Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem
Abstract
:1. Introduction
Study Area and Sampling
2. Methodology
2.1. Climate Change Modelling
2.2. Fuzzy Cognitive Mapping
2.2.1. Main Aspects of Fuzzy Cognitive Maps
2.2.2. Constructing Fuzzy Cognitive Maps
- What are the changes in summer and winter temperature observed over the past 10 to 15 years?
- What are the changes in rainfall variability observed over the past 10 to 15 years?
- What are the changes in extreme climatic events (cyclone, flood, etc.) observed over the past 10 to 15 years?
- What are the resulting impacts arising from direct effects due to climate variability, sea-level rise, and changes and climatic extremes?
- How have your lives and livelihoods been affected due to these changes?
- What adaptation practices have been taken up for enhancing climate resilience?
2.3. FCM-Based Simulations
3. Results
3.1. Projections of Climate Change in the Study Area
3.2. Climate-Related Impacts as Perceived by the Communities
3.3. Climate Change Adaptations in the Area
3.4. FCM-Based Simulations
4. Discussions
5. Conclusions and Research Directions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Scenarios | Input Vector Concepts Used for Simulations |
---|---|
Baseline | C1—Climate variability and change, C2—Climatic extremes |
Scenario 1 | C14—Dykes and embankments |
Scenario 2 | C15—Water resource management |
Scenario 3 | C18—Sustainable agriculture and aquaculture practices |
Scenario 4 | C22—Strengthening local institutions |
Scenario 5 | C14—Dykes and embankments, C15—Water resource management, C18—Sustainable agriculture and aquaculture practices, and C22—Strengthening local institutions |
Climatic Parameters | Reference Climate * (1981‒2010) | ** E-Mean of Projections during 2050s (2041–2070) | ** E-Mean of Projections during 2080s (2071–2100) | ||
---|---|---|---|---|---|
RCP 2.6 | RCP 8.5 | RCP 2.6 | RCP 8.5 | ||
Mean Temperature (°C) | 26.8 | 27.9 | 29.2 | 27.9 | 30.8 |
Mean Precipitation (mm) | 1744 | 1832 | 1872 | 1912 | 1872 |
Accumulated precipitation on consecutive rainy day (above 30 mm) | 118 | 375 | 328 | 372 | 376 |
Concepts | Baseline | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
IV | Steady State Value | IV | Steady State Value | IV | Steady State Value | IV | Steady State Value | IV | Steady State Value | IV | Steady State Value | |
C1: Climate variability and change | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
C2: Climatic extremes | 1 | 0.9297 | 1 | 0.9297 | 1 | 0.9297 | 1 | 0.9297 | 1 | 0.9297 | 1 | 0.9297 |
C3: Sea-level rise | 0 | 0.9564 | 0 | 0.6891 | 0 | 0.9564 | 0 | 0.9564 | 0 | 0.9564 | 0 | 0.6897 |
C4: Seawater intrusion | 0 | 0.9986 | 0 | 0.9839 | 0 | 0.9986 | 0 | 0.9986 | 0 | 0.9986 | 0 | 0.984 |
C5: Soil fertility | 0 | −0.9884 | 0 | −0.9881 | 0 | −0.9275 | 0 | −0.9276 | 0 | −0.8334 | 0 | −0.8284 |
C6: Water resources | 0 | −0.9921 | 0 | −0.9921 | 0 | −0.9605 | 0 | −0.9605 | 0 | 0.7482 | 0 | 0.7482 |
C7: Pest invasion | 0 | 0.9477 | 0 | 0.9477 | 0 | 0.9477 | 0 | 0.9477 | 0 | 0.9477 | 0 | 0.9477 |
C8: Agriculture productivity | 0 | −0.9999 | 0 | −0.9999 | 0 | −0.9961 | 0 | −0.9961 | 0 | 0.8096 | 0 | 0.8116 |
C9: Environmental degradation | 0 | 0.9444 | 0 | 0.9445 | 0 | 0.9445 | 0 | 0.9445 | 0 | 0.5532 | 0 | 0.5532 |
C10: Livestock productivity | 0 | −0.9804 | 0 | −0.9804 | 0 | −0.9231 | 0 | −0.9232 | 0 | 0.4799 | 0 | 0.48 |
C11: Loss of infrastructure | 0 | 0.9884 | 0 | 0.8717 | 0 | 0.9884 | 0 | 0.9884 | 0 | 0.9884 | 0 | 0.8721 |
C12: Health and quality of life | 0 | −0.9806 | 0 | −0.9806 | 0 | −0.9806 | 0 | −0.9806 | 0 | −0.9031 | 0 | −0.9031 |
C13: Economic poverty | 0 | 0.9935 | 0 | 0.9935 | 0 | 0.9746 | 0 | 0.9746 | 0 | −0.9981 | 0 | −0.9981 |
C14: Dykes and embankments | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
C15: Water resource management | 0 | 0 | 0 | 0 | 1 | 0.9392 | 0 | 0.9391 | 0 | 0.9885 | 1 | 0.9885 |
C16: Water infrastructure | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
C17: Agriculture inputs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
C18: Sustainable agriculture and aquaculture practices | 0 | 0 | 0 | 0 | 0 | 0.9402 | 1 | 0.9402 | 0 | 0.9455 | 1 | 0.9455 |
C19: Pest control measures | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
C20: Healthcare facilities | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9199 | 0 | 0.9199 |
C21: Livelihood diversification | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
C22: Strengthening local institutions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
C23: Credits and subsidies | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.9228 | 0 | 0.9228 |
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Singh, P.K.; Papageorgiou, K.; Chudasama, H.; Papageorgiou, E.I. Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem. Sustainability 2019, 11, 6655. https://doi.org/10.3390/su11236655
Singh PK, Papageorgiou K, Chudasama H, Papageorgiou EI. Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem. Sustainability. 2019; 11(23):6655. https://doi.org/10.3390/su11236655
Chicago/Turabian StyleSingh, Pramod K., Konstantinos Papageorgiou, Harpalsinh Chudasama, and Elpiniki I. Papageorgiou. 2019. "Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem" Sustainability 11, no. 23: 6655. https://doi.org/10.3390/su11236655