“一带一路”沿线国家水资源量数据集（2015）

水资源估算的主要思路是使用径流系数和径流影响因子（气候、地形、土地利用、土壤）建立机器学习模型，然后再将模型估算的径流系数还原成径流深，进一步计算水资源量。基于全球公开数据资料，建立径流系数和地形、气候、土壤、土地利用的机器学习模型，估算“一带一路”地区多年径流系数，根据2015年的降水资料，计算2015年的径流量，再根据各个国家的面积，估算“一带一路”沿线各国的水资源量。生成了“一带一路”地区高分辨率的径流系数分布图，为“一带一路”地区水资源评价、跨境水分配等提供基础数据支撑。The core approach for water resource estimation is to develop a machine learning model using runoff coefficients and runoff influencing factors (climate, topography, land use, and soil). Subsequently, the runoff coefficients predicted by the model are converted into runoff depth, and the total water resource volume is further calculated. Based on globally open public datasets, a machine learning model linking runoff coefficients with topography, climate, soil and land use is established to estimate the multi-year average runoff coefficients in the Belt and Road (B&R) region. The 2015 runoff volume is calculated using the 2015 precipitation data, and the water resource volumes of each country along the B&R are then estimated based on their territorial areas. A high-resolution runoff coefficient distribution map of the B&R region has been generated, which provides basic data support for water resource assessment and transboundary water allocation in the B&R region.