Soil tillage is an anthropogenic factor affecting hydrological processes in agricultural fields. To demonstrate a novel method for increasing mechanistic understanding of the tillage impacts, we combined a process-based three-dimensional (3D) hydrological model with terrestrial laser scanning (TLS) data. This study aimed to (1) exploit high-resolution TLS data to estimate soil surface depression storage (SSDS) and roughness after different soil tillage operations (ploughing, harrowing vs direct drilling) on a heavy clay soil, (2) combine the estimates of SSDS and roughness with a 3D hydrological model to demonstrate the potential of the method in analyzing hydrological impacts of tillage, and (3) evaluate the value and uncertainties in coupling spatial microtopographical data and process-based modeling. The SSDS values showed low mean (0.3–2.9 mm) and rare high values (>5 mm) with clearly the highest variation of SSDS and roughness values in the ploughed conditions. These tillage impacts were reflected in the amounts of surface layer runoff (max. increase 23% due to the SSDS and roughness changes) and drain discharge (max. decrease 8%). The impact on other water balance components was negligible, and factors such as drainage methods and terrain slope were shown to have more control on the water balance. The tillage impacts had also a clear effect on peak surface runoff values. Most (99%) of the hydrological changes were caused by the SSDS variation whereas the roughness had a minor role. The simulations showed that tillage-induced long-term changes on soil hydraulic properties (such as conductivity and macroporosity), which were not detected by the TLS data, can have a high impact on surface layer runoff generation, as these tillage-impacts increased runoff by 25%. While the simulations showed how the various hydrological impacts of tillage can be discerned, a more comprehensive assessment would require more data derived from several sites and tillage conditions.

土壤耕作是影响农田水文过程的人为因素。为了演示增加对耕作影响的机械理解的新颖方法，我们将基于过程的三维（3D）水文模型与地面激光扫描（TLS）数据相结合。这项研究旨在（1）利用高分辨率TLS数据来估计在重黏土上进行不同耕作操作（耕作，耙耕与直接钻孔之后）的土壤表面凹陷存储（SSDS）和粗糙度，（2）结合对SSDS和粗糙度与3D水文模型一起展示了该方法在分析耕作水文影响方面的潜力，并且（3）评估了将空间微地形数据与基于过程的建模耦合在一起的价值和不确定性。SSDS值显示出较低的平均值（0.3–2。9毫米）和罕见的高值（> 5毫米），在耕种条件下，SSDS和粗糙度值的变化最大。这些耕作影响反映在表层径流量（由于SSDS和粗糙度变化而导致的最大增加23％）和排水量（最多减少8％）中。对其他水平衡组成部分的影响可以忽略不计，并且排水方法和地形坡度等因素显示出对水平衡的更多控制。耕作影响对地表径流峰值也有明显影响。大多数（99％）的水文变化是由SSDS的变化引起的，而粗糙度的影响较小。模拟结果显示，耕作引起的土壤水力特性（例如电导率和大孔隙度）的长期变化并未通过TLS数据检测到，可能对表层径流产生很大影响，因为这些耕作影响使径流增加了25％。虽然模拟显示了如何辨别耕种的各种水文影响，但更全面的评估将需要更多来自多个地点和耕种条件的数据。