Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5463-2022
© Author(s) 2022. 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-14-5463-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Dec 2022
Data description paper |
| 15 Dec 2022
| 15 Dec 2022
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Shaoyang He
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100040, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China
Jing Tian
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China
Dongdong Kong
Department of Atmospheric Science, School of Environmental Studies,
China University of Geosciences, Wuhan 430074, China
Changming Liu
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, China
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Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
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Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Yuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
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This study developed an analytical ecohydrological model that considers three aspects of vegetation response to eCO2 (i.e., stomatal response, LAI response, and rooting depth response) to detect the impact of eCO2 on continental runoff over the past 3 decades globally. Our findings suggest a minor role of eCO2 on the global runoff changes, yet highlight the negative runoff–eCO2 response in semiarid and arid regions which may further threaten the limited water resource there.
Xinyao Zhou, Yonghui Yang, Zhuping Sheng, and Yongqiang Zhang
Hydrol. Earth Syst. Sci., 23, 2491–2505, https://doi.org/10.5194/hess-23-2491-2019, https://doi.org/10.5194/hess-23-2491-2019, 2019
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Quantifying the impact of upstream water use on downstream water scarcity is critical for water management. Comparing natural and observed runoff in China's 12 basins, this study found surface water use increased 1.6 times for the 1970s-2000s, driving most arid and semi-arid (ASA) basins into water scarcity status. The water stress decreased downstream in ASA basins due to reduced upstream inflow since the 2000s. Upstream water use caused over a 30 % increase in water scarcity in ASA basins.
Yongqiang Zhang and David Post
Hydrol. Earth Syst. Sci., 22, 4593–4604, https://doi.org/10.5194/hess-22-4593-2018, https://doi.org/10.5194/hess-22-4593-2018, 2018
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It is a critical step to gap-fill streamflow data for most hydrological studies, such as streamflow trend, flood, and drought analysis and predictions. However, quantitative evaluation of the gap-filled data accuracy is not available. Here we conducted the first comprehensive study, and found that when the missing data rate is less than 10 %, the gap-filled streamflow data using hydrological models are reliable for annual streamflow and its trend analysis.
Jianyu Liu, Qiang Zhang, Vijay P. Singh, Changqing Song, Yongqiang Zhang, Peng Sun, and Xihui Gu
Hydrol. Earth Syst. Sci., 22, 4047–4060, https://doi.org/10.5194/hess-22-4047-2018, https://doi.org/10.5194/hess-22-4047-2018, 2018
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Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration,
E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0.
Junlong Zhang, Yongqiang Zhang, Jinxi Song, Lei Cheng, Rong Gan, Xiaogang Shi, Zhongkui Luo, and Panpan Zhao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-737, https://doi.org/10.5194/hess-2017-737, 2017
Revised manuscript not accepted
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Estimating baseflow is critical for water balance budget, water resources management, and environmental evaluation. To predict baseflow index (the ratio of baseflow to total streamflow), this study introduces a new method, multilevel regression approach for predicting baseflow index for 596 Australian catchments, which outperformed two traditional methods: linear regression and hydrological modelling. Our results suggest that it is very promising to use this method to other parts of world.
Xiaomang Liu, Tiantian Yang, Koulin Hsu, Changming Liu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 21, 169–181, https://doi.org/10.5194/hess-21-169-2017, https://doi.org/10.5194/hess-21-169-2017, 2017
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A long-term, global, high-resolution, satellite-based precipitation estimation database (PERSIANN-CDR) was recently released. We evaluate the streamflow simulation capability of PERSIANN-CDR over two major river basins on the Tibetan Plateau. Results show that PERSIANN-CDR is a good alternative for a sparse gauge network and has the potentials for future hydrological and climate studies. The streamflow uncertainties are due to the hydrological model parameters and the length of calibration data.
Hongxia Li and Yongqiang Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-464, https://doi.org/10.5194/hess-2016-464, 2016
Manuscript not accepted for further review
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Numerous regionalisation studies have been conducted to predict the runoff time series in ungauged catchments. However, there are few studies investigating their benefits for predicting runoff time series on a continental scale. This study uses four regionalisation approaches to regionalise two rainfall–runoff models for continental Australia, demonstrates that the gridded IS approach outperforms other three in data-sparse regions, and is recommendated for large-scale hydrological predictions.
J. Vaze, Y. Q. Zhang, and L. Zhang
Proc. IAHS, 371, 215–221, https://doi.org/10.5194/piahs-371-215-2015, https://doi.org/10.5194/piahs-371-215-2015, 2015
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Most of the forested headwater catchments are an important source of water supply in many parts of the world. A prime example is southeast Australia where forests supply major river systems and towns and cities with water. It is critical for an informed and adaptive water resource management to understand changes in streamflow caused by vegetation changes in these headwater forest catchments. Natural disturbances such as bushfires and anthropogenic activities like forestation, deforestation, or
R. Ouyang, W. Liu, G. Fu, C. Liu, L. Hu, and H. Wang
Hydrol. Earth Syst. Sci., 18, 3651–3661, https://doi.org/10.5194/hess-18-3651-2014, https://doi.org/10.5194/hess-18-3651-2014, 2014
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
Y. Zhou, Y. Zhang, J. Vaze, P. Lane, and S. Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-4397-2013, https://doi.org/10.5194/hessd-10-4397-2013, 2013
Revised manuscript not accepted
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The Portuguese Large Wildfire Spread database (PT-FireSprd)
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Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
EUPollMap: The European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
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Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
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Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
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Harmonized gap-filled datasets from 20 urban flux tower sites
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The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
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Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
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This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
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Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
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We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
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The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
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Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
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We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Joao Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara F. Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-272, https://doi.org/10.5194/essd-2023-272, 2023
Revised manuscript accepted for ESSD
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Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it’s important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
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This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
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Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-364, https://doi.org/10.5194/essd-2022-364, 2023
Revised manuscript accepted for ESSD
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Modern and fossil pollen data contain precious information to reconstruct pas climate and environment. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
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The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
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We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
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Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
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Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
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Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
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Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
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Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Adzhar, R., Kelley, D. I., Dong, N., George, C., Torello Raventos, M., Veenendaal, E., Feldpausch, T. R., Phillips, O. L., Lewis, S. L., Sonké, B., Taedoumg, H., Schwantes Marimon, B., Domingues, T., Arroyo, L., Djagbletey, G., Saiz, G., and Gerard, F.: MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas, Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, 2022.
Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S.,
Lounsbury, D., and Matonse, A. H.: Examination of change factor
methodologies for climate change impact assessment, Water Resour.
Res., 47, W03501, https://doi.org/10.1029/2010WR009104, 2011.
Anon: Evaluation of MODIS gross primary productivity for Africa using eddy
covariance data, Remote Sens. Environ., 131, 275–286,
https://doi.org/10.1016/j.rse.2012.12.023, 2013.
Arain, M. A., Yuan, F., and Black, T. A.: Soil–plant nitrogen cycling
modulated carbon exchanges in a western temperate conifer forest in Canada,
Agr. Forest Meteorol., 140, 171–192,
https://doi.org/10.1016/j.agrformet.2006.03.021,2006.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, Ch., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the Annual Net Carbon and Water Exchange of Forests: The EUROFLUX Methodology, in: Advances in Ecological Research, edited by: Fitter, A. H. and Raffaelli, D. G., Academic Press, vol. 30, 113–175, https://doi.org/10.1016/S0065-2504(08)60018-5, 1999.
Baldocchi, D.: Measuring fluxes of trace gases and energy between ecosystems
and the atmosphere – the state and future of the eddy covariance method,
Glob. Change Biol., 20, 3600–3609, https://doi.org/10.1111/gcb.12649,
2014.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein,
A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel,
W., U, K. T. P., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S.,
Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the
Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water
Vapor, and Energy Flux Densities, B. Am. Meteorol.
Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Beaudoing, H. and Rodell, M. J. G.: Maryland: GLDAS Noah Land Surface Model
L4 monthly 0.25 x 0.25 degree V2.1, NASA/GSFC/HSL: Greenbelt, Maryland,
USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set],
https://doi.org/10.5067/SXAVCZFAQLNO, 2016.
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, Sci. Data, 5, 180214,
https://doi.org/10.1038/sdata.2018.214, 2018.
Bodner, G., Nakhforoosh, A., and Kaul, H.-P.: Management of crop water under
drought: a review, Agron. Sustain. Dev., 35, 401–442,
https://doi.org/10.1007/s13593-015-0283-4, 2015.
Brust, C., Kimball, J. S., Maneta, M. P., Jencso, K., He, M., and Reichle,
R. H.: Using SMAP Level-4 soil moisture to constrain MOD16
evapotranspiration over the contiguous USA, Remote Sens. Environ.,
255, 112277, https://doi.org/10.1016/j.rse.2020.112277, 2021.
Cao, S., Chen, L., and Yu, X.: Impact of China's Grain for Green Project on
the landscape of vulnerable arid and semi-arid agricultural regions: a case
study in northern Shaanxi Province, J. Appl. Ecol., 46,
536–543, https://doi.org/10.1111/j.1365-2664.2008.01605.x, 2009.
Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., and Yu, Z.: Geographically
weighted regression based methods for merging satellite and gauge
precipitation, J. Hydrol., 558, 275–289,
https://doi.org/10.1016/j.jhydrol.2018.01.042, 2018.
Chen, B., Coops, N. C., Fu, D., Margolis, H. A., Amiro, B. D., Black, T. A.,
Arain, M. A., Barr, A. G., Bourque, C. P.-A., Flanagan, L. B., Lafleur, P.
M., McCaughey, J. H., and Wofsy, S. C.: Characterizing spatial
representativeness of flux tower eddy-covariance measurements across the
Canadian Carbon Program Network using remote sensing and footprint analysis,
Remote Sens. Environ., 124, 742–755,
https://doi.org/10.1016/j.rse.2012.06.007, 2012.
Chen, S., Chen, J., Lin, G., Zhang, W., Miao, H., Wei, L., Huang, J., and
Han, X.: Energy balance and partition in Inner Mongolia steppe ecosystems
with different land use types, Agr. Forest Meteorol., 149,
1800–1809, https://doi.org/10.1016/j.agrformet.2009.06.009, 2009.
Cheng, M., Jiao, X., Li, B., Yu, X., Shao, M., and Jin, X.: Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation, Earth Syst. Sci. Data, 13, 3995–4017, https://doi.org/10.5194/essd-13-3995-2021, 2021.
Chu, H., Baldocchi, D. D., John, R., Wolf, S., and Reichstein, M.: Fluxes
all of the time? A primer on the temporal representativeness of FLUXNET,
J. Geophys. Res.-Biogeo., 122, 289–307,
https://doi.org/10.1002/2016JG003576, 2017.
Dong, G., Guo, J., Chen, J., Sun, G., Gao, S., Hu, L., and Wang, Y.: Effects
of spring drought on carbon sequestration, evapotranspiration and water use
efficiency in the songnen meadow steppe in northeast China, Ecohydrol., 4,
211–224, https://doi.org/10.1002/eco.200, 2011.
Feng, M. and Bai, Y.: A global land cover map produced through integrating multi-source datasets, Big Earth Data, 3, 191–219,
https://doi.org/10.1080/20964471.2019.1663627, 2019.
Feng, T., Su, T., Ji, F., Zhi, R., and Han, Z.: Temporal Characteristics of
Actual Evapotranspiration Over China Under Global Warming, J.
Geophys. Res.-Atmos., 123, 5845–5858,
https://doi.org/10.1029/2017JD028227, 2018.
Foken, T.: The energy balance closure problem: an overview, Ecol. Appl., 18,
1351–1367, https://doi.org/10.1890/06-0922.1, 2008.
Gan, R., Zhang, Y., Shi, H., Yang, Y., Eamus, D., Cheng, L., Chiew, F. H.
S., and Yu, Q.: Use of satellite leaf area index estimating
evapotranspiration and gross assimilation for Australian ecosystems,
Ecohydrology, 11, e1974, https://doi.org/10.1002/eco.1974, 2018.
Gevaert, C. M. and García-Haro, F. J.: A comparison of STARFM and an
unmixing-based algorithm for Landsat and MODIS data fusion, Remote Sens.
Environ., 156, 34–44, https://doi.org/10.1016/j.rse.2014.09.012, 2015.
Graf, A., Bogena, H. R., Drüe, C., Hardelauf, H., Pütz, T.,
Heinemann, G., and Vereecken, H.: Spatiotemporal relations between water
budget components and soil water content in a forested tributary catchment,
Water Resour. Res., 50, 4837–4857,
https://doi.org/10.1002/2013WR014516, 2014.
Grant, R. F., Arain, A., Arora, V., Barr, A., Black, T. A., Chen, J., Wang,
S., Yuan, F., and Zhang, Y.: Intercomparison of techniques to model high
temperature effects on CO2 and energy exchange in temperate and boreal
coniferous forests, Ecol. Modell., 188, 217–252,
https://doi.org/10.1016/j.ecolmodel.2005.01.060, 2005.
Hanson, P. J., Amthor, J. S., Wullschleger, S. D., Wilson, K. B., Grant, R.
F., Hartley, A., Hui, D., Hunt Jr., E. R., Johnson, D. W., Kimball, J. S.,
King, A. W., Luo, Y., McNulty, S. G., Sun, G., Thornton, P. E., Wang, S.,
Williams, M., Baldocchi, D. D., and Cushman, R. M.: Oak Forest Carbon and
Water Simulations: Model Intercomparisons and Evaluations Against
Independent Data, Ecol. Monogr., 74, 443–489,
https://doi.org/10.1890/03-4049, 2004.
Hao, Y., Wang, Y., and Yu, G.: A dataset of carbon and water fluxes over
Xilinhot temperate steppe in Inner Mongolia (2003–2010),
Science Data Bank [data set], https://doi.org/10.11922/sciencedb.996, 2020.
Haro-Monteagudo, D., Palazón, L., and Beguería, S.: Long-term
sustainability of large water resource systems under climate change: A
cascade modeling approach, J. Hydrol., 582, 124546,
https://doi.org/10.1016/j.jhydrol.2020.124546, 2020.
He, H., Wu, Z., Li, D., Zhang, T., Pan, F., Yuan, H., Jiang, S., Shi, Z.,
Yang, S., and Wang, F.: Characteristics of Winter Wheat Evapotranspiration
in Eastern China and Comparative Evaluation of Applicability of Different
Reference Evapotranspiration Models, J. Soil Sci. Plant Nutr., 22, 2078–2091,
https://doi.org/10.1007/s42729-022-00795-y, 2022.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Heinsch, F. A., Zhao, M., Running, S. W., Kimball, J. S., Nemani, R.
R., Davis, K. J., Bolstad, P. V., Cook, B. D., Desai, A. R., Ricciuto, D.
M., Law, B. E., Oechel, W. C., Hyojung Kwon, Hongyan Luo, Wofsy, S. C.,
Dunn, A. L., Munger, J. W., Baldocchi, D. D., Liukang Xu, Hollinger, D. Y.,
Richardson, A. D., Stoy, P. C., Siqueira, M. B. S., Monson, R. K., Burns, S.
P., and Flanagan, L. B.: Evaluation of remote sensing based terrestrial
productivity from MODIS using regional tower eddy flux network observations,
IEEE T. Geosci. Remote, 44, 1908–1925,
https://doi.org/10.1109/TGRS.2005.853936, 2006.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Hilker, T., Coops, N. C., Wulder, M. A., Black, T. A., and Guy, R. D.: The
use of remote sensing in light use efficiency based models of gross primary
production: A review of current status and future requirements, Sci.
Total Environ., 404, 411–423,
https://doi.org/10.1016/j.scitotenv.2007.11.007, 2008.
Holland, J. H.: Genetic Algorithms, Sci. Am., 267, 66–73, 1992.
Houborg, R., Cescatti, A., Migliavacca, M., and Kustas, W. P.: Satellite
retrievals of leaf chlorophyll and photosynthetic capacity for improved
modeling of GPP, Agr. Forest Meteorol., 177, 10–23,
https://doi.org/10.1016/j.agrformet.2013.04.006, 2013.
Huang, Y., Nicholson, D., Huang, B., and Cassar, N.: Global Estimates of
Marine Gross Primary Production Based on Machine Learning Upscaling of Field
Observations, Global Biogeochem. Cy., 35, e2020GB006718,
https://doi.org/10.1029/2020GB006718, 2021.
Hui, Y., Dehua, Q., Yiping, Z., Qinghai, S., Xuehai, F., Liqing, S.,
Yuntong, L., Wenjun, Z., Liguo, Z., Xiaobao, D., Yan, L., Yun, D., and
Donghai, Y.: An observation dataset of carbon and water fluxes in
Xishuangbanna rubber plantations from 2010 to 2014, Science Data
Bank [data set], https://doi.org/10.11922/sciencedb.j00001.00123, 2021.
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F. P.: Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, 2015.
Jia, B., Luo, X., Cai, X., Jain, A., Huntzinger, D. N., Xie, Z., Zeng, N., Mao, J., Shi, X., Ito, A., Wei, Y., Tian, H., Poulter, B., Hayes, D., and Schaefer, K.: Impacts of land use change and elevated CO2 on the interannual variations and seasonal cycles of gross primary productivity in China, Earth Syst. Dynam., 11, 235–249, https://doi.org/10.5194/esd-11-235-2020, 2020.
Joiner, J. and Yoshida, Y.: Satellite-based reflectances capture large
fraction of variability in global gross primary production (GPP) at weekly
time scales, Agr. Forest Meteorol., 291, 108092,
https://doi.org/10.1016/j.agrformet.2020.108092, 2020.
Kato, T., Tang, Y., Gu, S., Hirota, M., Du, M., Li, Y., and Zhao, X.:
Temperature and biomass influences on interannual changes in CO2 exchange in
an alpine meadow on the Qinghai-Tibetan Plateau, Glob. Change Biol., 12,
1285–1298, https://doi.org/10.1111/j.1365-2486.2006.01153.x, 2006.
Konak, A., Coit, D. W., and Smith, A. E.: Multi-objective optimization using
genetic algorithms: A tutorial, Reliab. Eng. Sys. Safe.,
91, 992–1007, https://doi.org/10.1016/j.ress.2005.11.018, 2006.
Kong, D., Zhang, Y., Gu, X., and Wang, D.: A robust method for
reconstructing global MODIS EVI time series on the Google Earth Engine,
ISPRS J. Photogramm., 155, 13–24,
https://doi.org/10.1016/j.isprsjprs.2019.06.014, 2019.
Landerer, F.: TELLUS_GRAC_L3_CSR_RL06_LND_v04. Ver. RL06 v04, PO.DAAC, CA, USA [data set],
https://doi.org/10.5067/TELND-3AC64, 2021.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates, Water Resour. Res., 48, W04531,
https://doi.org/10.1029/2011wr011453, 2012.
Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., and Tu, K.: A simple
surface conductance model to estimate regional evaporation using MODIS leaf
area index and the Penman-Monteith equation, Water Resour. Res., 44, W10419,
https://doi.org/10.1029/2007WR006562, 2008.
Li, S., Wang, G., Sun, S., Chen, H., Bai, P., Zhou, S., Huang, Y., Wang, J.,
and Deng, P.: Assessment of Multi-Source Evapotranspiration Products over
China Using Eddy Covariance Observations, Remote Sensing, 10, 1692,
https://doi.org/10.3390/rs10111692, 2018.
Li, X., Li, X., Li, Z., Ma, M., Wang, J., Xiao, Q., Liu, Q., Che, T., Chen,
E., Yan, G., Hu, Z., Zhang, L., Chu, R., Su, P., Liu, Q., Liu, S., Wang, J.,
Niu, Z., Chen, Y., Jin, R., Wang, W., Ran, Y., Xin, X., and Ren, H.:
Watershed Allied Telemetry Experimental Research, J. Geophys.
Res.-Atmos., 114, D22103, https://doi.org/10.1029/2008JD011590, 2009.
Liang, D., Zuo, Y., Huang, L., Zhao, J., Teng, L., and Yang, F.: Evaluation
of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30
m GlobeLand30 Datasets: A Case Study in Anhui Province, China, ISPRS
Int. J. Geo-Inf., 4, 2519–2541,
https://doi.org/10.3390/ijgi4042519, 2015.
Liu, J., Rambal, S., and Mouillot, F.: Soil Drought Anomalies in MODIS GPP
of a Mediterranean Broadleaved Evergreen Forest, Remote Sensing, 7,
1154–1180, https://doi.org/10.3390/rs70101154, 2015.
Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y., and Qiao, Z.: Mapping
cropping intensity in China using time series Landsat and Sentinel-2 images
and Google Earth Engine, Remote Sens. Environ., 239, 111624,
https://doi.org/10.1016/j.rse.2019.111624, 2020.
Liu, S., Bliss, N., Sundquist, E., and Huntington, T. G.: Modeling carbon
dynamics in vegetation and soil under the impact of soil erosion and
deposition, Global Biogeochem. Cy., 17, 1074,
https://doi.org/10.1029/2002GB002010, 2003.
Liu, S., Xu, Z., Zhu, Z., Jia, Z., and Zhu, M.: Measurements of
evapotranspiration from eddy-covariance systems and large aperture
scintillometers in the Hai River Basin, China, J. Hydrol., 487,
24–38, https://doi.org/10.1016/j.jhydrol.2013.02.025, 2013.
Liu, S., Xu, Z., Song, L., Zhao, Q., Ge, Y., Xu, T., Ma, Y., Zhu, Z., Jia,
Z., Zhang, F.: Upscaling evapotranspiration measurements from multi-site to
the satellite pixel scale over heterogeneous land surfaces, Agr.
Forest Meteorol., 230, 97–113,
https://doi.org/10.1016/j.agrformet.2016.04.008, 2016.
Liu, S., Li, X., Xu, Z., Che, T., Xiao, Q., Ma, M., Liu, Q., Jin, R., Guo,
J., Wang, L., Wang, W., Qi, Y., Li, H., Xu, T., Ran, Y., Hu, X., Shi, S.,
Zhu, Z., Tan, J., Zhang, Y., and Ren, Z.: The Heihe Integrated Observatory
Network: A Basin-Scale Land Surface Processes Observatory in China, Vadose
Zone J., 17, 180072, https://doi.org/10.2136/vzj2018.04.0072, 2018.
Liu, S. M., Xu, Z. W., Wang, W. Z., Jia, Z. Z., Zhu, M. J., Bai, J., and Wang, J. M.: A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem, Hydrol. Earth Syst. Sci., 15, 1291–1306, https://doi.org/10.5194/hess-15-1291-2011, 2011.
Liu, W., Wang, L., Zhou, J., Li, Y., Sun, F., Fu, G., Li, X., and Sang,
Y.-F.: A worldwide evaluation of basin-scale evapotranspiration estimates
against the water balance method, J. Hydrol., 538, 82–95,
https://doi.org/10.1016/j.jhydrol.2016.04.006, 2016.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020.
Ma, J., Xiao, X., Zhang, Y., Doughty, R., Chen, B., and Zhao, B.:
Spatial-temporal consistency between gross primary productivity and
solar-induced chlorophyll fluorescence of vegetation in China during
2007–2014, Sci. Total Environ., 639, 1241–1253,
https://doi.org/10.1016/j.scitotenv.2018.05.245, 2018.
Ma, J., Xiao, X., Miao, R., Li, Y., Chen, B., Zhang, Y., and Zhao, B.:
Trends and controls of terrestrial gross primary productivity of China
during 2000–2016, Environ. Res. Lett., 14, 084032,
https://doi.org/10.1088/1748-9326/ab31e4, 2019.
Ma, N. and Zhang, Y.: Increasing Tibetan Plateau terrestrial
evapotranspiration primarily driven by precipitation, Agr.
Forest Meteorol., 317, 108887, https://doi.org/10.1016/j.agrformet.2022.108887,
2022.
Ma, N., Szilagyi, J., Zhang, Y., and Liu, W.:
Complementary-Relationship-Based Modeling of Terrestrial Evapotranspiration
Across China During 1982–2012: Validations and Spatiotemporal Analyses,
J. Geophys. Res.-Atmos., 124, 4326–4351,
https://doi.org/10.1029/2018jd029850, 2019.
Ma, T., Sun, S., Fu, G., Hall, J. W., Ni, Y., He, L., Yi, J., Zhao, N., Du,
Y., Pei, T., Cheng, W., Song, C., Fang, C., and Zhou, C.: Pollution
exacerbates China's water scarcity and its regional inequality, Nat.
Commun., 11, 650, https://doi.org/10.1038/s41467-020-14532-5, 2020.
Ma, Y., Hu, Z., Xie, Z., Ma, W., Wang, B., Chen, X., Li, M., Zhong, L., Sun, F., Gu, L., Han, C., Zhang, L., Liu, X., Ding, Z., Sun, G., Wang, S., Wang, Y., and Wang, Z.: A long-term (2005–2016) dataset of hourly integrated land–atmosphere interaction observations on the Tibetan Plateau, Earth Syst. Sci. Data, 12, 2937–2957, https://doi.org/10.5194/essd-12-2937-2020, 2020.
Mao, Y. and Wang, K.: Comparison of evapotranspiration estimates based on
the surface water balance, modified Penman-Monteith model, and reanalysis
data sets for continental China, J. Geophys. Res.-Atmos., 122, 3228–3244, https://doi.org/10.1002/2016JD026065, 2017.
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.
Mauder, M., Desjardins, R. L., and MacPherson, I.: Scale analysis of
airborne flux measurements over heterogeneous terrain in a boreal ecosystem,
J. Geophys. Res.-Atmos., 112, D13112,
https://doi.org/10.1029/2006JD008133, 2007.
Medvigy, D., Wofsy, S., Munger, J., Hollinger, D., and Moorcroft, P.:
Mechanistic scaling of ecosystem function and dynamics in space and time:
Ecosystem Demography model version 2, J. Geophys. Res.-Biogeo., 114, G01002, https://doi.org/10.1029/2008JG000812, 2009.
Miao, C., Gou, J., Fu, B., Tang, Q., Duan, Q., Chen, Z., Lei, H., Chen, J.,
Guo, J., Borthwick, A. G. L., Ding, W., Duan, X., Li, Y., Kong, D., Guo, X.,
and Wu, J.: High-quality reconstruction of China's natural streamflow,
Sci. Bull., 67, 547–556, https://doi.org/10.1016/j.scib.2021.09.022,
2022.
Minx, J. C., Lamb, W. F., Andrew, R. M., Canadell, J. G., Crippa, M., Döbbeling, N., Forster, P. M., Guizzardi, D., Olivier, J., Peters, G. P., Pongratz, J., Reisinger, A., Rigby, M., Saunois, M., Smith, S. J., Solazzo, E., and Tian, H.: A comprehensive and synthetic dataset for global, regional, and national greenhouse gas emissions by sector 1970–2018 with an extension to 2019, Earth Syst. Sci. Data, 13, 5213–5252, https://doi.org/10.5194/essd-13-5213-2021, 2021.
Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., and Dolman, A. J.: Magnitude and variability of land evaporation and its components at the global scale, Hydrol. Earth Syst. Sci., 15, 967–981, https://doi.org/10.5194/hess-15-967-2011, 2011a.
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, 2011b.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global
terrestrial evapotranspiration algorithm, Remote Sens. Environ.,
115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Myneni, R., Knyazikhin, Y., Park, T.: MOD15A2H MODIS/Terra Leaf Area
Index/FPAR 8-Day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land
Processes DAAC [data set],
https://doi.org/10.5067/MODIS/MOD15A2H.006, 2015.
National Water Resources Bulletin: http://szy.mwr.gov.cn/gbsj/index.html, last access: 1 December 2021.
NOAA: NOAA CO2 concentration, ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_gl.txt, last access: 31 May 2021.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y. W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardo, J., et al.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P.
M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production: a
process model based on global satellite and surface data, Global
Biogeochem. Cy., 7, 811–841, https://doi.org/10.1029/93GB02725, 1993.
Rasmussen, J., Sonnenborg, T. O., Stisen, S., Seaby, L. P., Christensen, B. S. B., and Hinsby, K.: Climate change effects on irrigation demands and minimum stream discharge: impact of bias-correction method, Hydrol. Earth Syst. Sci., 16, 4675–4691, https://doi.org/10.5194/hess-16-4675-2012, 2012.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M.,
Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A.,
Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A.,
Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta,
F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M.,
Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., 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, https://doi.org/10.1111/j.1365-2486.2005.001002.x,
2005.
Ren, G., Zhan, Y., Ren, Y., Chen, Y., Wang, T., Liu, Y., and Sun, X.:
Spatial and temporal patterns of precipitation variability over mainland
China: I: climatology, Adv. Water Sci., 26, 299–310,
https://doi.org/10.14042/j.cnki.32.1309.2015.03.001, 2015.
Running, S., Mu, Q., and Zhao, M.: MOD17A2H MODIS/Terra Gross Primary
Productivity 8-Day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land
Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD17A2H.006,
2015.
Schaaf, C. and Wang, Z.: MCD43A1 MODIS/Terra + Aqua BRDF/Albedo Model
Parameters Daily L3 Global – 500 m V006, NASA EOSDIS Land
Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A1.006, 2015.
Su, T., Feng, T., Huang, B., Han, Z., Qian, Z., Feng, G., Hou, W., and Dong,
W.: Long-term mean changes in actual evapotranspiration over China under
climate warming and the attribution analysis within the Budyko framework,
Int. J. Climatol., 42, 1136–1147,
https://doi.org/10.1002/joc.7293, 2022.
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., and Friedl, M. A.:
Hierarchical mapping of annual global land cover 2001 to present: The MODIS
Collection 6 Land Cover product, Remote Sens. Environ., 222,
183–194, https://doi.org/10.1016/j.rse.2018.12.013, 2019.
Tang, G., Ma, Y., Long, D., Zhong, L., and Hong, Y.: Evaluation of GPM Day-1
IMERG and TMPA Version-7 legacy products over Mainland China at multiple
spatiotemporal scales, J. Hydrol., 533, 152–167,
https://doi.org/10.1016/j.jhydrol.2015.12.008, 2016.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456–457, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
Thornley, J. H. M.: Mathematical models in plant physiology, Academic Press (Inc.) London, Ltd., 1976.
Tong, X., Brandt, M., Yue, Y., Horion, S., Wang, K., Keersmaecker, W. D.,
Tian, F., Schurgers, G., Xiao, X., Luo, Y., Chen, C., Myneni, R., Shi, Z.,
Chen, H., and Fensholt, R.: Increased vegetation growth and carbon stock in
China karst via ecological engineering, Nat. Sustain., 1, 44–50,
https://doi.org/10.1038/s41893-017-0004-x, 2018.
Turner, D. P., Urbanski, S., Bremer, D., Wofsy, S. C., Meyers, T., Gower, S.
T., and Gregory, M.: A cross-biome comparison of daily light use efficiency
for gross primary production, Glob. Change Biol., 9, 383–395,
https://doi.org/10.1046/j.1365-2486.2003.00573.x, 2003.
Villarreal, S. and Vargas, R.: Representativeness of FLUXNET Sites Across
Latin America, J. Geophys. Res.-Biogeosci., 126, 289–307,
https://doi.org/10.1029/2020JG006090, 2021.
Wan, Z., Hook, S., and Hulley, G.: MYD11A2 MODIS/Aqua Land Surface Temperature/
Emissivity 8-Day L3 Global 1 km SIN Grid V006, NASA EOSDIS Land
Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD11A2.006, 2015.
Wang, G. Q., Zhang, J. Y., Jin, J. L., Pagano, T. C., Calow, R., Bao, Z. X., Liu, C. S., Liu, Y. L., and Yan, X. L.: Assessing water resources in China using PRECIS projections and a VIC model, Hydrol. Earth Syst. Sci., 16, 231–240, https://doi.org/10.5194/hess-16-231-2012, 2012.
Wang, S., Li, J., Zhang, B., Lee, Z., Spyrakos, E., Feng, L., Liu, C., Zhao,
H., Wu, Y., and Zhu, L.: Changes of water clarity in large lakes and
reservoirs across China observed from long-term MODIS, Remote Sens.
Environ., 247, 111949, https://doi.org/10.1016/j.rse.2020.111949, 2020.
Wen, X., Yu, G., Sun, X., Li, Q., Liu, Y., Zhang, L., Ren, C., Fu, Y., and
Li, Z.: Soil moisture effect on the temperature dependence of ecosystem
respiration in a subtropical Pinus plantation of southeastern China,
Agr. Forest Meteorol., 137, 166–175,
https://doi.org/10.1016/j.agrformet.2006.02.005, 2006.
Wofsy, S. C., Goulden, M. L., Munger, J. W., Fan, S. M., Bakwin, P. S.,
Daube, B. C., Bassow, S. L., and Bazzaz, F. A.: Net Exchange of CO2 in a
Mid-Latitude Forest, Science, 260, 1314–1317,
https://doi.org/10.1126/science.260.5112.1314, 1993.
Wolanin, A., Camps-Valls, G., Gómez-Chova, L., Mateo-García, G.,
van der Tol, C., Zhang, Y., and Guanter, L.: Estimating crop primary
productivity with Sentinel-2 and Landsat 8 using machine learning methods
trained with radiative transfer simulations, Remote Sens. Environ.,
225, 441–457, https://doi.org/10.1016/j.rse.2019.03.002, 2019.
Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M.: Basic and extensible post-processing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015–5030, https://doi.org/10.5194/bg-15-5015-2018, 2018.
Xiao, J., Sun, G., Chen, J., Chen, H., Chen, S., Dong, G., Gao, S., Guo, H.,
Guo, J., Han, S., Kato, T., Li, Y., Lin, G., Lu, W., Ma, M., McNulty, S.,
Shao, C., Wang, X., Xie, X., Zhang, X., Zhang, Z., Zhao, B., Zhou, G., and
Zhou, J.: Carbon fluxes, evapotranspiration, and water use efficiency of
terrestrial ecosystems in China, Agr. Forest Meteorol.,
182–183, 76–90, https://doi.org/10.1016/j.agrformet.2013.08.007, 2013.
Xie, P., Wu, Z., Sang, Y.-F., Gu, H., Zhao, Y., and Singh, V. P.: Evaluation
of the significance of abrupt changes in precipitation and runoff process in
China, J. Hydrol., 560, 451–460,
https://doi.org/10.1016/j.jhydrol.2018.02.036, 2018.
Xu, F., Wang, W., Wang, J., Xu, Z., Qi, Y., and Wu, Y.: Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis, Hydrol. Earth Syst. Sci., 21, 4037–4051, https://doi.org/10.5194/hess-21-4037-2017, 2017.
Yang, D., Li, C., Hu, H., Lei, Z., Yang, S., Kusuda, T., Koike, T., and
Musiake, K.: Analysis of water resources variability in the Yellow River of
China during the last half century using historical data, Water Resour.
Res., 40, W06502, https://doi.org/10.1029/2003wr002763, 2004.
Yang, F., Sarathchandra, C., Liu, J., Huang, H., Gou, J.-Y., Li, Y., Mao,
X., Wen, H., Zhao, J., Yang, M., Homya, S., and Prueksakorn, K.: How fern
and fern allies respond to heterogeneous habitat – a case in Yuanjiang
dry-hot valley, Communicative and Integrative Biology, 14, 248–260,
https://doi.org/10.1080/19420889.2021.2007591, 2021.
Yang, Y., Shi, Y., Sun, W., Chang, J., Zhu, J., Chen, L., Wang, X., Guo, Y.,
Zhang, H., Yu, L., Zhao, S., Xu, K., Zhu, J., Shen, H., Wang, Y., Peng, Y.,
Zhao, X., Wang, X., Hu, H., Chen, S., Huang, M., Wen, X., Wang, S., Zhu, B.,
Niu, S., Tang, Z., Liu, L., and Fang, J.: Terrestrial carbon sinks in China
and around the world and their contribution to carbon neutrality, Sci. China
Life Sci., 65, 861–895, https://doi.org/10.1007/s11427-021-2045-5, 2022.
Yao, Y., Wang, X., Li, Y., Wang, T., Shen, M., Du, M., He, H., Li, Y., Luo,
W., Ma, M., Ma, Y., Tang, Y., Wang, H., Zhang, X., Zhang, Y., Zhao, L.,
Zhou, G., and Piao, S.: Spatiotemporal pattern of gross primary productivity
and its covariation with climate in China over the last thirty years, Glob.
Change Biol., 24, 184–196, https://doi.org/10.1111/gcb.13830, 2018.
Yin, L., Tao, F., Chen, Y., Liu, F., and Hu, J.: Improving terrestrial
evapotranspiration estimation across China during 2000–2018 with machine
learning methods, J. Hydrol., 600,
https://doi.org/10.1016/j.jhydrol.2021.126538, 2021.
Yu, G.-R., Wen, X.-F., Sun, X.-M., Tanner, B. D., Lee, X., and Chen, J.-Y.:
Overview of ChinaFLUX and evaluation of its eddy covariance measurement,
Agr. Forest Meteorol., 137, 125–137,
https://doi.org/10.1016/j.agrformet.2006.02.011, 2006.
Yu, H., Qi, D., Zhang, Y., Sha, L., Liu, Y., Zhou, W., Deng, Y., and Song,
Q.: An observation dataset of carbon and water fluxes in Xishuangbanna
rubber plantations from 2010 to 2014, Science Data Bank [data set],
https://cstr.cn/31253.11.sciencedb.j00001.00123 (last access: 1 April 2022), 2021.
Yu, Q., Zhang, Y., Liu, Y., and Shi, P.: Simulation of the Stomatal
Conductance of Winter Wheat in Response to Light, Temperature and CO2
Changes, Ann. Botany, 93, 435–441, https://doi.org/10.1093/aob/mch023,
2004.
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D.,
Bernhofer, C., Gholz, H., Goldstein, A. H., and Goulden, M. L.: Deriving a light
use efficiency model from eddy covariance flux data for predicting daily
gross primary production across biomes, Agr. Forest Meteorol.,
143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007.
Yue, T., Zhao, N., Liu, Y., Wang, Y., Zhang, B., Du, Z., Fan, Z., Shi, W.,
Chen, C., and Zhao, M.: A fundamental theorem for eco-environmental surface
modelling and its applications, Sci. China Earth Sci., 63, 1092–1112,
https://doi.org/10.1007/s11430-019-9594-3, 2020.
Zhang, F., Li, H., Zhao, L., Zhang, L., Chen, Z., Zhu, J., Xu, S., Yang, Y.,
Zhao, X., and Yu, G.: An observation dataset of carbon, water and heat
fluxes of alpine wetland in Haibei (2004–2009), Science Data
Bank [data set], https://doi.org/10.11922/sciencedb.1010, 2020.
Zhang, J.-H., Han, S.-J., and Yu, G.-R.: Seasonal variation in carbon dioxide
exchange over a 200-year-old Chinese broad-leaved Korean pine mixed forest,
Agr. Forest Meteorol., 137, 150–165,
https://doi.org/10.1016/j.agrformet.2006.02.004, 2006.
Zhang, Y. and He, S.: PML-V2(China): evapotranspiration and gross primary
production (2000.02.26–2020.12.31), National Tibetan Plateau Data
Center [data set], https://doi.org/10.11888/Terre.tpdc.272389, 2022.
Zhang, Y., Leuning, R., Hutley, L. B., Beringer, J., McHugh, I., and Walker, J. P.: Using long-term water balances to parameterize surface conductances and calculate evaporation at 0.05° spatial resolution, Water Resour. Res., 46, W05512, https://doi.org/10.1029/2009wr008716, 2010.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A
global moderate resolution dataset of gross primary production of vegetation
for 2000–2016, Sci. Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165,
2017a.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.:
Global gross primary production from vegetation photosynthesis model for
2000–2016, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.879560, 2017b.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q.,
and Yang, Y.: Coupled estimation of 500 m and 8 d resolution global
evapotranspiration and gross primary production in 2002–2017, Remote
Sens. Environ., 222, 165–182,
https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., Dong, J., and
Doughty, R.: Global gross primary production from vegetation photosynthesis
model for 2017–2019, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.928381, 2021.
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,
https://doi.org/10.1029/2018JD030174, 2019.
Zhang, Z., Zhang, Y., Zhang, Y., Gobron, N., Frankenberg, C., Wang, S., and
Li, Z.: The potential of satellite FPAR product for GPP estimation: An
indirect evaluation using solar-induced chlorophyll fluorescence, Remote
Sens. Environ., 240, 111686,
https://doi.org/10.1016/j.rse.2020.111686, 2020.
Zhao, F., Li, F., Zhan, C., Zhang, L., and Chen, Z.: A carbon and water
fluxes dataset over farmland ecosystem of winter wheat and summer corn in
Yucheng (2003–2010), Science Data Bank [data set],
https://doi.org/10.11922/sciencedb.j00001.20002, 2021.
Zheng, Y., Shen, R., Wang, Y., Li, X., Liu, S., Liang, S., Chen, J. M., Ju, W., Zhang, L., and Yuan, W.: Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017, Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, 2020.
Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H., and Wu, X.:
Drought monitoring utility of satellite-based precipitation products across
mainland China, J. Hydrol., 568, 343–359,
https://doi.org/10.1016/j.jhydrol.2018.10.072, 2019.
Short summary
This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
This study developed a daily, 500 m evapotranspiration and gross primary production product...
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