Predicting ephemeral gully (EG) location is essential for many land management decisions and for accurate application of widely used soil erosion models. Topographic indexes are widely used for identifying EG head locations, even though the calibration of the topographic index models limits their use for broad scales. The objective of this research was to test the accuracy and usability of grid order in predicting EG head locations at the regional scale compared to other topographic indexes commonly used for this purpose. Three hundred fifty-three EGs in eight watersheds located within different landforms (Major Land Resource Areas) in Iowa, USA, were visually digitized, georeferenced, and analyzed using 3-meter LiDAR-derived elevation datasets and orthoimages. The location prediction accuracy, spatial location prediction error stability, and the variation of grid order critical threshold (CT) values were compared to predictions from four commonly used topographic indexes, specific contributing drainage area (A_s), Compound Topographic Index (CTI), Stream Power Index (SPI), and a variation of Stream Power Index (AS²). Results indicate that using A_s, CTI, SPI, and AS² to identify flow length upstream from predicted EG head locations would require careful calibration for each watershed or substantial errors in prediction accuracy would occur. In contrast, the accuracy of using grid order was acceptable at the regional scale even without model calibration with Nash Sutcliff Efficiency estimates ranging from 0.29 to 0.59; the distance between surveyed and predicted EG head locations was within 20 m for all studied EGs. The grid order value of 4 was the best CT value when the slope gradient was less than 10%, and 3 for steeper slopes. These results could be easily applied for different landforms across wider areas. The result of this research supports the prediction of EGs using automated systems without calibration at a broad scale where quality DEMs are available.

对于许多土地管理决策以及对广泛使用的土壤侵蚀模型的准确应用，预测短暂沟壑（EG）的位置至关重要。地形指数被广泛用于识别EG头部位置，即使地形指数模型的校准限制了它们在大范围内的使用。这项研究的目的是测试网格顺序在区域规模上预测EG头部位置的准确性和可用性，与通常用于此目的的其他地形指标相比。对位于美国爱荷华州不同地貌（主要土地资源区域）内八个流域的333个EG进行了可视化数字化，地理参考，并使用3米距离LiDAR得出的海拔数据集和正射影像进行了分析。位置预测精度，空间位置预测误差稳定性，_s），复合地形指数（CTI），流功率指数（SPI）和流功率指数的变化（AS ²）。结果表明，使用A _s，CTI，SPI和AS ²要确定预测的EG头部位置上游的流量长度，需要对每个分水岭进行仔细的校准，否则将在预测精度上产生重大误差。相反，即使不使用Nash Sutcliff效率估计值介于0.29至0.59进行模型校准，在区域范围内使用网格顺序的准确性也是可以接受的。所有研究的EG的被测和预测的EG头部位置之间的距离在20 m以内。当斜率梯度小于10％时，网格阶数为4是最佳CT值，对于较陡的斜率，网格阶数为3。这些结果可以轻松地应用于更广泛区域的不同地貌。这项研究的结果支持使用自动化系统对EG进行预测，而无需大规模校准即可获得高质量DEM。