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