The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1–30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modeling methods and 5,200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest r² (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.

中文翻译：

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作为土壤水分代理的地形湿度指数：流量路由算法和网格分辨率的重要性

地形湿度指数 (TWI) 是土壤湿度的常用代表。TWI 的预测能力受流量路由算法和 TWI 所源自的数字高程模型 (DEM) 的分辨率的影响。在这里，我们使用 11 种流量路由算法以 1-30 m 的 DEM 分辨率检查 TWI 的预测能力。我们使用统计建模方法和 5,200 个研究地块以及超过 46,000 个土壤湿度测量值来分析 TWI 与现场量化土壤湿度之间的关系。此外，我们测试了流路由算法对不同分辨率下 DEM 中垂直高度误差的敏感性。结果表明，TWI 的整体预测能力适中。最高 r ²(23.7%) 是在 2 m 分辨率下使用多流向算法达到的。此外，对高度误差的敏感性测试表明，多流向算法也比单流向算法对 DEM 误差具有更强的鲁棒性。结果提供的现场证据表明，在其最佳 TWI 是土壤水分的适度代理，其预测能力受流量路由算法和 DEM 分辨率的影响。因此，我们鼓励在使用 TWI 作为土壤水分代理时仔细评估算法和分辨率。