Publications

Burman, PKD; Das, P (2024). A Data-Driven Approach to Assess the Impact of Climate Change on a Tropical Mangrove in India. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 129(8), e2023JG007911.

Abstract
As a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem-atmosphere CO2 exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel-2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R2 = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R2 = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1-1.26 and SSP5-8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%-20.3% from 2050 to 2060 and of 4.82%-28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018). Mangroves are natural ecosystems found at the confluence of land and water. Their productivity is regulated by the complex environmental conditions prevailing at the interface of terrestrial, marine, and freshwater realms. Geographically, they mostly occur between 25 degrees N and 25 degrees S with a significant presence in the Indian subcontinent. The climate and ecosystem models used for predicting the changes in land and aquatic ecosystems have limited predictive capability for mangroves. Machine learning (ML) models are capable of capturing the coupled non-linear relationships among various processes and thus can improve such predictions. The present study uses in situ and satellite data to develop long-term productivity and canopy growth in the Pichavaram mangrove in India, which are subsequently used to develop ML models. These ML models are applied to identify the dominant mangrove species in this ecosystem and predict the changes in its photosynthetic carbon uptake in the middle-term (2050-2060) and long-term (2090-2100) climate change. The future carbon uptake increases (decreases) during wet (dry) periods. Overall, the projected decrease in annual photosynthesis ranges from 3.73% to 28.15%, compared to its current average. The seasonal variation in air temperature plays a key role in regulating the mangrove carbon uptake. this is graphical abstract caption so fix after graphical abstract image, fix before the rule as per style. image

DOI:
10.1029/2023JG007911

ISSN:
2169-8961