Transcript The effectiveness of conservation efforts in the Little Bear River Watershed Douglas Jackson-Smith: SSWA Dept, USU Nancy Mesner: WATS Dept, USU David Stevens, Jeff Horsburgh, Darwin.

The effectiveness of
conservation efforts in the
Little Bear River
Watershed
Douglas Jackson-Smith: SSWA Dept, USU
Nancy Mesner: WATS Dept, USU
David Stevens, Jeff Horsburgh, Darwin Sorensen:
CEE Dept, USU
Overview
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Background
Analysis of Existing WQ Data
Implementation & Maintenance Study
Alternative Approaches – Riparian Study
Targeting Critical Areas
Common BMP Monitoring Problems
Rethinking Monitoring
USDA’s Conservation Effectiveness Assessment Projects
National Assessment
Watershed Studies
Bibliographies and Lit Reviews
CEAP Program Objectives
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Determine whether publicly-funded programs to reduce
phosphorus loadings from nonpoint sources into
surface waters in the Little Bear River watershed are
effective;
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Examine the strengths and weaknesses of different
water quality monitoring programs; and
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Make recommendations to stakeholders to ensure that
future agricultural management efforts are targeted
towards the most effective and socioeconomically
viable BMPs.
USU Project Overview
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Original LBR watershed project (~1990-2002)
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Funds from HUA; EPA 319; EQIP
USU Conservation Effects Assessment Program (CEAP)
Grant – 2005-2009
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Assess effects of historical conservation practices
Review historical data
Map practices and their implementation
Model watershed and stream response
Outreach and education
Establish water quality monitoring network
Little Bear
Watershed
Little Bear River Hydrologic Unit Project
Pre-treatment problems:
Bank erosion, manure management, flood irrigation problems
Treatments:
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bank stabilization,
river reach restoration,
off-stream watering,
manure and water management,
grazing management
Analysis of Historic Water
Quality Trends
Seasonal Kendall Trend for TP concentration at
Mendon Rd (mouth of LBR).
1.0
Conservation
project
initiation
Total Phosphorus, mg/L
0.8
Slope
-0.0043 mg/L yr
Since 1992
No Significant
0.6
slope before 1990
0.4
0.2
0.0
1980
1985
1990
Date
1995
2000
2005
Flow data may drag down
‘post’ estimates
Ambient Monitoring Data
Little Bear at Paradise
Moist
Dry Cons ‘Normal’
Projects
Dry
OBSERVATIONS
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Trends suggest water quality improvements
Data Record Insufficient to
Tease out Exogenous Variables – project coincided with
changes in background climate conditions
 Link Trends to BMP Implementation
 Support Traditional Modeling Approaches
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Implementation and
Maintenance of BMPs
Socioeconomic Component
PROGRAM
SIGNUP
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BEHAVIOR
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WATER
QUALITY
Socioeconomic Methods
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Gather formal practice info from NRCS files
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Went through every file – 90 landowners
Create master list of practices (871 total)
Copied key maps for interviews
Conduct field interviews with participants
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Validate file information
Contacted 70 of 90 participants
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55 agreed to be interviewed
61% of all landowners; 79% of those we contacted
Conducted field interviews - ~90 minutes
Detailed discussoin about BMP experience
Findings - Implementation
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Individual BMPs
83% of BMPs successfully implemented
 Reasons for non-implementation (17%)
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Some cases – not recognized as contracted BMP
 Many – management practices that did not change
behavior (based on interview discussion)
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Farm-Level
32% farms implemented all BMPs
 60% farms implemented more than ½
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Maintenance of BMPs
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Is it still there? If not, why not?
Overall –
21% of implemented BMPs not still there
 Combined with non-implemented practices = 1/3 of
all originally contracted BMPs not currently there
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Why not maintained?
No longer farming or sold land – 32%
 Still farming, no longer use – 68%
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BMP Implementation & Maintenance by "Type"
100
90
83
83
80
70
60
49
50
40
30
20
10
0
Structural
Planting, Clearing and
Leveling
Percent implemented
Percent original BMPs still there
Management
Percent maintained
Implications: Maintenance
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Good news:
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Not so good news:
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Producers did not discontinue the practices because they
did not like them
The management practices had the shortest lifespan
ALSO: Nonfarm Development and Farm Changes
can also affect long term impacts
Implications: Implementation
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Management practices are the heart of
conservation programs
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Failure to fully implement may have huge
impacts on success
Big Question: How can management
behaviors be implemented more
effectively?
Analysis of Riparian Area
BMPs
Videography Analysis
Component
Limitations to WQ monitoring data in
1990s
 Search for alternative indicators of BMP
impact
 Discussions with colleagues led to
discovery of 1992 aerial 3-band
videography for stretches of LBR
 Arranged to re-fly the river in 2007
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Analysis Strategy
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Match images from 1992 and 2007
Classify vegetative conditions for both time periods
within identical riparian zones
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Riparian trees
Small shrubs & grasses
Bare soil
Water & Shadows
Quantify changes in riparian vegetation and stream
geomorphology between 1992-2007
Associate presence or absence of ‘riparian-relevant’
BMPs to these changes
‘Riparian Area’ Focused BMPs
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Stream channel structural BMPs
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Stream access controls for livestock
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Clearing & snagging (326)
Streambank and shoreline protection (580)
Stream channel stabilization (584)
Riparian fencing (5383) – subset of 382
Stream crossing (578)
Riparian vegetation BMPs
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Channel vegetation (322)
Critical area planting (342)
Tree/Shrub establishment (612)
(13,825’)
2007 digital images
1992 video images
Site: Upstream from Hyrum Dam
1992 Multispectral Mosaic
Detail
2007 Multispectral Mosaic
Initial Observations
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Significant vegetation growth
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Trees significantly larger throughout watershed
Significant geomorphologic changes in main
stream channel path
Moving centerline
 New ‘islands’
 Major bank cuts & shifts in some new erosion
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BIG QUESTION: Is it because of
BMPs?
1992
2007
1992
2007
STATISTICAL RESULTS
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Calculate area for each of 5 different vegetative
classes
PREVIEW: analysis approach
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Document overall patterns of change
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Shows the ‘background’ trends
Compare changes in “BMP impact zones”
Aggregated riparian-relevant BMPs
 Individual riparian-relevant BMPs
 Comparison to Non-BMP areas
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Percent of Riparian Zone by Vegetation Type,
BMP and Non-BMP Impacted Zones
80%
66%
70%
61%
Percent of Riparian Zone
60%
53%
50%
39%
40%
30%
20%
20%
24%
17%
15%
17%
17%
9%
10%
18%
15%
13%
8%
9%
0%
Water/Shadow
Riparian Trees
Shrubs & Grasses
Bare Soil
BMP areas 1992
BMP areas 2007
Non-BMP areas 1992
Non-BMP areas 2007
Percent Change in Riparian Vegetation by
BMP Status, 1992 to 2007
80%
Percent Change 1992-2007
60%
55%
40%
33%
25%
20%
0%
(-20%)
(-40%)
(-46%) (-47%) (-46%)
(-47%) (-45%) (-48%)
Shrubs & Grasses
Bare Soil
(-60%)
Riparian Trees
BMP area
NonBMParea
Overall
Quick Summary
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Riparian conditions improving throughout watershed
(more trees, less exposed soil)
BMPs installed in areas with less vegetation
BMPs associated with much more rapid growth in tree
cover, similar rates of decline in exposed soil
Fences = reduced exposed soil most
Instream work = increased trees the most
Targeting Critical Areas
Idea behind Targeting…
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Growing Recognition of Landscape
Variability
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Research Q: Is there evidence that the BMPs
implemented in LBR specifically targeted critical
areas?
 Critical Areas: areas where the potential contribution of
pollutants (i.e., sediments, phosphorus) to the receiving water is
significantly higher than other areas
Combined Map of
Risk Zones
Description of LBR Area
Lowinfluence
km2 (%)
Low-risk
km2 (%)
Sub-risk
km2 (%)
Risk
km2
(%)
Total
km2
LBR Watershed (total)
365 (53%)
225 (33%)
57 (8%)
35 (5%)
682
Farm Field Area
173 (67%)
47 (18%)
20 (8%)
19 (7%)
259
Contracted Farm
Field Area
38 (48%)
21(26%)
12(15%)
9(11%)
80
Non-Contract Farm
Field Area
135 (75%)
26 (15%)
8 (4%)
10 (6%)
179
Sub-Risk
Low-Influence
23%
Covered
By
BMPs
62%
62 %
23%
Low Risk
Risk
47%
47 %
47%
47 %
Implications: Spatial Analysis
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Evidence exists that higher risk zones were
targeted with BMPs (not random)
More than ½ of riskiest areas covered by BMPs
More than 70% of BMPs in zones that are not
considered at high risk for runoff erosion
 Suggests opportunity for greater targeting &
efficiency
 Related to structure of program
Common Problems in
BMP Monitoring
Programs
Lessons Learned: Common problems in
BMP monitoring programs
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Failure to design monitoring plan around BMP objectives
Failure to identify and quantify sources of variability in
these dynamic systems.
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Failure to understand pollutant pathways and
transformations  choosing inappropriate monitoring
approaches
v
Little Bear River Watershed, Utah
Total Observations at Watershed Outlet site
Discharge
1976 - 2004:
1994 - 2004:
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Total phosphorus
162
72
241
99
11
10
10
11
6
7
6
4
2
4
1
13
13
13
4
10
10
5
7
8
8
8
Number of
observations
each year
Was the original UDWQ monitoring program a failure?
No….Program was intended to detect exceedences of water
quality criteria.
The failure was ours…. In attempting to use these
monitoring data for detecting change in loads
• Failure to design monitoring plan around BMP objectives
• Failure to identify and quantify sources of variability in
these dynamic system.
• A failure to understand pollutant pathways and
transformations  choosing inappropriate monitoring
approaches
“lower watershed site”
“upper watershed site”
Monitoring stations
Data Processing
Applications
Internet
Monitoring/data system
Base Station
Computer(s)
Data discovery, visualization,
analysis, and modeling through
Internet enabled applications
Workgroup HIS
Server
Programmer interaction through
web services
Internet
Telemetry
Network
Observations
Database
(ODM)
Environmental Sensors
Workgroup HIS Tools
Upper Site
Flow (cfs)
Turbidity (NTU)
• Seasonal and
annual variation
• Variation
between sites
• Different
pathways of
pollutants
Lower Site
Flow (cfs)
Turbidity (NTU)
January – December 2006
Sample Data
Surrogate monitoring results
Sources of variability in sampling data
• Relationship of surrogate to target
pollutant
• Sampling frequency
• Timing of sampling
• Rare events
Turbidity vs TSS at Upper Site
• Variability in correlations between turbidity and
water quality parameters (TSS and TP)
Impact of “rare” events
TSS Load
Upper Site
Lower Site
8.9 X 106
1.4 X 107
Runoff (% of total)
89%
54%
Baseflow (% of total)
11%
46%
Storms (% of baseflow)
<1%
16%
Annual (kg)
• Failure to design monitoring plan around BMP objectives
• Failure to identify and quantify sources of variability in
these dynamic system.
• A failure to understand pollutant pathways and
transformations  choosing inappropriate
monitoring approaches
Problems with “one-size-fits-all” monitoring design
Rees Creek TSS load
50000
45000
40000
kg / day
35000
30000
25000
20000
Above
15000
Problem:
excess sediment
10000
Below
Average
flow
=
20
cfs
5000
BMP = series
of in-stream sediment basins
0
1
2
3
4
5
weeks
6
7
8
9
Bear River phosphorus load
400
350
load (kg/day)
300
250
200
150
100
Problem:
excess phosphorus
50
Average
flow = 1000 cfs
0
BMP = fence
cattle
OUT
of 4riparian
1
2
3
5 area
6 and 7revegetate
8
weeks
9
Rethinking Monitoring
Designing Monitoring Programs to
Evaluate BMP Effectiveness
Nancy Mesner, Dept of Watershed Sciences
Utah State University
[email protected]; 435 797 7541
Ginger Paige, University of Wyoming
University of Wyoming
[email protected]; (307) 766-2200
The road to more effective monitoring….
 Monitoring plans require careful thought before anything is
implemented.
 Consider how the data will be used to demonstrate change.
 Use your understanding of your watershed and how the
pollutants of concern behave to target monitoring most
effectively.
 Use different approaches for different BMPs.
The road to more effective monitoring….
 Keep project goals and objectives in mind when
monitoring BMPs
 Monitor at an appropriate scale
 Keep time lags in mind
Be selective, consider individual situations
Monitor surrogates when appropriate
Control or measure human behaviors / other watershed
changes.
Focuses on the considerations
and decisions necessary as a
project is first being
considered.
NOT a “how-to” manual of
protocols
Online, interactive version
Currently being used to
develop monitoring plans in
MT, CO, WY, UT and tribes
Target Audience
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State Environmental Agencies
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Conservation Groups
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Land Management Agencies
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Citizen Monitoring Groups
Table of Contents
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INTRODUCTION
SECTION 1 What is Your Monitoring Objective?
SECTION 2 Understanding Your Pollutant and Your Natural System
SECTION 3 Consider the Scale
SECTION 4 Monitoring versus Modeling: Different Approaches to
Detecting Impacts
SECTION 5 Choosing the Best Monitoring Design
SECTION 6 Site Specific Considerations
SECTION 7 Protocols
SECTION 8 Quality Assurance and Quality Control
SECTION 9 Data Management
SECTION 10 Analysis of Data
SECTION 11 Interpreting and Using the Data
REFERENCES
APPENDIX A-C: DEFINITIONS & RESOURCES
Additional Resources - Tools
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Check list
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Decision Tree
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identify KEY components of a monitoring program
non- linear process – very interactive
Web Version of the Guidance Document:
(Under Development)
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active links to the information and references in the
Guidance Document
Check List
► Method
to help identify
KEY components that
need to be considered
► Takes
one through the
thought process.
Decision Tree
► Identifies
KEY
components
► Shows links between
components
► Links to information
in the Guidance doc
► Non – linear!!
Next Steps
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Finalizing document
Available as a document & online as pdf
Northern Plains and Mountains Website
http://region8water.colostate.edu/
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Developing web version
Links to “key” information
 models
 websites
 water quality standards
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Using in watershed WQ monitoring programs
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Getting and incorporating feedback
Additional Conclusions
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Formal USDA Program files are imperfect guide to actual
BMP implementation & maintenance
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Fieldwork can generate important insights into waterquality relevant behaviors
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More accurate behavioral component of models
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Understanding barriers to implementation & maintenance
Face to Face Contact = particularly useful
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Takes time & money
Future Actions
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Assistance with Watershed Coordinators in
developing effective monitoring plans;
Application of many of the lessons learned on a
Utah watershed project
Evaluation of effectiveness of Utah’s NPS
program.
QUESTIONS?
CONTACT INFO:
[email protected]
[email protected]
[email protected]
This research is supported by CSREES CEAP Competitive Watershed
Grant UTAW-2004-05671