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TATOO-Python']Python Topographic Analysis Tool Library for semi-automated setup of high-resolution integrated hydrologic models
被引:1
|作者:
Mitterer, Johannes A.
[1
]
机构:
[1] Tech Univ Munich, Arcisstr 21, D-80333 Munich, BY, Germany
关键词:
Hydrology;
Modelling;
Preprocessing;
!text type='Python']Python[!/text;
Cross-section;
LARSIM;
IHM;
WATER-QUALITY MODEL;
PREPROCESSING PROGRAM;
CHANNEL GEOMETRY;
DEM RESOLUTION;
RIVER-BASIN;
INPUT DATA;
FLOW;
CATCHMENT;
FRAMEWORK;
NETWORKS;
D O I:
10.1016/j.envsoft.2022.105406
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
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.
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页数:21
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