Articles | Volume 15, issue 10
https://doi.org/10.5194/essd-15-4571-2023
© 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-15-4571-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins
Jinghua Xiong
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei 430072, China
Abhishek
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
Hrishikesh A. Chandanpurkar
Centre for Sustainability, Environment, and Climate Change, FLAME University, Pune, India
James S. Famiglietti
Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
Global Futures Laboratory, Arizona State University, Tempe, AZ 85281, United States
Chong Zhang
Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
Gionata Ghiggi
Environmental Remote Sensing Laboratory (LTE), EPFL, 1005 Lausanne, Switzerland
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei 430072, China
Yun Pan
CORRESPONDING AUTHOR
Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
Bramha Dutt Vishwakarma
Interdisciplinary Centre for Water Research, Indian Institute of Science, Bengaluru 560012, India
Centre for Earth Sciences, Indian Institute of Science, Bengaluru 560012, India
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F. Sabzehee, V. Nafisi, S. Iran Pour, and B. D. Vishwakarma
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F. Sabzehee, V. Nafisi, S. Iran Pour, and B. D. Vishwakarma
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 931–934, https://doi.org/10.5194/isprs-archives-XLII-4-W18-931-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-931-2019, 2019
Hui-Min Wang, Jie Chen, Chong-Yu Xu, Hua Chen, Shenglian Guo, Ping Xie, and Xiangquan Li
Hydrol. Earth Syst. Sci., 23, 4033–4050, https://doi.org/10.5194/hess-23-4033-2019, https://doi.org/10.5194/hess-23-4033-2019, 2019
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When using large ensembles of global climate models in hydrological impact studies, there are pragmatic questions on whether it is necessary to weight climate models and how to weight them. We use eight methods to weight climate models straightforwardly, based on their performances in hydrological simulations, and investigate the influences of the assigned weights. This study concludes that using bias correction and equal weighting is likely viable and sufficient for hydrological impact studies.
Yanlai Zhou, Fi-John Chang, Shenglian Guo, Huanhuan Ba, and Shaokun He
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-457, https://doi.org/10.5194/hess-2017-457, 2017
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Chao Deng, Pan Liu, Shenglian Guo, Zejun Li, and Dingbao Wang
Hydrol. Earth Syst. Sci., 20, 4949–4961, https://doi.org/10.5194/hess-20-4949-2016, https://doi.org/10.5194/hess-20-4949-2016, 2016
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Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate change and anthropogenic activities. The technique of the ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variations.
Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-619, https://doi.org/10.5194/hess-2016-619, 2016
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The study offers insights into future design floods that are inferred with both AM and POT samplings under nonstationarity caused by changing climate. Future design floods in nonstationarity context are usually (lower than) but not necessarily more different from stationary estimates. AM-based projection is more sensitive to climate change than POT estimates. The over-dispersion in POT arrival rate leads to the invalidation of Poisson assumption that the misuse may induce overestimated floods.
Chao Deng, Pan Liu, Shenglian Guo, Zejun Li, and Dingbao Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2015-407, https://doi.org/10.5194/hess-2015-407, 2016
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Hydrological model parameters may not be constant in a changing environment, i.e., climate change and human activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variation. The temporal variation parameter can be explained by catchment characteristic.
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
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Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
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Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
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We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
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The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Yinghong Jing, Xinghua Li, and Huanfeng Shen
Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
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Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
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Short summary
To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
To overcome the shortcomings associated with limited spatiotemporal coverage, input data...
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