Demand for Stream Mitigation in Colorado, USA
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Interviews with Experts, Stakeholders, Resource Managers, and Regulators
2.2.2. Stream Impacts and Compensatory Mitigation via ORM
2.2.3. Mitigation Bank Data via RIBITS
2.2.4. Hydrography Data and Waters of the United States (WOTUS)
2.2.5. Demand-Related Data
2.3. Methodology
3. Results
3.1. Spatial Distribution of Recent Stream Impacts Requiring Compensatory Mitigation
3.2. RIBITS and Current Supply of Mitigation Credits
3.3. Forecasting Impacts and Demand for Stream Mitigation in Colorado Using Suitability Analysis
- Demand for stream mitigation is an increasing function of land use changes related to growth and development.
- Demand for stream mitigation is higher for land use changes in areas with greater concentrations of streams.
- Which qualitative impact types (e.g., residential development, transportation) have occurred most frequently in Colorado in the past ~5 years?
- Which qualitative impact types have required mitigation most frequently in Colorado in the past ~5 years?
- Do proxy variables for recent land use change (e.g., population change, housing unit change) differ significantly between impact sites that required mitigation and those that did not?
- Where are impacts likely to occur in Colorado in the next ~5 years?
- Which stream segments in Colorado are most at risk for impacts in the next ~5 years?
- How many linear feet (LF) of streams in Colorado are at risk for impacts in the next ~5 years?
- How many LF of streams in each HUC-8 unit in Colorado are at risk for impacts in the next ~5 years?
- Which HUC-8 units in Colorado are (most) likely to experience impactful land use change in the next ~5 years?
- ▪
- Low: where the demand index is within one standard deviation of the statewide average;
- ▪
- Moderate: where the demand index is between one and two standard deviations higher than the statewide average;
- ▪
- High: where the demand index is between two and three standard deviations higher than the statewide average; and
- ▪
- Very High: where the demand index is three or more standard deviations higher than the statewide average.
3.4. Stakeholder Insights on Planned Stream Mitigation in Colorado
4. Discussion
4.1. Need and Desire for Stream Mitigation Banks in Colorado
4.2. Future Demand for Stream Mitigation Credits in Colorado
4.3. Regulatory Climate in Colorado as It Pertains to Stream Mitigation
4.4. Challenges for Stream Mitigation Banks
4.5. Will Stream Mitigation Banks Accomplish the Objective of Restoring and Maintaining the Chemical, Physical, and Biological Integrity of Colorado’s Waters?
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. List of People We Interviewed to Assess Regulatory Climate and Provide Expertise on Compensatory Stream Mitigation in Colorado
- Linda Bassi, Section Chief for Stream & Lake Protection Section (Instream Flows Program), Colorado Water Conservation Board
- Brett Bovee, Regional Director at WestWater Research, LLC
- Tim Covino, Watershed Hydrologist and Stream Ecologist, Colorado State University
- Jeff Deatherage, Chief of Water Supply, Colorado Division of Water Resources
- Stephen Decker, Mitigation Banking Specialist, Omaha District, USACE
- Martin Doyle, Director of Water Policy Program, Nicholas Institute for Environmental Policy Solutions, Duke University
- Kara Hellige, Senior Project Manager, Durango Field Office, Sacramento District, USACE
- Brad Johnson, Private Consultant, Primary Investigator on the FACWet developmental team
- Sara Johnson, Executive Director of the Ecological Restoration Business Association
- Sarah Marshall, Wetland Hydrologist/Ecologist, Colorado Natural Heritage Program
- Steve Martin, Environmental Planner, USACE – Institute for Water Resources
- Julia McCarthy, Environmental Scientist, U.S. Environmental Protection Agency
- Joe McMahan, Chief-Regulatory Branch, Omaha District, USACE
- Matt Montgomery, Senior Project Manager, Grand Junction Regulatory Office, Sacramento District, USACE
- Adam Riggsbee, President, RiverBank Conservation
- Joel Sholtes, Hydraulic Engineer, Bureau of Reclamation, Lakewood, CO
- Jay Skinner, Colorado Parks & Wildlife Instream Flow Specialist
- Zach Smith, Water rights attorney, Colorado Water Trust, Denver, CO
- Allan Steinle, Chief-Regulatory Division, Albuquerque District, USACE
- Chris Sturm, Stream Restoration Coordinator, Colorado Water Conservation Board
- Luke Swan, Lead Geomorphologist and Senior Project Manager, Otak, Inc., Boulder, CO
- Reagan Waskom, Director of Colorado Water Institute
- Ellen Wohl, Fluvial Geomorphologist and Environmental Scientist, Colorado State University
Appendix B. Data Manipulation, Analytical Methods, and Intermediary Results
Appendix B.1. Data Manipulation and Analytical Methods
# | Research Question | Protocol for Answering Question |
---|---|---|
3 | Do proxy variables for recent land use change (e.g., population change, housing unit change) differ significantly between impact sites that required mitigation and those that did not? | Impact sites from the ORM dataset are represented as discrete points. Raster datasets must be created to describe the continuous land use properties and changes surrounding these locations to better characterize differences, if any, between sites that required mitigation and those that did not. |
4 | Where are impacts likely to occur in Colorado in the next ~5 years? | Because land use change can occur anywhere, it is necessary to represent the potential for development-related land use change (and impacts) across the full extent of the study area. Raster modeling can be used to combine risk factors. |
5 | Which stream segments in Colorado are most at risk for impacts in the next ~5 years? | Stream (vector) data can be overlaid onto the potential development impacts (raster) data created in the previous step to identify streams that are likely to be impacted in the near future. |
6 | How many linear feet of streams in Colorado are at risk for impacts in the next ~5 years? | Using the output from the preceding step, the length of stream segments that intersect with high-development-risk areas can be summed. |
7 | How many linear of feet of streams in each HUC-8 unit in Colorado are at risk for impacts in the next ~5 years? | The total length of at-risk streams identified in response to research question #6 above can be broken down by HUC-8 unit. |
8 | Which HUC-8 units in Colorado are (most) likely to experience impactful land use change in the next ~5 years? | The quantitative information from the previous step can be summarized in more qualitative terms. |
- ▪
- Esri projected population and housing unit growth rates from 2016 to 2021;
- ▪
- Projected job growth from 2015 to 2021 (Note: 2015 is the most recent year of data available in the U.S. Census LODES dataset described above. To arrive at future job forecasts, the team relied on a type of polynomial autoregressive model in which 2015 job counts were modeled as a function of job counts in each year from 2010 to 2014, as well as the squares of those past job counts. Backwards selection revealed that only the two most recent periods (2013 and 2014), as well as their squares, had sufficient explanatory power for the 2015 job count. The parameters from a model with those four terms and an intercept were then applied iteratively to estimate job counts for 2016-2021. In other words, the job forecasts are based on somewhat of a two-period moving average. To mitigate the high uncertainty involved in the forecast process, the average of the six [moving-average] estimates were taken and used to represent “future job counts” in the demand analyses. These forecasts estimate 3.4% annual growth in jobs through 2021, which is admittedly overly optimistic relative to the 2.7% annual growth that occurred between 2010 and 2015, and the 2.2% growth rate that was expected to occur between 2016 and 2017 (Svaldi, 2016). It is recommended that future studies conduct sensitivity analyses and compare and contrast estimates from additional projection methods to quantify expected job change at the census block level. For the present pilot study, we rely on the optimistic forecasts only.);
- ▪
- USGS EERMA energy potential (gas and oil; geothermal);
- ▪
- CDOT authorized transportation projects;
- ▪
- Developable sites featured on Colorado InSite; and
- ▪
- Local and major roads currently classified by CDOT as Congested.
Impact Category | Impact Strength | Impact Variable(s) |
---|---|---|
Transportation | Major | Density of local roads built in 2010–2016 Density of major roads built in 2010–2016 |
Development | Major | Change in population density, 2010–2016 Change in housing unit density, 2010–2016 Change in job density, 2010–2015 |
Energy Generation | Minor | Density of oil and gas facilities |
Mining and Drilling | Minor | Density of active construction mines |
Impact Category | Impact Strength | Impact Variable(s) |
---|---|---|
Transportation | Major | Density of congested local roads Density of congested major roads Density of authorized CDOT projects |
Development | Major | Change in population density, 2016–2021 Change in housing unit density, 2016–2021 Change in job density, 2015–2021 Colorado InSite developable land density |
Energy Generation | Minor | Density of oil and gas facilities Oil and gas potential (binary) Geothermal potential (binary) |
Mining and Drilling | Minor | Density of active construction mines |
Appendix B.2. Intermediary Results
Mitigation Not Required (n = 4602) | Mitigation Required (n = 449) | |||
---|---|---|---|---|
Variable | Mean | Median | Mean | Median |
Density of local roads built in 2010–2016 | 32.66 | 6.21 | 46.44 *** | 28.92 *** |
Density of major roads built in 2010–2016 | 6.13 | 0.36 | 6.65 | 1.70 *** |
Change in population density, 2010–2016 | 38.02 | 7.57 | 60.96 *** | 48.05 *** |
Change in housing unit density, 2010–2016 | 13.70 | 3.80 | 19.75 *** | 16.38 *** |
Change in job density, 2010–2015 | 37.38 | 1.78 | 95.78 *** | 7.87 *** |
Density of oil and gas facilities | 0.10 | 0.02 | 0.13 *** | 0.05 *** |
Density of active construction mines | 2.24 | 0.68 | 2.65 * | 0.82 ** |
No Impact (2010–2016) Present in Location (n = 2392) | Location of Known (2010–2016) Stream Impact (n = 1206) | |||
---|---|---|---|---|
Variable | Mean | Median | Mean | Median |
Current Demand | 13.07 | 10.37 | 33.45 *** | 31.11 *** |
Current Demand (z-score) | −0.04 | −0.30 | 1.89 *** | 1.67 *** |
- ▪
- Low: Current Demand (z-score) < 1.0
- ▪
- Moderate: 1.0 ≤ Current Demand (z-score) < 2.0
- ▪
- High: 2.0 ≤ Current Demand (z-score) < 3.0
- ▪
- Very High: Current Demand (z-score) ≥ 3.0
References
- Murphy, J. Chart: Colorado Is the Second-Fastest Growing State in the U.S. The Denver Post. 7 July 2016. Available online: https://www.denverpost.com/2016/2007/2007/colorado-second-population-growth-2015/ (accessed on 8 March 2018).
- Weissmann, J. The Fastest-Growing States in America (and Why They‘re Booming). The Atlantic. 12 December 2012. Available online: https://www.theatlantic.com/business/archive/2012/2012/the-fastest-growing-states-in-america-and-why-theyre-booming/266541/ (accessed on 8 March 2018).
- Svaldi, A. Colorado Economy Set for Continued Growth in 2017, Despite Worker Shortages. The Denver Post. 5 December 2016. Available online: https://www.denverpost.com/2016/2012/2005/colorado-economy-growth-2017/ (accessed on 8 March 2018).
- U.S. Department of Energy. 2017 U.S. Energy and Jobs Report State Charts. 2017; p. 32. Available online: https://energy.gov/sites/prod/files/2017/2001/f2034/2017%2020US%2020Energy%2020and%2020Jobs%2020Report%2020State%2020Charts%2202_2010.pdf (accessed on 8 March 2018).
- Eason, B. Colorado’s Growing Pains: From Roads to Water, Here Are 5 Key Issues as the State’s Population Swells. The Denver Post. 15 October 2017. Available online: https://www.denverpost.com/2017/2010/2015/colorado-growing-population-issues/ (accessed on 8 March 2018).
- Weaver, R.; Bagchi-Sen, S.; Knight, J.; Frazier, A.E. Shrinking Cities: Understanding Urban Decline in the United States; Routledge: New York, NY, USA, 2016; p. 245. [Google Scholar]
- Leo, C.; Anderson, K. Being realistic about urban growth. J. Urban Aff. 2006, 28, 169–189. [Google Scholar] [CrossRef]
- Daly, H.E.; Farley, J. Ecological Economics: Principles and Applications; Island Press: Washington, DC, USA, 2004; p. 450. [Google Scholar]
- BenDor, T.; Sholtes, J.; Doyle, M.W. Landscape characteristics of a stream and wetland mitigation banking program. Ecol. Appl. 2009, 19, 2078–2092. [Google Scholar] [CrossRef] [PubMed]
- Davis, S.K. The politics of water scarcity in the western states. Soc. Sci. J. 2001, 38, 527–542. [Google Scholar] [CrossRef] [Green Version]
- Peterson, J. Water Experts Prepare for Colorado’s Population Boom. The Durango Herald. 25 June 2017. Available online: https://durangoherald.com/articles/167803 (accessed on 8 March 2018).
- BenDor, T.; Stewart, A. Land Use Planning and Social Equity in North Carolina’s Compensatory Wetland and Stream Mitigation Programs. Environ. Manag. 2011, 47, 239–253. [Google Scholar] [CrossRef] [PubMed]
- Lave, R.; Robertson, M.M.; Doyle, M.W. Why You Should Pay Attention to Stream Mitigation Banking. Ecol. Restor. 2008, 26, 287–289. [Google Scholar] [CrossRef]
- Institute for Water Resources. The Mitigation Rule Retrospective: A Review of the 2008 Regulations Governing Compensatory Mitigation for Losses of Aquatic Resources; USACE, 2015-R-03; Institute for Water Resources: Alexandria, VA, USA, 2015. Available online: https://www.iwr.usace.army.mil/Portals/70/docs/iwrreports/2015-R-03.pdf (accessed on 18 January 2019).
- Kampf, S.K.; Puntenney, K.; Martin, C.; Weber, R.; Gerlich, J.; Hammond, J.C.; Lefsky, M.A. Controls on streamflow intermittence in the Colorado Front Range. In Proceedings of the American Geophysical Union Fall Meeting, New Orleans, LA, USA, 11–15 December 2017. [Google Scholar]
- Poff, N.L.; Allan, J.D.; Bain, M.B.; Karr, J.R.; Prestegaard, K.L.; Richter, B.D.; Sparks, R.E.; Stromberg, J.C. The natural flow regime. Bioscience 1997, 47, 769–784. [Google Scholar] [CrossRef]
- Rosgen, D.L. Watershed Assessment of River Stability and Sediment Supply, 2nd ed.; Wildland Hydrology: Fort Collins, CO, USA, 2008; p. 684. [Google Scholar]
- Hough, P.; Robertson, M. Mitigation under Section 404 of the Clean Water Act: Where it comes from, what it means. Wetl. Ecol. Manag. 2009, 17, 15–33. [Google Scholar] [CrossRef]
- Allan, J.D. Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems. Annu. Rev. Ecol. Evol. Syst. 2004, 35, 257–284. [Google Scholar] [CrossRef] [Green Version]
- Wohl, E.E. Rivers in the Landscape: Science and Management; Wiley-Blackwell: Oxford, UK, 2014; p. 330. [Google Scholar]
- Wohl, E. Wide Rivers Crossed the South Platte and the Illinois of the American Prairie; University Press of Colorado: Boulder, CO, USA, 2013; p. 408. [Google Scholar]
- Wohl, E. Virtual Rivers: Lessons from the Mountain Rivers of the Colorado Front Range; Yale University Press: New Haven, CT, USA, 2001; p. 224. [Google Scholar]
- Wohl, E.; Lininger, K.B.; Fox, M.; Baillie, B.R.; Erskine, W.D. Instream large wood loads across bioclimatic regions. For. Ecol. Manag. 2017, 404, 370–380. [Google Scholar] [CrossRef]
- National Research Council. Compensating for Wetland Losses under the Clean Water Act; The National Academies Press: Washington, DC, USA, 2001; p. 348. [Google Scholar]
- Harman, W.; Starr, R.; Carter, M.; Tweedy, K.; Clemmons, M.; Suggs, K.; Miller, C. A Function-Based Framework for Stream Assessment and Restoration Projects; EPA 843-K-12-006; U.S. Environmental Protection Agency, Office of Wetlands, Oceans, and Watersheds: Washington, DC, USA, 2012.
- Ward, J.V. The four-dimensional nature of lotic ecosystems. J. N. Am. Benthol. Soc. 1989, 8, 2–8. [Google Scholar] [CrossRef]
- Julian, J.P.; Podolak, C.J.P.; Meitzen, K.M.; Doyle, M.W.; Manners, R.B.; Hester, E.T.; Ensign, S.; Wilgruber, N.A. Shaping the physical template: Biological, hydrological, and geomorphic connections in stream channels. In Stream Ecosystems in a Changing Environment; Jones, J.B., Stanley, E.H., Eds.; Elsevier: London, UK, 2016; pp. 85–133. [Google Scholar]
- Garrard, G.E.; Williams, N.S.G.; Mata, L.; Thomas, J.; Bekessy, S.A. Biodiversity Sensitive Urban Design. Conserv. Lett. 2018, 11, e12411. [Google Scholar] [CrossRef]
- Hester, R.T. Design for Ecological Democracy; MIT Press: Cambridge, CA, USA, 2006; p. 528. [Google Scholar]
- Doyle, M.W.; Singh, J.; Lave, R.; Robertson, M.M. The morphology of streams restored for market and nonmarket purposes: Insights from a mixed natural-social science approach. Water Resour. Res. 2015, 51, 5603–5622. [Google Scholar] [CrossRef] [Green Version]
- Swan, L.; Beeby, J.; Smull, E.; Bledsoe, B.; Gurnee, G.; Auckland, J. Wyoming Stream Quantification Tool Field Testing and Review; Meridian Institute: Washington, DC, USA, 2017; p. 58. [Google Scholar]
Category | Dataset | Key Variable(s) | Level of Analysis | Coverage |
---|---|---|---|---|
Population and Residential Development | Esri data | -Population (current and future) -Housing units (current and future) | Block/Block group | Statewide |
Jobs and Economic Development | LODES | -Job count (current; future to be forecasted) | Block/Block group | Statewide |
Colorado InSite | -Shovel ready sites | Point/impact area | Statewide | |
Transportation | CDOT planned projects | -Planned transportation projects | Line/impact area | Statewide |
CDOT road segments | -Road type (highway, major, local) -Year built (for major and local roads only) -Congested (Yes or No) | Line/road segment | Statewide | |
Energy Development and Mining and Drilling | USGS EERMA Colorado Division of Reclamation Mining & Safety | -Existing oil and gas facilities -Permitted hard rock and active construction mines -Future energy development | Point (current) Area (future) | Statewide |
Variable | Summary | |
---|---|---|
Population | 2010 Total: | 5,029,196 persons |
2016 Estimate: | 5,425,481 persons | |
2021 Projection: | 5,831,123 persons | |
Housing | 2010 Total: | 2,212,898 units |
2016 Estimate: | 2,359,070 units | |
2021 Projection: | 2,522,289 units | |
Jobs | 2010 Total: | 2,129,886 jobs |
2015 Total: | 2,441,882 jobs | |
Transportation | Highways: | 14,649.4 km |
Major Roads: | 27,896.6 km | |
13.0% Congested | ||
1% with YearBuilt >= 2012 | ||
Local Roads: | 105,945.2 km | |
15.2% Congested | ||
1.3% with YearBuilt >= 2012 | ||
Planned Roads: | 1774.1 km | |
Oil/Gas | Facilities: | 13,416 features |
Directional Lines: | 8755.0 km | |
Mining | Hardrock Mines: | 110 features (25,052.5 affected acres) |
Construction Mines: | 826 features (83,104.4 affected acres) | |
Streams | Order ≥ 2: | 60,443.7 km |
Impact Category | Impact Strength | Impact Variable(s) |
---|---|---|
Transportation | Major | Density of local roads built in 2010–2016 Density of major roads built in 2010–2016 |
Development | Major | Change in population density, 2010–2016 Change in housing unit density, 2010–2016 Change in job density, 2010–2015 |
Energy Generation | Minor | Density of oil and gas facilities |
Mining and Drilling | Minor | Density of active construction mines |
Impact Category | Impact Strength | Impact Variable(s) |
---|---|---|
Transportation | Major | Density of congested local roads Density of congested major roads Density of authorized CDOT projects |
Development | Major | Change in population density, 2016–2021 Change in housing unit density, 2016–2021 Change in job density, 2015–2021 Colorado InSite developable land density |
Energy Generation | Minor | Density of oil and gas facilities Oil and gas potential (binary) Geothermal potential (binary) |
Mining and Drilling | Minor | Density of active construction mines |
Work Type | LF Statistics | AC Statistics |
---|---|---|
Residential development | Min = 0 | Min = 0 |
Max = 800 | Max = 1.47 | |
Median = 0 | Median = 0.25 | |
Mean = 99.4 | Mean = 0.39 | |
Sum = 1392 | Sum = 5.51 | |
Commercial development | Min = 0 | Min = 0.13 |
Max = 3700 | Max = 0.95 | |
Median = 0 | Median = 0.47 | |
Mean = 740 | Mean = 0.56 | |
Sum = 3700 | Sum = 2.81 | |
Road Improvement | Min = 0 | Min = 0.01 |
Max = 5300 | Max = 32.50 | |
Median = 0 | Median = 0.02 | |
Mean = 259.6 | Mean = 2.00 | |
Sum = 5452 | Sum = 42.06 | |
Culvert and Non-bridge crossing | Min = 0 | Min = 0 |
Max = 400 | Max = 0.44 | |
Median = 0 | Median = 0.05 | |
Mean = 20 | Mean = 0.10 | |
Sum = 400 | Sum = 1.97 | |
Bridge Construction/Maintenance | Min = 0 | Min = 0 |
Max = 1300 | Max = 2.56 | |
Median = 0 | Median = 0.06 | |
Mean = 118.9 | Mean= 0.25 | |
Sum = 2854 | Sum = 6.00 | |
Mitigation and Bank stabilization | Min = 0 | Min = 0 |
Max = 2647 | Max = 8.6 | |
Median = 0 | Median = 0.01 | |
Mean = 298.2 | Mean= 0.34 | |
Sum = 16700 | Sum = 18.94 | |
Energy generation and Mining | Min = 0 | Min = 0.02 |
Max = 0 | Max = 0.63 | |
Median = 0 | Median = 0.21 | |
Mean = 0 | Mean = 0.24 | |
Sum = 0 | Sum = 1.70 | |
Dam construction and maintenance | Min = 0 | Min = 0 |
Max = 750 | Max = 1.7 | |
Median = 0 | Median = 0.04 | |
Mean = 134.2 | Mean = 0.21 | |
Sum = 1745 | Sum = 2.78 | |
Other development, structures, and dredging | Min = 0 | Min = 0 |
Max = 3413 | Max = 3.68 | |
Median = 0 | Median = 0.13 | |
Mean = 140.7 | Mean = 3.2 | |
Sum = 6049 | Sum = 17.67 |
Impact Type (Generalized) | Observed Frequency | Percent of Total (%) |
---|---|---|
Transportation | 1740 | 34.4 |
Other | 1180 | 23.4 |
Structure | 1122 | 22.2 |
Mitigation | 415 | 8.2 |
Development | 380 | 7.5 |
Dredging | 152 | 3.0 |
Energy Generation | 29 | 0.6 |
Mining and Drilling | 27 | 0.5 |
Agriculture | 7 | 0.2 |
Total | 5052 | 100.0 |
Impact Type | Mitigation Required | Mitigation Not Required | Percent of All Mitigation Cases (%) | Included in Demand Analyses |
---|---|---|---|---|
Transportation | 167 | 1573 | 37.2 | Yes |
Development | 95 | 285 | 21.2 | Yes |
Other | 81 | 1099 | 18.0 | No |
Structure | 54 | 1068 | 12.0 | No |
Mitigation | 29 | 386 | 6.4 | No |
Dredging | 13 | 139 | 2.9 | No |
Mining and Drilling | 4 | 23 | 0.9 | Yes |
Agriculture | 3 | 4 | 0.7 | No |
Energy Generation | 3 | 26 | 0.7 | Yes |
Risk/Demand Level | LF (000s) at Risk | % of All Streams (order 2 or above) | |
---|---|---|---|
Streams at Risk | High | 9650.980 | 4.9 |
Very High | 7851.528 | 4.0 | |
All Streams with High or Very High Risk | 17,502.508 | 8.9 | |
Total Length of All Streams (order 2 or above) | 198,306.073 | 100.0 |
HUC-8 | Watershed Name | Total Stream Length | Total with High or Very High Risk | High Risk | Very High Risk | % at High or Very High Risk |
---|---|---|---|---|---|---|
10190008 | Lone Tree-Owl | 1094.91 | 705.59 | 317.26 | 388.33 | 64.4 |
10190003 | Middle South Platte-Cherry Creek | 4709.74 | 2792.33 | 1081.51 | 1710.82 | 59.3 |
10190010 | Kiowa | 1373.46 | 690.28 | 359.03 | 331.25 | 50.3 |
10190005 | St. Vrain | 1987.85 | 928.28 | 382.55 | 545.73 | 46.7 |
10190006 | Big Thompson | 1710.59 | 663.29 | 202.02 | 461.27 | 38.8 |
10190004 | Clear | 924.43 | 339.81 | 116.24 | 223.57 | 36.8 |
11020003 | Fountain | 2509.04 | 900.22 | 467.23 | 432.99 | 35.9 |
14080104 | Animas | 1865.91 | 531.76 | 193.86 | 337.9 | 28.5 |
10190002 | Upper South Platte | 3026.06 | 855.88 | 333.33 | 522.55 | 28.3 |
10190007 | Cache La Poudre | 4760.43 | 1307.52 | 585.47 | 722.05 | 27.5 |
14020006 | Uncompahgre | 2112.35 | 482.56 | 236.99 | 245.57 | 22.8 |
10260001 | Smoky Hill Headwaters | 578.57 | 128.55 | 80.28 | 48.27 | 22.2 |
10250012 | South Fork Beaver | 254.8 | 52.58 | 52.58 | 0 | 20.6 |
10190014 | Pawnee | 1423.81 | 215.33 | 196.28 | 19.05 | 15.1 |
14010005 | Colorado Headwaters-Plateau | 7814.90 | 1149.46 | 681.94 | 467.52 | 14.7 |
11020002 | Upper Arkansas | 5819.92 | 831.75 | 426.29 | 405.46 | 14.3 |
10190012 | Middle South Platte-Sterling | 5783.34 | 706.35 | 434.57 | 271.78 | 12.2 |
11020005 | Upper Arkansas-Lake Meredith | 2637.85 | 297.68 | 240.96 | 56.72 | 11.3 |
Challenge | Description |
---|---|
Hydrological Assurance | USACE will only approve banks that demonstrate hydrological assurance, in terms of both flow reliability (flow guaranteed by the State) and project endurance (restoration will withstand floods and droughts). |
Water Developers | Environmental instream flows that are required above reduce available water for development, and are thus being contested by water developers. |
Uncertainty of a functional assessment tool | The earliest that CO SQT would be approved is late-2019; but even then, this tool may not be the sole determinant of ‘functional feet’ used to quantify/debit stream mitigation credits. |
Complex river systems | CO rivers, particularly along the Front Range, have complex geometries (e.g., multi-thread channels and hard-to-define riverbanks) that may not fit easily into the SQT. |
Uncertainty of new Clean Water Rule | The uncertainty on the definition of jurisdictional waters creates a lot of risk. |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Julian, J.P.; Weaver, R.C. Demand for Stream Mitigation in Colorado, USA. Water 2019, 11, 174. https://doi.org/10.3390/w11010174
Julian JP, Weaver RC. Demand for Stream Mitigation in Colorado, USA. Water. 2019; 11(1):174. https://doi.org/10.3390/w11010174
Chicago/Turabian StyleJulian, Jason P., and Russell C. Weaver. 2019. "Demand for Stream Mitigation in Colorado, USA" Water 11, no. 1: 174. https://doi.org/10.3390/w11010174
APA StyleJulian, J. P., & Weaver, R. C. (2019). Demand for Stream Mitigation in Colorado, USA. Water, 11(1), 174. https://doi.org/10.3390/w11010174