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dc.contributor.advisorRavindranath, N H
dc.contributor.advisorBala, G
dc.contributor.authorSharma, Jagmohan
dc.date.accessioned2018-06-05T10:32:27Z
dc.date.accessioned2018-07-31T05:34:26Z
dc.date.available2018-06-05T10:32:27Z
dc.date.available2018-07-31T05:34:26Z
dc.date.issued2018-06-05
dc.date.submitted2015
dc.identifier.urihttp://etd.iisc.ac.in/handle/2005/3660
dc.identifier.abstracthttp://etd.iisc.ac.in/static/etd/abstracts/4530/G27312-Abs.pdfen_US
dc.description.abstractDuring the 21st century, climatic change and non-climatic stressors are likely to impact forests leading to large-scale forest and biodiversity loss, and diminished ecological benefits. Assessing the vulnerability of forests and addressing the sources of vulnerability is an important risk management strategy. The overall goal of this research work is to develop methodological approaches at different scales and apply them to assess the vulnerability of forests in India for developing strategies for forest adaptation. Indicator-based methodological approaches have been developed for vulnerability assessment at local, landscape and national scales under current climate scenario, and at national scale under future climate scenario. Under current climate scenario, the concept of inherent vulnerability of forests has emerged by treating vulnerability as a characteristic internal property of a forest ecosystem independent of exposure. This approach to assess vulnerability is consistent with the framework presented in the latest report of Intergovernmental Panel on Climate Change (IPCC AR5 2014). Assessment of vulnerability under future climate scenario is presented only at national scale due to challenges associated with model-based climate projections and impact assessment at finer scales. The framework to assess inherent vulnerability of forests at local scale involves selection of vulnerability indicators and pair wise comparison method (PCM) to assign the indicator weights. The methodology is applied in the field to a 300-ha moist deciduous case study forest (Aduvalli Protected Forest, Chikmagalur district) in the Western Ghats area, where a vulnerability index value of 0.248 is estimated. Results of the study indicate that two indicators - ‘preponderance of invasive species’ and ‘forest dependence of community’ - are the major drivers of inherent vulnerability at present. The methodology developed to assess the inherent vulnerability at landscape scale involves use of vulnerability indicators, the pair wise comparison method, and geographic information system (GIS) tools. Using the methodology, assessment of inherent vulnerability of Western Ghats Karnataka (WGK) landscape forests is carried out. Four vulnerability indicators namely, biological richness, disturbance index, canopy cover and slope having weights 0.552, 0.266, 0.123 and 0.059, respectively are used. The study shows that forests at one-third of the grid points in the landscape have high and very high inherent vulnerability, and natural forests are inherently less vulnerable than plantation forests. The methodology used for assessment of forest inherent vulnerability at the national scale was same as used at landscape scale. 40% of forest grid points in India are assessed with high and very high inherent vulnerability. Except in pockets, the forests in the three biodiversity hotspots in India i.e., the Western Ghats in peninsular India, northeastern India, and the northern Himalayan region are assessed to have low to medium inherent vulnerability. Vulnerability of forests under future climate scenario at national scale is estimated by combining the results of assessment of climate change impact and inherent vulnerability. In the present study, ensemble climatology from five CMIP5 (Coupled Model Intercomparison Project phase 5) climate models for RCP (Representative Concentration Pathways) 4.5 and 8.5 in short (2030s) and long term (2080s) is used as input to IBIS (Integrated Biosphere Simulator) dynamic vegetation model. Forest grid points projected to experience vegetation-shift to a new plant functional type (PFT) under future climate are categorized under ‘extremely high’ vulnerability category. Such forest grid points in India are 22 and 23% in the short term under RCP4.5 and 8.5 respectively, and these percentages increase to 31 and 37% in the long term. IBIS simulated vegetation projections are also compared with LPJ (Lund-Potsdam-Jena) simulated projections. Both the vegetation models agree that forests at about one-third of the grid points could be impacted by future climate but the spatial distribution of impacted grid points differs between the models. Vulnerability assessment is a powerful tool for building long-term resilience in the forest sector in the context of projected climate change. From this study, three forest scenarios emerge in India for developing adaptation strategies namely: (a) less disturbed primary forests; (b) degraded and fragmented primary forests; and (c) secondary (plantation) forests. Minimizing anthropogenic disturbance and conserving biodiversity are critical to reduce forest vulnerability of less disturbed primary forests. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build resilience. Mainstreaming forest adaptation in India through Forest Working Plans and realignment of the forestry programs is necessary to manage the risk to forests under climate change.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG27312en_US
dc.subjectIndian Forestsen_US
dc.subjectClimate Stressorsen_US
dc.subjectClimate Change Impact Assessmenten_US
dc.subjectVulnerability of Forestsen_US
dc.subjectForest-Climate Interactionen_US
dc.subjectIndian Forests - Impact Assessmenten_US
dc.subjectForest Vulnerability Assessmenten_US
dc.subjectInherent Vulnerability - Western Ghatsen_US
dc.subject.classificationSustainable Technologiesen_US
dc.titleVulnerability of Forests to Climatic and Non-Climatic Stressors : A Multi-Scale Assessment for Indian Forestsen_US
dc.typeThesisen_US
dc.degree.namePhDen_US
dc.degree.levelDoctoralen_US
dc.degree.disciplineFaculty of Engineeringen_US


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