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CoSWAT Model v1: A high-resolution global SWAT+ hydrological model

蒸散量 水土评价工具 环境科学 水循环 水文模型 环境资源管理 水资源 计算机科学 大洪水 气候变化 全球变化 先进星载热发射反射辐射计 水文学(农业) 气候模式 土地利用 水流 持续性 全球变暖 过程(计算) 仿真建模 可持续发展 水资源管理 遥感 全球气候 SWAT模型 气象学 数据集 水平衡 校准 洪水(心理学) 数据管理 地理信息系统 土壤图 集合(抽象数据类型) 土地利用、土地利用的变化和林业 土地覆盖 空间分析 影响评估
作者
Celray James Chawanda,Ann van Griensven,Albert Nkwasa,Jose Pablo Teran Orsini,Jaehak Jeong,Soon-Kun Choi,Raghavan Srinivasan,Jeffrey G. Arnold
出处
期刊:Hydrology and Earth System Sciences [Copernicus Publications]
卷期号:29 (23): 6901-6916 被引量:2
标识
DOI:10.5194/hess-29-6901-2025
摘要

Abstract. Global hydrological models are essential tools for understanding water resources and assessing Climate Change (CC) impacts at planetary scales, supporting water management, flood risk assessment, and sustainable development initiatives worldwide. The Soil and Water Assessment Tool (SWAT+) has demonstrated robust performance across various environments and scales, from local to continental applications. However, despite its widespread use, a global implementation of SWAT+ is currently lacking due to computational demands and data management challenges, while existing global models often lack the detailed process representation and high spatial resolution needed for comprehensive hydrological analysis. A global SWAT+ model would offer unique advantages through its integrated simulation of water quantity, quality, and land management processes, while supporting multiple UN Sustainable Development Goals and enhancing research opportunities in global hydrology. This study aimed to develop a High-resolution Global SWAT+ Model and establish a reproducible framework for large-scale SWAT+ applications. We developed the Community SWAT (CoSWAT) modeling framework, an open-source solution that automates data retrieval, preprocessing, and model configuration using Python, while maximizing parallel processing for computational efficiency. The global model was then set up using the framework at 2 km resolution using ASTER DEM, ESA land use data, FAO soil data, and ISIMIP climate data, with performance evaluated against GRDC flow data and GLEAM evapotranspiration dataset. Results without calibration showed reasonable spatial patterns in evapotranspiration simulation with 78.54 % of sampled points showing differences within ± 100 mm compared to GLEAM data, though river discharge performance was limited due to lack of reservoir implementation with 23.02 % of stations showing positive Kling–Gupta Efficiency values. The development of this first global SWAT+ model demonstrates the feasibility of high-resolution global hydrological modeling using SWAT+, while the CoSWAT framework provides a robust foundation for reproducible large-scale modeling. These advances enable more detailed analysis of global water resources and CC impacts, though future work should focus on incorporating water management practices, improving process representation with calibration, and enhancing computational efficiency.

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