MLoE Error BoundTransition from Low-Risk of Impairment to Unlikely to be Impaired State
Central Value of MLoETransition from Low-Risk to Moderate Risk state
Upper MLoE Error BoundTransition from Moderate- to High-Risk of Impairment states
Low Risk of Impairment
Aquatic ecosystem is in good condition, but some ecosystem components may be stressed
Moderate Risk of Impairment
Some ecosystem components may be operational, but most are likely to be altered
High Risk of Impairment
High degree of alteration in most ecosystem components
Example: Preliminary Parkland Objectives
Objectives = Mean ± Standard Error; Derived from 7x Ecosystem Response Metrics
0.18 mg TP/L
0.23 mg TP/L
0.28 mg TP/L
1.47 mg TN/L
2.03 mg TN/L
2.60 mg TN/L
Summer TP
Summer TNA BMaintain Improve
Targ
et 1
Targ
et 2
Alternate Classification
Watershed Characteristics
• Drainage area
• Watershed Slope
• Drainage density
• Major Soil Types
• Climatic Variables
• Average Annual Runoff Volumes
1
2 Cluster analysis yielded three distinct watershed types:
1. Low slope, moderate-to-large basin, drier, higher Solonetzic soil type
2. Low slope, moderate basin size, moderate precipitation, low-Solonetzic soil type
3. High slope, small basin, higher precipitation/runoff
Regional Objectives
1
2
Spatial Applicability
• Natural region basis
• Watershed-based classification proposed
• 3rd and 4th Strahler order streams
Seasonal Objectives
• Spring (April – May)
• Summer (June – August)
3 Objectives defined by weighted Multiple Lines of Evidence (MLoE) approach
• Weight = Statistical Error
• Error propagation yields uncertainty around nutrient objective
• Uncertainty/error bounds used as management triggers/targets
Site-Specific Nutrient Objectives: QUAL2K Model
Segment StreamIdentify Sources and Abstractions
Model Reaches
SSO: General Process
Does the model error overlap with the regional nutrient objective ranges?
How do SSOs for different stream reaches compare to regional objectives?
SSO: Study Sites
Parkland – Threehills Creek
Grassland – Indianfarm Creek
Threehills Creek
Achievability of Nutrient Objectives: Watershed-Scale BMP Modeling
Watershed-Scale BMP Modeling: Study Sites
Parkland – Threehills Creek
Grassland – Indianfarm Creek
CEEOT Model: Simulated Load Reductions
Indianfarm Creek – BMP Simulations
Threehills Creek – BMP Simulations
TN ↓60%TP ↓50%
TN ↓25% TP ↓15%
Questions to Answer with BMP Modeling
1
2
Can we achieve desired in-stream nutrient concentrations through BMP application?
• Alter model outputs to simulate in-stream concentrations vs. loads
What level of effort (and at what cost) is required to achieve the derived nutrient objectives?
3 Which is better suited as management targets, site-specific or regional nutrient objectives?
• Assuming there are differences between them
Acknowledgements
Questions
