Carbon stocks under various forest management regimes: Implications for climate change adaptation and mitigation in Nepal

Abstract

Forest degradation and deforestation is the main contributor to the climate change from the green sector. Land use, land use change and forestry (LULUCF) sector is considered as one of major sources of Greenhouse gas (GHG) emission to the atmosphere. The major indicator of climate change such as global warming and variations in rainfall are due primarily to the accumulation of carbon (C) along with other GHGs in the atmosphere. The quantification of the C emission from the forestry and land use sector has been adopted by Intergovernmental Panel on Climate Change (IPCC) considering the activity data and emission factors separately for deforestation and degradation of the forest. The product of the activity data and emission factors for each activity data is the gross emission from the forestry and land use sector. The REDD mechanism assumed to develop the reference emission level and the historical baseline so that the C emission reduction from the deforestation and forest degradation could be quantified to support the Reducing Emission from Deforestation and forest Degradation (REDD) implementation in the firm of MRV, REL which are these days considered as the part of National Forest Monitoring System (NFMS). The research work on the quantification of forest and soil organic carbon (SOC), assessment of land use and land use changes and its predictions is important in the developing nations to fulfil knowledge, data and technical gap for the development of REL, MRV and NFMS in these nations. We selected forests of Gorkha, Rasuwa and Chitawan districts of Nepal as our study sites, representing altitudinal variations and maintaining the representation of various eco-climatic zones. This research specifically tried to explore the methods of land use and land cover mapping using the appropriate methods taking inputs from the remote sensing. The object based image analysis (OBIA) techniques were applied for the image classification to support as a land cover maps. The spatial and temporal changes in the land use and land cover were assessed to get the quantification of those over time and space. The land change modeller (LCM) technique was used for the modelling of land use change (LUC) for 2030, which ultimately tells about the future trend of the forest cover change considering the current trend of forest cover change and the potential transition between 1990 and 2010 forest cover. The accuracy of the prediction matters on the parameters we select as indicators. The research focused to quantify the biomass and SOC stock in the forest. It is considered that AGB, LHG, BGB and SOC are the major C pool in the forested lands. Considering xix the local scenario in the countries like Nepal has different land management practices are under implementation such as Agriculture, Agroforestry, Community forestry (CF). Different land management practices may have different contribution to the C sequestration because of its very nature of land management techniques and adopted ways of land reclamations. Thus, during this research, the forest and SOC were assessed in different land management practices such as CF, Agroforestry (AGF) and Agriculture (AG). CFs have high contribution in the C sequestration in ABG and SOC pool. Agriculture has contribution in the C sequestration in SOC pool only but it provides the livelihood and food security contribution to the farmer. AGF is the ideal case to restore the degraded land to support on C sequestration and a level of contribution to the local people’s livelihood through income generation and food. That may be not only the tangible goods in term of fuel wood, timber, Non-timber forestry products (NTFPs) and fodder but it can also contribute providing the ecosystem services to the environmental balance such as water restoration, land restoration, biodiversity maintenance and C enhancement. Thus, the research also focused on the multiple benefits from the managed forest. The result of the study shows that land cover change in Gorkha has positive change in Forest land by 3%, other land by 3%. Rasuwa district loses annually 2% of forest land and 1% of agricultural land by increasing 3% of other land uses. Chitwan district shows the balance change of 1% increment in forest land and 1% decrease in agricultural land. AGBC was expectantly highest in Rasuwa district CF (ranging from 107.3 to 260.3 t ha-1) due to dense growth and colder climate, followed by Gorkha (3.1 to 118.4 t ha-1), and least in Chitwan (17.6 to 95.2 t ha-1). Forest land in higher elevation serves as more C pool due to higher elevation with colder climate. Carbon mapping in CF of Rasuwa and Gorkha had major areas with higher total C-stocks of 80-250 tons per ha compared to least in Chitwan district which has less than 80 t ha-1. Above ground C biomass highly depends upon the wood diameter, height and crown density. The highest C-stocks for agroforestry system in both above and below ground were observed in Rasuwa, followed by Chitwan district. Besides forest, agroforestry practices also hold a good potential to accumulate and store C. Hence they have scope for contributing to climate change mitigation and adaption with cobenefits. A family receives on average total amounts of timber, fuelwood, fodder and grasses from forest, agroforestry and agricultural land of 18.1 cft., 171.5 kg, 150.6 kg and 127.4 kg, respectively. This is equivalent to on average per family a total monetary value xx of timber, fuel-wood, fodder and grasses from CF, agroforestry and agriculture land of NRs. 112,803, 61,338 and 17,528, respectively (1US$ = 100 NRs). The research findings indicate that agro-forestry practice can contribute in both C sequestration and the livelihood support to the people. This system also buffers the requirement of local people to sustain their livelihood by restoration of the degraded lands and support to mitigate climate change impacts by sequestering C in the grown trees and underneath the soil. The remote sensing data can be used as the best option to monitor the resource level in terms of temporal and spatial variations such as land cover changes, its modelling and their relationship with the C flux. The adopted methodology can contribute to establishing the system of MRV and NFMS which are basis for the REDD mechanism.