Result: TATOO – Python Topographic Analysis Tool Library for semi-automated setup of high-resolution integrated hydrologic models.

While modelling includes many detailed processes, the model setup gets costly. Delineating distributed parameters requires advanced GIS processing techniques and programming expertise, limiting models' usage to researchers and practitioners with sufficient resources. Although high-resolution input data get increasingly available, only a few preprocessing algorithms of integrated hydrological models can handle these in a reasonable time and for both subcatchment and raster model architectures. Here the collaborative open-source Python-3.6 Topographic Analysis Tool (TATOO) library is presented, integrating different models' preprocessing into one processing environment and combining not model-specific topographic preprocessing functions with model-specific parameter calculation functions. Utilising high-resolution DEMs and flow network shapefiles, TATOO offers algorithms to delineate (1) raster- or subcatchment-based model networks, (2) runoff generation, concentration and routing parameters, and (3) channel and foreland cross-section geometries including bankfull water levels. TATOO's capabilities and time requirements are demonstrated for the Large Area Runoff Simulation Model's preprocessing. • Open-source Python-3.6 Topographic Analysis Tools coupled with ESRI ArcGIS Pro. • Preprocessing of data for raster- and subcatchment-based hydrological models. • Utilization of high-resolution digital elevation models and complex flow networks. • Delineation of structure, runoff generation, concentration and routing parameters. • Automated river channel and foreland cross-section geometry delineation. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)