Evaluation of land use/land cover datasets in hydrological modelling using the SWAT model

IF 1.5 Q4 WATER RESOURCES H2Open Journal Pub Date : 2023-03-08 DOI:10.2166/h2oj.2023.062
Sayed Amir Alawi, Sevinç Özkul
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引用次数: 3

Abstract

Land use/land cover (LULC) is a key influencer for runoff generation and the estimation of evapotranspiration in the hydrology of watersheds. Therefore, it is essential to use accurate and reliable LULC data in hydrological modelling. Ground-based data deficiencies are a big challenge in most parts of developing countries and remote areas around the globe. The main objective of this research was to evaluate the accuracy of LULC data from two different sources in hydrological modelling using the soil and water assessment tool (SWAT). The first LULC data was prepared by the classification of Landsat 8 satellite imagery, and the second LULC data was extracted from the ESRI 2020 global LULC dataset. The study was conducted on the Kokcha Watershed, a mountainous basin partly covered by permanent snow and glaciers. The accuracy assessment was done based on a comparison between observed river discharge and simulated river flow, utilizing each LULC dataset separately. After calibration and validation of the models, the acquired result was approximately similar and slightly (5.5%) different. However, due to the higher resolution and easily accessible ESRI 2020 dataset, it is recommended to use ESRI 2020 in hydrological modelling using the SWAT model.
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使用SWAT模型对水文建模中的土地利用/土地覆盖数据集进行评估
土地利用/土地覆盖(LULC)是流域水文中径流生成和蒸散估算的关键影响因素。因此,在水文建模中使用准确可靠的LULC数据至关重要。地面数据不足是发展中国家大部分地区和全球偏远地区面临的一大挑战。本研究的主要目的是使用土壤和水评估工具(SWAT)评估来自两个不同来源的LULC数据在水文建模中的准确性。第一个LULC数据是通过陆地卫星8号卫星图像的分类编制的,第二个LULC是从ESRI 2020全球LULC数据集中提取的。这项研究是在Kokcha流域进行的,这是一个部分被永久性积雪和冰川覆盖的山区盆地。精度评估是基于观测到的河流流量和模拟河流流量之间的比较进行的,分别使用每个LULC数据集。在对模型进行校准和验证后,获得的结果大致相似,但略有不同(5.5%)。然而,由于ESRI 2020数据集分辨率更高且易于访问,建议在使用SWAT模型的水文建模中使用ESRI 2020。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
H2Open Journal
H2Open Journal Environmental Science-Environmental Science (miscellaneous)
CiteScore
3.30
自引率
4.80%
发文量
47
审稿时长
24 weeks
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