A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin

Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiaoman Jiang [verfasserIn] Guoqiang Wang [verfasserIn] Yuntao Wang [verfasserIn] Jiping Yao [verfasserIn] Baolin Xue [verfasserIn] Yinglan A [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	machine learning random forest actual evapotranspiration spatiotemporal distribution

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 9, p 2234
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:9, p 2234

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15092234

Katalog-ID:	DOAJ090340752

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520			\|a Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.
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allfields	10.3390/rs15092234 doi (DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e DE-627 ger DE-627 rakwb eng Xiaoman Jiang verfasserin aut A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q Guoqiang Wang verfasserin aut Yuntao Wang verfasserin aut Jiping Yao verfasserin aut Baolin Xue verfasserin aut Yinglan A verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2234 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2234 https://doi.org/10.3390/rs15092234 kostenfrei https://doaj.org/article/0d851570738d43c586c4be9fd8b59f4e kostenfrei https://www.mdpi.com/2072-4292/15/9/2234 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2234
spelling	10.3390/rs15092234 doi (DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e DE-627 ger DE-627 rakwb eng Xiaoman Jiang verfasserin aut A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q Guoqiang Wang verfasserin aut Yuntao Wang verfasserin aut Jiping Yao verfasserin aut Baolin Xue verfasserin aut Yinglan A verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2234 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2234 https://doi.org/10.3390/rs15092234 kostenfrei https://doaj.org/article/0d851570738d43c586c4be9fd8b59f4e kostenfrei https://www.mdpi.com/2072-4292/15/9/2234 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2234
allfields_unstemmed	10.3390/rs15092234 doi (DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e DE-627 ger DE-627 rakwb eng Xiaoman Jiang verfasserin aut A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q Guoqiang Wang verfasserin aut Yuntao Wang verfasserin aut Jiping Yao verfasserin aut Baolin Xue verfasserin aut Yinglan A verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2234 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2234 https://doi.org/10.3390/rs15092234 kostenfrei https://doaj.org/article/0d851570738d43c586c4be9fd8b59f4e kostenfrei https://www.mdpi.com/2072-4292/15/9/2234 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2234
allfieldsGer	10.3390/rs15092234 doi (DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e DE-627 ger DE-627 rakwb eng Xiaoman Jiang verfasserin aut A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q Guoqiang Wang verfasserin aut Yuntao Wang verfasserin aut Jiping Yao verfasserin aut Baolin Xue verfasserin aut Yinglan A verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2234 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2234 https://doi.org/10.3390/rs15092234 kostenfrei https://doaj.org/article/0d851570738d43c586c4be9fd8b59f4e kostenfrei https://www.mdpi.com/2072-4292/15/9/2234 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2234
allfieldsSound	10.3390/rs15092234 doi (DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e DE-627 ger DE-627 rakwb eng Xiaoman Jiang verfasserin aut A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations. machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q Guoqiang Wang verfasserin aut Yuntao Wang verfasserin aut Jiping Yao verfasserin aut Baolin Xue verfasserin aut Yinglan A verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2234 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2234 https://doi.org/10.3390/rs15092234 kostenfrei https://doaj.org/article/0d851570738d43c586c4be9fd8b59f4e kostenfrei https://www.mdpi.com/2072-4292/15/9/2234 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2234
language	English
source	In Remote Sensing 15(2023), 9, p 2234 volume:15 year:2023 number:9, p 2234
sourceStr	In Remote Sensing 15(2023), 9, p 2234 volume:15 year:2023 number:9, p 2234
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	machine learning random forest actual evapotranspiration spatiotemporal distribution Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Xiaoman Jiang @@aut@@ Guoqiang Wang @@aut@@ Yuntao Wang @@aut@@ Jiping Yao @@aut@@ Baolin Xue @@aut@@ Yinglan A @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ090340752
language_de	englisch
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By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. 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author	Xiaoman Jiang
spellingShingle	Xiaoman Jiang misc machine learning misc random forest misc actual evapotranspiration misc spatiotemporal distribution misc Science misc Q A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
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topic_title	A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin machine learning random forest actual evapotranspiration spatiotemporal distribution
topic	misc machine learning misc random forest misc actual evapotranspiration misc spatiotemporal distribution misc Science misc Q
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title	A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
ctrlnum	(DE-627)DOAJ090340752 (DE-599)DOAJ0d851570738d43c586c4be9fd8b59f4e
title_full	A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
author_sort	Xiaoman Jiang
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author_browse	Xiaoman Jiang Guoqiang Wang Yuntao Wang Jiping Yao Baolin Xue Yinglan A
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title_sort	hybrid framework for simulating actual evapotranspiration in data-deficient areas: a case study of the inner mongolia section of the yellow river basin
title_auth	A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
abstract	Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.
abstractGer	Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.
abstract_unstemmed	Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC<sub<2021</sub<) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R<sup<2</sup< = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.
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title_short	A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin
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