Office of Research and DevelopmentNational Risk Management Research Laboratory
Understanding the Uncertainties Associated with Watershed Modeling for Water Quality Trading
Christopher T. Nietch, Matthew T. Heberling, and Amr Safwat
2
• Local, state, and federal agencies want water quality trading (WQT) to work, but there are many barriers. We focus on two:
– Uncertainties associated with modeling the watershed system
– Thin markets (too few participants)
• We use monitoring, modeling, and economic tools to address these barriers.
• Our approach is to use a case-study with traditional actors and potentially alternative participants at a tractable scale.― The study had to be large scale, but not so large that the spatial extent would make
capturing the uncertainties associated with credit generation and WQ attainment untraceable.
― We hypothesized that with a good monitoring program and an appropriate model we cold link all future trades to water quality consequence.
3
Research Conceptual framework:Making WQT more viable
We developed a framework in response to questions:1) In the face watershed
uncertainties and existing structure of point and non-point sources is water quality trading feasible?
2) If not, can we increase participation by considering non-typical participants?
3) Are there unintended consequences and/or additional benefits of these approaches?
We define feasibility as the preliminary assessment for the potential of trading to meet water quality goals.
East Fork Watershed Case Study and Stakeholders-EFWCoop:
4
“Only through adequate testing, refinement, and calibration….And review and buy-in from stakeholders will model related uncertainties be overcome” (Walker and Selman 2014)
1300 km2 or321,236 acres
5
East Fork Watershed System:Good monitoring program design
Spatially and temporally dense monitoring program
Nested monitoring schemes within different land use types (agr & urban)
6
• Different types of uncertainty are encountered in WQT
• Walker and Selman (2014) categorize them as:– Biophysical and Scientific Uncertainty– Extreme Event Uncertainty– Behavioral Uncertainty– Regulatory Uncertainty– Market Uncertainty
• We address the Biophysical and Scientific Uncertainty by conducting research that integrates and evaluates all five aspects that are defined as potential mitigation strategies:
– Improved Science– Direct Measurement– Effectiveness Estimates– Estimation tools and models– Trading Ratios
7
• Most interested in quantify uncertainty inTotal Nitrogen (TN) and Total Phosphorus (TP) loads
– from farm fields– from septic systems– after AgBMPs are implemented– at the watershed outlet– attenuated
• Fixed Inputs (need to rationalize uncertainty as “captured”)
– Weather Data– Land uses– Point source loads (after satisfying model input format)– Fertilizer application rates (??)– Model adjustments to simulate BMPs (??)– BMP implementation costs (both for point and non-points
sources)• Soil and Water Assessment Tool (SWAT) – Key
Modeling PackageSemi-distributed, physically based, divided into land and channel phases.
• Stormwater Management Model (SWMM) used for fine scale modeling of urban area.
Neitsch et al., 2011, http://swat.tamu.edu/media/99192/swat2009-theory.pdf
EPA-SWMM 5.0, Rossman 2010
Ranked TN Loads among Farmer BMP applicants
SWAT Model Output
8
Nitrate Loading at Parcel Scale – HRU
Level
High-resolution SWAT at watershed scale has trade-offs: Long runtimes and high data management requirements.
Nietch and others, 2013 Alternative land use method for spatially-informed watershed management decision-making using SWAT, JEE,139:1413-1423.
9
• Feasibility analysis makes cost comparisons among supply and demand for credits. That is, see if traditional WQT is even an economically viable option
• Analyze data on cost per pound of nutrient reduction for many different BMPs and at different WWTP control levels.
– However, out of 10 WWTPs in the UEFW only 5 had incentive to trade. – Also given the density of NPS relative to the location and size of PS demand, trading would
not result in necessary WQ improvement.
≈$50KAnnual
≈$1KAnnualwithout trading, vs. with tradingOr,
+Urban GI-types
CTIC. 2011.
10
• Determining the significance of BMP scenarios:
• The uncertainty in the load reduction is needed to define credit availability
• If I’m a farmer, and I want to start using Cover Crops, how do I know how many credits I will get?
1) We need to know what effect the Cover Crop BMP type has on N and/or P runoff reduction. I.e., the difference between before and after BMP placement,
2) If one credit = 1lbs TN removed, then TNreduced = number of credits available (not accounting for uncertainty)
Corn growing through decomposing cover of annual ryegrass in Clermont County farmer’s field
11
So, how do we capture uncertainty at Farm Field Scale using SWAT Model while maintaining tractability at watershed scale?
Say there is 11 AgLandUses x 10 soil types x 3 slope classes = 333 unique Field Scale Conditions:333 conditions x 2 scenarios x 1000 runs x 2 constituents = 1,332,000 individual model runs
We can’t manage the data from that many model runs!
As an alternative, we obtain the “field-scale” uncertainty by conducting an analysis at the headwatershed scale in small agricultural drainages
We still have to run the watershed model on the order of:
1000 runs x 2 scenarios x 2 constituents = 4000 times
We have to apply the same fractional uncertainty to individual fields.
12
Watershed Scale: Flow (cms)
TN (kg/month)
TP (kg/month)
p-factor 0.69r-factor 0.83R2 0.82NSE 0.75PBIAS 4.4KGE 0.62
p-factor 0.32r-factor 0.56R2 0.56NSE 0.49PBIAS 19KGE 0.58
p-factor 0.52r-factor 0.84R2 0.53NSE 0.51PBIAS 10.2KGE 0.66
Nitrogen Load Distribution for the Upper EFW – best model
simulation 1000 runs
13
Headwatershed Scale
TN (kg/month) p-factor 0.61
r-factor 1.73
R2 0.56
NSE 0.55
PBIAS 0.6
KGE 0.57
Headwatershed Monitoring
14
Uncertainty at headwatershed scale
Uncertainty at watershed scale
Scaled to Annual w/ 95%CI for
mean estimate
. ±3.5%
.
. ±4.0%
.
15
Scenario gMean L90PPU U90PPU %LE %UE EfficiencyBaseCase‐NoTill 15.0 3.1 90.8BMP‐CoverCrop 7.2 2.1 25.3Difference 8.7 ‐21.9 46.2 352 431 0.58
TN reduced normalized to cover crop area (lbs/yr.acre cc )) at headwatershed scale
Scenario gMean L90PPU U90PPU %LE %UE EfficiencyBaseCase‐NoTill 18.9 4.6 97.7BMP‐CoverCrop 12.3 4.7 36.8Difference 8.4 ‐21.8 47.8 358 467 0.45
TN reduced normalized to cover crop area (lbs/yr.acre cc )) at watershed scale
– Results suggest that a 3.5 to 4.0 Trade Ratio should be adopted for cover crops.
– On average there is about a 10% loss in treatment efficiency at the watershed scale.
16
NPS dominates PS dominates
Under assumed sufficient credit demand
Target still violated
Flow exceedance frequencyUncertainty Changes
• Determining the significance of BMP scenarios II:
• Meeting the WQ Target
WQ Target
TP (m
gL)
WQ Target
TP (m
gL)
Measured Uncertaintye.g., from the Great Miami River
Conclusions
• With a scalable model application we can rationalize credit availability and gauge risk of not meeting WQ target, therefore can help reduce regulatory uncertainty.
• A handful of detailed case studies like this one should be able to provide probability distributions to BMP scenarios that would allow estimating confidence ranges outside of complex modeling environment.
• In our system, uncertainty captured or not, WQT using traditional participants is not feasible for meeting WQ Targets.
17
• BMP uncertainty includes, baseline model uncertainty, BMP-specific effect uncertainty, and how model is simulating BMP effect at large scale. This is “Prediction Uncertainty” and can be quite large.
• In our system a 3.5 Trade Ratio minimum would be needed to account for prediction uncertainty.
• There is currently no guidance for capturing prediction uncertainty!
References
18
– Arabi, M., R.S. Govindaraju, and M.M. Hantush. 2007. A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices. Journal of Hydrology,333:459-471.
– Arabi, M., J.R. Frankenberger, B.A. Engel, and J.G. Arnold. 2008. Representation of agricultural conservation practices with SWAT. Hydrological Processes. 22, 3042-3055.
– Walker, S. and M.Selman. 2014. Addressing risk and uncertainty in water quality trading markets. World Resources Institute. Issue Brief. 10G St NE, Suite 800, Washington, DC. 20002. www.wri.org.
– Horan, R.D. and J. S. Shortle. 2011. Economic and Ecological Rules for Water Quality Trading. JAWRA, 47(1):59-69.– Woznicki, S. A., and A.P. Nejdhashemi. 2014. Sensitivity analysis of BMPs under climate change scenarios. JAWRA,1-23.,DOI:
10.1111/j.1752-1688.2011.00598.x– Karchar, S.C., J.M. VanBriesen, and C.T. Nietch. 2013. Alternative land use method for spatially informed watershed
management decision making using SWAT. Journal of Environmental Engineering. 139:1413-1423.– Wabash River Watershed Water Quality Trading Feasibility Study – Final Report. 2011. Prepared by the Conservation
Technology Information Center. http://www.ctic.org/media/pdf/Wabash%20WQT%20Feasibility%20Study_091411_final%20report.pdf
– Neitsch, S. L., J.G. Arnold, J.R. Kiniry, and J.R. Williams. 2011, Soil and Water Assessment Tool Theoretical Documentation Version 2009. Texas Water Resources Institute Technical Report No. 406. Texas A&M University System, College Station, Texas 77843-2118. http://swat.tamu.edu/media/99192/swat2009-theory.pdf.
– Rossman, L.A. 2010. Stormwater Management Model User’s Manual Version 5.0. EPA/600/R-05/040.
The data, expressed ideas and opinions, and interpretation herein are those of the primary presenter (Nietch) and do not reflect official EPA position or policy.
Top Related