• C S Jha

      Articles written in Journal of Earth System Science

    • Predictive modelling of the spatial pattern of past and future forest cover changes in India

      C Sudhakar Reddy Sonali Singh V K Dadhwal C S Jha N Rama Rao P G Diwakar

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      This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km², which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multilayer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

    • Monitoring of fire incidences in vegetation types and Protected Areas of India: Implications on carbon emissions

      C Sudhakar Reddy V V L Padma Alekhya K R L Saranya K Athira C S Jha P G Diwakar V K Dadhwal

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      Carbon emissions released from forest fires have been identified as an environmental issue in the context of global warming. This study provides data on spatial and temporal patterns of fire incidences, burnt area and carbon emissions covering natural vegetation types (forest, scrub and grassland) and Protected Areas of India. The total area affected by fire in the forest, scrub and grasslands have been estimated as 48765.45, 6540.97 and 1821.33 km², respectively, in 2014 using Resourcesat-2 AWiFS data. The total CO₂ emissions from fires of these vegetation types in India were estimated to be 98.11 Tg during 2014. The highest emissions were caused by dry deciduous forests, followed by moist deciduous forests. The fire season typically occurs in February, March, April and May in different parts of India. Monthly CO₂ emissions from fires for different vegetation types have been calculated for February, March, April and May and estimated as 2.26, 33.53, 32.15 and 30.17 Tg, respectively. Protected Areas represent 11.46% of the total natural vegetation cover of India. Analysis of fire occurrences over a 10-year period with two types of sensor data, i.e., AWiFS and MODIS, have found fires in 281 (out of 614) Protected Areas of India. About 16.78 Tg of CO₂ emissions were estimated in Protected Areas in 2014. The natural vegetation types of Protected Areas have contributed for burnt area of 17.3% and CO₂ emissions of 17.1% as compared to total natural vegetation burnt area and emissions in India in 2014. 9.4% of the total vegetation in the Protected Areas was burnt in 2014. Our results suggest that Protected Areas have to be considered for strict fire management as an effective strategy for mitigating climate change and biodiversity conservation.

    • Prediction of heavy rainfall days over a peninsular Indian station using the machine learning algorithms

      KANDULA V SUBRAHMANYAM C RAMSENTHIL A GIRACH IMRAN ANIKET CHAKRAVORTY R SREEDHAR E EZHILRAJAN D BALA SUBRAHAMANYAM RADHIKA RAMACHANDRAN KARANAM KISHORE KUMAR M RAJASEKHAR C S JHA

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      Advance prediction of heavy rainfall days over a given location is of paramount importance as heavy rainfall impacts ecosystems, leads to foods, accounts largely for the total rainfall over the region and its prediction is highly desired for the efficient management of weather-dependent activities. Traditionally, Numerical Weather Prediction models serve the purpose of weather predictions, but they have their constraints and limitations. In this regard, artificial intelligence and machine learning tools have gained popularity in recent years. In the present study, we have employed the Gaussian Process Regression (GPR) approach, one of the machine learning methods, on a long time-series rainfall data for the determination of heavy and light rainfall days. Climatological data of daily rainfall for a period of 116 years from 1901 to 2016 over Sriharikota (13.66°N, 80.23°E), a coastal island location in India, is used for training the GPR model for the identification of the heavy and light category of rainy days. The performance of the GPR model is investigated by predicting the heavy and light rainfall days per year over Sriharikota. K-nearest neighbour, random forest, and decision tree models are also used and results are compared. The validation of GPR results shows that the performance of the proposed model is satisfying (root mean square error = 0.161; mean absolute error = 0.126; mean squared error = 0.026), especially for the heavy rainfall days. Furthermore, GPR model is extended to prediction of spatial distribution of monthly rainfall over the Indian region. Results obtained from the present study encourages the utilization of the GPR model as one of the promising machine learning tools for the prediction of heavy rainfall days over a given location.

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