地形湿度指数可定量模拟流域内土壤水分的干湿状况,是静态土壤含水量的最常用指标,具有明确的物理意义。但是,由于DEM本身的结构特点,其提取的地形湿度指数具有尺度依赖性。本文主要探讨因DEM水平分辨率不同而导致的DEM栅格单元异质性,对地形湿度指数提取的影响。以厦门市地貌类型比较复杂的西源溪流域为实验区,使用1 ∶1万等高线生成的2.5m和20m分辨率DEM数据,分别提取地形湿度指数并计算栅格单元地形异质性指数,分析DEM栅格单元异质性指数与地形湿度指数之间的关系。研究表明,基于高程标准差、地势起伏度、景观破碎度和多样性的栅格单元异质性指数与地形湿度指数偏差之间均存在显著的负相关性,这4个异质性指数对地形湿度指数差值的对数回归模拟效果良好且显著有效。这对低分辨率DEM提取地形湿度指数的误差纠正,以及描述区域土壤含水量等地形湿度指数的应用研究具有积极意义。

The Topographic Wetness Index (TWI) is frequently used to simulate the soil moisture conditions in a watershed quantitatively. The index that extracted from DEM is an important factor with explicit physical significance in the rainfall-runoff process models. Some previous studies discovered that the TWI would change with the resolution of DEM from which it was derived and this change, and terrain heterogeneity of DEM grids were employed to explain the scale dependency. In this paper, we explored the resolution effects of TWI and the influence of terrain heterogeneity of DEM grids. The research area is located in Xiyuanxi watershed, Xiamen City of Fujian Province, which includes different terrain types such as hill, platform, plain, etc. A fine-resolution DEM with 2.5m grid size was used to investigate the scale dependency of TWI values when converting DEM with resolution of 20m. Four terrain and heterogeneity attributes were employed as the quantitative indices of terrain heterogeneity of DEM grids, i.e. standard deviation of elevation, relief, landscape fragmentation index and landscape diversity index. The correlation and regression analysis was performed to identify sensitive and correlative responses between each terrain heterogeneity index and TWI variance as resolution was changed. The results indicated significant negative correlations between the terrain heterogeneity indices and the TWI variance (the Pearson correlation coefficients were -0.707, -0.712, -0.779 and -0.841 respectively, all based on a high confidence level of 0.01). By extrapolating the fitted curve for the terrain heterogeneity indices and TWI variance, the logarithmic curves fit the optimal equations well (the coefficient of determination are all greater than 0.9, F>F_0.01). So, these indices can be used to evaluate the impact of the terrain heterogeneity on the TWI. Meanwhile, the regression models can improve the accuracy of the TWI derived from a coarse resolution DEM. The effect discussed in this study is helpful in providing a more accurate data for the TWI applications.