Rasheed Nisreen Jawad, Al-Khafaji Mahmoud S, Alwan Imzahim A, Al-Suwaiyan Mohammad Saleh, Doost Ziaul Haq, Yaseen Zaher Mundher
Soil and Water Resources Department, College of Agriculture, University of Diyala, Baqubah, 32001, Diyala, Iraq.
Department of Water Resources Engineering, University of Baghdad, Baghdad, Iraq.
Heliyon. 2024 Sep 24;10(19):e38348. doi: 10.1016/j.heliyon.2024.e38348. eCollection 2024 Oct 15.
This review was conducted to highlight the most influential factors and specify the trends reducing uncertainty and increasing the accuracy of soil and water assessment tool (SWAT)-based hydrological models. Although the resolution of input data on the results of SWAT-based hydrological models has been extensively determined. There is still a gap in providing comprehensive review framework to be emerged for identifying the impact of the data resolution and accuracy. The factors taken into consideration in this study were the impact of digital elevation model (DEM) resolution, soil data resolution, land use and land cover (LULC) resolution, and the impact of weather data resolution. Identifying the best DEM resolution depends on the watershed response and hydrological processes. However, for sediment yield estimation, more attention should be paid to the accuracy of soil data. Furthermore, the impact of LULC resolution on the accuracy of streamflow is still not sufficiently understood, whereas fine resolution is required for an accurate simulation of the sediment yield. Sub-daily precipitation data is essential for an accurate estimation of streamflow. Despite the fact that climate forecast system reanalysis (CFSR) and tropical rainfall measuring mission (TRMM) are the most widely used climate products, climate hazards group infrared precipitation with station data (CHIRPS) produces an adequate estimation for streamflow when there is insufficient gauged data. However, other aspects have not been deeply taken into consideration, including the interactive and complementary impacts of these factors. Thus, more attention and focus should be given to these issues. This review and evaluation can be a significant guide for selecting the suitable input data to implement efficient SWAT-based watershed models.
本综述旨在突出最具影响力的因素，并明确降低不确定性和提高基于土壤与水资源评估工具（SWAT）的水文模型准确性的趋势。尽管基于SWAT的水文模型结果的输入数据分辨率已得到广泛研究。但在提供全面的综述框架以确定数据分辨率和准确性的影响方面仍存在差距。本研究考虑的因素包括数字高程模型（DEM）分辨率、土壤数据分辨率、土地利用与土地覆盖（LULC）分辨率以及气象数据分辨率的影响。确定最佳DEM分辨率取决于流域响应和水文过程。然而，对于产沙量估算，应更多关注土壤数据的准确性。此外，LULC分辨率对径流准确性的影响仍未得到充分理解，而准确模拟产沙量则需要高分辨率。次日降水数据对于准确估算径流至关重要。尽管气候预报系统再分析（CFSR）和热带降雨测量任务（TRMM）是使用最广泛的气候产品，但当实测数据不足时，气候灾害组红外降水与站点数据（CHIRPS）对径流能做出充分估算。然而，其他方面尚未得到深入考虑，包括这些因素的交互和互补影响。因此，应更多关注这些问题。本综述和评估可为选择合适的输入数据以实施高效的基于SWAT的流域模型提供重要指导。
