Articles | Volume 15, issue 1
https://doi.org/10.5194/esd-15-75-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-15-75-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2024
Research article |
| 29 Jan 2024
| 29 Jan 2024
Historical and projected future runoff over the Mekong River basin
Chao Wang
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Stephen Leisz
Department of Anthropology and Geography, Colorado State University, Fort Collins, CO 80523, USA
College of Arts and Sciences, VinUniversity, Ocean Park, Hanoi, Vietnam
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Xiaoying Shi
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Jiafu Mao
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA
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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.
Yaoping Wang, Daniel M. Ricciuto, Jiafu Mao, Sören E. Weber, Verity G. Salmon, Xiaoying Shi, Xiaojuan Yang, Natalie A. Griffiths, Paul J. Hanson, Anthony P. Walker, Jonathan Stelling, Katherine Duchesneau, Camille E. Defrenne, Jeffrey M. Warren, Stephen D. Sebestyen, Kyle J. Pearson, Keith Oleheiser, Joshua M. Birkebak, Mark Guilliams, Misha B. Krassovski, Melanie A. Mayes, and Peter E. Thornton
Geosci. Model Dev., 19, 5827–5855, https://doi.org/10.5194/gmd-19-5827-2026, https://doi.org/10.5194/gmd-19-5827-2026, 2026
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Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
Geosci. Model Dev., 19, 3427–3453, https://doi.org/10.5194/gmd-19-3427-2026, https://doi.org/10.5194/gmd-19-3427-2026, 2026
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Yue Li, Gang Tang, Eleanor O'Rourke, Samar Minallah, Martim Mas e Braga, Sophie Nowicki, Robin S. Smith, David M. Lawrence, George C. Hurtt, Daniele Peano, Gesa Meyer, Birgit Hassler, Jiafu Mao, Yongkang Xue, and Martin Juckes
Geosci. Model Dev., 19, 3129–3155, https://doi.org/10.5194/gmd-19-3129-2026, https://doi.org/10.5194/gmd-19-3129-2026, 2026
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Land and Land Ice Theme Opportunities describe a list that contains 25 variable groups with 716 variables, which are potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology.
Songjun Wu, Doerthe Tetzlaff, Yi Zheng, and Chris Soulsby
Geosci. Model Dev., 19, 2257–2278, https://doi.org/10.5194/gmd-19-2257-2026, https://doi.org/10.5194/gmd-19-2257-2026, 2026
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We developed EcoTWIN v1.0, a new fully distributed tracer-aided ecohydrological model that tracks water, isotopes, and nutrients fluxes. The model was successfully tested in 17 large European catchments across diverse geological and climatic backgrounds. As a tracer-aided model, EcoTWIN not only captures flow paths but also estimates water ages/travel times, thus bridging hydrology with water quality. This opens new possibilities for understanding the synergy between water and nitrogen cycles.
Bin Yu, Yanan Chen, and Yi Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2026-1098, https://doi.org/10.5194/egusphere-2026-1098, 2026
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We introduce a deep learning approach that extracts shared operating patterns from diverse, data-rich reservoirs and transfers this knowledge to limited-data areas, yielding skillful simulations of storage and release. Using satellite observations, the model also reconstructs historical behavior in no-data systems. Together, these advances provide a scalable foundation for addressing pressing data gaps in reservoir operations and better supporting large-scale and long-term water management.
Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
Geosci. Model Dev., 18, 9497–9540, https://doi.org/10.5194/gmd-18-9497-2025, https://doi.org/10.5194/gmd-18-9497-2025, 2025
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Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci., 29, 6393–6417, https://doi.org/10.5194/hess-29-6393-2025, https://doi.org/10.5194/hess-29-6393-2025, 2025
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Lena Wang, Sharon A. Billings, Li Li, Daniel R. Hirmas, Keira Johnson, Devon Kerins, Julio Pachon, Curtis Beutler, Karla M. Jarecke, Vaishnavi Varikuti, Micah Unruh, Hoori Ajami, Holly Barnard, Alejandro N. Flores, Kenneth Williams, and Pamela L. Sullivan
Biogeosciences, 22, 6097–6117, https://doi.org/10.5194/bg-22-6097-2025, https://doi.org/10.5194/bg-22-6097-2025, 2025
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Kachinga Silwimba, Alejandro N. Flores, Irene Cionni, Sharon A. Billings, Pamela L. Sullivan, Hoori Ajami, Daniel R. Hirmas, and Li Li
Geosci. Model Dev., 18, 7707–7734, https://doi.org/10.5194/gmd-18-7707-2025, https://doi.org/10.5194/gmd-18-7707-2025, 2025
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Land models need reliable soil properties to simulate water, but these settings are uncertain. We analyzed Community Land Model version 5 simulations for the United States from 1980 to 2010 to see how different soil settings shape patterns of soil moisture. Compared with an independent global land dataset, patterns align in many regions but differ in water-limited areas such as the Great Plains. Our maps show where to improve settings and guide future tests with observations.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The loss and gain of surface water (variable inundation) are common processes across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review the literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
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Gary Sterle, Julia Perdrial, Dustin W. Kincaid, Kristen L. Underwood, Donna M. Rizzo, Ijaz Ul Haq, Li Li, Byung Suk Lee, Thomas Adler, Hang Wen, Helena Middleton, and Adrian A. Harpold
Hydrol. Earth Syst. Sci., 28, 611–630, https://doi.org/10.5194/hess-28-611-2024, https://doi.org/10.5194/hess-28-611-2024, 2024
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We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. The newly developed dataset, termed CAMELS-Chem, includes common stream water chemistry constituents and wet deposition chemistry in 516 catchments. Examples show the value of CAMELS-Chem to trend and spatial analyses, as well as its limitations in sampling length and consistency.
Wei Zhi, Yuning Shi, Hang Wen, Leila Saberi, Gene-Hua Crystal Ng, Kayalvizhi Sadayappan, Devon Kerins, Bryn Stewart, and Li Li
Geosci. Model Dev., 15, 315–333, https://doi.org/10.5194/gmd-15-315-2022, https://doi.org/10.5194/gmd-15-315-2022, 2022
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Watersheds are the fundamental Earth surface functioning unit that connects the land to aquatic systems. Here we present the recently developed BioRT-Flux-PIHM v1.0, a watershed-scale biogeochemical reactive transport model, to improve our ability to understand and predict solute export and water quality. The model has been verified against the benchmark code CrunchTope and has recently been applied to understand reactive transport processes in multiple watersheds of different conditions.
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Short summary
Climate change can significantly impact river runoff; however, predicting future runoff is challenging. Using historical runoff gauge data to evaluate model performances in runoff simulations for the Mekong River, we quantify future runoff changes in the Mekong River with the best simulation combination. Results suggest a significant increase in the annual runoff, along with varied seasonal distributions, thus heightening the need for adapted water resource management measures.
Climate change can significantly impact river runoff; however, predicting future runoff is...
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