Treffer: Enhancing river flow predictions in MOHID-Land through integration of gridded soil data and hydraulic parameters using the MOHID SOIL TOOL.

Accurate soil hydraulic parameters are essential for hydrological modeling, yet their spatial variability challenges parameterization. This study presents the MOHID SOIL TOOL (MST) to automate the integration of Brazilian Agricultural Research Corporation (EMBRAPA) soil texture data with Rosetta, an artificial neural network tool for estimating soil hydraulic parameters, enhancing hydrological simulations. The methodology involved programming automation routines to process soil data, ensuring compatibility with MOHID-Land adjusting soil hydraulic parameters to identify more realistic values that better represent local conditions. Developed in Python 3 with a Windows-compatible interface, MST automates the import, processing, and conversion of soil data. Testing in the Pedro do Rio watershed (Petrópolis, Brazil) demonstrated its efficiency in preparing soil input files for subsequent model calibration while reducing human errors. By optimizing workflow and ensuring precise data processing, MST advances hydrological research and supports sustainable water resource management, with flexibility for global raster-based soil datasets. • The MOHID Soil Tool (MST) automates the pre-processing of soil data for MOHID-Land. • The tool integrates gridded soil data with Rosetta, enhancing hydrological accuracy. • It allows calibration of soil hydraulic parameters to improve streamflow prediction. • MST is globally compatible with raster-based soil data sources, ensuring flexibility. • This tool boosts modeling efficiency, supporting sustainable global water management. [ABSTRACT FROM AUTHOR]

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