Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective
Abstract Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using G...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-024-02345-6 |
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| author | Sheheryar Khan Wang Huiliang Umer Nauman Muhammad Waseem Boota Zening Wu |
| author_facet | Sheheryar Khan Wang Huiliang Umer Nauman Muhammad Waseem Boota Zening Wu |
| author_sort | Sheheryar Khan |
| collection | DOAJ |
| description | Abstract Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm. |
| format | Article |
| id | doaj-art-862cdfb0f43a4872a2abce7e0575f0d3 |
| institution | Kabale University |
| issn | 2190-5487 2190-5495 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj-art-862cdfb0f43a4872a2abce7e0575f0d32025-01-26T12:47:09ZengSpringerOpenApplied Water Science2190-54872190-54952024-12-0115112310.1007/s13201-024-02345-6Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspectiveSheheryar Khan0Wang Huiliang1Umer Nauman2Muhammad Waseem Boota3Zening Wu4School of Water Conservancy and Transportation, Zhengzhou UniversitySchool of Water Conservancy and Transportation, Zhengzhou UniversityInformation Science and Engineering College, Henan University of TechnologyCollege of Geography and Environmental Science, Henan UniversitySchool of Water Conservancy and Transportation, Zhengzhou UniversityAbstract Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm.https://doi.org/10.1007/s13201-024-02345-6Yellow River Basin ChinaETGSEBALRemote sensingLand use change (LULC)Random forest classifiers (RF) |
| spellingShingle | Sheheryar Khan Wang Huiliang Umer Nauman Muhammad Waseem Boota Zening Wu Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective Applied Water Science Yellow River Basin China ET GSEBAL Remote sensing Land use change (LULC) Random forest classifiers (RF) |
| title | Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective |
| title_full | Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective |
| title_fullStr | Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective |
| title_full_unstemmed | Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective |
| title_short | Evaluating land use ımpact on evapotranspiration in Yellow River Basin China through a novel GSEBAL model: a remote sensing perspective |
| title_sort | evaluating land use impact on evapotranspiration in yellow river basin china through a novel gsebal model a remote sensing perspective |
| topic | Yellow River Basin China ET GSEBAL Remote sensing Land use change (LULC) Random forest classifiers (RF) |
| url | https://doi.org/10.1007/s13201-024-02345-6 |
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