Watershed Diagnostics for Improved Adoption of Management Practices: Integrating Biophysical and Social Factors
•Download as PPTX, PDF•
1 like•962 views
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Watershed Diagnostics for Improved Adoption of Management Practices: Integrating Biophysical and Social Factors
Similar to Watershed Diagnostics for Improved Adoption of Management Practices: Integrating Biophysical and Social Factors (20)
More from National Institute of Food and Agriculture
More from National Institute of Food and Agriculture (20)
Recently uploaded
Recently uploaded (20)
Watershed Diagnostics for Improved Adoption of Management Practices: Integrating Biophysical and Social Factors
- 1. Watershed Diagnostics for Improved Adoption of Management Practices: Integrating Biophysical and Social Factors PAUL T. LEISNHAM DEPT. ENVIRONMENTAL SCIENCE & TECHNOLOGY, COLLEGE OF AGRICULTURE & NATURAL RESOURCES, UNIVERSITY OF MARYLAND NIWQP: 2012-51130-20209; $631,500
- 2. Talk Outline 1. Background 2. Impacts of climate change on pollution Critical Source Areas (CSAs) 3. Impacts of climate change on Best Management Practice (BMP) effectiveness 4. Competing views on water pollution and BMPs among stakeholders
- 3. Paul Leisnham, Socio-Ecology Dan Boward, MD-DNR Ecology Tom Hutson & Nicole Barth, Extension David Lansing, UMBC Sociology Adel Shirmohammadi, Hydrology Victoria Chanse, Amanda Rockler & numerous students Extension & Sociology Hubert Montas, Diagnostic Tools Jaison Rekenberger, Diagnostic Tools Daniel Schall, UMBC Sociology Julianna Brightman, Sociology
- 4. Develop, implement, and evaluate a cross-disciplinary approach for watershed management that considers both technical (biophysical) and social factors Objectives 1. Evaluate community attitudes towards water resources and identify barriers to BMP adoption 2. Develop a Diagnostic Decision Support System (DDSS) to identify CSAs and prescribe targeted BMPs 3. Develop and evaluate strategies for increasing awareness and behaviors towards water quality to increase BMP adoption
- 5. Tom Fisher, Horn Point Laboratory, UMCES Algae and turbidity are increasing over time, and summer bottom DO is decreasing Little improvement in Chesapeake Bay in past 40 years
- 6. 0 0.2 0.4 0.6 0.8 1 1.2 5/1/87 5/6/87 5/11/87 5/16/87 5/21/87 5/26/87 5/31/87 Date Precipitation(inches/day) Study Area Spatial Database Pollutant Transport Model Diagnosis Expert System Pollutant Export Hot Spots BMP Allocation Plan Prescription Expert SystemExcess Export Causes Pollutant Hot Spots Excess Export Causes Bio- Dynamic Models DDSS Development Components: GIS, Models, Expert Systems Models: SWAT (SUSTAIN, AQUATOX, FIBI, EPA BASINS) Expert Systems: MATLAB (predicate calculus to decision trees) • DDSS will rank environmental causes to pollution and prescribe a BMP allocation plan • Geo-referenced biophysical and land management data • Simulate watershed responses to selected stressors • Identify CSAs & prescribe appropriate BMPs • More “bang for the buck”
- 7. Climate Change Forecasts for the Northeastern United States ↑ Elevated concentrations of atmospheric CO2 ↑ Elevated mean temperature ↑ Increased mean rainfall ↑ Shift in seasonal rainfall patterns o Wetter spring o Drier late summer ↑ Increases in extreme weather events
- 8. Climate Change: Precipitation Changes Precipitation Volume: Very Heavy Events i.e., top 1% (1958-2012)
- 9. Model Construction and Analysis Software Purpose and Progression ArcGIS Spatial Data Analysis (Graphics and Database) ArcSWAT Model development SWAT input file Generation SWAT-CUP Model calibration (SUFI-2 Method) SWAT Experimental engine (SWAT.exe) Model Calibration Warm-up (3 yrs): 1/1/1990 to 12/31/1992 Calibration Period (15 yrs): 1/1/1990 to 12/31/2004 Data Type Characteristics Topography/DEM 10 meter Landuse/Land Cover NLCD 2006 HRU Soils NRCS SSURGO Weather (Calibration) 3 stations NCEP CFSR Flow, Nutrients and Sediment 1 USGS Gauging Station Greensboro Climate Change GFDL-CM2.1 CMIP3 (B1, A1B, A2) (Mid and End Century) Model Validation Warm-up (2 yrs): 1/1/2005 to 12/31/2006 Validation Period (6 yrs): 1/1/2005 to 12/31/2010 Downscaled climate predictions from global model based around 3 IPCC scenarios that lead to low, medium, and high future levels of CO2
- 10. Definition of a Critical Source Area (CSA) Rank SurQ (mm H20) TSS (tonnes/ha/yr) TN (kg/ha/yr) TP (kg/ha/yr) Top 10% >406 >1.03 >24 >1.9 Top 20% >359 >0.73 >16 >1.6 Top 10%: Value for which the top ~770 HRUs is separated from the other 7705 HRUs Top 20%: Value for which the top ~1540 HRUs is separated from the other 7705 HRUs An area that exports a target pollutant at concentrations significantly above average Always defined a watershed area (or HRU) a CSA if it exported a given pollutant at these fixed thresholds
- 11. Impacts of Climate Change on Pollution CSAs
- 12. Watershed Response: Baseline and Climate Change Scenarios 400 450 500 550 600 650 700 750 800 850 B ASELINE M ID C ENTUR Y (2 0 4 6 -2 06 4) END C ENTUR Y (2 0 8 1 -2 10 0) STREAMFLOW (MM/YR) B1 A1B A2 10 12 14 16 18 20 22 B ASELINE M ID C ENTUR Y (2 0 4 6 -2 06 4) END C ENTUR Y (2 0 8 1 -2 10 0) TOTALNITROGEN (KG/HA/YR) B1 A1B A2 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 B ASELINE M ID C ENTUR Y (2 0 4 6 -2 06 4) END C ENTUR Y (2 0 8 1 -2 10 0) TOTALPHOSPHORUS (KG/HA/YR) B1 A1B A2 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 B ASELINE M ID C ENTUR Y (2 0 4 6 -2 06 4) END C ENTUR Y (2 0 8 1 -2 10 0) SEDIMENT (MG/HA/YR) B1 A1B A2
- 13. Surface Run-off & Total Suspended Solids Present Day CSAs SRES A2 End Century CSAs 25-30% ↑ rainfall 3.9X ↑ 3.0X ↑
- 14. Present Day CSAs SRES A2/A1B End Century CSAs Nitrogen & Phosphorous 25-30% ↑ rainfall 3.7X ↑ 2.5X ↑
- 15. Impacts of Climate Change on BMP Effectiveness
- 16. Targeting Method: Dense CSAs 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝐷𝑒𝑛𝑠𝑒 𝐴𝑟𝑒𝑎𝑠 𝑎𝑡 𝑡ℎ𝑒 𝑡𝑜𝑝 20% 𝐵𝑟𝑒𝑎𝑘 𝑉𝑎𝑙𝑢𝑒 Rank SurQ (mm H20) TSS (tonnes/ha/yr) TN (kg/ha/yr) TP (kg/ha/yr) Top 10% >406 >1.03 >24 >1.9 Top 20% >359 >0.73 >16 >1.6
- 17. Targeting Dense CSAs of Today Residual CSA Density with Baseline BMP Design Subjected to Current, A1B and A2 Climate Conditions TMDL targets met 64%-143% above TMDL Targets
- 18. Targeting Dense CSAs of the Future Residual CSA Density with A2 BMP Design Subjected to Current, A1B and A2 Climate Conditions. TMDL targets met TMDL targets met
- 19. Competing Views on Water Pollution & Management Among Stakeholders
- 20. Two dominant discourses from Interviews and Q-sort Scientists and environmental professionals: Stressed the role of agriculture in water degradation, & importance of regulating all adverse land-use practices Farmers and agricultural extension agents: Stressed the importance of industry-driven regulation, local knowledge & were skeptical of “outside” knowledge, including science. Axes of Regulation and Cultural Knowledge Legend Increasing Regulation Deregulation Outside knowledge Local knowledge
- 21. Highlights Summary 1. Climate change will likely increase areas that export excessive levels of pollutants 2. Targeting CSAs works, but a BMP allocation plan based on today’s CSAs fail to meet TMDL goals 3. We need a BMP allocation plan based on future climate scenarios demanding greater community-based participation 4. We need to overcome existing divergent viewpoints among stakeholders on pollution and its management
- 22. Final Work: Integrating Social Dimensions into DDSS KAP surveys with farmers and urban residents.
- 23. Example Research-Education- Extension Outputs 3 journal papers in press or advanced peer-review o 2 in Special Climate Change edition of Transactions of the ASABE 3 journal articles in prep. 12 presentations at national conferences o Best student poster (Renkenberger) at 2014 AWRA Annual Meeting 2 MS students graduated 6 undergraduates trained Graduate class developed (“Watershed Ecosystem Sustainability”) 4-H EcoStewards program developed (850 participants) o 4-H youth and adult water quality education aids Adult water quality in-service trainings for educators (190 participants)
- 24. Extra Slides
- 25. Extension Outreach 59 12 0 20 40 60 80 Interested Not interested Numberoffarmers Interest Farmer interest of additional information on BMPs (n=71)
- 26. Improving awareness Leveraging connections with trusted knowledge sources (Extension & State agencies) 4-H youth programs County fair exhibits Town-hall meetings
- 27. Extension Outreach 12 2 14 40 19 3 29 5 20 57 42 3 0 10 20 30 40 50 60 Pamphet Youtube Workshop Extension Govt Other Numberofresponses Delivery Method BMP information delivery of 59 respondent farmers 'Existing Delivery (n=90) 'Desired Delivery (n=156) 59 12 0 50 100 Interested Not interested Numberoffarmers Interest Farmer interest of additional information on BMPs (n=71)
- 28. Polarizing statements There is very strong evidence that agriculture is the single largest source of nitrogen, phosphorous and sediment going into the Chesapeake Bay. Water problems like Pfiesteria should not be blamed on chicken manure because of all of the sewage being pumped into the bay right in the port of Baltimore. Pollutants like this in the bay aren’t from the farmers. The most effective regulators are people who came up from the industry of whatever it is they’re regulating because they understand what actually happens. Large companies like Perdue control all aspects of growing chickens, they should pay for Agricultural Best Management Practices for farmers.
- 29. 1 2 3 4 Water problems like Pfiesteria should not be blamed on chicken... The most effective regulators are people that come up from industry... There is very strong evidence that agriculture is the single largest source of nitrogen... Large companies like Perdue control all aspects of growing chickens... Strongly Disagree Disagree Agree Strongly Agree Farmer (n = 66) responses as part of survey These statements stimulated the strongest responses and were the most polarizing, from 26 stakeholder interviews and Q-sort analysis Paired T-TEST: T = -11.60, p < 0.0001
- 30. Biophysical Modeling Once we have a working model… 1. We can use best available science to find Critical Source Areas (CSAs) 2. We don’t have to physically install BMPs (now or in the future), saving money 3. We can use the model to test BMP configurations that will achieve TMDL targets Effects of Climate Change B1 A2 A1B SWAT (Soil Water and Assessment Tool) Land Use/Cover Topography Soils Weather Other Flow Prediction Sediment Loadings Nutrient Loadings Water Balance/Availability Nutrient Output x BMP Removal Eff (%) = TMDL Attainment
- 31. BMP Design and Targeting Methods Typical Design Strategy Baseline Model (SWAT) Apply targeting method and calculate BMP efficiencies Repeat for A2 and A1B scenarios using baseline conditions Future Proof Design Strategy End Century Model (SRES A2 SWAT) Apply targeting method and calculate BMP efficiencies Repeat for Baseline and A1B scenarios using A2 conditions Examine effect of climate change on BMP implementation Compare/Evaluate Two Targeting Methods: • CSA Based (Dense CSAs) • Non-CSA Based (Land Use) Apply Generic SWAT BMP Removal Required to Meet Bay TMDL Targets Scenario TSS TN TP Baseline 33% 45% 23% B1 63% 64% 49% A1B 69% 70% 53% A2 71% 67% 57%
- 32. Results: In-Stream Validation, Monthly Points falling on SWAT generated streams were correlated by month 1.Reach Export Correlation ◦ Discharge is significant in the same month as well as the month prior ◦ TP is significant in the same month of sampling ◦ TSS and TN are significant in month prior to sampling 2. Two month lag was considered but may be anomalous due to infrequent sampling Constituent IBI (%) Habitat Index (%) Month Discharge (cm/s) -0.83 -0.86 Sediment (mg/L) 0.38 -0.16 Total N (mg/L) 0.80 0.35 Total P (mg/L) -0.83 -0.52 Month-1 Discharge (cm/s) -0.79 -0.98 Sediment (mg/L) -0.60 -0.67 Total N (mg/L) -0.32 -0.66 Total P (mg/L) 0.05 0.52 Month-2 Discharge (cm/s) -0.12 -0.19 Sediment (mg/L) 0.20 -0.36 Total N (mg/L) 0.82 0.39 Total P (mg/L) -0.91 -0.58
- 33. 𝑇𝑀𝐷𝐿 = 𝑊𝐿𝐴 + 𝐿𝐴 + 𝑀𝑂𝑆 Point Sources + Non-Point Sources + Margin of Safety Motivation: EO 13508 and Regulation TSS, TN and TP
Editor's Notes
- My name is Paul Leisnham from the University of Maryland I want to thank Jim, Nancy and all the organizers for this conference, and it’s a pleasure to kick off the project talks today. It’s with some intrepidation that I give a talk after communication experts, but I’ll try my best to showcase some highlights from this project. It was funded by the NIWQP in 2012 and still has until 2017 to complete, and has a total budget of just over $600K.
- In my talk, I am going to address three main highlights so far, after a very brief background to the project. CSAs are watershed areas that export target pollutants at rates significantly higher than others,
- As you may have gleaned from the outline, this is a integrated research-extension-education project and cross-disciplinary in nature. My expertise is as a socio-ecologist, but we have personnel with expertise in hydrology and the development of diagnostic tools, sociology and education and extension. I have highlighted Jasion and Daniel who are you Masters students supported by the project and who did much of the work, along with numerous undergraduates.
- The highlights in this talk will center on the first two objectives.
- The focus of our work as been the Choptank watershed on the Delmarva Peninsula, and in particular a sub-watershed called the Greensboro watershed which is predominantly agricultural. The Greensboro watershed is characteristic of many watersheds in the Chesapeake Bay basin in that we are seeing little improvement in water quality despite considerable research and outreach to reduce non-point source pollutants from agricultural practices, such as sediment, nitrogen and phosphorous. Water quality data for the Greensboro watershed over the 40 years shows…
- Our approach to addressing such water degradation is to build a diagnostic decision support system tools to help watershed managers develop BMP allocations plans. The diagnostic tool will…There are three main sub-components to the : 1. Geo-referenced… of the study area. 2. Bio-dynamic models that…. 3. Set of two expert systems that will… With this approach we can get the most bang for the buck. Details: Why is a hotspot a hotspot? Is it because of topography, is it because of soil depth? Is it because of type of agricultural practice? ID appropriate practice for each hotspot. Data: Hydrology, water quality, ecosystem health Source: ARS, MBSS, student-led surveys (Leisnham) Methods: compare values, trends, orders of magnitude, timing. Nash-Sutcliffe and Pearson coefficients. Sensitivity analysis. Field inspection for hot-spot verification. Correlate predicted water quality with BIBI from field observations.
- However, when considering BMP implementation a complicating factor is climate change. The Northeast has experienced and will continue to experience a range of impacts…and of particular importance to this talk the greatest is quite possibly increases in extreme weather events.
- In particular, the northeast has already experienced a dramatic increase in the amount in very heavy events (i.e., top 1% of daily events). We may expect CSAs identified under current climate may no longer be sufficient to control NPS pollution with future climate change. Thus, we also need to identify CSAs and prescribe BMPs under different climate change scenarios. Precipitation and Temperature are the primary forcers driving watershed response. North Eastern United States: % change in amount of precipitation falling in top 1% of heaviest daily rainfall events. Temperature changes as predicted by SRES B1, A1B and A2 climate scenarios (Working Group III).
- This slide simply has some details of the data and model construction, calibration, validation based on observations at the gauging station. This study used downscaled climate predictions from global GFDL CM2.1 based around the 3 socio-economic development scenarios of the IPCC Special Report on Emission Scenarios (SRES) that lead to low, medium and high future levels of atmospheric CO2. To note, the Greensboro watershed consisted of a total of 7705 distinct HRUs based on the combinations environmental data. Detail: Used past weather to calibrated so that output in watershed model looked at watershed output in real life. The B1 storyline is characterized by a world that tends toward a service based economy and technologies that are resource efficient. The A1B scenario reflects a balance between fossil and non-fossil fuel sources, in combination with rapid economic and technological growth worldwide. The A2 storyline is one where economies and social boundaries remain heterogeneous into the future and economic and technological growth are slower than for B1 and A1B. Changes in land use not taken into account. PPT does change and
- And we defined a CSA as….. Our breakpoint was the top 10% or 20% of watershed areas, or HRUs, that exported a given constituent, and then always defined areas a CSA if they exported pollutants at these fixed excessive levels This is important to identify for focusing our BMP implementation. We want to do this as effectively and efficiently as possible. I should note here that the point of defining CSAs is to reduce the amount of land area targeted by less-strategic placement of BMPs. Not based on TMDLs or water quality. Ranking is only relative to present day model.
- All Scenarios: 25-30% more rain Changes between scenarios are also affected by rainfall intensity Produces: SurQ: 56% - 90% 1.60x TSS: 79% - 132% 1.67x TN: 56% - 88% 1.57x TP: 52% - 80% 1.54x
- Over the next two slides I am going to show you the worst-case increase in land area considered a CSA based on our fixed thresholds. We expect to see a 25-30% increase in rainfall under the A2 emissions scenario and this leads to massive increases excessive surface runoff and sediment. Keep in kind that I’m showing large increases with the worst case scenario by increases under milder emissions scenarios at mid-century also see large increases. Details: Average annual concentration and total export Percent Change in Area SurQ: 21% - 81% 3.9x TSS: 18% - 45% 2.5x Percent Change in Export SurQ: 31% - 89% 3x TSS: 46% - 81% 1.5x
- Worst case emissions scenario for nitrogen pollution is A1B which just nearly as much total rainfall but less extreme events which allows more infiltration and subsurface flow which leaches more Nitrogen Average annual concentration and total export Percent Change in Area TN: 11% - 41% 3.7x TP: 13% - 32% 2.46x Percent Change in Export TN: 31% - 72% 2.3x TP: 39% - 66% 1.70x
- To assess the impacts of climate change on BMP effectiveness we first want to consider how we will prescribe BMPs when we have four constituent pollutants.
- One method is to apply BMPs to watershed areas, or HRUs, with multiple CSAs. Here I am showing you maps with 3 CSAs overlaid each other, and areas are given a CSA Denisty. We have baseline scenario and the two higher emissions scenarios at end of century, which have more critically dense areas. Ok So what may we expect when we allocate targeted BMPs? A single HRU can be a CSA for more than one pollutant. These HRUs are more critically dense than other HRUs. Hence a CSA density. Critical Break Values are the same as defined earlier. They are fixed. BMPs at the 20% CBV need to be applied to all CSAs
- What we found is that if we implement BMPs on dense CSAs of today, we may STILL end up with significant proportions of watershed that are still critically dense CSAs in the future, under climate change, and TMDL targets are missed. BMP progress is lost due to climate change. When relating BMP effectives to TMDL targets, the take home message is that we will meet TMDL targets if we ignore climate change but miss TMDL targets by 64-143% under the worst case emissions scenario Keep in mind, that we are just focusing on TSS, N, P b/c everywhere would be a CSA with Surface runoff. Using this strategy we target 31% of the watershed area. Details: Based on Stationary design we target 31% of the watershed area BMP Eff% TSS = 61%; TN = 79%; TP = 43% However, Future Scenarios show that in the future we will still end up with 64%-143% above TMDL Targets Concluding Statement for this slide: BMP progress is lost due to climate change
- In comparison, if we target critically dense CSAs under high emissions climate change conditions, we address CSAs now and into the future and TMDL targets are met. Under this strategy we are targeting 58% of the watershed, which is more than the 31% of the prior targeting strategy, but we achieve much better outcomes. But by needing to target are larger proportion of the watershed we demand greater stakeholder commitment. Details: A future design with a 20% threshold requires targeting 58% of the land area BMP efficiencies of 82%, 74% and 72% for TSS, TN and TP respectively TMDLs are met now and in the future (Except for TN >100%)
- This slide shows the results of a Q-sort and factor analysis of stakeholder attitudes from stakeholder interviews. Q-sort is a social science instrument to understand relationships between various stakeholder viewpoints on a topic that are gathered form semi-structured interviews. For brevity, I won’t go into details of our interviews as part of this project but highlight two key elements in interpreting this figure: There were two dominant discourses from stakeholder interviews. Regulation and Knowledge Source. Each symbol represents an individual participant and symbols close to together represent similar attitudes. We can see a clear, statistically significant division, between stakeholder groups. [note that while farmers may be considered environmentalists, we considered “environmentalists” here as those people who did not farm but worked on environmental issues (e.g., scientists, environmental professionals) Scientists emphasized agricultural run-off was a significant contributor of water degradation in the region but wanted to independent regulation all land uses that contribute to water degradation. Farmers didn’t load as high on that discourse, and while acknowledging some regulation was necessary preferred it to come from within the industry and only if it did not interfere with their ability to secure a livelihood. Farmers on the other hand emphasized local knowledge as being important. Details: From these interviews two dominant discourses emerged: Regulation and Knowledge Source. Each symbol represents an individual participant and symbols close to together represent similar attitudes. We can see clear, statistically significant division, between stakeholder groups. Scientists emphasized agricultural run-off was a significant contributor of water degradation in the region but wanted to independent regulation all land uses that contribute to water degradation. Farmers didn’t load as high on that discourse, and while acknowledging some regulation was necessary preferred it to come from within the industry and only if it did not interfere with free enterprise to secure a livelihood. Farmers on the other hand emphasized local knowledge as being important. This work was led by our colleague, Dr. David Lansing from UMBC. Differences of opinions between environmentalist and farmers are not a surprise to many of you and we have to acknowledge that this is a challenge that we have to overcome to properly address watershed management. These results are impt b/c they highlight that the type of knowledge and how it is delivered is important, and that stakeholder attitudes are likely shaped by a wide range of influences, underlying values, and ideologies. The interviews revealed clear differences in stakeholder perceptions of water resources with two discourses, regulation and knowledge source, being particularly dominant. [READ SLIDE] 26 semi-structured interviews were conducted and Q methods were applied to transcripts to analyze responses. 34 statements were pulled from the interviews that covered a selection around one or two topics of what the interviewees stated. Interviewees and additional research subjects were revisited are then asked conduct a Q survey. The respondents must place the statements into a normalized distribution (individual sorts), ranking the statements from “most strongly agree” which equals positive (+4) to “most strongly disagree” which equals negative (–4). After pulling statements, each was assigned to one of two different domains and subcategories or sub-themes within each domain: 1) What is an Agricultural Best Management Practice? and 2) What drives land-use decision making on the Eastern Shore? I performed a principal components factor analysis. Two factors that were identified as significant at distinguishing groups of respondents: Regulation and Construction of Knowledge. Causes of pollution Who should regulate Role of Poultry Industry
- One of the last research steps of this project is integrating social data into the DDSS in the form of a BMP adoption sub-model. We want to estimate what the likelihood that stakeholders are likely to implement BMPs given their demographic, socio-economic and cultural conditions. We have gathered quantities data to address this in surveys and currently completing that sub-model. data We can also plan targeted education strategies to specific demographic groups that may have knowledge or attitudes that are barriers to BMP adoption, and we could model how changes in knowledge and behavior affect water quality. To do this we have administered in-depth face-to-face KAP surveys with over 72 farmers and 84 residents in the wider Choptank watershed. These numbers may not seem high but they are very sufficient to detect relationships between demographic variables, metrics of knowledge and attitude and BMP practice of the participants. We are in the process of developing this sub-model but I will simply share two results.
- Some god news from the surveys, is that the majority of surveyed farmers are interested in receiving information on BMPs.
- Some god news from the surveys, is that the majority of surveyed farmers are interested in receiving information on BMPs, and their preferred sourse of delivery is from extension agents and govt professionals. interThe second results was that farmers were generally receptive to receiving additional information on BMPs, and that their most desired delivery is via Extension Agents
- To give you a better sense of the diverse viewpoints, I want to share with you the statements that were most polarizing between stakeholders. As a part of Q-sort, these were statements were taken directly from interview transcripts, and then ranked as being most strongly agreed or disagreed among 30 such statements then presented to a second set of stakeholders. Note that Pfiesteria are heterotrophic dinoflagellates that has been associated with harmful algal blooms and fish kills
- The first is that the survey results were generally consist with the interview in that there was divergent agreement on the most polarizing statements. Surveyed farmers agreed with the two most strongly supported statements from the interviews, and disagreed with the statements that interviewed farmers disagreed with
- How can watershed modeling be useful for adaptation and climate resilience? When we talk about climate change we are either talking about adaptation or mitigation. Here we will focus on adaptation. Physical, chemical, biological changes Distributed parameter model Where do we implement our tools? BMP’s are tools Tracking vulnerability into the future. This is a time sensitive problem How is the landscape changed by climate change? Policy makers need to know what to expect We have the Chesapeake bay model. Computer Modeling Pros Identify areas that need BMPs Test BMP application Close the “monitoring gap” Cons Hydrology is imprecise BMP modeling assumes average effectiveness Limited ability to represent the “real world”. Distributed Problems and Distributed Solutions Vulnerable areas need to be identified Vulnerable areas will change with a variable climate BMPs can be expensive BMPs take time to implement BMP effectiveness changes with climate change
- Purpose of Targeting Methods: Minimize or optimize balance between BMP area placement and BMP efficiency. Table is minimum BMP efficiency: Assumes 100% aerial placement. This minimizes efficiency CSA Based: Computationally simpler but with over design
- These are the results of a time series correlation analysis between sampling points (IBI and HI vs Discharge, TSS, TN and TP). The same analysis is done but shifted by up to two months prior to the time the sample was taken (by month not by day as these are monthly average SWAT output). We see that HI is similarly significant but slightly different than IBI. HI is the physical condition of the habitat with regard to bank erosion/stability and vegetative cover. We see some correlation in the Month-2 scenario but it is to be taken with a grain of salt. All sampling points are taken only once a year so there is insufficient information to rule this out as an anomaly. However, there may be a sort of IBI and HI “decay rate” for which benthic life will respond over a time period longer than a month to various kinds of rainfall events. There is a hint of this in month-2 (Orange box) for HI looking at the negative correlations (which is good) for discharge and sediment. However, this would be difficult to determine using monthly average as it could be just spilled over correlation from data points taken very close to the beginning or end of the month of sampling. Lastly, phosphorus may have a much more immediate effect which is why correlation skips a month and is negative again in month-2. We would need data from months-3 and -4 to determine if this is a natural fluctuation response or just an anomaly.
- Executive Order and the Clean Water Act of 1972: Fishable, swimmable and drinkable Point Sources: Industry outfalls, untreated poultry farming litter, Wastewater treatment plants Non-Point Sources: Agriculture, urban and suburban open spaces, impervious areas This is our current approach to addressing water quality problems here in the US. TMDLs issued at the federal level are important because watersheds cross county, state and even country boundaries. What is missing? A consideration of climate change in the WIP framework