Estimations of forest water retention across China from an observation site-scale to a national-scale

Hydrological models or remote sensing methods are often used to estimate water retention amounts and to analyze large spatial variations in water retention across large scales. However, the accuracies of hydrological models and remote sensing data are far lower than those of site-observational data. Here, we used 1045 observational sites of forest ecosystem across China and a random forest (RF) model for more accurate spatial prediction of canopy interception amount (CIA), litter maximum water-holding amount (LWHA), soil water storage amount (SSA), and forest water retention amount (WRA) of China. We found that the total forest WRA in China was 2325.87 x 108 m3, and the CIA, LWHA and SSA contributed 21.24%, 5.37% and 73.39%, respectively, to the WRA. The forest WRA in the basins of southern China including the Yangtze River Basin (YTRB), Songhua River Basin (SHRB), Southwest Rivers Basin (SWRB) and Pearl River Basin (PRB) accounted for 78.33% of the total forest WRA. The WRAs of subtropical needleleaf forest, temperate deciduous broadleaf forest, subtropical and tropical mountainous needleleaf forest, subtropical evergreen broadleaf forest, and cold and temperate mountainous needleleaf forest accounted for 50.61% of the total WRA in Chinese forests. The WRAs of cold and temperate forest types were relatively higher in the SHRB, Liao River Basin (LRB), Northwest Rivers Basin (NWRB), Hai River Basin (HRB) and Yellow River Basin (YRB) than in other basins, and the WRAs of subtropical and tropical forests types were relatively higher in the YTRB, Southeast Rivers Basin (SERB), SWRB, and PRB than in the other basins. This observation-based assessment can provide insight into spatial variations in different layers of the WRA including canopy, litter, and soil which can be used for forest resource management in China.

Automated summary

Hydrological models and remote sensing methods are often used to estimate water retention amounts and analyze spatial variations. However, their accuracies are lower than site-observational data. This study used observational sites and a RF model to predict canopy interception, litter water-holding amount, soil water storage, and forest water retention in China, finding significant spatial variations in different forest types across different basins.