Freshwater Ecosystem Services in Mining Regions: Modelling Options for Policy Development Support
Abstract
:1. Introduction
1.1. Freshwater Ecosystem Services in Mining Regions
1.2. Justification of This Review
2. Materials and Methods
3. Results and Discussions
3.1. Methodological Choices for ESA in Mining Contexts
3.1.1. Focus on Mining and Its Impacts
3.1.2. Data for ES Valuation in Mining Contexts
3.1.3. Indicators of Water Biophysical State
3.1.4. Complexity, Human–Ecosystem Integration and Ecological Functionality in ES Models
3.2. Policy-Oriented Efforts in Reviewed Assessments
3.2.1. Scenario Simulation, Trade-Offs and Uncertainties
3.2.2. Stakeholders Involvement and ESA Outcome
3.3. Fitting the Practice in an Environmental Management Scheme
4. Main Discussion
4.1. Data Science, Acquisition and Transformation
4.2. Process-Oriented Modelling
4.3. Monitoring Freshwater ES in Mining Contexts
4.4. Implications for Institutional Stakeholders
5. Conclusions
Acknowledgments
Conflicts of Interest
Appendix A
Author (Year) | Water ES Model Foundation. | Final Output 1 | Public Participation | Simulation | Expert Knowledge | Trade-Off Assessment | Explicit Uncertainty |
---|---|---|---|---|---|---|---|
1. Zhang et al. (2010) | LULC-based provision. | V | |||||
2. Bai et al. (2011) | Water productivity from statistics. | V+R | X | ||||
3. Li et al. (2011) | Water loss cost analysis. | V | |||||
4. Hogan et al. (2012) | LULC-based water quality. Validated models. | V | X | X | |||
5. Haase et al. (2012) | LULC-based cultural value regulation. | V | X | ||||
6. Larondelle et al. (2012) | Groundwater recharge model. | V | X | ||||
7. Boissiere et al. (2013) | Not directly assessed. Only rainfall. | K | X | ||||
8. Breffle et al. (2013) | Cultural water ES from rainfall. | K | X | X | |||
9. Evans et al. (2013) | Not directly assessed. | K | X | ||||
10. Bian and Lu (2013) | LULC-based. | V | |||||
11.Zhang et al. (2013) | LULC-based. | V | X | ||||
12. Woziwoda et al. (2014) | Not directly assessed. | R | X | ||||
13. Fu et al. (2015) | Water yield modelling. | V | X | ||||
14. Pandit et al. (2015) | Market price in a river stretch. | V | X | ||||
15. Fan and Ding (2015) | LULC-based. | V | X | X | |||
16. Mazzotta et al. (2015) | Fish habitat. Recreational fishing. | V+R | X | X | |||
17. Zhang et al. (2016) | LULC-based. Use of rainfall. | V+R | X | X | |||
18. Pullanikkatil et al. (2016) | LULC-based cultural value. | R | X | ||||
19. Blaen et al. (2016) | Not directly assessed. | V | X | X | |||
20. Fan et al. (2016) | LULC-based. Use of rainfall. | V | X | ||||
21. Molina et al. (2016) | Cultural value of photographs. | V | X | X | X | ||
22. Preece et al. (2016) | LULC-based. | V | X | X | |||
23. Duarte et al. (2016) | Not directly assessed. | V | X | X | X | ||
24. Wilker et al. (2016) | Not directly assessed, only scoped. | V | X | X | |||
25. Burges et al. (2016) | Proxies from field experiments. | V | X | ||||
26. Rosa et al. (2016) | Not directly assessed, only scoped. | R | X |
Criteria | Class | Amount | Ratio | Criteria | Class | Amount | Ratio |
---|---|---|---|---|---|---|---|
Mining focus | primary | 20 | 77% | Ecological functionality | null | 6 | 19% |
secondary | 6 | 23% | low | 12 | 46% | ||
Basis for ES valuation | LULC | 16 | 62% | mid | 6 | 19% | |
cultural | 10 | 39% | high | 2 | 8% | ||
ƒ(eco) # | 11 | 42% | Human–ecosystem integration | low | 2 | 8% | |
+proxies * | 24 | 92% | mid | 16 | 62% | ||
Main data sources | GIS | 16 | 62% | high | 8 | 30% | |
Interviews | 13 | 50% | Model complexity | low | 7 | 27% | |
field | 9 | 35% | mid | 10 | 39% | ||
experiment | 2 | 8% | high | 9 | 35% | ||
secondary data | 18 | 70% | ESA Output | value | 20 | 77% | |
Indicator of water ES | area | 14 | 54% | response | 6 | 23% | |
flow | 7 | 27% | only knowledge | 3 | 12% | ||
quality | 3 | 12% | Scenario simulation | 7 | 27% | ||
soil moisture | 2 | 8% | Trade-offs | 3 | 12% | ||
Expert knowledge | 6 | 23% | Uncertainties | 6 | 23% |
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Criteria |
|
Mining focus |
|
Basis for ES valuation |
|
Data sources |
|
Indicator of water biophysical state |
|
Ecological functionality 1 |
|
Human–ecosystem integration |
|
Model complexity |
|
ESA output |
|
Policy-oriented aspects in models: (1) scenario simulation; (2) trade-offs analysis; (3) uncertainty assessment; and (4) stakeholder participation. |
Ecological Functionality | High Complexity | Medium Complexity | Low Complexity | |
---|---|---|---|---|
High ecological functionality | Burges et al. (2013) | Larondelle et al. (2012) | ||
Medium ecological functionality | Li et al. (2011) Hogan et al. (2012) Evans et al. (2013) | Haase et al. (2012) Bai et al. (2011) | Wilker et al. (2016) | |
Low ecological functionality | Zhang et al. (2010) Duarte et al. (2016) Zhang et al. (2016) Mazzotta et al. (2015) Fu et al. (2015) | Molina et al. (2016) Blaen et al. (2016) Bian et al. (2013) | Pullanikkatil et al. (2016) Pandit et al. (2015) Boissiere et al. (2013) Woziwoda et al. (2014) | |
Null ecological functionality | Rosa et al. (2016) Preece et al. (2016) Fan et al. (2016) Zhang et al. (2013) | Fan et al. (2015) Breffle et al. (2013) |
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Mercado-Garcia, D.; Wyseure, G.; Goethals, P. Freshwater Ecosystem Services in Mining Regions: Modelling Options for Policy Development Support. Water 2018, 10, 531. https://doi.org/10.3390/w10040531
Mercado-Garcia D, Wyseure G, Goethals P. Freshwater Ecosystem Services in Mining Regions: Modelling Options for Policy Development Support. Water. 2018; 10(4):531. https://doi.org/10.3390/w10040531
Chicago/Turabian StyleMercado-Garcia, Daniel, Guido Wyseure, and Peter Goethals. 2018. "Freshwater Ecosystem Services in Mining Regions: Modelling Options for Policy Development Support" Water 10, no. 4: 531. https://doi.org/10.3390/w10040531