Dear Colleagues,

Land Use/Land Cover impacts on the hydrology and sediment transport in catchments have challenged modelers and environmental scientists worldwide. There are many problems in simulating the LULC impacts on the surface hydrological processes. One of these problems is improving the soil-plant-root relationships to capture the fundamental changes in the surface runoff and sediment transport processes. Relations between plants’ roots and biota with the soil surface layer are relevant as the preferential flows are formed in the soil profile, increasing the soil infiltration capacity, reducing the surface runoff, and increasing groundwater recharge. The relationship between plants’ canopy and rainfall partitioning is also a key factor for surface runoff generation. Most hydrological and sedimentological models need to adequately cope with the abovementioned relations, which generate a calibration process that can not represent the soil-water-atmosphere processes, resulting in poor or biased surface runoff simulation. 

We can tackle these problems by improving our understanding of the soil’s physical infiltration processes and using alternatives such as Artificial Intelligence that allows training the machine models to capture the influence of preferential flows and organic matter on the infiltration and, consequently, in surface runoff.

The purpose of this SI is to invite scientists from several areas of knowledge to contribute with their expertise in surface runoff and sediment transport modeling, considering the recent advancements in this challenging subject that are very important for the management of the land, considering the surface hydrological processes. Studies related to preferential flows in forested areas, novelties in rainfall canopy partitioning, machine learning useful in simulating surface runoff and sediment transport using robust datasets, and the incorporation of new techniques for physical-based model calibration aiming to simulate LULC scenarios are welcome.

Dr. Carlos Rogério Mello
Guest Editor

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• land use/cover change
• hydrological modeling
• sediment transport
• machine learning
• environmental science
• climate change
• soil physics