Article
Engineering, Civil
Huiling Chen, Gaofeng Zhu, Shasha Shang, Wenhua Qin, Yang Zhang, Yonghong Su, Kun Zhang, Yongtai Zhu, Cong Xu
Summary: This study evaluated the performances of two remote sensing-based ET models at different scales and found that GLEAM performed relatively poorly in some regions, with significant discrepancies in ET partitioning between the two models.
JOURNAL OF HYDROLOGY
(2022)
Article
Agronomy
Chunan Xiao, Jiabing Cai, Baozhong Zhang, Hongfang Chang, Zheng Wei
Summary: Field surface temperature data were analyzed and optimized using thermal infrared sensors and an algorithm, improving the accuracy of evapotranspiration estimation in the S-I model. The estimation of soil evaporation and canopy transpiration was refined through the Revised S-W model. This study provides valuable insights into the accurate estimation of evapotranspiration using different models in different regions, and its application in precision irrigation management.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Agronomy
D. Kool, W. P. Kustas, A. Ben-Gal, N. Agam
Summary: Partitioning of evapotranspiration into soil water evaporation and transpiration allows separate assessment of soil and plant water, energy, and carbon exchange. Remote sensing-based models are ideally suited to monitor ET over large areas, but ET partitioning estimates vary widely. The two-source energy balance (TSEB) model was evaluated for seasonal ET partitioning, showing improved results with adaptations to plant transpiration parameters and soil heat flux, yet inferior performance when using measured soil and vine temperatures.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Agronomy
Z. Ntshidi, S. Dzikiti, D. Mazvimavi, N. T. Mobe
Summary: Orchard evapotranspiration (ET) is a complex process that includes tree transpiration, understorey vegetation transpiration, soil evaporation, and mulches. The study found that in young orchards, orchard floor evaporative fluxes accounted for over 80% of the measured ET, while in mature orchards, these losses were less significant.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Agronomy
Yueyue Wang, Robert Horton, Xuzhang Xue, Tusheng Ren
Summary: Partitioning evapotranspiration (ET) into soil water evaporation (E) and crop transpiration (T) accurately is crucial for effective irrigation management. The study successfully used compatible sensors to measure E and T, showing good agreement with lysimeter ET data overall, but slight discrepancies at different ET rates. Combining different measurement approaches can achieve satisfactory accuracy in partitioning ET.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Agronomy
Jing Zheng, Junliang Fan, Fucang Zhang, Qianlai Zhuang
Summary: Soil mulching can improve crop yield and water productivity by promoting plant transpiration and suppressing soil evaporation.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Plant Sciences
Mingjie Xu, Qianhui Ma, Shengtong Li, Fengting Yang, Tao Zhang, Fei Xu, Bin Yang, Hui Zhang, Shu Zhang, Qianyu Wang, Yuanyuan Tang, Huimin Wang
Summary: This study applied the improved SWH model to estimate forest evapotranspiration and its components in a subtropical plantation. The model performed relatively well in tracking seasonal variations but was weaker in estimating interannual variabilities. The results provided a basis for further improving and optimizing the modeled results under different climate backgrounds.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Agronomy
Xingyu Hu, Huimin Lei
Summary: Partitioning of evapotranspiration (ET) is crucial for understanding surface-atmosphere interactions and plant water use strategy. In this study, seven different partitioning methods were compared, with the machine learning method TEA showing the best performance in simulating crop transpiration and soil evaporation. A new method based on the USO model also demonstrated good partitioning results. The study also revealed the interannual variability of crop T/ET, as well as the differences in ET components.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Environmental Sciences
Wanqiu Xing, Weiguang Wang, Quanxi Shao, Linye Song, Mingzhu Cao
Summary: By incorporating soil moisture into the evapotranspiration algorithm, the accuracy of estimating evapotranspiration and transpiration can be improved, especially in water-limited regions. In China, the updated ET algorithm shows differences in transpiration and soil evaporation estimates across different regions, with higher transpiration estimates in the southern regions overall.
Article
Water Resources
Haoyu Zhang, Shuai Wang, Chongchong Ye, Yaping Wang
Summary: This study investigated the trend and attribution of evapotranspiration (ET) on the Tibetan Plateau (TP) using ET products obtained from a model. The results showed that ET, soil evaporation (E), and vegetation transpiration (T) presented a similar spatial pattern, with E contributing the most to ET. Additionally, the variations of modelled ET on the TP were distinguished into energy-restricted and water-restricted regions.
HYDROLOGICAL PROCESSES
(2022)
Article
Agronomy
Pei Wang, Jingjing Ma, Juanjuan Ma, Haitao Sun, Qi Chen
Summary: This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and successfully applied it to an agricultural ecosystem, validating its accuracy and identifying net radiation and shortwave radiation as the main drivers of ET0 at the study site. The proposed R-SPAC model can be used for predicting ET0 under various future environment conditions and exploring interactions between climate, crop type, and soil.
Article
Energy & Fuels
Seth E. Younger, C. Rhett Jackson, Mackenzie J. Dix, Peter V. Caldwell, Doug P. Aubrey
Summary: The increasing demand for woody crops for bioenergy raises concerns about water yield due to increased evapotranspiration. This study compared the water budgets of loblolly pine and Eucalyptus benthamii and found that Eucalyptus had higher evapotranspiration and biomass production. The results also showed that evapotranspiration was not affected by groundwater depth.
BIOENERGY RESEARCH
(2023)
Article
Environmental Sciences
Changlong Li, Zengyuan Li, Zhihai Gao, Bin Sun
Summary: Based on the specific characteristics of drylands, this study optimized the ET estimation model and found improved results in extreme value regions. Different vegetation regions can represent different climate areas, with precipitation being the decisive factor affecting ET.
Article
Environmental Sciences
Lifeng Zhang, Zhiguang Chen, Xiang Zhang, Liang Zhao, Qi Li, Dongdong Chen, Yanhong Tang, Song Gu
Summary: The study used the S-W model to quantify ET partitioning in a degraded alpine meadow on the QTP, showing that soil evaporation was higher than plant transpiration, with Rn, LAI, and SWC5cm being important factors influencing ET partitioning.
Article
Environmental Sciences
Jiaming Xu, Bingfang Wu, Dongryeol Ryu, Nana Yan, Weiwei Zhu, Zonghan Ma
Summary: The improved canopy conductance model considers radiation and vapor pressure deficit as the main influencing factors, quantifies the temporal variation in stomatal responses to radiation, and enhances estimation accuracy. The model exhibits high applicability in various climate zones and surface types in Northern China.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Engineering, Multidisciplinary
Chengcheng Chen, Qian Zhang, Mahsa H. Kashani, Changhyun Jun, Sayed M. Bateni, Shahab S. Band, Sonam Sandeep Dash, Kwok-Wing Chau
Summary: In this study, a deep-learning-based LSTM model is developed for the first time to forecast monthly rainfall data and compared with the RF model. The results show that the LSTM model outperforms the RF model in rainfall forecasting at both stations, with reasonable accuracy under any global climatic conditions.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
(2022)
Article
Environmental Sciences
Zijin Yuan, Nusseiba NourEldeen, Kebiao Mao, Zhihao Qin, Tongren Xu
Summary: The evaluation of long-term spatiotemporal variability in soil moisture in Africa reveals a declining trend in soil moisture levels, particularly in central Africa. The study employs a time series reconstruction model and a Spatially Weighted Downscaling Model to overcome the limitations of different microwave sensors. The analysis found that spring exhibits the most significant drop in soil moisture, mainly in eastern and central Africa.
Article
Astronomy & Astrophysics
Roohollah Noori, Sayed M. Bateni, Markus Saari, Mansour Almazroui, Ali Torabi Haghighi
Summary: High-latitude lakes are warming faster than the global average, which has significant implications for life on Earth. The study found that lake surface-water temperature warms faster than local air temperature, while lake deep-water temperature and depth-weighted mean water temperature warm at slower rates. The number of ice-free days is also increasing.
EARTH AND SPACE SCIENCE
(2022)
Article
Environmental Sciences
Muhammad Jamal Nasir, Waqas Ahmad, Javed Iqbal, Burhan Ahmad, Hazem Ghassan Abdo, Rafiq Hamdi, Sayed M. Bateni
Summary: Over the past few decades, the majority of the global population has been residing in urban areas, leading to a strain on land resources. This research investigates the land-use dynamics and their impact on the Land Surface Temperature in Kohat City, Pakistan.
EARTH SYSTEMS AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Daouda Nsangou, Amidou Kpoumie, Zakari Mfonka, Sayed M. Bateni, Abdou Nasser Ngouh, Jules Remy Ndam Ngoupayou
Summary: The paper proposes a conceptual approach to modeling flood risk-sensitive areas in the Mfoundi watershed, using multi-criteria decision analysis, the analytical hierarchy process, and the geographical information system. The study successfully generated a flood susceptibility map for the Yaounde town area, providing a basic tool for more efficient environmental management. The research also serves as an experimental study representative of the humid tropical forest zone in Cameroon.
EARTH SYSTEMS AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Zixuan Hu, Linna Chai, Wade T. Crow, Shaomin Liu, Zhongli Zhu, Ji Zhou, Yuquan Qu, Jin Liu, Shiqi Yang, Zheng Lu
Summary: This study presents an optimized wavelet-coupled fitting method (OWCM) to enhance the fitting accuracy of general models for soil moisture analysis. By introducing a wavelet transform technique, the OWCM improves the capturing of high- and low-frequency image features. The results demonstrate that the OWCM-derived soil moisture products are closer to in situ observations and better capture the dynamic changes during the unfrozen season.
Article
Environmental Sciences
Yuan Zhang, Shaomin Liu, Lisheng Song, Xiang Li, Zhenzhen Jia, Tongren Xu, Ziwei Xu, Yanfei Ma, Ji Zhou, Xiaofan Yang, Xinlei He, Yunjun Yao, Guangcheng Hu
Summary: This study proposes an integrated validation framework for evaluating the accuracy of remotely sensed evapotranspiration (RS_ET) products over different land surfaces. The framework is applied to five widely used RS_ET products in the Heihe River Basin, analyzing their accuracy, spatiotemporal variations, error sources, and uncertainties during the validation process.
Article
Environmental Sciences
Lang Han, Gui-Rui Yu, Zhi Chen, Xian-Jin Zhu, Wei-Kang Zhang, Tie-Jun Wang, Li Xu, Shi-Ping Chen, Shao-Min Liu, Hui-Min Wang, Jun-Hua Yan, Jun-Lei Tan, Fa-Wei Zhang, Feng-Hua Zhao, Ying-Nian Li, Yi-Ping Zhang, Li-Qing Sha, Qing-Hai Song, Pei-Li Shi, Jiao-Jun Zhu, Jia-Bing Wu, Zhong-Hui Zhao, Yan-Bin Hao, Xi-Bin Ji, Liang Zhao, Yu-Cui Zhang, Shi-Cheng Jiang, Feng-Xue Gu, Zhi-Xiang Wu, Yang-Jian Zhang, Zhou Li, Ya-Kun Tang, Bing-Rui Jia, Gang Dong, Yan-Hong Gao, Zheng-De Jiang, Dan Sun, Jian-Lin Wang, Qi-Hua He, Xin-Hu Li, Fei Wang, Wen-Xue Wei, Zheng-Miao Deng, Xiang-Xiang Hao, Xiao-Li Liu, Xi-Feng Zhang, Xing-Guo Mo, Yong-Tao He, Xin-Wei Liu, Hu Du, Zhi-Lin Zhu
Summary: Accurately estimating ecosystem respiration is crucial for understanding terrestrial carbon cycles and predicting global carbon budgets. In this study, an Intelligent Random Forest (IRF) model was developed to estimate ecosystem respiration in China, integrating ecological understanding with machine learning techniques. The results showed that the IRF model outperformed other models and algorithms. The study highlighted the importance of gross primary productivity, living plant biomass, and soil organic carbon in controlling the spatiotemporal variability of ecosystem respiration in China.
GLOBAL BIOGEOCHEMICAL CYCLES
(2022)
Article
Environmental Sciences
Yingze Tian, Tongren Xu, Fei Chen, Xinlei He, Shi Li
Summary: This study demonstrates that data assimilation can significantly improve the accuracy of short-term land surface variable predictions, with the assimilation impact lasting up to 60-100 days.
Article
Environmental Sciences
Xin Li, Guodong Cheng, Bojie Fu, Jun Xia, Ling Zhang, Dawen Yang, Chunmiao Zheng, Shaomin Liu, Xiubin Li, Changqing Song, Shaozhong Kang, Xiaoyan Li, Tao Che, Yi Zheng, Yanzhao Zhou, Haibo Wang, Youhua Ran
Summary: The Heihe River basin (HRB), with its unique mountain cryosphere-oasis-desert landscapes and human-nature competition for water resources, has provided an excellent case for the study of critical zones (CZs) and watershed systems. Significant progress and breakthroughs have been made in understanding cryospheric hydrological processes, ecological and hydrological interactions, and surface-groundwater interactions in the HRB. However, challenges remain in observing and modeling geochemical and geomorphological processes in integrated watershed studies.
Article
Water Resources
Hamidreza Vosoughifar, Helaleh Khoshkam, Sayed M. Bateni, Changhyun Jun, Tongren Xu, Shahab S. Band, Christopher M. U. Neale
Summary: This study developed MARS and GEP models for estimating ETo in coastal regions and evaluated their performances. The results showed that the generalized MARS1-MARS5 and GEP1-GEP5 models accurately estimated ETo in regions other than their training region, with MARS1 and GEP1 performing the best. The study also found that MARS outperformed GEP in estimating ETo and improved the estimation of ETo.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Environmental Sciences
Kebiao Mao, Han Wang, Jiancheng Shi, Essam Heggy, Shengli Wu, Sayed M. M. Bateni, Guoming Du
Summary: In this study, a novel fully-coupled paradigm combining deep learning, physical methods, and statistical methods is developed to robustly retrieve soil moisture (SM) and land surface temperature (LST) from passive microwave data, improving retrieval accuracy. The physical method is derived based on the energy radiation balance equation, while the statistical method is constructed using multi-source data. The mean absolute error of the retrieved SM and LST data are 0.027 m(3)/m(3) and 1.38 K, respectively, at an incidence angle of 0-65 degrees. This model-data-knowledge-driven and deep learning method overcomes the shortcomings of traditional methods and provides a paradigm for retrieval of other geophysical variables.
Article
Environmental Studies
Rahim Tavakolifar, Himan Shahabi, Mohsen Alizadeh, Sayed M. Bateni, Mazlan Hashim, Ataollah Shirzadi, Effi Helmy Ariffin, Isabelle D. Wolf, Saman Shojae Chaeikar
Summary: This study compared the predictive capacities of fuzzy logic-ANP (FLANP) and fuzzy logic-TOPSIS (FLTOPSIS) for mapping landslide susceptibility along the Saqqez-Marivan main road in Kurdistan province, Iran. The FLTOPSIS method showed better prediction accuracy with an AUCROC of 0.983 compared to 0.938 for the FLANP method. The susceptibility map developed through the FLTOPSIS method is suitable for informing management and planning in landslide-prone areas in mountainous regions.
Article
Environmental Sciences
Ruyu Mei, Kebiao Mao, Jiancheng Shi, Jeffrey Nielson, Sayed M. Bateni, Fei Meng, Guoming Du
Summary: This study developed a novel fully-coupled paradigm for the robust retrieval of integrated water vapor content (WVC) from thermal infrared remote sensing data using deep learning. Two conditions were determined for the deep learning retrieval paradigm of WVC: the input and output parameters need to form a complete set of solvable equations; if there is a strong relationship between input and output parameters, direct retrieval is possible. When a strong correlation exists between input and output parameters, high-precision retrieval can be achieved without prior knowledge.
Article
Environmental Sciences
Mahyat Shafapourtehrany, Fatemeh Rezaie, Changhyun Jun, Essam Heggy, Sayed M. Bateni, Mahdi Panahi, Haluk Ozener, Farzin Shabani, Hamidreza Moeini
Summary: This study used deep learning models and remote sensing data to generate landslide susceptibility maps, showing that areas with steep slopes, high rainfall, and soil wetness are more susceptible to landslides. This contributes to a better understanding of deep learning applications in the field of natural hazards.
