Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region. (1st June 2020) - Sannigrahi, Srikanta; Zhang, Qi; Joshi, P.K.; Sutton, Paul C.; Keesstra, Saskia; Roy, P.S.; Pilla, Francesco; Basu, Bidroha; Wang, Ying; Jha, Shouvik; Paul, Saikat Kumar; Sen, Somnath
Abstract: Ecosystem Service Valuation (ESV) is a process of evaluating and quantifying the monetary values of ESs and their functions. Using both biophysical and spatially explicit integrated models, biophysical and monetary values of key Ecosystem Services (ESs) were estimated in the Sundarbans Biosphere Region (SBR), India. Quantification was made both in time series (1982–2017) and individual years (1973, 1988, 2003, 2013, 2018, 2025, 2035, 2045) to understand the impact of climate change and land-use dynamics on the long-term ecological status of the region. The monetary and biophysical values of the ESs were then obtained from Net Primary Productivity (NPP) models, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Cellular Automata Markov Chain Model (CA-Markov). NPP increased significantly during the first half period (1982–1999), but significantly declined during the second period (2000–2017). The highest estimated ESVs (US$ ha −1 ) was found for habitat service (30780), nutrient cycling (12626), and gas regulation (7224.81), whereas, lower ESVs were approximated for water regulation (347.81), raw material production (777.82) and waste treatment (13.57) services. Among the nine ESs evaluated, climate regulation, gas regulation, and disturbance regulation were the most important regulating services of the SBR. The combined effects of climate change and land-use dynamics on ESs are much stringent in a vulnerable region like the SBR. Most of the regulating services were closely associated with the fluctuation of land use land cover input. Thus, land management policies and land reform strategies that will encourage the conversion of productive land, especially the highly productive mangrove forest, for the development or any other financial benefits, would disturb the ideal human-nature balance of this ecosystem. The outcomes of this study also provide an important reference to the land administrators, researchers, and decision-makers to comprehend the expected social-ecological juxtaposition in a protected natural reserve region like the Sundarbans. Highlights: 5 ecosystem models were utilized for estimating the carbon sequestration. Both biophysical and spatially explicit models were used for estimating ecosystem services (ESs). 10 machine learning models were utilized for land use land cover classification. ESs were estimated for future scenarios. Spatial and non-spatial trade-offs and synergies among the ESs were evaluated.