Revegetation program in North China potentially increase carbon sequestration and mitigate climate change. However, the responses of water yield to climate factors are still unclear among different vegetation types, which is critically important to select appropriate species for revegetation. Exploring the sensitivity of water yield to climate factors is also essential for predicting how future climate change would affect water yield. The aim of this study is to quantify the water yield of North China and its sensitivity among different land cover types. Based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we estimated the temporal changes and its associated factors in water yield in North China during the period 2000-2015.To investigate the relationship between water yield and climate factors, linear regressions were performed to calculate the sensitivity (slope) of water yield to climate variables ( in each 100 mm/yr precipitation bin and 1°C temperature bin).
Results/Conclusions
The result showed that the InVEST performed well in water yield estimation (R2 = 0.76), and thus can be successfully applied across the study area. The total water yield across North China is 19.3±2.22 *1010 mm /yr during the period of 2000–2015, with a mean water yield (MWY) of 167.68±10.06 mm. Large spatial difference in MWY was found, that is strongly related to temperature, precipitation and land use types. The responses of MWY to mean annual precipitation (MAP) are closely tied to temperature conditions in forest and grassland. The sensitivities of MWY to MAP in forest showed a significant decreasing trend with the temperature gradient, while opposite results were shown in grassland. Our findings confirmed the impact of climatic variables on water yield, and highlighted that the climatic controls varied among vegetation types. Our results also suggest that shrub and grass would be more suitable in North China’s revegetation programs to improve water yield capacity, especially in future warming conditions.