Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4849-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4849-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
31 Oct 2023
Data description paper |
| 31 Oct 2023
| 31 Oct 2023
A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model
Shanlei Sun
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China
Zaoying Bi
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, USA
Yi Liu
School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
Ge Sun
Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, Raleigh, USA
Weimin Ju
International Institute for Earth System Science, Nanjing University, Nanjing, China
Chunwei Liu
Jiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
Mengyuan Mu
ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, Australia
Jinjian Li
School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, China
Yang Zhou
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China
Xiaoyuan Li
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China
Jiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
Haishan Chen
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China
Related authors
No articles found.
Karim Pyarali, Lulu Zhang, Ning Liu, Abdulhakeem Al-Qubati, and Ge Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1629, https://doi.org/10.5194/egusphere-2025-1629, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
An ecosystem services model was applied across Germany to estimate water supply and carbon sequestration. The results showed that total annual water discharge and carbon sequestration for Germany is 85 billion m3 and 106 TgC, respectively. Furthermore, we found that croplands provide the largest amount of water, deciduous broadleaf forests sequester most of the carbon, and wetlands are very effective in absorbing carbon. During extreme events, we noticed a real impact on both services.
Yiming Wang, Yi Zhang, Yilun Han, Wei Xue, Yihui Zhou, Xiaohan Li, and Haishan Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2790, https://doi.org/10.5194/egusphere-2025-2790, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This work explores the use of global storm-resolving model (GSRM) simulation data to enhance global climate modeling (GCM) through a machine learning–based model physics suite. Stable multiyear climate simulations with improved precipitation characteristics are achieved by using 80-day GSRM data.
Shuzhuang Feng, Fei Jiang, Yongguang Zhang, Huilin Chen, Honglin Zhuang, Shumin Wang, Shengxi Bai, Hengmao Wang, and Weimin Ju
EGUsphere, https://doi.org/10.5194/egusphere-2025-2669, https://doi.org/10.5194/egusphere-2025-2669, 2025
Short summary
Short summary
Using satellite data and advanced modeling, this study inverted daily high-resolution anthropogenic CH4 emissions across China and Shanxi Province. We found that China's 2022 CH4 emissions were 45.1 TgCH4·yr⁻¹, significantly lower than previous estimates, especially in coal mining and waste sectors. The inversion substantially reduced emission uncertainties and improved CH4 concentration simulations. These results suggest China’s climate mitigation burden may have been overestimated.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
Short summary
Short summary
Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Liang Feng, Paul Palmer, Luke Smallman, Jingfeng Xiao, Paulo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen Noe, Stephen Platt, Xinrong Ren, Martin Steinbacher, and Irene Xueref-Remy
EGUsphere, https://doi.org/10.5194/egusphere-2025-1793, https://doi.org/10.5194/egusphere-2025-1793, 2025
Short summary
Short summary
2023 saw an unexpectedly high global atmospheric CO2 growth. Satellite data reveal a role for increased emissions over the tropics. Larger emissions over eastern Brazil can be explained by warmer temperatures, while changes in rainfall and soil moisture play more of a role in emission increases elsewhere in the tropics.
Kyaw Than Oo, Chen Haishan, Kazora Jonah, and Du Xinguan
EGUsphere, https://doi.org/10.5194/egusphere-2025-1159, https://doi.org/10.5194/egusphere-2025-1159, 2025
Short summary
Short summary
The study examines the delayed withdrawal of the Mainland Indochina Southwest Monsoon by exploring spatial trends. The new Cumulative Change-Point Monsoon index effectively describes seasonal shifts. Results indicate stronger subtropical westerly jets and weaker tropical easterly jets in recent years, impacting wind patterns and delaying monsoon withdrawal.
Xufeng Wang, Tao Che, Jingfeng Xiao, Tonghong Wang, Junlei Tan, Yang Zhang, Zhiguo Ren, Liying Geng, Haibo Wang, Ziwei Xu, Shaomin Liu, and Xin Li
Earth Syst. Sci. Data, 17, 1329–1346, https://doi.org/10.5194/essd-17-1329-2025, https://doi.org/10.5194/essd-17-1329-2025, 2025
Short summary
Short summary
In this study, carbon flux and auxiliary meteorological data are post-processed to create an analysis-ready dataset for 34 sites across six ecosystems in the Heihe River basin. Overall, 18 sites have multi-year observations, while 16 were observed only during the 2012 growing season, totaling 1513 site months. This dataset can be used to explore carbon exchange, assess ecosystem responses to climate change, support upscaling studies, and evaluate carbon cycle models.
Yi Liu, Jingfeng Xiao, Xing Li, and Yue Li
Hydrol. Earth Syst. Sci., 29, 1241–1258, https://doi.org/10.5194/hess-29-1241-2025, https://doi.org/10.5194/hess-29-1241-2025, 2025
Short summary
Short summary
This work demonstrates that multi-source satellite-based water and carbon fluxes can capture critical soil moisture at a large spatial scale. In particular, grassland and clay with critical soil moisture higher than average soil moisture may be in a state of water limitation for long periods. Increased water demand could expose western grassland to more vulnerability.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3672, https://doi.org/10.5194/egusphere-2024-3672, 2025
Short summary
Short summary
The Russia-Ukraine war in 2022 severely disrupted Ukraine’s economy, with significant reductions in industrial, transportation, and residential activities. Our research used satellite data to track changes in nitrogen oxide emissions, a key indicator of human activity, during the war. We found a 28 % decline in emissions, which was twice of the decrease caused by the COVID-19 pandemic. This study highlights how modern warfare can deeply impact both the environment and economic stability.
Xingyu Wang, Fei Jiang, Hengmao Wang, Zhengqi Zhang, Mousong Wu, Jun Wang, Wei He, Weimin Ju, and Jing M. Chen
Atmos. Chem. Phys., 25, 867–880, https://doi.org/10.5194/acp-25-867-2025, https://doi.org/10.5194/acp-25-867-2025, 2025
Short summary
Short summary
The role of OCO-3 XCO2 retrievals in estimating global terrestrial carbon fluxes is unclear. We investigate this by assimilating OCO-3 XCO2 retrievals alone and in combination with OCO-2 XCO2. The assimilation of OCO-3 XCO2 alone underestimates global land sinks, mainly at high latitudes, due to the lack of observations beyond 52° S and 52° N, large variations in the number of data, and varying observation times, while the joint assimilation of OCO-2 and OCO-3 XCO2 has the best performance.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Mana Gharun, Ankit Shekhar, Jingfeng Xiao, Xing Li, and Nina Buchmann
Biogeosciences, 21, 5481–5494, https://doi.org/10.5194/bg-21-5481-2024, https://doi.org/10.5194/bg-21-5481-2024, 2024
Short summary
Short summary
In 2022, Europe's forests faced unprecedented dry conditions. Our study aimed to understand how different forest types respond to extreme drought. Using meteorological data and satellite imagery, we compared 2022 with two previous extreme years, 2003 and 2018. Despite less severe drought in 2022, forests showed a 30 % greater decline in photosynthesis compared to 2018 and 60 % more than 2003. This suggests an alarming level of vulnerability of forests across Europe to more frequent droughts.
Ran Yan, Jun Wang, Weimin Ju, Xiuli Xing, Miao Yu, Meirong Wang, Jingye Tan, Xunmei Wang, Hengmao Wang, and Fei Jiang
Biogeosciences, 21, 5027–5043, https://doi.org/10.5194/bg-21-5027-2024, https://doi.org/10.5194/bg-21-5027-2024, 2024
Short summary
Short summary
Our study reveals that the effects of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's gross primary production (GPP) are basically opposite, with obvious seasonal changes. Soil moisture primarily influences GPP during ENSO events (except spring) and temperature during IOD events (except fall). Quantitatively, China's annual GPP displays modest positive anomalies during La Niña and negative anomalies in El Niño years, driven by significant seasonal variations.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
Short summary
The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
Short summary
Short summary
Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Atmos. Meas. Tech., 17, 4411–4424, https://doi.org/10.5194/amt-17-4411-2024, https://doi.org/10.5194/amt-17-4411-2024, 2024
Short summary
Short summary
This study explores the problems of surface reflectance estimation from previous MISR satellite remote sensing images and develops an error correction model to obtain a higher-precision aerosol optical depth (AOD) product. High-accuracy AOD is important not only for the daily monitoring of air pollution but also for the study of energy exchange between land and atmosphere. This will help further improve the retrieval accuracy of multi-angle AOD on large spatial scales and for long time series.
Shuzhuang Feng, Fei Jiang, Tianlu Qian, Nan Wang, Mengwei Jia, Songci Zheng, Jiansong Chen, Fang Ying, and Weimin Ju
Atmos. Chem. Phys., 24, 7481–7498, https://doi.org/10.5194/acp-24-7481-2024, https://doi.org/10.5194/acp-24-7481-2024, 2024
Short summary
Short summary
We developed a multi-air-pollutant inversion system to estimate non-methane volatile organic compound (NMVOC) emissions using TROPOMI formaldehyde retrievals. We found that the inversion significantly improved formaldehyde simulations and reduced NMVOC emission uncertainties. The optimized NMVOC emissions effectively corrected the overestimation of O3 levels, mainly by decreasing the rate of the RO2 + NO reaction and increasing the rate of the NO2 + OH reaction.
Yi Y. Liu, Albert I. J. M. van Dijk, Patrick Meir, and Tim R. McVicar
Biogeosciences, 21, 2273–2295, https://doi.org/10.5194/bg-21-2273-2024, https://doi.org/10.5194/bg-21-2273-2024, 2024
Short summary
Short summary
Greenness of the Amazon forest fluctuated during the 2015–2016 drought, but no satisfactory explanation has been found. Based on water storage, temperature, and atmospheric moisture demand, we developed a method to delineate the regions where forests were under stress. These drought-affected regions were mainly identified at the beginning and end of the drought, resulting in below-average greenness. For the months in between, without stress, greenness responded positively to intense sunlight.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
Short summary
Short summary
Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
Short summary
Short summary
Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
Short summary
Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Jing Fang, Xing Li, Jingfeng Xiao, Xiaodong Yan, Bolun Li, and Feng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-452, https://doi.org/10.5194/essd-2021-452, 2022
Revised manuscript not accepted
Short summary
Short summary
The dataset provided the vegetation photosynthetic phenology instead of traditional phenology to represent plant seasonal activities. This dataset had the latest period (2001–2020) and a fine spatial resolution (0.05 degree). Our phenology metrics revealed the spatial-temporal patterns of the multiple growing seasons in the Northern Hemisphere. The dataset will facilitate various research such as developing models, evaluating phenology shifts, and monitoring climate change worldwide.
Jiehao Zhang, Yulong Zhang, Ge Sun, Conghe Song, Matthew P. Dannenberg, Jiangfeng Li, Ning Liu, Kerong Zhang, Quanfa Zhang, and Lu Hao
Hydrol. Earth Syst. Sci., 25, 5623–5640, https://doi.org/10.5194/hess-25-5623-2021, https://doi.org/10.5194/hess-25-5623-2021, 2021
Short summary
Short summary
To quantify how vegetation greening impacts the capacity of water supply, we built a hybrid model and conducted a case study using the upper Han River basin (UHRB) that serves as the water source area to the world’s largest water diversion project. Vegetation greening in the UHRB during 2001–2018 induced annual water yield (WY) greatly decreased. Vegetation greening also increased the possibility of drought and reduced a quarter of WY on average during drought periods.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
Short summary
Short summary
Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Teresa E. Gimeno, Belinda E. Medlyn, Dani Or, Jinyan Yang, and David S. Ellsworth
Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, https://doi.org/10.5194/hess-25-447-2021, 2021
Short summary
Short summary
Land surface model (LSM) is a critical tool to study land responses to droughts and heatwaves, but lacking comprehensive observations limited past model evaluations. Here we use a novel dataset at a water-limited site, evaluate a typical LSM with a range of competing model hypotheses widely used in LSMs and identify marked uncertainty due to the differing process assumptions. We show the extensive observations constrain model processes and allow better simulated land responses to these extremes.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop Evapotranspiration: Guidelines for computing crop water requirements, Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome, https://www.fao.org/3/X0490E/x0490e00.htm#Contents (last access: 18 July 2021), 1998.
Aminzadeh, M., Roderick, M. L., and Or, D.: A generalized complementary relationship between actual and potential evaporation defined by a reference surface temperature, Water Resour. Res., 52, 385–406, 2016.
Aouissi, J., Benabdallah, S., Chabaâne, Z. L., and Cudennec, C.: Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT – Application in data-scarce rural Tunisia, Agr. Water Manage., 174, 39–51, 2016.
Aschonitis, V. G., Demertzi, K., Papamichail, D., Colombani, N., and Mastrocicco, M.: Revisiting the Priestley-Taylor method for the assessment of reference crop evapotranspiration in Italy, Ital. J. Agrometeorol., 20, 5–18, 2015.
Aschonitis, V. G., Papamichail, D., Demertzi, K., Colombani, N., Mastrocicco, M., Ghirardini, A., Castaldelli, G., and Fano, E.-A.: High-resolution global grids of revised Priestley–Taylor and Hargreaves–Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation, Earth Syst. Sci. Data, 9, 615–638, https://doi.org/10.5194/essd-9-615-2017, 2017.
Aubin, I., Beaudet, M., and Messier, C.: Light extinction coefficients specific to the understory vegetation of the southern boreal forest, Quebec, Can. J. Forest Res., 30, 168–177, 2000.
Barbero, R., Fowler, H. J., Lenderink, G., and Blenkinsop, S.: Is the intensification of precipitation extremes with global warming better detected at hourly than daily resolutions?, Geophys. Res. Lett., 44, 974–983, 2017.
Barbour, M. M. and Buckley, T. N.: The stomatal response to evaporative demand persists at night in Ricinus communis plants with high nocturnal conductance, Plant Cell Environ., 30, 711–721, 2007.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Beck, H. E., Van Dijk, A. I. J. M., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., and Miralles, D. G.: MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles, B. Am. Meteorol. Soc., 103, E710–E732, 2022.
Berengena, J. and Gavilán, P.: Reference evapotranspiration estimation in a highly advective semiarid environment, J. Irrig. Drain. E., 131, 147–163, 2005.
Beven, K.: Rainfall-Runoff Modelling: The Primer, 2nd edn., John Wiley & Sons Ltd, Oxford (UK), 457 pp., ISBN 978-0-470-71459-1, 2012.
Brisson, N., Itier, B., L'Hotel, J. C., and Lorendeau, J. Y.: Parameterisation of the Shuttleworth-Wallace model to estimate daily maximum transpiration for use in crop models, Ecol. Model., 107, 159–169, 1998.
Cavalcante, R. B. L., Pontes, P. R. M., Souza, P. W. M., and de Souza, E. B.: Opposite effects of climate and land use changes on the annual water balance in the Amazon arc of deforestation, Water Resour. Res., 55, 3092–3106, 2019.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturved, R. K., Fuchs, R., Brovkin, V., Ciacis, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu, Z., Nemani, R. R., and Myneni, R. B.: China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122–129, 2019.
Chen, H., Jiang, A. Z., Huang, J. J., Li, H., McBean, E., Singh, V. P., Zhang, J., Lan, Z., Gao, J., and Zhou, Z.: An enhanced Shuttleworth-Wallace model for simulation of evapotranspiration and its components, Agr. Forest Meteorol., 313, 108769, https://doi.org/10.1016/j.agrformet.2021.108769, 2022.
Cheng, W., Dan, L.i., Deng, X., Feng, J., Wang, Y., Peng, J., Tian, J., Qi, W., Liu, Z., Zheng, X., Zhou, D., Jiang, S., Zhao, H., and Wang, X.: Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios, Scientific Data, 9, 83, https://doi.org/10.1038/s41597-022-01196-7, 2022.
Crago, R. and Crowley, R.: Complementary relationships for near-instantaneous evaporation, J. Hydrol., 300, 199–211, 2005.
Crow, W. T. and Kustas, W. P.: Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model, Remote Sens. Environ., 112, 1268–1281, 2008.
Dai, Y., Wei, N., Yuan, H., Zhang, S., Shangguan, W., Liu, S., Lu, X., and Xin, Y.: Evaluation of soil thermal conductivity schemes for use in land surface modelling, J. Adv. Model. Earth Sy., 11, 3454–3473, 2019a.
Dai, Y., Xin, Q., Wei, N., Zhang, Y., Shangguan, W., Yuan, H., Zhang, S., Liu, S., and Lu, X.: A global high-resolution dataset of soil hydraulic and thermal properties for land surface modeling, J. Adv. Model. Earth Sy., 11, 2996–3023, 2019b.
Dallaire, G., Poulin, A., Arsenault, R., and Brissette, F.: Uncertainty of potential evapotranspiration modelling in climate change impact studies on low flows in North America, Hydrolog. Sci. J., 66, 689–702, 2021.
Damour, G., Simonneau, T., Cochard, H., and Urban, L.: An overview of models of stomatal conductance at the leaf level, Plant Cell Environ., 33, 1419–1438, 2010.
Dawson, T. E., Burgess, S. S., Tu, K. P., Oliveira, R. S., Santiago, L. S., Fisher, J. B., Simonin, K. A., and Ambrose A. R.: Nighttime transpiration in woody plants from contrasting ecosystems, Tree Physiol., 27, 561–575, 2007.
De Dios, V. R., Roy, J., Ferrio, J. P., Alday, J. G., Landais, D., Milcu, A., and Gessler, A.: Processes driving nocturnal transpiration and implications for estimating land evapotranspiration, Scientific Reports, 5, 10975, https://doi.org/10.1038/srep10975, 2015.
Douglas, E. M., Jacobs, J. M., Sumner, D. M., and Ray, R. L.: A comparison of models for estimating potential evapotranspiration for Florida land cover types, J. Hydrol., 373, 366–376, 2009.
Duursma, R. A., Blackman, C. J., Lopéz, R., Martin-StPaul, K., Cochard, H., and Medlyn, B. E.: On the minimum leaf conductance: Its role in models of plant water use, and ecological and environmental controls, New Phytol., 221, 693–705, 2019.
Eckhardt, K. and Ulbrich, U.: Potential impacts of climate change on groundwater recharge and streamflow in a central European low mountain range. J. Hydrol., 284, 244–252, 2003.
ECMWF: IFS Documentation-Cy31r1 Part IV: Physical Processes, http://www.ecmwf.int/sites/default/files/elibrary/2007/9221-part-iv-physical-processes.pdf (last access: 12 December 2022), 2007.
Elfarkh, J., Er-Raki, S., Ezzahar, J., Chehbouni, A., Aithssaine, B., Amazirh, A., Khabba, S., and Jarlan, L.: Integrating thermal stress indexes within Shuttleworth-Wallace model for evapotranspiration mapping over a complex surface, Irrigation Sci., 39, 45–61, 2021.
Emami-Bistghani, Z., Siadat, S. A., Torabi, M., Bakhshande, A., Alami, S. K., and Shiresmaeili, H.: Influence of plant density on light absorption and light extinction coefficient in sunflower cultivars, Res. Crop., 13, 174–179, 2012.
Espadafor, M., Lorite, I. J., Gavilán, P., and Berengena, J.: An analysis of the tendency of reference evapotranspiration estimates and other climate variables during the last 45 years in Southern Spain, Agr. Water Manage., 98, 1045–1061, 2011.
Fang, H., Jiang, C., Li, W., Wei, S., Baret, F., Chen, J. M., Garcia-Haro, J., Liang, S., Liu, R., Myneni, R. B., Pinty, B., Xiao, Z., and Zhu, Z.: Characterization and intercomparison of Global Moderate Resolution Leaf Area Index (LAI) products: Analysis of climatologies and theoretical uncertainties, J. Geophys. Res.-Biogeo., 118, 529–548, 2013.
Fauset, S., Gloor, M. U., Aidar, M. P. M., Freitas, H. C., Fyllas, N. M., Marabesi, M. A., Rochelle, A. L. C. A., Shenkin, Vieira, S. A., and Joly, C. A.: Tropical forest light regimes in a human-modified landscape, Ecosphere, 8, e02002, https://doi.org/10.1002/ecs2.2002, 2017.
Field, C. B., Jackson, R. B., and Mooney, H. A.: Stomatal responses to increased CO2: implications from the plant to the global scale, Plant Cell Environ., 18, 1214–1225, 1995.
Fisher, J. B., DeBiase, T. A., Qi, Y., Xu, M., and Goldstein, A. H.: Evapotranspiration models compared on a Sierra Nevada forest ecosystem, Environ. Modell. Softw., 20, 783–796, 2005.
Fisher, J. B., Whittaker, R. J., and Malhi, Y.: ET come home: potential evapotranspiration in geographical ecology, Global Ecol. Biogeogr., 20, 1–18, https://doi.org/10.1111/j.1466-8238.2010.00578.x, 2011.
Foken, T.: The energy balance closure problem: an overview, Ecol. Appl., 18, 1351–1367, 2008.
Franks, P. J. and Beerling, D. J.: Maximum leaf conductance driven by CO2 effects on stomatal size and density over geologic time, P. Natl. Acad. Sci. USA, 106, 10343–10347, 2008.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 68–182, 2010.
Gang, C.-C, Wang, Z.-Q, Yang, Y., Chen, Y.-Z., Zhang, Y.-Z., Li, J.-L., and Cheng, J.-M.: The NPP spatiotemporal variation of global grassland ecosystems in response to climate change over the past 100 years, Acta Prataculturae Sinica, 25, 1–14, https://doi.org/10.11686/cyxb2016148, 2016 (in Chinese with English Abstract).
Gardiol, J. M., Serio, L. A., and Maggiora, A. I. D.: Modeling evapotranspiration of corn (Zea mays) under different plant densities, J. Hydrol., 217, 188–196, 2003.
Gardner, A., Jiang, M., Ellsworth, D., MacKenzie, A. R., Pritchard, J., Bader, M. K.-F., Barton, C., Bernacchi, C., Calfapietra, C., Crous, K. Y., Dusenge, M. E., Gimeno, T. E., Hall, M., Lamba, S., Leuzinger, S., Uddling, J., Warren, J., Wallin, G., and Medlyn, B.: Optimal stomatal theory predicts CO2 responses of stomatal conductance in both gymnosperm and angiosperm trees, New Phytol., 153, 477–484, 2022.
Gash, J. H. C., Lloyd, C. R., and Lachaud, G.: Estimating sparse forest rainfall interception with an analytical model, J. Hydrol., 170, 79–86, 1995.
Gedney, N., Cox, P. M., Betts, R. A., Boucher, O., Huntingford, C., and Stott, P. A.: Detection of a direct carbon dioxide effect in continental river runoff records, Nature, 439, 835–838, 2006.
Gentine, P., Entekhabi, D., Chehbouni, A., Boulet, G., and Duchemin, B.: Analysis of evaporative fraction diurnal behavior, Agr. Forest Meteorol., 143, 13–29, 2007.
Gentine, P., Entekhabi, D., and Polcher, J.: The diurnal behavior of evaporative fraction in the soil-vegetation-atmospheric boundary layer continuum, J. Hydrometeorol., 12, 1530–1546, 2011.
Gong, X., Liu, H., Sun, J., Gao, Y., Zhang, X., Jha, S. K., Zhang, H., Ma, X., and Wang, W.: A proposed surface resistance model for the Penman-Monteith formula to estimate evapotranspiration in a solar greenhouse, J. Arid Land, 9, 530–546, 2017.
Groh, J., Pütz, T., Gerke, H., Vanderborght, J., and Vereecken, H.: Quantification and prediction of nighttime evapotranspiration for two distinct grassland ecosystems, Water Resour. Res., 55, 2961–2975, 2019.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, 2012.
Han, Q., Wang, T., Wang, L., Smettem, K., Mai, M., and Chen X.: Comparison of nighttime with daytime evapotranspiration responses to environmental controls across temporal scales along a climate gradient, Water Resour. Res., 57, e2021WR029638, https://doi.org/10.1029/2021WR029638, 2021.
Hargreaves, G. H. and Samani, Z. A.: Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, Proceedings of the American Society of Civils Engineers, 108, 225–230, 1983.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Scientific Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hawkins, B. A, Field, R., Cornell, H. V., Currie, D. J., Guégan, J. F., Kaufman, D. M., Kerr, J. T., Mittelbach, G. G., Oberdorff, T., and O'Brien, E. M.: Energy, water, and broad-scale geographic patterns of species richness, Ecology, 84, 3105–3117, 2003.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, , M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hinkelman, L. M.: The Global Radiative Energy Budget in MERRA and MERRA-2: Evaluation with Respect to CERES EBAF Data, J. Climate, 32, 1973–1994, 2019.
Hu, Z., Yu, G., Zhou, Y., Sun, X., Li, Y., Shi, P., Wang, Y., Song, X., Zheng, Z., Zhang, L., and Li, S.: Partitioning of evapotranspiration and its controls in four grassland ecosystems: Application of a two-source model, Agr. Forest Meteorol., 149, 1410–1420, 2009.
Hu, Z., Li, S., Yu, G., Sun, X., Zhang, L., and Han, S.: Modeling evapotranspiration by combing a two-source model, a leaf stomatal model, and a light-use efficiency model, J. Hydrol., 501, 186–192, 2013.
Huang, H., Liu, C., Wang, X., Biging, G. S., Chen, Y., Yang, J., and Gong, P.: Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data, ISPRS J. Photogramm., 129, 189–199, https://doi.org/10.1016/j.isprsjprs.2017.04.020, 2017.
Huang, S., Yan, H., Zhang, C., Wang, G., Acquah, S. J., Yu, J., Li, L., Ma, J., and Darko, R. O.: Modeling evapotranspiration for cucumber plants based on the Shuttleworth-Wallace model in a Venlo-type greenhouse, Agr. Water Manage., 228, 105861, https://doi.org/10.1016/j.agwat.2019.105861, 2020.
International Food Policy Research Institute: Global spatially-disaggregated crop production statistics data for 2010 version 2.0, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/PRFF8V, 2019.
IPCC: Summary for policymakers, in: Climate change 2014: impacts, adaptation, and vulnerability. part a: global and sectoral aspects, edited by: Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., and White, L. L., contribution of working group ii to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, 1–32, https://www.ipcc.ch/site/assets/uploads/2018/02/ar5_wgII_spm_en.pdf (last access: 29 July 2021), 2014.
Iritz, Z., Lindroth, A., Heikinheimo, M., Grelle, A., and Kellner, E.: Test of a modified Shuttleworth-Wallace estimate of boreal forest evaporation, Agr. Forest Meteorol., 98–99, 605–619, 1999.
Itenfisu, D., Elliot, R., Allen, R., and Walter, I.: Comparison of reference evapotranspiration calculations across a range of climates, in: Proceedings of the 4th National Irrigation Symposium, Phoenix, Arizona, USA, 14–16 November, 2000, St. Joseph, ASAE Edn., 216–227, https://www.cabdirect.org/cabdirect/abstract/20003037387 (last access: 1 May 2022), 2000.
Jarvis, P. G.: Interpretation of variations in leaf water potential and stomatal conductance found in canopies in field, Philos. T. Roy. Soc. B, 273, 593–610, 1976.
Jensen, M., Burman, R., and Allen, R.: Evapotranspiration and irrigation water requirements, in: ASCE manual No. 70, ASCE Edn., New York, 332 pp., ISBN 0872627632, 1990.
Jiang, Y., Tang, R., and Li, Z. L.: A physical full-factorial scheme for gap-filling of eddy covariance measurements of daytime evapotranspiration, Agr. Forest Meteorol., 323, 109087, https://doi.org/10.1016/j.agrformet.2022.109087, 2022.
Jourdier, B.: Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France, Adv. Sci. Res., 17, 63–77, 2020.
Kadeba, A., Nacoulma, B. M. I., Ouédraogo, A., Bachmann, Y., Thiombiano, A., Schmidt, M., and Boussim, J. I.: Land cover change and plants diversity in the Sahel: a case study from northern Burkina Faso, Ann. For. Res., 58, 109–123, 2015.
Kahler, D. M. and Brutsaert, W.: Complementary relationship between daily evaporation in the environment and pan evaporation, Water Resour. Res., 42, W05413, https://doi.org/10.1029/2005WR004541, 2006.
Kerr, J.: Butterfly species richness patterns in Canada: energy, heterogeneity, and the potential consequences of climate change, Conserv. Ecol., 5, 10, https://doi.org/10.5751/ES-00246-050110, 2001.
Kool, D., Agam, N., Lazarovitch, N., Heitman, J. L., Sauer, T. J., and Ben-Gal, A.: A review of approaches for evapotranspiration partitioning, Agr. Forest Meteorol., 184, 56–70, 2014.
Lagos, L. O., Martin, D. L., Verma, S. B., Irmak, S., Irmak, A., Eisenhauer, D., and Suyker, A.: Surface energy balance model of transpiration from variable canopy cover and evaporation from residue-covered or bare soil systems: model evaluation, Irrigation Sci., 31, 135–150, 2013.
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., and Wegner, J. D.: Global canopy top height estimates from GEDI LIDAR waveforms for 2019, Zenodo [data set], https://doi.org/10.5281/zenodo.5112903, 2021.
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., and Wegner, J. D.: Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles, Remote Sens. Environ., 268, 112760, https://doi.org/10.1016/j.rse.2021.112760, 2022.
Lawrence, D. M., Thornton, P. E., Oleson, K. W., and Bonan, G. B.: The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: impacts on land-atmosphere interaction, J. Hydrometeorol., 8, 862–880, 2007.
Lhomme, J. P.: Stomatal control of transpiration: Examination of the Jarvis-type representation of canopy resistance in relation to humidity, Water Resour. Res., 37, 689–699, 2001.
Li, H. and Ma, Y.: Application on classification of Qinghai grassland by advanced comprehensive and sequential classification, Acta Prataculturae Sinica, 18, 76–82, 2009 (in Chinese with English Abstract).
Liang, S., Zhao, X., Liu, S., Yuan, W., Cheng, X., Xiao, Z., Zhang, X., Liu, Q., Cheng, J., Tang, H., Qu, Y., Bo, Y., Qu, Y., Ren, H., Yu, K., and Twonshend, J.: A long-term Global Land Surface Satellite (GLASS) data-set for environmental studies, Int. J. Digit. Earth, 6, 5–33, 2013.
Liang, T. G., Feng, Q. S., Huang, X. D., and Ren, J. D.: Review in the study of comprehensive sequential classification system of grassland, Acta Prataculturae Sinica, 20, 252–258, 2011 (in Chinese with English Abstract).
Lindroth, A. and Perttu, K.: Simple calculation of extinction coefficient of forest stands, Agr. Meteorol., 25, 97–110, 1981.
Liu, C., Sun G., McNulty, S. G., and Kang, S.: An improved evapotranspiration model for an apple orchard in northwestern China, Transactions of the American Society of Agricultural and Biological Engineers, 58, 1253–1264, 2015.
Liu, C., Sun, G., McNulty, S. G., Noormets, A., and Fang, Y.: Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements, Hydrol. Earth Syst. Sci., 21, 311–322, https://doi.org/10.5194/hess-21-311-2017, 2017.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020a.
Liu, H., Gong, P., Wang, J., Nicholas, C., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.913496, 2020b.
Liu, J., Chen, J. M., and Cihlar, J.: Mapping evapotranspiration based on remote sensing: an application to Canada's landmass, Water Resour. Res., 39, 1189, https://doi.org/10.1029/2002WR001680, 2003.
Liu, Q., McVicar, T. R., Yang, Z., Donohue, R. J., Liang, L., and Yang, Y.: The hydrological effects of varying vegetation characteristics in a temperate water-limited basin: development of the dynamic Budyko-Choudhury-Porporato (dBCP) model, J. Hydrol., 543, 595–611, 2016.
Liu, X., Xu, C., Zhong, X., Li, Y., Yuan, X., and Cao, J.: Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement, Agr. Water Manage., 184, 145–155, 2017.
Liu, Y., Liu, R., and Chen, J. M.: Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data, J. Geophys. Res.-Biogeo., 117, G04003, https://doi.org/10.1029/2012JG002084, 2012.
Liu, Y., Xiao, J., Ju, W., Xu, K., Zhou, Y., and Zhao, Y.: Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield, Environ. Res. Lett., 11, 094010, https://doi.org/10.1088/1748-9326/11/9/094010, 2016.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan, W., Li, D., and Zhou, Y.: Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes, Remote Sens. Environ., 206, 174–188, 2018.
Lo Seen, D., Chehbouni, A., Njoku, E., Saatchi, S., Mougin, E., and Monteny, B.: An approach to couple vegetation functioning and soil-vegetation-atmosphere-transfer models for semiarid grasslands during the HAPEX-Sahel experiment, Agr. Forest Meteorol., 83, 49–74, 1997.
Lu, J., Sun, G., McNulty, S. G., and Amatya, D. M.: Modeling actual evapotranspiration from forested watersheds across the southeastern United States, J. Am. Water Resour. As., 39, 886–896, 2003.
Lu, J., Sun, G., McNulty, S. G., and Amatya, D. M.: A comparison of six potential evapotranspiration methods for regional use in the southeastern United States, J. Am. Water Resour. As., 41, 621–633, 2005.
Maddoni, G. A., Otegui, M. E., and Cirilo. A. G.: Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation, Field Crop. Res., 71, 183–193, 2001.
Maes, W. H. and Steppe, K.: Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review, J. Exp. Bot., 63, 4671–4712, 2012.
Maes, W. H., Pashuysen, T., Trabucco, A., Veroustraete, F., and Muys, B.: Does energy dissipation increase with ecosystem succession? Testing the ecosystem exergy theory combining theoretical simulations and thermal remote sensing observations, Ecol. Model., 23–24, 3917–3941, 2011.
Maes, W. H., Gentine, P., Verhoest, N. E. C., and Miralles, D. G.: Potential evaporation at eddy-covariance sites across the globe, Hydrol. Earth Syst. Sci., 23, 925–948, https://doi.org/10.5194/hess-23-925-2019, 2019.
Maki, T., Ikegami, M., Fujita, T., Hirahara, T., Yamada, K., Mori, K., Takeuchi, A., Tsutsumi, Y., Suda, K., and Conway, T. J.: New technique to analyse global distributions of CO2 concentration and fluxes from non-processed observational data, Tellus B, 62, 797–809, https://doi.org/10.1111/j.1600-0889.2010.00488.x, 2010.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martínez-Vilalta, J., Poyatos, R., Aguadé, D., Retana, J., and Mencuccini, M.: A new look at water transport regulation in plants, New Phytol., 204, 105–115, 2014.
McVicar, T. R., Van Niel, T. G., Li, L. T., Hutchinson, M. F., Mu, X. M., and Liu, Z. H.: Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences, J. Hydrol., 338, 196–220, 2007.
Medlyn, B. E., Barton, C. V. M., Broadmeadow, M. S. J., Ceulemans, R., De Angelis, P., Forstreuter, M., Freeman, M., Jackson, S. B., Kellomäki, S., Laitat, E., Rey, A., Roberntz, P., Sigurdsson, B. D., Strassemeyer, J., Wang, K., Curtis, P. S., and Jarvis, P. G.: Stomatal conductance of forest species after long-term exposure to elevated CO2 concentrations: a synthesis, New Phytol., 149, 247–264, 2001.
Milly, P. C. and Dunne, K. A.: Potential evapotranspiration and continental drying, Nat. Clim. Change, 6, 946–951, 2016.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Mizutani, K., Yamanoi, K., Ikeda, T., and Watanabe, T.: Applicability of the eddy correlation method to measure sensible heat transfer to forest under rainfall conditions, Agr. Forest Meteorol., 86, 193–203, 1997.
Mo, X., Liu, S., Lin, Z., and Zhao, W.: Simulating temporal and spatial variation of evapotranspiration over the Lushi basin, J. Hydrol., 285, 125–142, 2004.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Monteith, J. L.: Evaporation and environment, Sym. Soc. Exp. Biol., 19, 205–234, 1965.
Moore, G. W., Cleverly, J., and Owens, M. K.: Nocturnal transpiration in riparian Tamarix thickets authenticated by sap flux, eddy covariance and leaf gas exchange measurements, Tree Physiol., 28, 521–528, 2008.
Morison, J. I. L. and Gifford, R. M.: Plant growth and water use with limited water supply in high CO2 concentrations. I. Leaf area, water use and transpiration, Funct. Plant Biol., 11, 361–374, 1984.
Mu, Q., Zhao, M., and Running, S. W.: MODIS Global Terrestrial Evapotranspiration (ET), Product (NASA MOD16A2/A3), Algorithm Theoretical Basis Document, Collection 5, NASA Headquarters, https://lpdaac.usgs.gov/documents/93/MOD16_ATBD.pdf (last access: 30 June 2022), 2013.
Mu, Q. Z., Zhao, M. S., and Running, S.W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, 2011.
Nakamura, T., Maki, T., Machida, T., Matsueda, H., Sawa, Y., and Niwa, Y.: Improvement of atmospheric CO2 inversion analysis at JMA, A31B-0033, in: Proceedings of the AGU Fall Meeting, San Francisco, CA, USA, 14–18 December 2015, 2015AGUFM.A31B0033N, https://agu.confex.com/agu/fm15/webprogram/Paper64173.html (last access: 28 May 2022), 2015.
Neitsch, S. L, Arnold, J. G., Kiniry, J. R., Williams, J. R., and King, K. W.: Soil and Water Assessment Tool Theoretical Documentation: Version 2000, U.S. Department of Agriculture – Agricultural Research Service, Grassland Soil and Water Research Laboratory and Texas A&M University, Blackland Research and Extension Center, Temple, TX, https://swat.tamu.edu/media/1290/swat2000theory.pdf (last access: 3 March 2022), 2002.
Noilhan, J. and Planton, S.: A simple parameterization of land surface processes for meteorological models, Mon. Weather Rev., 117, 536–549, 1989.
Norby, R. J., Delucia, E. H., Gielen, B., Calfapietra, C., Giardina, C. P., King, J. S., Ledford, J., McCarthy, H. R., Moore, D. J., Ceulemans, R., De Angelis, P., Finzi, A. C., Karnosky, D. F., Kubiske, M. E., Lukac, M., Pregitzer, K. S., Scarascia-Mugnozza, G. E., Schlesinger, W. H., and Oren, R.: Forest response to elevated CO2 is conserved across a broad range of productivity, P. Natl. Acad. Sci. USA, 102, 18052–18056, 2005.
Novick, K. A., Oren, R., Stoy, P. C., Siqueira, M., and Katul, G. G.: Nocturnal evapotranspiration in eddy-covariance records from three co-located ecosystems in the Southeastern US: Implications for annual fluxes, Agr. Forest Meteorol., 149, 1491–1504, 2009.
Nutini, F., Boschetti, M., Candiani, G., Bocchi, S., and Brivio, P. A.: Evaporative fraction as an indicator of moisture condition and water stress status in semi-arid rangeland ecosystems, Remote Sensing, 6, 6300–6323, 2014.
Odhiambo, L. O. and Irmak, S.: Performance of extended Shuttleworth-Wallace model for estimating and partitioning of evapotranspiration in a partial residue-covered subsurface drip-irrigated soybean field, Transactions of the American Society of Agricultural and Biological Engineers, 54, 915–930, 2011.
O'Keefe, K. and Nippert, J. B.: Drivers of nocturnal water flux in a tallgrass prairie, Funct. Ecol., 32, 1155–1167, 2018.
Padrón, R. S., Gudmundsson, L., Michel, D., and Seneviratne, S. I.: Terrestrial water loss at night: global relevance from observations and climate models, Hydrol. Earth Syst. Sci., 24, 793–807, https://doi.org/10.5194/hess-24-793-2020, 2020.
Papagiannopoulou, C., Miralles, D., Dorigo, W. A., Verhoest, N. E. C., Depoorter, M., and Waegeman, W.: Vegetation anomalies caused by antecedent precipitation in most of the world, Environ. Res. Lett., 12, 074016, https://doi.org/10.1088/1748-9326/aa7145, 2017.
Pastorello, G., Trotta, C., Canfora, E., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Peng, Z., Tang, R., Jiang, Y., Liu, M., and Li, Z. L.: Global estimates of 500 m daily aerodynamic roughness length from MODIS data, ISPRS J. Photogramm., 183, 336–351, https://doi.org/10.1016/j.isprsjprs.2021.11.015, 2022.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. Roy. Soc. Lond. A Mat., 1032, 120–145, 1948.
Phillips, L. B., Hansen, A. J., Flather, C. H., and Robison-Cox, J.: Applying species-energy theory to conservation: a case study for North American birds, Ecol. Appl., 20, 2007–2023, 2010.
Phillips, N. G., Lewis, J. D., Logan, B. A., and Tissue, D. T.: Inter- and intra-specific variation in nocturnal water transport in Eucalyptus, Tree Physiol., 30, 586–596, 2010.
Piao, S. L., Friedlingstein, P., Ciais, P., de Noblet-Ducoudré, N., Labat, D., and Zaehle, S.: Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends, P. Natl. Acad. Sci. USA, 104, 5242–15247, 2007.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2020.
Powell, T. L., Bracho, R., Li, J., Dore, S., Hinkle, C. R., and Drake, B. G.: Environmental controls over net ecosystem carbon exchange of scrub oak in central Florida, Agr. Forest Meteorol., 141, 19–34, 2006.
Prakash, V., Bera, T., Pradhan, S., and Acharya, S. K.: Potential of Syngonanthus nitens fiber as a reinforcement in epoxy composite and its mechanical characterization, Journal of the Indian Academy of Wood Science, 17, 73–81, 2020.
Raab, N., Meza, F. J., Frank, N., and Bambach, N.: Empirical stomatal conductance models reveal that the isohydric behavior of an Acacia caven Mediterranean Savannah scales from leaf to ecosystem, Agr. Forest Meteorol., 213, 203–216, 2015.
Rao, L. Y., Sun, G., Ford, C. R., and Vose, J. M.: Modeling potential evapotranspiration of two forested watersheds in the southern Appalachians, T. ASABE, 54, 2067–2078, 2011.
Reddy, S. J.: An empirical method for estimating sunshine from total cloud amount, Sol. Energy, 15, 281–285, 1974.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, 2005.
Roderick, M. L., Greve, P., and Farquhar G. D.: On the assessment of aridity with changes in atmospheric CO2, Water Resour. Res., 51, 5450–5463, 2015.
Running, S., Mu, Q., and Zhao, M.: MOD16A2 MODIS/Terra 95Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD16A2.006, 2017.
Saxe, H., Ellsworth, D., and Heath, J.: Tree and forest functioning in an enriched CO2 atmosphere, New Phytol., 139, 395–436, 1998.
Scheff, J.: Drought indices, drought impacts, CO2, and warming: A historical and geologic perspective, Current Climate Change Reports, 4, 202–209, 2018.
Seiller, G. and Anctil, F.: How do potential evapotranspiration formulas influence hydrological projections?, Hydrolog. Sci. J., 61, 2249–2266, 2016.
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, 2012.
Shuttleworth, W. J., and Gurney, R. J.: The theoretical relationship between foliage temperature and canopy resistance in sparse crops, Q. J. Roy. Meteor. Soc., 116, 497–519, 1990.
Shuttleworth, W. J. and Wallace, J. S.: Evaporation from sparse crops: An energy combination theory, Q. J. Roy. Meteor. Soc., 111, 839–855, 1985.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Space, 116, G0402, https://doi.org/10.1029/2011JG001708, 2011.
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential evapotranspiration at 0.1∘ resolution for the global land surface from 1981–present, Scientific Data, 8, 224, https://doi.org/10.1038/s41597-021-01003-9, 2021.
Singh, V. P. and Xu, C.-Y.: Evaluation and generalization of 13 equations for determining free water evaporation, Hydrol. Process., 11, 311–323, 1997.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185. 2003.
Stannard, D. I.: Comparison of Penman-Monteith, Shuttleworth-Wallace, and modified Priestley-Taylor evapotranspiration models for wildland vegetation in semiarid rangeland, Water Resour. Res., 29, 1379–139, 1993.
Sun, G., Alstad, K., Chen, J., Ford, C. R., Lin, G., Liu, C., Nan, L., McNulty, S. G., and Miao, H.: A general predictive model for estimating monthly ecosystem evapotranspiration, Ecohydrology, 4, 245–255, 2011a.
Sun, G., Caldwell, P., Noormets, A., McNulty, S. G., Cohen, E., Myers, J. M., Domec, J.-C., Treasure, E., Mu, Q., Xiao, J., John, R., and Chen, J.: Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model, J. Geophys. Res.-Biogeo., 116, G00J05, https://doi.org/10.1029/2010JG001573, 2011b.
Sun, S., Chen, H., Ju, W., Yu, M., Hua, W., and Yin, Y.: On the attribution of the changing hydrological cycle in Poyang Lake Basin, China. J. Hydrol., 514, 214–225, 2014.
Sun, S., Chen, H., Sun, G., Ju, W., Wang, G., Huang, J., Zhang, F., Zhu, S., and Hua, W.: Attributing the changing reference evapotranspiration in Southwest China using a new separating method, J. Hydrometeorol., 18, 777–798, 2017.
Sun, S., Bi, Z., Zhou, S., Wang, H., Li, Q., Liu, Y., Wang, G., Li, S., Chen, H., and Zhou, Y.: Spatiotemporal shifts in key hydrological variables and dominant factors over China, Hydrol. Process., 35, e14319, https://doi.org/10.1002/hyp.14319, 2021.
Sun, S., Liu, Y., Chen, Ju, W., Xu, C.-Y., Liu, Y., Zhou, B., Zhou, Y., Zhou, Y., and Yu, M.: Causes for the increases in both evapotranspiration and water yield over vegetated mainland China during the last two decades, Agr. Forest Meteorol., 324, 109118, https://doi.org/10.1016/j.agrformet.2022.109118, 2022.
Sun, S., Bi, Z., and Chen, H.: A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth-Wallace model, National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Terre.tpdc.300193, 2023.
Suttie, J. M., Reynolds, S. G., and Batello, C.: Grasslands of the World. Plant Production and Protection Series No. 34, Food and Agriculture Organization of the United Nations, Rome, https://www.fao.org/3/y8344e/y8344e00.htm (last access: 7 January 2022), 2005.
Tabari, H. and Talaee, P. H.: Local calibration of the Hargreaves and Priestley-Taylor equations for estimating reference evapotranspiration in arid and cold climates of Iran based on the Penman-Monteith model, J. Hydrol. Eng., 16, 837–845, 2011.
Tahiri, A. Z., Anyoji, H., and Yasuda, H.: Fixed and variable light extinction coefficients for estimating plant transpiration and soil evaporation under irrigated maize, Agr. Water Manage., 84, 184–192, 2006.
Tanguy, M., Prudhomme, C., Smith, K., and Hannaford, J.: Historical gridded reconstruction of potential evapotranspiration for the UK, Earth Syst. Sci. Data, 10, 951–968, https://doi.org/10.5194/essd-10-951-2018, 2018.
Thornthwaite, C. W.: An approach toward a rational classification of climate, Geogr. Rev., 38, 55–94, 1948.
Tomas-Burguera, M., Vicente-Serrano, S. M., Beguería, S., Reig, F., and Latorre, B.: Reference crop evapotranspiration database in Spain (1961–2014), Earth Syst. Sci. Data, 11, 1917–1930, https://doi.org/10.5194/essd-11-1917-2019, 2019.
Tourula, T. and Heikinheimo, M.: Modelling evapotranspiration from a barley field over the growing season, Agr. Forest Meteorol., 91, 237–250, 1998.
Trajkovic, S.: Hargreaves versus Penman-Monteith, J. Irrig. Drain. E., 133, 38–42, 2007.
Trenberth, K. E., Dai, A., van der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought, Nat. Clim. Change, 4, 17–22, 2012.
Turc, L.: Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date, Ann. Agron., 12, 13–49, 1961.
Urraca, R., Huld, T., Amillo, A. M. G., Martinez-de-Pison, F. J., Kaspar, F., and Sanz-Garcia, A.: Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data, Sol. Energy, 164, 339–354, 2018.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J.: A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718, 2010.
Vicente-Serrano, S. M., McVicar, T. R., Miralles, D. G., Yang, Y., and Tomas-Burguera, M.: Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change, WIREs Clim. Change, 11, e632, https://doi.org/10.1002/wcc.632, 2020.
Villagarcía, L., Were, A., Garcíac, M., and Domingo, F.: Sensitivity of a clumped model of evapotranspiration to surface resistance parameterisations: Application in a semi-arid environment, Agr. Forest Meteorol., 150, 1065–1078, 2010.
Wallace, J. S., Roberts, J. M., and Sivakumar, M. V. K.: The estimation of transpiration from sparse dryland millet using stomatal conductance and vegetation area indices, Agr. Forest Meteorol., 51, 35–49, 1990.
Wand, S. J. E., Midgley, G. F., Jones, M. H., and Curtis, P. S.: Responses of wild C4 and C3 grass (Poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of current theories and perceptions, Glob. Change Biol., 5, 723–741, 1999.
Wang, H., Guan, H., Liu, N., Soulsby, C., Tetzlaff, D., and Zhang, X.: Improving the Jarvis-type model with modified temperature and radiation functions for sap flow simulations, J. Hydrol., 587, 124981, https://doi.org/10.1016/j.jhydrol.2020.124981, 2020.
Wang, K. C. and Dickinson, R. E.: A review on global terrestrial evapotranspiration: observation, modeling, climatology, and Climatic Variability, Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373, 2012.
Wang, Y., Li, G., Ding, J., Guo, Z., Tang, S., Wang, C., Huang, Q., Liu, R., and Chen, J. M.: A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height, Remote Sens. Environ., 74, 24–43, 2016.
Wei, G., Zhou, L., Liu, H., Tian, Q., Ding, L., and Ran, X.: Improving evapotranspiration model performance by treating energy imbalance and interaction, Water Resour. Res., 56, e2020WR027367, https://doi.org/10.1029/2020WR027367, 2020.
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating palmer drought severity index, J. Climate, 17, 2335–2351, 2004.
Wever, L. A., Flanagan, L. B., and Carlson, P. J.: Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland, Agr. Forest Meteorol., 112, 31–49, 2002.
White, F.: The vegetation of Africa: a descriptive memoir to accompany the Unesco/AETFAT/UNSO vegetation map of Africa, in: Natural Resources Research 20, Unesco, Paris, ISBN 9231019554, https://unesdoc.unesco.org/ark:/48223/pf0000058054/PDF/058054engo.pdf.multi (last access: 26 January 2022), 1983.
Wild, M.: Global dimming and brightening: A review, J. Geophys. Res.-Atmos., 114, D00D16, https://doi.org/10.1029/2008JD011470, 2009.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B. E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R., and Verma, S.: Energy balance closure at FLUXNET sites, Agr. Forest Meteorol., 113, 223–243, 2002.
Winkel, T., Payne, W., and Renno, J. F.: Ontogeny modifies the effects of water stress on stomatal control, leaf area duration and biomass partitioning of Pennisetum glaucum, New Phytol., 149, 71–82, 2001.
Wu, L., Min, L. L., Shen, Y. J., Zhou, X. X., and Liu, F. G.: Simulation of maize evapotranspiration at different growth stages using revised dual-layered model in arid Northwest China, Chinese Journal of Eco-Agriculture, 25, 634–646, 2017 (in Chinese with English Abstract).
Xiang, K., Li, Y., Horton, R., and Feng, H.: Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – A review, Agr. Water Manage., 232, 106043, https://doi.org/10.1016/j.agwat.2020.106043, 2020.
Xiao, X. M., Zhang, Q. Y., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore III, B., and Ojima, D.: Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data, Remote Sens. Environ., 91, 256–270, 2004.
Xiao, Z., Liang, S., Wang, J., Chen, P., Yin, X., and Song, J.: Use of general regression neural networks for generating the GLASS Leaf Area Index product from time-series MODIS surface reflectance, IEEE T. Geosci. Remote, 52, 209–223, 2014.
Xiao, Z., Liang, S., Wang, J., and Zhao, X.: Long time series Global Land Surface Satellite (GLASS) Leaf Area Index product derived from MODIS and AVHRR data, IEEE T. Geosci. Remote, 54, 5301–5318, 2016.
Xiao, Z., Liang, S., and Jiang, B.: Evaluation of four long time-series global leaf area index products, Agr. Forest Meteorol., 246, 218–230, 2017.
Xu, B., Li, J., Park, T., Liu, Q., Zeng, Y., Yin, G., Zhao, J., Fan, W., Yang, L., and Knyazikhin, Y.: An integrated method for validating long-term leaf area index products using global networks of site-based measurements, Remote Sens. Environ., 209, 134–151, 2018.
Xu, C.-Y. and Singh, V. P.: Evaluation and generalization of radiation-based methods for calculating evaporation, Hydrol. Process., 14, 339–349, 2000.
Xu, C.-Y. and Singh, V. P.: Evaluation and generalization of temperature-based methods for calculating evaporation, Hydrol. Process., 15, 305–319, 2001.
Yang, Y. and Shang, S.: Comparison of dual-source evapotranspiration models in estimating potential evaporation and transpiration, Transactions of the Chinese Society of Agricultural Engineering, 28, 85–91, 2012.
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R., and Donohue, R. J.: Hydrological implications of vegetation responses to elevated CO2 in climate projections, Nat. Clim. Change, 9, 44–48, 2019.
Yin, J., Feng, Q., Liang, T., Meng, B., Yang, S., Gao, J., Ge, J., Hou, M., Liu, J., Wang, W., Yu, H., and Liu, B.: Estimation of grassland height based on the random forest algorithm and remote sensing in the Tibetan Plateau, IEEE J. Sel. Top. Appl., 13, 178–186, 2019.
Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, 2020.
Zeppel, M. J. B., Lewis, J. D., Phillips, N. G., and Tissue, D. T.: Consequences of nocturnal water loss: A synthesis of regulating factors and implications for capacitance, embolism and use in models, Tree Physiol., 34, 1047–1055, 2014.
Zhan, C., Orth, R., Migliavacca, M., Zaehle, S., Reichstein, M., Engel, J., Ramming, A., and Winkler, A. J.: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water, Glob. Change Biol., 28, 7313–7326, 2022.
Zhang, B., Kang, S., Li, F., and Zhang, L.: Comparison of three evapotranspiration models to Bowen ratio-energy balance method for a vineyard in an arid desert region of northwest China, Agr. Forest Meteorol., 148, 1629–1640, 2008.
Zhang, J., Zhao, T., Li, Z., Li, C., Li, Z., Ying, K., Shi, C., Jiang, L., and Zhang, W.: Evaluation of Surface Relative Humidity in China from the CRA-40 and Current Reanalyses, Adv. Atmos. Sci., 38, 1958–1976, 2021.
Zhang, L., Hu, Z., Fan, J., Zhou, D., and Tang, F.: A meta-analysis of the canopy light extinction coefficient in terrestrial ecosystems, Front. Earth Sci.-PRC, 8, 599–609, 2014.
Zhang, Z., Arnault, J., Wagner, S., Laux, P., and Kunstmann, H.: Impact of lateral terrestrial water flow on land-atmosphere interactions in the Heihe River Basin in China: Fully coupled modeling and precipitation recycling analysis, J. Geophys. Res.-Atmos., 124, 8401–8423, 2019.
Zhao, M. and Cao, L.: Regional response of land hydrology and carbon uptake to different amounts of solar radiation modification, Earth's Future, 10, e2022EF003288, https://doi.org/10.1029/2022EF003288, 2022.
Zhao, P., Li, S. E., Li, F. S., Du, T. S., Tong, L., and Kang, S. Z.: Comparison of dual crop coefficient method and Shuttleworth-Wallace model in evapotranspiration partitioning in a vineyard of northwest China, Agr. Water Manage., 160, 41–56, 2015.
Zhou, M. C., Ishidaira, H., Hapuarachchi, H. P., Magome, J., Kiem, A. S., and Takeuchi, K.: Estimating potential evapotranspiration using Shuttleworth-Wallace model and NOAA-AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin, J. Hydrol., 327, 151–173, 2006.
Zhou, S., Yu, B., Zhang, Y., Huang, Y., and Wang, G.: Partitioning evapotranspiration based on the concept of underlying water use efficiency, Water Resour. Res., 52, 1160–1175, 2016.
Zhou, S., Yu, B., Zhang, Y., Huang, Y., and Wang, G.: Water use efficiency and evapotranspiration partitioning for three typical ecosystems in the Heihe River Basin, Agr. Forest Meteorol., 253–254, 261–273, 2018.
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R. R., and Myneni, R. B.: Global data sets of vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011, Remote Sensing, 5, 927–948, 2013.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xioa, Z., Yang, H., Zaehe, S., and Zeng, N.: Greening of the earth and its drivers, Nat. Clim. Change, 6, 791–795, 2016.
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Based on various existing datasets, we comprehensively considered spatiotemporal differences in...
Altmetrics
Final-revised paper
Preprint