Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China

Abstract

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas.

Drought Sensitivity and Vulnerability of Rubber Plantation GPP—Insights from Flux Site-Based Simulation

“…Moreover, tropical regions experience high cloud cover throughout the year, limiting remote sensing methods [20]. Consequently, models have been essential tools for understanding and forecasting the responses of tropical forests to climate change [21,22]. Completed studies have focused on carbon fluxes, water fluxes, biomass, and the productivity of rubber forests, such as using the soil vegetation atmosphere transfer (SVAT) model to simulate CO 2 and H 2 O fluxes in the canopy of rubber trees and the process-based Land Use Change Impact Assessment tool (LUCIA) to simulate the biomass and rubber yield of rubber plantations at the scale of individual trees, plots, and landscapes [23,24].…”