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.
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
Yi Zheng
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
Related authors
No articles found.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3207, https://doi.org/10.5194/egusphere-2025-3207, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3408, https://doi.org/10.5194/egusphere-2025-3408, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
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
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Kachinga Silwimba, Alejandro N. Flores, Irene Cionni, Sharon A. Billings, Pamela L. Sullivan, Hoori Ajami, Daniel R. Hirmas, and Li Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-713, https://doi.org/10.5194/egusphere-2025-713, 2025
Short summary
Short summary
This study evaluates the influence of soil hydraulic parameterizations on soil moisture simulations in CLM5 across the CONUS (1980–2010) using Empirical Orthogonal Function (EOF) analysis. Results reveal significant regional discrepancies, particularly in the Great Plains, where parameter uncertainty drives biases in soil moisture variability. Comparisons with ERA5-Land highlight seasonal mismatches, underscoring the need for improved soil parameterization to enhance land surface model accuracy.
James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
Biogeosciences, 22, 995–1034, https://doi.org/10.5194/bg-22-995-2025, https://doi.org/10.5194/bg-22-995-2025, 2025
Short summary
Short summary
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.
Lena Wang, Sharon Billings, Li Li, Daniel Hirmas, Keira Johnson, Devon Kerins, Julio Pachon, Curtis Beutler, Karla Jarecke, Vaishnavi Varikuti, Micah Unruh, Hoori Ajami, Holly Barnard, Alejandro Flores, Kenneth Williams, and Pamela Sullivan
EGUsphere, https://doi.org/10.5194/egusphere-2025-70, https://doi.org/10.5194/egusphere-2025-70, 2025
Short summary
Short summary
Our study looked at how different forest types and conditions affected soil microbes, and soil carbon and stability. Aspen organic matter led to higher microbial activity, smaller soil aggregates, and more stable soil carbon, possibly reducing dissolved organic carbon movement from hillslopes to streams. This shows the importance of models like the Microbial Efficiency – Matrix Stabilization framework for predicting CO2 release, soil carbon stability, and carbon movement.
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. Discuss., https://doi.org/10.5194/hess-2024-340, https://doi.org/10.5194/hess-2024-340, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We explore tile drainage’ impacts on the integrated hydrology-biogeochemistry-plant system, using ecosys with soil oxygen and microbe dynamics. We found that tile drainage lowers soil water content and improves soil oxygen levels, which helps crops grow better, especially during wet springs, and the developed root system also helps mitigate drought stress on dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.
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
Short summary
Short summary
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
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
Short summary
Short summary
Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
Short summary
Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Hang Wen, Pamela L. Sullivan, Gwendolyn L. Macpherson, Sharon A. Billings, and Li Li
Biogeosciences, 18, 55–75, https://doi.org/10.5194/bg-18-55-2021, https://doi.org/10.5194/bg-18-55-2021, 2021
Short summary
Short summary
Carbonate weathering is essential in regulating carbon cycle at the century timescale. Plant roots accelerate weathering by elevating soil CO2 via respiration. It however remains poorly understood how and how much rooting characteristics modify flow paths and weathering. This work indicates that deepening roots in woodlands can enhance carbonate weathering by promoting recharge and CO2–carbonate contact in the deep, carbonate-abundant subsurface.
Cited articles
Adamson, P. T., Rutherfurd, I. D., Peel, M. C., and Conlan, I. A.: The hydrology of the Mekong River, in: The Mekong, edited by: Campbell, I. C., Elsevier, 53–76, https://doi.org/10.1016/B978-0-12-374026-7.00004-8, 2009. a
Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., and Siebert, S.: Development and testing of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J., 48, 317–337, 2003. a
Arnell, N. W. and Gosling, S. N.: The impacts of climate change on river flood risk at the global scale, Climatic Change, 134, 387–401, 2016. a
Arnell, N. W., van Vuuren, D. P., and Isaac, M.: The implications of climate policy for the impacts of climate change on global water resources, Global Environ. Chang., 21, 592–603, 2011. a
Bihrat, Ö. and Bayazit, M.: The power of statistical tests for trend detection, Turkish Journal of Engineering and Environmental Sciences, 27, 247–251, 2003. a
Brient, F.: Reducing uncertainties in climate projections with emergent constraints: concepts, examples and prospects, Adv. Atmos. Sci., 37, 1–15, 2020. a
Chen, H., Liu, J., Mao, G., Wang, Z., Zeng, Z., Chen, A., Wang, K., and Chen, D.: Intercomparison of ten ISI-MIP models in simulating discharges along the Lancang-Mekong River basin, Sci. Total Environ., 765, 144494, https://doi.org/10.1016/j.scitotenv.2020.144494, 2021. a, b, c, d
Cochrane, T. A., Arias, M. E., and Piman, T.: Historical impact of water infrastructure on water levels of the Mekong River and the Tonle Sap system, Hydrol. Earth Syst. Sci., 18, 4529–4541, https://doi.org/10.5194/hess-18-4529-2014, 2014. a
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Climate Change 2013: The Physical Science Basis, IPCC Working Group I Contribution to AR5, Chapter 12 – Long-term climate change: Projections, commitments and irreversibility, IPCC, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107415324.024, 2013. a
Eyler, B.: Last days of the mighty Mekong, Bloomsbury Publishing, Zed Books Ltd, London, https://doi.org/10.5040/9781350221031, 2019. a, b
Field, C. B. and Barros, V. R.: Climate Change 2014–Impacts, Adaptation and Vulnerability: Global and Sectoral Aspects, Cambridge University Press, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107415386, 2014. a
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 ∘C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017. a
Giuntoli, I., Vidal, J.-P., Prudhomme, C., and Hannah, D. M.: Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models, Earth Syst. Dynam., 6, 267–285, https://doi.org/10.5194/esd-6-267-2015, 2015. a
Gosling, S. N. and Arnell, N. W.: Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis, Hydrol. Process., 25, 1129–1145, 2011. a
Guan, X., Zhang, J., Bao, Z., Liu, C., Jin, J., and Wang, G.: Past variations and future projection of runoff in typical basins in 10 water zones, China, Sci. Total Environ., 798, 149277, https://doi.org/10.1016/j.scitotenv.2021.149277, 2021. a, b, c
Hagemann, S., Chen, C., Clark, D. B., Folwell, S., Gosling, S. N., Haddeland, I., Hanasaki, N., Heinke, J., Ludwig, F., Voss, F., and Wiltshire, A. J.: Climate change impact on available water resources obtained using multiple global climate and hydrology models, Earth Syst. Dynam., 4, 129–144, https://doi.org/10.5194/esd-4-129-2013, 2013. a
Hall, A., Cox, P., Huntingford, C., and Klein, S.: Progressing emergent constraints on future climate change, Nat. Clim. Change, 9, 269–278, 2019. a
Hamed, K. H.: Improved finite-sample Hurst exponent estimates using rescaled range analysis, Water Resour. Res., 43, https://doi.org/10.1029/2006WR005111, 2007. a
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. a
Hoang, L. P., Lauri, H., Kummu, M., Koponen, J., van Vliet, M. T. H., Supit, I., Leemans, R., Kabat, P., and Ludwig, F.: Mekong River flow and hydrological extremes under climate change, Hydrol. Earth Syst. Sci., 20, 3027–3041, https://doi.org/10.5194/hess-20-3027-2016, 2016. a, b
Hoang, L. P., van Vliet, M. T., Kummu, M., Lauri, H., Koponen, J., Supit, I., Leemans, R., Kabat, P., and Ludwig, F.: The Mekong's future flows under multiple drivers: How climate change, hydropower developments and irrigation expansions drive hydrological changes, Sci. Total Environ., 649, 601–609, 2019. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, vol. in press, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021. a, b
Johnston, R. and Kummu, M.: Water resource models in the Mekong Basin: a review, Water Resour. Manag., 26, 429–455, 2012. a
Kingston, D. G., Thompson, J. R., and Kite, G.: Uncertainty in climate change projections of discharge for the Mekong River Basin, Hydrol. Earth Syst. Sci., 15, 1459–1471, https://doi.org/10.5194/hess-15-1459-2011, 2011. a, b
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A climate model projection weighting scheme accounting for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, 2017. a
Lauri, H., de Moel, H., Ward, P. J., Räsänen, T. A., Keskinen, M., and Kummu, M.: Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge, Hydrol. Earth Syst. Sci., 16, 4603–4619, https://doi.org/10.5194/hess-16-4603-2012, 2012. a
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, 2015. a
Baiyinbaoligao, Liu, H., Chen, X., and Mu, X.: Overview of the Mekong River Basin, in: Flood Prevention and Drought Relief in Mekong River Basin, edited by: Liu, H., Springer Singapore, Singapore, 1–25, https://doi.org/10.1007/978-981-15-2006-8_1, 2020. a
Liu, J., Chen, D., Mao, G., Irannezhad, M., and Pokhrel, Y.: Past and future changes in climate and water resources in the lancang–mekong River Basin: Current understanding and future research directions, Engineering, 13, 144–152, 2022. a
Lu, X. X. and Siew, R. Y.: Water discharge and sediment flux changes over the past decades in the Lower Mekong River: possible impacts of the Chinese dams, Hydrol. Earth Syst. Sci., 10, 181–195, https://doi.org/10.5194/hess-10-181-2006, 2006. a
Lv, X., Zuo, Z., Ni, Y., Sun, J., and Wang, H.: The effects of climate and catchment characteristic change on streamflow in a typical tributary of the Yellow River, Sci. Rep.-UK, 9, 14535, https://doi.org/10.1038/s41598-019-51115-x, 2019. a
Milly, P. C., Dunne, K. A., and Vecchia, A. V.: Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, 347–350, 2005. a
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, 2016. a
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, 2014. a
Ruiz-Barradas, A. and Nigam, S.: Hydroclimate variability and change over the Mekong River basin: Modeling and predictability and policy implications, J. Hydrometeorol., 19, 849–869, 2018. a
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, 2014. a
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020. a, b
Shiogama, H., Watanabe, M., Kim, H., and Hirota, N.: Emergent constraints on future precipitation changes, Nature, 602, 612–616, 2022. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., and Thonicke, K.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003. a
Takata, K., Emori, S., and Watanabe, T.: Development of the minimal advanced treatments of surface interaction and runoff, Global Planet. Change, 38, 209–222, 2003. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Tran, D. D., van Halsema, G., Hellegers, P. J., Ludwig, F., and Wyatt, A.: Questioning triple rice intensification on the Vietnamese mekong delta floodplains: An environmental and economic analysis of current land-use trends and alternatives, J. Environ. Manage., 217, 429–441, 2018. a
Ul Hasson, S., Pascale, S., Lucarini, V., and Böhner, J.: Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections, Atmos. Res., 180, 42–63, 2016. a
Wang, F., Shao, W., Yu, H., Kan, G., He, X., Zhang, D., Ren, M., and Wang, G.: Re-evaluation of the power of the mann-kendall test for detecting monotonic trends in hydrometeorological time series, Front. Earth Sci., 8, 14, https://doi.org/10.3389/feart.2020.00014, 2020. a
Wang, W., Lu, H., Ruby Leung, L., Li, H.-Y., Zhao, J., Tian, F., Yang, K., and Sothea, K.: Dam construction in Lancang-Mekong River Basin could mitigate future flood risk from warming-induced intensified rainfall, Geophys. Res. Lett., 44, 10–378, 2017. a
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The inter-sectoral impact model intercomparison project (ISI–MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, 2014. a
Yun, X., Tang, Q., Wang, J., Liu, X., Zhang, Y., Lu, H., Wang, Y., Zhang, L., and Chen, D.: Impacts of climate change and reservoir operation on streamflow and flood characteristics in the Lancang-Mekong River Basin, J. Hydrol., 590, 125472, https://doi.org/10.1016/j.jhydrol.2020.125472, 2020. a
Yun, X., Tang, Q., Sun, S., and Wang, J.: Reducing Climate Change Induced Flood at the Cost of Hydropower in the Lancang-Mekong River Basin, Geophys. Res. Lett., 48, e2021GL094243, https://doi.org/10.1029/2021GL094243, 2021. a
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...
Altmetrics
Final-revised paper
Preprint