Articles | Volume 29, issue 14
https://doi.org/10.5194/hess-29-3119-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3119-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2025
Research article |
| 23 Jul 2025
| 23 Jul 2025
Representation of a two-way coupled irrigation system in the Common Land Model
Shulei Zhang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
Hongbin Liang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Xingjie Lu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
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Zhilong Fan, Lu Li, Zhongwang Wei, Shupeng Zhang, Sisi Chen, Xingjie Lu, Wei Shangguan, Nan Wei, Hua Yuan, Pan Zhang, Jiaxin Tian, Jianhong Zhou, Baoxiang Pan, Wei Han, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-5358, https://doi.org/10.5194/egusphere-2026-5358, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We developed a new land data assimilation framework based on deep generative models. Compared with existing methods, it better handles non-Gaussian model errors and noisy observations. Tests in both an idealized system and a realistic land data assimilation experiment showed more accurate and consistent estimates, demonstrating its potential for improving land-state estimation with complex land surface models.
Xiaoying Shi, Daniel M. Ricciuto, Yaoping Wang, Joe R. Melton, Fortunat Joos, Yiqi Luo, Xiaofeng Xu, Dafeng Hui, Qianlai Zhuang, Chunjing Qiu, Hongxing He, Siya Shao, Xingjie Lu, Yongjiu Dai, Akihiko Ito, Qing Sun, Nitin Chaudhary, Ayesha Hussain, Mousong Wu, Jian Zhou, Jingwei Zhang, Ye Yuan, Rachael H. Turton, Eleanor J. Burke, Jiafu Mao, Yuefeng Hao, Natalie A. Griffiths, Verity Salmon, Paul J. Hanson, Melanie A. Mayes, and Peter E. Thornton
EGUsphere, https://doi.org/10.5194/egusphere-2026-5193, https://doi.org/10.5194/egusphere-2026-5193, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Northern peatlands store vast amounts of carbon, but their response to climate change remains uncertain. We compared 15 ecosystem models with observations from a long-term warming and elevated carbon dioxide experiment in Minnesota. Models differed widely in their carbon responses, reflecting differences in ecosystem processes and model parameters. The results identify priorities for improving peatland models and projections of future carbon–climate feedbacks.
Xueqi Cao, Nan Wei, Hua Yuan, Shupeng Zhang, Zhongwang Wei, Xingjie Lu, Lu Li, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-4210, https://doi.org/10.5194/egusphere-2026-4210, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Lakes influence weather by storing and releasing heat, but models often oversimplify how sunlight travels through water, snow, and ice. We improved how a model handles light entering a lake. Tests across nine diverse lakes show surface waters warm more, deep waters warm less, and under-ice heating is better captured. Benefits vary among lakes, so better light treatment must pair with better mixing and heat storage.
Peng Zhou, Jieming Chou, Li Dan, Jean-François Lamarque, Muhammad Bilal, Fang Li, Mengting Sun, Rebecca Buchholz, Desneiges Murray, Zhaoxiang Cao, Jing Peng, Kai Li, Fuqiang Yang, Wei Pan, Jinyan Chen, and Liwen Xing
Geosci. Model Dev., 19, 5491–5513, https://doi.org/10.5194/gmd-19-5491-2026, https://doi.org/10.5194/gmd-19-5491-2026, 2026
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We assessed the impact of ozone damage representations in a land-surface model on simulations of vegetation productivity. Results varied depending on how ozone effects were triggered and how vegetation recovery was modeled. Schemes that incorporated vegetation-specific thresholds and memory effects on photosynthesis and water loss more accurately reflected spatial patterns, indicating directions for enhancing model realism and improving projections of ecosystem responses to ozone pollution.
Chen Yang, Hui Huang, Zeyu Tang, Yongjiu Dai, and Xiaobin Chang
EGUsphere, https://doi.org/10.31223/X5PR3B, https://doi.org/10.31223/X5PR3B, 2026
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Groundwater plays an important role in how water and energy move between land and atmosphere, but simulating it in detail is very slow. We developed a faster method that keeps the key processes while reducing computing cost. Tested in a large river basin in China, it ran about twenty times faster and still closely matched detailed simulations. This approach makes it easier to include groundwater in large-scale and long-term environmental modeling.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada-Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Linn Hamester, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, Matthieu Lengaigne, Rohini Kumar, and Maryna Strokal
Geosci. Model Dev., 19, 4095–4135, https://doi.org/10.5194/gmd-19-4095-2026, https://doi.org/10.5194/gmd-19-4095-2026, 2026
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Fang Li, David M. Lawrence, Brendan M. Rogers, Chantelle Burton, Huilin Huang, Yiquan Jiang, Johannes W. Kaiser, Matthew Kasoar, Hanna Lee, Ruby Leung, Lars Nieradzik, Aihui Wang, Daniel S. Ward, Ligeer Ce, Yangchun Li, Zhongda Lin, Apostolos Voulgarakis, and Yongkang Xue
Geosci. Model Dev., 19, 3989–4007, https://doi.org/10.5194/gmd-19-3989-2026, https://doi.org/10.5194/gmd-19-3989-2026, 2026
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Extreme fire events are increasing, and fires are projected to rise across most regions, posing growing risks to ecosystems and society. As a key Earth system process, fire is now modeled in most Earth System Models (ESMs). FireMIP (Fire Modeling Intercomparison Project) within CMIP7 will evaluate fire simulations in state‑of‑the‑art ESMs, project future fire changes, and provide quantitative, process‑based understanding of fire's role in the Earth system.
Zezhen Wu, Zhongwang Wei, Xingjie Lu, Nan Wei, Lu Li, Shupeng Zhang, Hua Yuan, Shaofeng Liu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 30, 2651–2666, https://doi.org/10.5194/hess-30-2651-2026, https://doi.org/10.5194/hess-30-2651-2026, 2026
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Land surface models simulate exchanges at the Earth's surface. Traditional evaluation methods can be misleading because they may hide errors or be overly sensitive to outliers. We introduce the Model Fidelity Metric, which measures model performance in terms of accuracy, variability and distribution similarity. Tests with synthetic data and real streamflow observations show that this metric provides more stable and informative assessments of model performance.
Esther Karin Bender, Matvey Debolskiy, Kjetil Aas, Sebastian Westermann, Fang Li, Jiawen Zhu, and Hanna Lee
EGUsphere, https://doi.org/10.5194/egusphere-2026-1039, https://doi.org/10.5194/egusphere-2026-1039, 2026
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This paper introduces a new method to represent ice-rich permafrost soils in the Community Land Model used for climate prediction. The approach includes key processes previously missing, enabling simulation of rapid thaw events from ground-ice melt. It improves representation of soil temperature, hydrology, and landscape dynamics, leading to more accurate future greenhouse-gas estimates.
Tingting Wu, Shupeng Zhang, Xiaofan Yang, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-2275, https://doi.org/10.5194/egusphere-2026-2275, 2026
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We coupled a land surface model (CoLM) with an analysis tool (PSUADE) to evaluate processes controlling water table depth (WTD) through sensitivity analysis and parameter optimization. A small set of sensitive parameters governing WTD was identified, showing subsurface runoff controls average groundwater levels, while soil properties govern variability. A stepwise calibration was then suggested to improve accuracy. This framework offers a transferable pathway to enhance groundwater simulation.
Mara Y. McPartland, Tomas Lovato, Charles Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
Geosci. Model Dev., 19, 2849–2880, https://doi.org/10.5194/gmd-19-2849-2026, https://doi.org/10.5194/gmd-19-2849-2026, 2026
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The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
Chen Yang, Aoqi Sun, Shupeng Zhang, Yongjiu Dai, Stefan Kollet, and Reed Maxwell
Geosci. Model Dev., 19, 1849–1866, https://doi.org/10.5194/gmd-19-1849-2026, https://doi.org/10.5194/gmd-19-1849-2026, 2026
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Groundwater plays a key role in land–atmosphere water and energy exchange, yet it is often simplified in large-scale Earth system models. We review 20 years of efforts to couple the groundwater model ParFlow with land surface and atmospheric models, showing how groundwater dynamics shape terrestrial fluxes. We also present an updated coupling framework that enhances model performance and flexibility, and outline a modular strategy to guide future development.
Wanyi Lin, Hua Yuan, Wenzong Dong, Zhuo Liu, Jiayi Xiang, Xinran Yu, Shupeng Zhang, Zhongwang Wei, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-21, https://doi.org/10.5194/essd-2026-21, 2026
Manuscript not accepted for further review
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Land surface models require information on vegetation composition and seasonal leaf area dynamics, yet long-term high-resolution datasets remain limited. We present a global dataset of plant functional types and corresponding leaf area index for 1985–2020, derived from multiple satellite observations. The dataset reduces uncertainties in vegetation representation and improves the description of phenological dynamics, supporting more realistic land surface and climate modeling.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibao Liu, and Yongjiu Dai
Geosci. Model Dev., 18, 6517–6540, https://doi.org/10.5194/gmd-18-6517-2025, https://doi.org/10.5194/gmd-18-6517-2025, 2025
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Land surface models are used for simulating how Earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes the evaluation process more comprehensive and efficient, stands out by incorporating various factors and working with data at any scale, enabling scientists to incorporate new types of models and measurements as our understanding of Earth's systems evolves.
Shuyang Guo, Yongjiu Dai, Hua Yuan, and Hongbin Liang
The Cryosphere, 19, 3553–3570, https://doi.org/10.5194/tc-19-3553-2025, https://doi.org/10.5194/tc-19-3553-2025, 2025
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The Snow, Ice, and Aerosol Radiation Model version 4 has only been used to evaluate bare-ice albedo in land surface models, with necessary ice property data lacking quality control. We integrated this model into our land surface model and improved bare-ice properties using quality-controlled satellite data. Our findings show regional warming and reduced snow cover in Greenland’s bare-ice region, driven by changes in bare-ice properties through bare-ice–snow albedo feedback.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
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We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
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In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
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SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
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Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
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The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Sandy P. Harrison, Roberto Villegas-Diaz, Esmeralda Cruz-Silva, Daniel Gallagher, David Kesner, Paul Lincoln, Yicheng Shen, Luke Sweeney, Daniele Colombaroli, Adam Ali, Chéïma Barhoumi, Yves Bergeron, Tatiana Blyakharchuk, Přemysl Bobek, Richard Bradshaw, Jennifer L. Clear, Sambor Czerwiński, Anne-Laure Daniau, John Dodson, Kevin J. Edwards, Mary E. Edwards, Angelica Feurdean, David Foster, Konrad Gajewski, Mariusz Gałka, Michelle Garneau, Thomas Giesecke, Graciela Gil Romera, Martin P. Girardin, Dana Hoefer, Kangyou Huang, Jun Inoue, Eva Jamrichová, Nauris Jasiunas, Wenying Jiang, Gonzalo Jiménez-Moreno, Monika Karpińska-Kołaczek, Piotr Kołaczek, Niina Kuosmanen, Mariusz Lamentowicz, Martin Lavoie, Fang Li, Jianyong Li, Olga Lisitsyna, José Antonio López-Sáez, Reyes Luelmo-Lautenschlaeger, Gabriel Magnan, Eniko Katalin Magyari, Alekss Maksims, Katarzyna Marcisz, Elena Marinova, Jenn Marlon, Scott Mensing, Joanna Miroslaw-Grabowska, Wyatt Oswald, Sebastián Pérez-Díaz, Ramón Pérez-Obiol, Sanna Piilo, Anneli Poska, Xiaoguang Qin, Cécile C. Remy, Pierre J. H. Richard, Sakari Salonen, Naoko Sasaki, Hieke Schneider, William Shotyk, Migle Stancikaite, Dace Šteinberga, Normunds Stivrins, Hikaru Takahara, Zhihai Tan, Liva Trasune, Charles E. Umbanhowar, Minna Väliranta, Jüri Vassiljev, Xiayun Xiao, Qinghai Xu, Xin Xu, Edyta Zawisza, Yan Zhao, Zheng Zhou, and Jordan Paillard
Earth Syst. Sci. Data, 14, 1109–1124, https://doi.org/10.5194/essd-14-1109-2022, https://doi.org/10.5194/essd-14-1109-2022, 2022
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We provide a new global data set of charcoal preserved in sediments that can be used to examine how fire regimes have changed during past millennia and to investigate what caused these changes. The individual records have been standardised, and new age models have been constructed to allow better comparison across sites. The data set contains 1681 records from 1477 sites worldwide.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
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This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Cited articles
Al-Yaari, A., Ducharne, A., Thiery, W., Cheruy, F., and Lawrence, D.: The Role of Irrigation Expansion on Historical Climate Change: Insights From CMIP6, Earth's Future, 10, e2022EF002859, https://doi.org/10.1029/2022EF002859, 2022.
Arboleda-Obando, P. F., Ducharne, A., Yin, Z., and Ciais, P.: Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2, Geoscientific Model Development, 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, 2024.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., Springer, Dordrecht, Netherlands, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987.
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W. A., Heinke, J., von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge and irrigation water supply during the 20th century, Water Resour. Res., 47, https://doi.org/10.1029/2009WR008929, 2011.
bin Abdullah, K.: Use of water and land for food security and environmental sustainability, Irrig. Drain., 55, 219–222, https://doi.org/10.1002/ird.254, 2006.
Blyth, E. M., Arora, V. K., Clark, D. B., Dadson, S. J., De Kauwe, M. G., Lawrence, D. M., Melton, J. R., Pongratz, J., Turton, R. H., Yoshimura, K., and Yuan, H.: Advances in Land Surface Modelling, Current Climate Change Reports, 7, 45–71, https://doi.org/10.1007/s40641-021-00171-5, 2021.
Bonan, B.: A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6DF6P5X, 1996.
Buckingham, E.: Studies on the Movement of Soil Moisture; US Department of Agriculture, Bureau of Soils: Washington, DC, USA, ISBN 978-1527798847, 1907.
Campbell, B., Beare, D., Bennett, E., Hall-Spencer, J., Ingram, J., Jaramillo, F., Ortiz, R., Ramankutty, N., Sayer, J., and Shindell, D.: Agriculture production as a major driver of the Earth system exceeding planetary boundaries, Ecol. Soc., 22, 8, https://doi.org/10.5751/ES-09595-220408, 2017.
Campbell, G. S.: A Simple Method for Determining Unsaturated Conductivity from Moisture Retention Data, Soil Sci., 117, 311–314, https://doi.org/10.1097/00010694-197406000-00001, 1974.
Chen, L. and Dirmeyer, P. A.: Global observed and modelled impacts of irrigation on surface temperature, Int. J. Climatol., 39, 2587–2600, https://doi.org/10.1002/joc.5973, 2019.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Collatz, G. J., Ribas-Carbo, M., and Berry, J. A.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/pp9920519, 1992.
Cook, B. I., Shukla, S. P., Puma, M. J., and Nazarenko, L. S.: Irrigation as an historical climate forcing, Clim. Dynam., 44, 1715–1730, https://doi.org/10.1007/s00382-014-2204-7, 2015.
Dai, Y. and Yuan, H.: CoLM2014, GitHub [code], https://github.com/Hi-Res-CoLM/CoLM2014, 2014.
Dai, Y. and Zeng, Q.: A land surface model (IAP94) for climate studies part I: Formulation and validation in off-line experiments, Adv. Atmos. Sci., 14, 433–460, https://doi.org/10.1007/s00376-997-0063-4, 1997.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W., Schlosser, C. A., and Yang, Z.-L.: The Common Land Model, B. Am. Meteorol. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Dai, Y., Dickinson, R. E., and Wang, Y.-P.: A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance, J. Climate, 17, 2281–2299, https://doi.org/10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2, 2004.
Dai, Y., Xin, Q., Wei, N., Zhang, Y., Shangguan, W., Yuan, H., Zhang, S., Liu, S., and Lu, X.: A Global High-Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling, J. Adv. Model. Earth Sy., 11, 2996–3023, https://doi.org/10.1029/2019MS001784, 2019.
Dessler, A. E. and Sherwood, S. C.: A Matter of Humidity, Science, 323, 1020–1021, https://doi.org/10.1126/science.1171264, 2009.
de Vrese, P., Hagemann, S., and Claussen, M.: Asian irrigation, African rain: Remote impacts of irrigation, Geophys. Res. Lett., 43, 3737–3745, https://doi.org/10.1002/2016GL068146, 2016.
de Vrese, P., Stacke, T., and Hagemann, S.: Exploring the biogeophysical limits of global food production under different climate change scenarios, Earth System Dynamics, 9, 393–412, https://doi.org/10.5194/esd-9-393-2018, 2018.
Dickinson, E., Henderson-Sellers, A., and Kennedy, J.: Biosphere-atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model, University Corporation for Atmospheric Research, https://doi.org/10.5065/D67W6959, 1993.
Dieter, C. A., Maupin, M. A., Caldwell, R. R., Harris, M. A., Ivahnenko, T. I., Lovelace, J. K., Barber, N. L., and Linsey, K. S.: Estimated use of water in the United States in 2015, Circular, U.S. Geological Survey, 65 pp., https://doi.org/10.3133/cir1441, 2018.
Döll, P., Müller Schmied, H., Schuh, C., Portmann, F. T., and Eicker, A.: Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites, Water Resour. Res., 50, 5698–5720, https://doi.org/10.1002/2014WR015595, 2014.
Döll, P., Trautmann, T., Gerten, D., Schmied, H. M., Ostberg, S., Saaed, F., and Schleussner, C.-F.: Risks for the global freshwater system at 1.5 °C and 2 °C global warming, Environ. Res. Lett., 13, 044038, https://doi.org/10.1088/1748-9326/aab792, 2018.
Douglas, E. M., Beltrán-Przekurat, A., Niyogi, D., Pielke, R. A., and Vörösmarty, C. J.: The impact of agricultural intensification and irrigation on land–atmosphere interactions and Indian monsoon precipitation – A mesoscale modeling perspective, Global Planet. Change, 67, 117–128, https://doi.org/10.1016/j.gloplacha.2008.12.007, 2009.
Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., and Jacob, R.: Modeling agriculture in the Community Land Model, Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, 2013.
Druel, A., Munier, S., Mucia, A., Albergel, C., and Calvet, J.-C.: Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1, Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, 2022.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022.
Gao, H. and Zhao, G.: Global Reservoir Surface Area Dataset (GRSAD), V3, Texas Data Repository [data set], https://doi.org/10.18738/T8/DF80WG, 2019.
Gosling, S., Müller Schmied, H., Betts, R., Chang, J., Ciais, P., Dankers, R., Döll, P., Eisner, S., Flörke, M., Gerten, D., Grillakis, M., Hanasaki, N., Hagemann, S., Huang, M., Huang, Z., Jerez, S., Kim, H., Koutroulis, A., Leng, G., Liu, X., Masaki, Y., Montavez, P., Morfopoulos, C., Oki, T., Papadimitriou, L., Pokhrel, Y., Portmann, F. T., Orth, R., Ostberg, S., Satoh, Y., Seneviratne, S., Sommer, P., Stacke, T., Tang, Q., Tsanis, I., Wada, Y., Zhou, T., Büchner, M., Schewe, J., and Zhao, F.: ISIMIP2a Simulation Data from Water (global) Sector (V.1.1), GFZ Data Services [data set], https://doi.org/10.5880/PIK.2019.003, 2019.
GRDC: The Global Runoff Database, Global Runoff Data Centre [data set], https://www.bafg.de/GRDC/EN/Home/ (last access: July 2025), 2023.
Hanasaki, N., Yoshikawa, S., Pokhrel, Y., and Kanae, S.: A global hydrological simulation to specify the sources of water used by humans, Hydrol. Earth Syst. Sci., 22, 789–817, https://doi.org/10.5194/hess-22-789-2018, 2018.
Hanazaki, R., Yamazaki, D., and Yoshimura, K.: Development of a Reservoir Flood Control Scheme for Global Flood Models, J. Adv. Model. Earth Sy., 14, e2021MS002944, https://doi.org/10.1029/2021MS002944, 2022.
Hu, Z., Xu, Z., Ma, Z., Mahmood, R., and Yang, Z.: Potential surface hydrologic responses to increases in greenhouse gas concentrations and land use and land cover changes, Int. J. Climatol., 39, 814–827, https://doi.org/10.1002/joc.5844, 2019.
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, 2015.
Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., and Franke, J. A.: Climate impacts on global agriculture emerge earlier in new generation of climate and crop models, Nature Food, 2, 873–885, https://doi.org/10.1038/s43016-021-00400-y, 2021a.
Jägermeyr, J., Müller, C., Minoli, S., Ray, D., and Siebert, S.: GGCMI Phase 3 crop calendar, Zenodo [data set], https://doi.org/10.5281/zenodo.5062513, 2021b.
Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R. G., Fallatah, O., and Kirchner, J. W.: Rapid groundwater decline and some cases of recovery in aquifers globally, Nature, 625, 715–721, https://doi.org/10.1038/s41586-023-06879-8, 2024.
Jones, E., Qadir, M., van Vliet, M. T. H., Smakhtin, V., and Kang, S.: The state of desalination and brine production: A global outlook, Sci. Total Environ., 657, 1343–1356, https://doi.org/10.1016/j.scitotenv.2018.12.076, 2019.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls,G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: FLUXCOM Global Land Energy Fluxes, Max Planck Institute for Biogeochemistry [data set], https://doi.org/10.17871/FLUXCOM_EnergyFluxes_v1, 2018.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scientific Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Kang, S. and Eltahir, E. A. B.: North China Plain threatened by deadly heatwaves due to climate change and irrigation, Nat. Commun., 9, 2894, https://doi.org/10.1038/s41467-018-05252-y, 2018.
Ketchum, D., Hoylman, Z. H., Huntington, J., Brinkerhoff, D., and Jencso, K. G.: Irrigation intensification impacts sustainability of streamflow in the Western United States, Communications Earth & Environment, 4, 479, https://doi.org/10.1038/s43247-023-01152-2, 2023.
Konar, M., Ruess, P. J., Wanders, N., and Bierkens, M. F. P.: Data for Total irrigation by crop in the Continental United States from 2008 to 2020, University of Illinois Urbana-Champaign [data set], https://doi.org/10.13012/B2IDB-2656127_V1, 2024.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011a.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: Global Reservoir and Dam Database, McGill University [data set], https://www.globaldamwatch.org/grand/ (last access: July 2025), 2011b.
Leng, G., Huang, M., Tang, Q., and Leung, L. R.: A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate, J. Adv. Model. Earth Sy., 7, 1285–1304, https://doi.org/10.1002/2015MS000437, 2015.
Leng, G., Leung, L. R., and Huang, M.: Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME Land Model, J. Adv. Model. Earth Sy., 9, 1665–1683, https://doi.org/10.1002/2016MS000885, 2017.
Li, C., Lu, H., Yang, K., Wright, J. S., Yu, L., Chen, Y., Huang, X., and Xu, S.: Evaluation of the Common Land Model (CoLM) from the Perspective of Water and Energy Budget Simulation: Towards Inclusion in CMIP6, Atmosphere, 8, 141, https://doi.org/10.3390/atmos8080141, 2017a.
Li, C., Yuan, X., Jiao, Y., Ji, P., and Huang, Z.: High-resolution land surface modeling of the irrigation effects on evapotranspiration over the Yellow River basin, J. Hydrol., 633, 130986, https://doi.org/10.1016/j.jhydrol.2024.130986, 2024a.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How much runoff originates as snow in the western United States, and how will that change in the future?, Geophys. Res. Lett., 44, 6163–6172, https://doi.org/10.1002/2017GL073551, 2017b.
Li, F., Zhou, Z., Levis, S., Sitch, S., Hayes, F., Feng, Z., Reich, P. B., Zhao, Z., and Zhou, Y.: Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses, Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, 2024b.
Li, H., Huang, M., Wigmosta, M. S., Ke, Y., Coleman, A. M., Leung, L. R., Wang, A., and Ricciuto, D. M.: Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed, J. Geophys. Res.-Atmos., 116, D24120, https://doi.org/10.1029/2011JD016276, 2011.
Li, H., Lo, M.-H., Ryu, D., Peel, M., and Zhang, Y.: Possible Increase of Air Temperature by Irrigation, Geophys. Res. Lett., 49, e2022GL100427, https://doi.org/10.1029/2022GL100427, 2022.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Liu, S., Zeng, X., Dai, Y., Yuan, H., Wei, N., Wei, Z., Lu, X., and Zhang, S.: A Surface Flux Estimation Scheme Accounting for Large-Eddy Effects for Land Surface Modeling, Geophys. Res. Lett., 49, e2022GL101754, https://doi.org/10.1029/2022GL101754, 2022.
Lombardozzi, D. L., Lu, Y., Lawrence, P. J., Lawrence, D. M., Swenson, S., Oleson, K. W., Wieder, W. R., and Ainsworth, E. A.: Simulating Agriculture in the Community Land Model Version 5, J. Geophys. Res.-Biogeo., 125, e2019JG005529, https://doi.org/10.1029/2019JG005529, 2020.
Lu, Y., Jin, J., and Kueppers, L. M.: Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop), Clim. Dynam., 45, 3347–3363, https://doi.org/10.1007/s00382-015-2543-z, 2015.
Malek, K., Stöckle, C., Chinnayakanahalli, K., Nelson, R., Liu, M., Rajagopalan, K., Barik, M., and Adam, J. C.: VIC–CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions, Geosci. Model Dev., 10, 3059–3084, https://doi.org/10.5194/gmd-10-3059-2017, 2017.
McDermid, S., Nocco, M., Lawston-Parker, P., Keune, J., Pokhrel, Y., Jain, M., Jägermeyr, J., Brocca, L., Massari, C., Jones, A. D., Vahmani, P., Thiery, W., Yao, Y., Bell, A., Chen, L., Dorigo, W., Hanasaki, N., Jasechko, S., Lo, M.-H., Mahmood, R., Mishra, V., Mueller, N. D., Niyogi, D., Rabin, S. S., Sloat, L., Wada, Y., Zappa, L., Chen, F., Cook, B. I., Kim, H., Lombardozzi, D., Polcher, J., Ryu, D., Santanello, J., Satoh, Y., Seneviratne, S., Singh, D., and Yokohata, T.: Irrigation in the Earth system, Nature Reviews Earth & Environment, 4, 435–453, https://doi.org/10.1038/s43017-023-00438-5, 2023.
McDermid, S. S., Mearns, L. O., and Ruane, A. C.: Representing agriculture in Earth System Models: Approaches and priorities for development, J. Adv. Model. Earth Sy., 9, 2230–2265, https://doi.org/10.1002/2016MS000749, 2017.
Mehta, P., Siebert, S., Kummu, M., Deng, Q., Ali, T., Marston, L., Xie, W., and Davis, K. F.: Half of twenty-first century global irrigation expansion has been in water-stressed regions, Nature Water, 2, 254–261, https://doi.org/10.1038/s44221-024-00206-9, 2024.
Merriam, J. L., Burt, C. M., Clemmens, A. J., Solomon, K. H., Howell, T. A., and Strelkoff, T. S.: Irrigation Performance Measures: Efficiency and Uniformity, J. Irrig. Drain. E., 125, 97–99, https://doi.org/10.1061/(ASCE)0733-9437(1999)125:2(97), 1999.
Mieno, T., Foster, T., Kakimoto, S., and Brozović, N.: Aquifer depletion exacerbates agricultural drought losses in the US High Plains, Nature Water, 2, 41–51, https://doi.org/10.1038/s44221-023-00173-7, 2024.
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E., Trautmann, T., and Döll, P.: The global water resources and use model WaterGAP v2.2d: model description and evaluation, Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, 2021.
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019.
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.
Nair, S., Johnson, J., and Wang, C.: Efficiency of Irrigation Water Use: A Review from the Perspectives of Multiple Disciplines, Agron. J., 105, 351–363, https://doi.org/10.2134/agronj2012.0421, 2013.
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand, Hydrol. Earth Syst. Sci., 19, 33–61, https://doi.org/10.5194/hess-19-33-2015, 2015.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., and Gulden, L. E.: A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models, J. Geophys. Res.-Atmos., 110, D21106, https://doi.org/10.1029/2005JD006111, 2005.
Nocco, M. A., Smail, R. A., and Kucharik, C. J.: Observation of irrigation-induced climate change in the Midwest United States, Glob. Change Biol., 25, 3472–3484, https://doi.org/10.1111/gcb.14725, 2019.
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data, J. Hydrometeorol., 11, 171–184, https://doi.org/10.1175/2009JHM1116.1, 2010.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Phillips, C. E., Nair, U. S., Mahmood, R., Rappin, E., and Pielke, R. A.: Influence of Irrigation on Diurnal Mesoscale Circulations: Results From GRAINEX, Geophys. Res. Lett., 49, e2021GL096822, https://doi.org/10.1029/2021GL096822, 2022.
Pokhrel, Y., Hanasaki, N., Koirala, S., Cho, J., Yeh, P. J.-F., Kim, H., Kanae, S., and Oki, T.: Incorporating Anthropogenic Water Regulation Modules into a Land Surface Model, J. Hydrometeorol., 13, 255–269, https://doi.org/10.1175/JHM-D-11-013.1, 2012.
Pokhrel, Y. N., Hanasaki, N., Wada, Y., and Kim, H.: Recent progresses in incorporating human land–water management into global land surface models toward their integration into Earth system models, WIREs Water, 3, 548–574, https://doi.org/10.1002/wat2.1150, 2016.
Puma, M. J. and Cook, B. I.: Effects of irrigation on global climate during the 20th century, J. Geophys. Res.-Atmos., 115, D16120, https://doi.org/10.1029/2010JD014122, 2010.
Rappin, E. D., Mahmood, R., Nair, U. S., and Pielke Sr., R. A.: Land–Atmosphere Interactions during GRAINEX: Planetary Boundary Layer Evolution in the Presence of Irrigation, J. Hydrometeorol., 23, 1401–1417, https://doi.org/10.1175/JHM-D-21-0160.1, 2022.
Richards, L. A.: Capillary Conduction of Liquids Through Porous Mediums, Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Rodell, M. and Reager, J. T.: Water cycle science enabled by the GRACE and GRACE-FO satellite missions, Nat. Water, 1, 47–59, https://doi.org/10.1038/s44221-022-00005-0, 2023.
Ruess, P. J., Konar, M., Wanders, N., and Bierkens, M. F. P.: Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, 11, 395, https://doi.org/10.1038/s41597-024-03244-w, 2024.
Russo, T. A. and Lall, U.: Depletion and response of deep groundwater to climate-induced pumping variability, Nat. Geosci., 10, 105–108, https://doi.org/10.1038/ngeo2883, 2017.
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., and Helkowski, J. H.: Effects of global irrigation on the near-surface climate, Clim. Dynam., 33, 159–175, https://doi.org/10.1007/s00382-008-0445-z, 2009.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting dates: an analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620, https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010.
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B., Dankers, R., Eisner, S., Fekete, B. M., Colón-González, F. J., Gosling, S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y., Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water scarcity under climate change, P. Natl. Acad. Sci. USA, 111, 3245–3250, https://doi.org/10.1073/pnas.1222460110, 2014.
Schlosser, C. A., Strzepek, K., Gao, X., Fant, C., Blanc, É., Paltsev, S., Jacoby, H., Reilly, J., and Gueneau, A.: The future of global water stress: An integrated assessment, Earth's Future, 2, 341–361, https://doi.org/10.1002/2014EF000238, 2014.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation, J. Climate, 9, 676–705, https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2, 1996.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014a.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: The Global Soil Dataset for Earth System Modeling, The Land-Atmosphere Interaction Research Group [data set], http://globalchange.bnu.edu.cn/research/data/ (last access: July 2025), 2014b.
Shen, X., Sun, Y., Hu, J., Wang, J., Sun, J., Xue, J., Han, W., Zhang, H., Lu, H., Zhang, H., Chen, Q., Liu, Y., Liu, Q., Ma, Z., Jin. Z., Li, X., Liu, K., Zhao, B., Zhou, B., Gong, J., Chen D., and Wang, J.: Development and operation transformation of GRAPES global middle-range forecast system, J. Appl. Meteor. Sci., 28, 1–10, https://doi.org/10.11898/1001-7313.20170101, 2017.
Siebert, S. and Döll, P.: Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation, J. Hydrol., 384, 198–217, https://doi.org/10.1016/j.jhydrol.2009.07.031, 2010.
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880, https://doi.org/10.5194/hess-14-1863-2010, 2010.
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5, FAO [data set], https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version (last access: July 2025), 2013.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
Tang, Q., Oki, T., Kanae, S., and Hu, H.: The Influence of Precipitation Variability and Partial Irrigation within Grid Cells on a Hydrological Simulation, J. Hydrometeorol., 8, 499–512, https://doi.org/10.1175/JHM589.1, 2007.
Taranu, S. I., Lawrence, D. M., Wada, Y., Tang, T., Kluzek, E., Rabin, S., Yao, Y., De Hertog, S. J., Vanderkelen, I., and Thiery, W.: Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1, Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, 2024.
Thiery, W., Davin, E. L., Lawrence, D. M., Hirsch, A. L., Hauser, M., and Seneviratne, S. I.: Present-day irrigation mitigates heat extremes, J. Geophys. Res.-Atmos., 122, 1403–1422, https://doi.org/10.1002/2016JD025740, 2017.
USDA/NASS: National Agricultural Statistics Service Cropland Data Layer, Published crop-specific data layer, Washington, DC, USDA/NASS [data set], https://nassgeodata.gmu.edu/CropScape/ (last access: July 2025), 2019.
USDA/NASS: Census of Agriculture, USDA – National Agricultural Statistics Service, USDA/NASS [data set], https://quickstats.nass.usda.gov/ (last access: July 2025), 2023.
USGS: Water Use in the United States, USGS Water Data for the Nation, USGS [data set], https://water.usgs.gov/watuse/data/ (last access: July 2025), 2023.
van Vliet, M. T. H., Jones, E. R., Flörke, M., Franssen, W. H. P., Hanasaki, N., Wada, Y., and Yearsley, J. R.: Global water scarcity including surface water quality and expansions of clean water technologies, Environ. Res. Lett., 16, 024020, https://doi.org/10.1088/1748-9326/abbfc3, 2021.
Verma, S., Kumar, N., Verma, A., Singh, H., Siddique, K. H. M., and Singh, N. P.: Novel approaches to mitigate heat stress impacts on crop growth and development, Plant Physiology Reports, 25, 627–644, https://doi.org/10.1007/s40502-020-00550-4, 2020.
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., and Landerer, F. W.: Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120, 2648–2671, https://doi.org/10.1002/2014JB011547, 2015.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014a.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set, International Institute for Applied Systems Analysis [data set], ftp://rfdata:forceDATA@ftp.iiasa.ac.at (last access: July 2025), 2014b.
Wiese, D. N., Landerer, F. W., and Watkins, M. M.: Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, 7490–7502, https://doi.org/10.1002/2016WR019344, 2016.
Wisser, D., Fekete, B. M., Vörösmarty, C. J., and Schumann, A. H.: Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network- Hydrology (GTN-H), Hydrol. Earth Syst. Sci., 14, 1–24, https://doi.org/10.5194/hess-14-1-2010, 2010.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description of floodplain inundation dynamics in a global river routing model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019.
Yang, Y., Jin, Z., Mueller, N. D., Driscoll, A. W., Hernandez, R. R., Grodsky, S. M., Sloat, L. L., Chester, M. V., Zhu, Y.-G., and Lobell, D. B.: Sustainable irrigation and climate feedbacks, Nature Food, 4, 654–663, https://doi.org/10.1038/s43016-023-00821-x, 2023.
Yao, Y., Vanderkelen, I., Lombardozzi, D., Swenson, S., Lawrence, D., Jägermeyr, J., Grant, L., and Thiery, W.: Implementation and Evaluation of Irrigation Techniques in the Community Land Model, J. Adv. Model. Earth Sy., 14, e2022MS003074, https://doi.org/10.1029/2022MS003074, 2022.
Yigzaw, W., Li, H.-Y., Demissie, Y., Hejazi, M. I., Leung, L. R., Voisin, N., and Payn, R.: A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models, Water Resour. Res., 54, 10372–10386, https://doi.org/10.1029/2017WR022040, 2018a.
Yigzaw, W., Li, H., Demissie, Y., Hejazi, M. I., Leung, L. R., Voisin, N., and Payn, R.: Global Reservoir Geometry Database, Zenodo [data set], https://doi.org/10.5281/zenodo.1322884, 2018b.
Yuan, H., Dai, Y., Dickinson, R. E., Pinty, B., Shangguan, W., Zhang, S., Wang, L., and Zhu, S.: Reexamination and further development of two-stream canopy radiative transfer models for global land modeling, J. Adv. Model. Earth Sy., 9, 113–129, https://doi.org/10.1002/2016MS000773, 2017.
Yuan, X. and Liang, X.-Z.: Evaluation of a Conjunctive Surface–Subsurface Process Model (CSSP) over the Contiguous United States at Regional–Local Scales, J. Hydrometeorol., 12, 579–599, https://doi.org/10.1175/2010JHM1302.1, 2011.
Zeng, X. and Dickinson, R. E.: Effect of Surface Sublayer on Surface Skin Temperature and Fluxes, J. Climate, 11, 537–550, https://doi.org/10.1175/1520-0442(1998)011<0537:EOSSOS>2.0.CO;2, 1998.
Zhang, H., Zhang, M., Jin, J., Fei, K., Ji, D., Wu, C., Zhu, J., He, J., Chai, Z., Xie, J., Dong, X., Zhang, D., Bi, X., Cao, H., Chen, H., Chen, K., Chen, X., Gao, X., Hao, H., Jiang, J., Kong, X., Li, S., Li, Y., Lin, P., Lin, Z., Liu, H., Liu, X., Shi, Y., Song, M., Wang, H., Wang, T., Wang, X., Wang, Z., Wei, Y., Wu, B., Xie, Z., Xu, Y., Yu, Y., Yuan, L., Zeng, Q., Zeng, X., Zhao, S., Zhou, G., and Zhu, J.: Description and Climate Simulation Performance of CAS-ESM Version 2, J. Adv. Model. Earth Sy., 12, e2020MS002210, https://doi.org/10.1029/2020MS002210, 2020a.
Zhang, S., Liang, H., Li, F., Lu, X., and Dai, Y.: Representation of a two-way coupled irrigation system in the Common Land Model, Zenodo [data set], https://doi.org/10.5281/zenodo.15826886, 2024.
Zhang, Z., Barlage, M., Chen, F., Li, Y., Helgason, W., Xu, X., Liu, X., and Li, Z.: Joint Modeling of Crop and Irrigation in the central United States Using the Noah-MP Land Surface Model, J. Adv. Model. Earth Sy., 12, e2020MS002159, https://doi.org/10.1029/2020MS002159, 2020b.
Zhao, G. and Gao, H.: Automatic Correction of Contaminated Images for Assessment of Reservoir Surface Area Dynamics, Geophys. Res. Lett., 45, 6092–6099, https://doi.org/10.1029/2018GL078343, 2018.
Zhou, T., Leung, L. R., Leng, G., Voisin, N., Li, H.-Y., Craig, A. P., Tesfa, T., and Mao, Y.: Global Irrigation Characteristics and Effects Simulated by Fully Coupled Land Surface, River, and Water Management Models in E3SM, J. Adv. Model. Earth Sy., 12, e2020MS002069, https://doi.org/10.1029/2020MS002069, 2020.
Short summary
This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
This study enhances irrigation modeling in the Common Land Model by capturing the full...
