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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.
- Clay soil
- Direct drilling
- Distributed modeling
- Spatial data
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- 2 Finished
Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing Point Cloud Ecosystem
01/01/2018 → 31/07/2021
Project: Academy of Finland: Strategic research funding