Hydrological Processes in Agricultural Watersheds
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 10875
Special Issue Editor
Special Issue Information
Dear Colleagues,
Hydrological processes in agricultural watershed issues are often very challenging. The pressures on agricultural water resources are increasing with different scales of watershed development involving ecological, pedological and hydrological consequences in river basins and groundwater aquifers, and water environment deterioration.
All this leads to an increasing need to investigate the effects of different human activities and natural impacts on the hydrological processes; water environments such as land-use changes, climatic variability and climate change; and intensified water and fertilizer practices. Moreover economic, environmental, and social issues are considered more and more in water resource research. In this context, computer-based models can help to choose the right plans, designs, and policies to obtain the desired impacts.
In recent years, advances in computer techniques have allowed water scientists to develop complex models at different scales to support hydrological processes in agricultural watersheds.
This Special Issue is focused on recent advances in models and methods for agricultural watersheds.
This Special Issue will showcase manuscripts that link the following themes:
- Watershed scale hydrological modeling;
- Algorithms based on deep learning and machine learning;
- Eco-hydrological modeling;
- Pedo-hydrological processes;
- Climate change impacts on watershed management;
- Carbon–hydrological cycle;
- Groundwater hydrology;
- Pollutant transport and water quality assessment;
- Parameter inversion.
We look forward to receiving your original research articles and reviews.
Dr. Lin Lin
Guest Editor
Manuscript Submission Information
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Keywords
- distributed hydrological model
- eco-hydrological modeling
- pedo-hydrology
- deep learning and machine learning
- climate change
- runoff forecasting
- carbon–hydrological cycle
- groundwater numerical simulation
- pollutant transport
- parameter inversion
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