Assessment of above ground biomass and soil organic carbon in the forests of Nepal under climate change scenario

Abstract

IntroductionMany factors, such as climate, topography, forest management, or tree/forest attributes, influence soil organic carbon (SOC) and above-ground tree biomass (AGTB). This study focuses on assessing relationship between various predictor variables and response variables (SOC and AGTB) in the perspective of climate change scenario. The study was conducted throughout in Nepal using forest resource assessment data (2010–2014).MethodsOur study applied a random forest model to assess the status of SOC and AGTB under future climate change scenarios using 19 bioclimatic variables accompanied by other variables such as altitude, aspect, basal area, crown cover development status, distance to settlement forest types, number of trees, macro-topography, management regime, physiographic zones, slope, and soil depth. The study used 737 (70%) samples as a training data for model development while 312 (30%) samples as a testing data for model validation.Results and discussionThe respective RMSE, RMSE% and adjusted R2 of the Random Forest Model for SOC estimation were found to be 9.53 ton/ha, 15% and 0.746 while same for the AGTB were 37.55 ton/ha, 21.74% and 0.743. Particularly, changes in temperature and precipitation showed an effect on the amount of SOC and AGTB in the projected scenario i.e., CMIP6, SSP2 4.5 for 2040–2060. The study found the amount of SOC decreased by 3.85%, while AGTB increased by 2.96% in the projected scenario. The proposed approach which incorporates the effect of bioclimatic variables can be a better option for understanding the dynamics of SOC and AGTB in the future using climatic variables.

Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

“…Lewis et al [79] found an increase in AGB in African tropical forests with precipitation during the driest nine months of the year and a decrease during the wettest three months of the year. Malla et al [71] reported a positive effect on AGB of the precipitation of the driest month and the maximum temperature of the warmest month in the forests throughout Nepal. The positive effect of precipitation during the driest month suggests that ensuring water availability during periods of rainfall can contribute to increased growth in the growing season [36], resulting in higher AGB.…”

“…The partial plot function under the :randomForest" package version 4.7.1.1 in the R 4.3.0 software was used, following the methodology proposed by [70] Friedman (2001). Partial dependence plots are commonly employed to examine the linearity, non-linearity, or other intricate relationships between predictors and response variables [71].…”