Articles | Volume 10, issue 12
https://doi.org/10.5194/gmd-10-4539-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-4539-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Coupling a three-dimensional subsurface flow and transport model with a land surface model to simulate stream–aquifer–land interactions (CP v1.0)
Gautam Bisht
Earth & Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Tian Zhou
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Xingyuan Chen
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Heng Dai
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Glenn E. Hammond
Applied Systems Analysis and Research Department, Sandia National Laboratories, Albuquerque, NM, USA
William J. Riley
Earth & Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Janelle L. Downs
Earth Systems Science Division, Pacific Northwest National Laboratory, Richland, WA, USA
Ying Liu
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
John M. Zachara
Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Related authors
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025, https://doi.org/10.5194/hess-29-3833-2025, 2025
Short summary
Short summary
Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
Short summary
Short summary
Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
Short summary
Short summary
Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
Short summary
Short summary
The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025, https://doi.org/10.5194/hess-29-3833-2025, 2025
Short summary
Short summary
Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Mingjie Shi, Nate McDowell, Huilin Huang, Faria Zahura, Lingcheng Li, and Xingyuan Chen
Biogeosciences, 22, 2225–2238, https://doi.org/10.5194/bg-22-2225-2025, https://doi.org/10.5194/bg-22-2225-2025, 2025
Short summary
Short summary
Using Moderate Resolution Imaging Spectroradiometer data products, we quantitatively estimate the resistance and resilience of ecosystem functions to wildfires that occurred in the Columbia River basin in 2015. The carbon state exhibits lower resistance and resilience than the ecosystem fluxes. The random forest feature importance analysis indicates that burn severity plays a minor role in the resilience of grassland and a relatively major role in the resilience of forest and savanna.
Elsa Abs, Christoph Keuschnig, Pierre Amato, Chris Bowler, Eric Capo, Alexander Chase, Luciana Chavez Rodriguez, Abraham Dabengwa, Thomas Dussarrat, Thomas Guzman, Linnea Honeker, Jenni Hultman, Kirsten Küsel, Zhen Li, Anna Mankowski, William Riley, Scott Saleska, and Lisa Wingate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1716, https://doi.org/10.5194/egusphere-2025-1716, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Meta-omics technologies offer new tools to understand how microbial and plant functional diversity shape biogeochemical cycles across ecosystems. This perspective explores how integrating omics data with ecological and modeling approaches can improve our understanding of greenhouse gas fluxes and nutrient dynamics, from soils to clouds, and from the past to the future. We highlight challenges and opportunities for scaling omics insights from local processes to Earth system models.
Jinyun Tang and William J. Riley
Biogeosciences, 22, 1809–1819, https://doi.org/10.5194/bg-22-1809-2025, https://doi.org/10.5194/bg-22-1809-2025, 2025
Short summary
Short summary
A new mathematical formulation of the dynamic energy budget model is presented for the growth of biological organisms. This new formulation combines mass conservation law and chemical kinetics theory and is computationally faster than the standard formulation of dynamic energy budget models. In simulating the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the new model is as good as the standard dynamic energy budget model using almost the same parameter values.
Ashley Brereton, Zelalem Mekonnen, Bhavna Arora, William Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler Anthony, and Adina Paytan
EGUsphere, https://doi.org/10.5194/egusphere-2025-361, https://doi.org/10.5194/egusphere-2025-361, 2025
Short summary
Short summary
Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
Maggi M. Laan, Stephanie G. Fulton, Vanessa A. Garayburu-Caruso, Morgan E. Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Amy E. Goldman, Samantha Grieger, Robert O. Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin L. M. Zionce, Sophia A. McKever, Allison Myers-Pigg, Opal Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1109, https://doi.org/10.5194/egusphere-2025-1109, 2025
Short summary
Short summary
Respiration is a process that combines carbon and oxygen to generate energy for living organisms. Within a river, respiration in sediments and water have variable contributions to respiration of the whole river system. Contrary to conventional wisdom, we found that water column respiration did not increase systematically moving from small streams to big rivers. Instead, it was locally influenced by temperature, nutrients and suspended solids.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
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.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
Short summary
A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Stephanie G. Fulton, Morgan Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Samantha Grieger, Robert Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin McCann, Sophia A. McKever, Allison Myers-Pigg, Opal C. Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3038, https://doi.org/10.5194/egusphere-2023-3038, 2024
Preprint archived
Short summary
Short summary
This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
Short summary
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
Short summary
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
Short summary
Short summary
Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
Short summary
Short summary
High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
Short summary
Short summary
Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
Short summary
Short summary
The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
Geosci. Model Dev., 15, 3941–3967, https://doi.org/10.5194/gmd-15-3941-2022, https://doi.org/10.5194/gmd-15-3941-2022, 2022
Short summary
Short summary
We document the model experiments for the future climate projection by E3SMv1.0. At the highest future emission scenario, E3SMv1.0 projects a strong surface warming with rapid changes in the atmosphere, ocean, sea ice, and land runoff. Specifically, we detect a significant polar amplification and accelerated warming linked to the unmasking of the aerosol effects. The impact of greenhouse gas forcing is examined in different climate components.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
Short summary
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
Short summary
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
Short summary
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Jinyun Tang, William J. Riley, and Qing Zhu
Geosci. Model Dev., 15, 1619–1632, https://doi.org/10.5194/gmd-15-1619-2022, https://doi.org/10.5194/gmd-15-1619-2022, 2022
Short summary
Short summary
We here describe version 2 of BeTR, a reactive transport model created to help ease the development of biogeochemical capability in Earth system models that are used for quantifying ecosystem–climate feedbacks. We then coupled BeTR-v2 to the Energy Exascale Earth System Model to quantify how different numerical couplings of plants and soils affect simulated ecosystem biogeochemistry. We found that different couplings lead to significant uncertainty that is not correctable by tuning parameters.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
Short summary
Short summary
We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Jing Tao, Qing Zhu, William J. Riley, and Rebecca B. Neumann
The Cryosphere, 15, 5281–5307, https://doi.org/10.5194/tc-15-5281-2021, https://doi.org/10.5194/tc-15-5281-2021, 2021
Short summary
Short summary
We improved the DOE's E3SM land model (ELMv1-ECA) simulations of soil temperature, zero-curtain period durations, cold-season CH4, and CO2 emissions at several Alaskan Arctic tundra sites. We demonstrated that simulated CH4 emissions during zero-curtain periods accounted for more than 50 % of total emissions throughout the entire cold season (Sep to May). We also found that cold-season CO2 emissions largely offset warm-season net uptake currently and showed increasing trends from 1950 to 2017.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
Short summary
Short summary
Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences, 17, 6185–6205, https://doi.org/10.5194/bg-17-6185-2020, https://doi.org/10.5194/bg-17-6185-2020, 2020
Short summary
Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
Biogeosciences, 17, 5849–5860, https://doi.org/10.5194/bg-17-5849-2020, https://doi.org/10.5194/bg-17-5849-2020, 2020
Short summary
Short summary
Methane (CH4) is a strong greenhouse gas that can accelerate climate change and offset mitigation efforts. A key assumption embedded in many large-scale climate models is that ecosystem CH4 emissions can be estimated by fixed temperature relations. Here, we demonstrate that CH4 emissions cannot be parameterized by emergent temperature response alone due to variability driven by microbial and abiotic interactions. We also provide mechanistic understanding for observed CH4 emission hysteresis.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023, https://doi.org/10.5194/bg-17-4999-2020, https://doi.org/10.5194/bg-17-4999-2020, 2020
Short summary
Short summary
The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Cited articles
Arnqvist, J. and Bergström, H.: Flux-profile relation with roughness sublayer correction, Q. J. Roy. Meteor. Soc., 141, 1191–1197, https://doi.org/10.1002/qj.2426, 2015.
Balay, S., Brown, J., Buschelman, K., Eijkhout, V., Gropp, W. D., Kaushik, D., Knepley, M. G., McInnes, L. C., Smith, B. F., and Zhang, H.: PETSc Users Manual, Tech. Rep. ANL-95/11 – Revision 3.5 Rep., Argonne National Laboratory, Lemont, Il, USA, 2015.
Basu, S. and Lacser, A.: A Cautionary Note on the Use of Monin–Obukhov Similarity Theory in Very High-Resolution Large-Eddy Simulations, Bound.-Lay. Meteorol., 163, 351–355, https://doi.org/10.1007/s10546-016-0225-y, 2017.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Bjornstad, B. N.: On the Trail of the Ice Age Floods: A Geological Field Guide to the Mid-Columbian Basin, KeoKee, Sandpoint, ID, USA, 2007.
Burdine, N. T.: Relative Permeability Calculations From Pore Size Distribution Data, Journal of Petroleum Technology, 5, SPE-225-G, https://doi.org/10.2118/225-G, 1953.
Chen, X., Murakami, H., Hahn, M. S., Hammond, G. E., Rockhold, M. L., Zachara, J. M., and Rubin, Y.: Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data, Water Resour. Res., 48, W06501, https://doi.org/10.1029/2011WR010675, 2012.
Chen, X., Hammond, G. E., Murray, C. J., Rockhold, M. L., Vermeul, V. R., and Zachara, J. M.: Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area, Water Resour. Res., 49, 7064–7076, https://doi.org/10.1002/2012WR013285, 2013.
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M., Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015.
Coleman, A., Larson, K., Ward, D., and Lettrick, J.: Development of a High-Resolution Bathymetry Dataset for the Columbia River through the Hanford Reach Rep. PNNL-19878, Pacific Northwest National Laboratory, Richland, WA, USA, 2010.
Condon, L. E., Maxwell, R. M., and Gangopadhyay, S.: The impact of subsurface conceptualization on land energy fluxes, Adv. Water Resour., 60, 188–203, https://doi.org/10.1016/j.advwatres.2013.08.001, 2013.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
Elsner, M. M., Cuo, L., Voisin, N., Deems, J. S., Hamlet, A. F., Vano, J. A., Mickelson, K. E. B., Lee, S. Y., and Lettenmaier, D. P.: Implications of 21st century climate change for the hydrology of Washington State, Climatic Change, 102, 225–260, https://doi.org/10.1007/s10584-010-9855-0, 2010.
Fan, Y. and Miguez-Macho, G.: A simple hydrologic framework for simulating wetlands in climate and earth system models, Clim. Dynam., 37, 253–278, https://doi.org/10.1007/s00382-010-0829-8, 2011.
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, 2013.
Fang, Y., Leung, L. R., Duan, Z., Wigmosta, M. S., Maxwell, R. M., Chambers, J. Q., and Tomasella, J.: Influence of landscape heterogeneity on water available to tropical forests in an Amazonian catchment and implications for modeling drought response, J. Geophys. Res.-Atmos., 122, 8410–8426, https://doi.org/10.1002/2017JD027066, 2017.
Fischer, H., Kloep, F., Wilzcek, S., and Pusch, M. T.: A river's liver – microbial processes within the hyporheic zone of a large lowland river, Biogeochemistry, 76, 349–371, 2005.
Gaillardet, J., Regnier, P., Lauerwald, R., and Ciais, P.: Geochemistry of the Earth's surface GES-10 Paris France, 18–23 August, 2014, Carbon Leakage through the Terrestrial-aquatic Interface: Implications for the Anthropogenic CO2 Budget, Proced. Earth Plan. Sc., 10, 319–324, https://doi.org/10.1016/j.proeps.2014.08.025, 2014.
Gao, Z., Russell, E. S., Missik, J. E. C., Huang, M., Chen, X., Strickland, C. E., Clayton, R., Arntzen, E., Ma, Y., and Liu, H.: A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods, J. Geophys. Res.-Atmos., 122, 6934–6949, https://doi.org/10.1002/2017JD027160, 2017.
Gebler, S., Hendricks Franssen, H. J., Kollet, S. J., Qu, W., and Vereecken, H.: High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data, J. Hydrol., 547, 309–331, https://doi.org/10.1016/j.jhydrol.2017.01.048, 2017.
Gilbert, J. M., Maxwell, R. M., and Gochis, D. J.: Effects of Water-Table Configuration on the Planetary Boundary Layer over the San Joaquin River Watershed, California, J. Hydrometeorol., 18, 1471–1488, https://doi.org/10.1175/jhm-d-16-0134.1, 2017.
Hamlet, A. F. and Lettenmaier, D. P.: Effects of climate change on hydrology and water resources in the Columbia River basin, J. Am. Water Resour. As., 35, 1597–1623, 1999.
Hammond, G. E. and Lichtner, P. C.: Field-scale model for the natural attenuation of uranium at the Hanford 300 Area using high-performance computing, Water Resour. Res., 46, W09527, https://doi.org/10.1029/2009WR008819, 2010.
Hammond, G. E., Lichtner, P. C., and Rockhold, M. L.: Stochastic simulation of uranium migration at the Hanford 300 Area, J. Contam. Hydrol., 120–121, 115–128, https://doi.org/10.1016/j.jconhyd.2010.04.005, 2011.
Hammond, G. E., Lichtner, P. C., and Mills, R. T.: Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN, Water Resour. Res., 50, 208–228, https://doi.org/10.1002/2012WR013483, 2014.
Harvey, J. and Gooseff, M.: River corridor science: Hydrologic exchange and ecological consequences from bedforms to basins, Water Resour. Res., 51, 6893–6922, https://doi.org/10.1002/2015WR017617, 2015.
Hauer, C., Siviglia, A., and Zolezzi, G.: Hydropeaking in regulated rivers – From process understanding to design of mitigation measures, Sci. Total Environ., 579, 22–26, https://doi.org/10.1016/j.scitotenv.2016.11.028, 2017.
Hou, Z., Huang, M., Leung, L. R., Lin, G., and Ricciuto, D. M.: Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model, J. Geophys. Res.-Atmos., 117, D15108, https://doi.org/10.1029/2012JD017521, 2012.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/bams-d-12-00121.1, 2013.
Ji, X., Shen, C., and Riley, W. J.: Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow, Adv. Water Resour., 86, 155–169, https://doi.org/10.1016/j.advwatres.2015.09.027, 2015.
Karra, S., Painter, S. L., and Lichtner, P. C.: Three-phase numerical model for subsurface hydrology in permafrost-affected regions (PFLOTRAN-ICE v1.0), The Cryosphere, 8, 1935–1950, https://doi.org/10.5194/tc-8-1935-2014, 2014.
Keune, J., Gasper, F., Goergen, K., Hense, A., Shrestha, P., Sulis, M., and Kollet, S.: Studying the influence of groundwater representations on land surface-atmosphere feedbacks during the European heat wave in 2003, J. Geophys. Res.-Atmos., 121, 13301–13325, https://doi.org/10.1002/2016JD025426, 2016.
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model, Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007WR006004, 2008.
Kollet, S. J., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler, C., Park, Y.-J., Refsgaard, J. C., Stisen, S., and Sudicky, E.: The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 53, 867–890, https://doi.org/10.1002/2016WR019191, 2017.
Kumar, J., Collier, N., Bisht, G., Mills, R. T., Thornton, P. E., Iversen, C. M., and Romanovsky, V.: Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape, The Cryosphere, 10, 2241–2274, https://doi.org/10.5194/tc-10-2241-2016, 2016.
Lawrence, P. J., Feddema, J. J., Bonan, G. B., Meehl, G. A., O'Neill, B. C., Oleson, K. W., Levis, S., Lawrence, D. M., Kluzek, E., Lindsay, K., and Thornton, P. E.: Simulating the Biogeochemical and Biogeophysical Impacts of Transient Land Cover Change and Wood Harvest in the Community Climate System Model (CCSM4) from 1850 to 2100, J. Climate, 25, 3071–3095, https://doi.org/10.1175/jcli-d-11-00256.1, 2012.
Lei, H., Huang, M., Leung, L. R., Yang, D., Shi, X., Mao, J., Hayes, D. J., Schwalm, C. R., Wei, Y., and Liu, S.: Sensitivity of global terrestrial gross primary production to hydrologic states simulated by the Community Land Model using two runoff parameterizations, J. Adv. Model. Earth Sys., 6, 658–679, 2014.
Leng, G., Huang, M., Voisin, N., Zhang, X., Asrar, G. R., and Leung, L. R.: Emergence of new hydrologic regimes of surface water resources in the conterminous United States under future warming, Environ. Res. Lett., 11, 114003, https://doi.org/10.1088/1748-9326/11/11/114003, 2016a.
Leng, G., Zhang, X., Huang, M., Yang, Q., Rafique, R., Asrar, G. R., and Leung, L. R.: Simulating county-level crop yields in the conterminous United States using the community land model: The effects of optimizing irrigation and fertilization, J. Adv. Model. Earth Sys., 8, 1912–1931, https://doi.org/10.1002/2016MS000645, 2016b.
Leung, L. R., Huang, M., Qian, Y., and Liang, X.: Climate–soil–vegetation control on groundwater table dynamics and its feedbacks in a climate model, Clim. Dynam., 36, 57–81, 2011.
Lewis, J. M.: The Story behind the Bowen Ratio, B. Am. Meteorol. Soc., 76, 2433–2443, https://doi.org/10.1175/1520-0477(1995)076<2433:tsbtbr>2.0.co;2, 1995.
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.
Lichtner, P. C. and Hammond, G. E.: Using High Performance Computing to Understand Roles of Labile and Nonlabile Uranium(VI) on Hanford 300 Area Plume Longevity, Vadose Zone J., 11, 0097, https://doi.org/10.2136/vzj2011.0097, 2012.
Lichtner, P. C., Hammond, G. E., Lu, C., Karra, S., Bisht, G., Andre, B., Mills, R. T., and Jitu, K.: PFLOTRAN User Manual: a Massively Parallel Reactive Flow and Transport Model for Describing Surface and Subsurface Processes, Rep., Los Alamos National Laboratory, Los Alamos, NM, USA, 2015.
Liu, Y., Bisht, G., Subin, Z. M., Riley, W. J., and Pau, G. S. H.: A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site, Vadose Zone J., 15, 0068, https://doi.org/10.2136/vzj2015.05.0068, 2016.
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016.
Maxwell, R. M. and Kollet, S. J.: Interdependence of groundwater dynamics and land-energy feedbacks under climate change, Nat. Geosci., 1, 665–669, 2008.
Maxwell, R. M. and Miller, N. L.: Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 6, 233–247, https://doi.org/10.1175/JHM422.1, 2005.
Maxwell, R. M., Putti, M., Meyerhoff, S.,Delfs, J.-O., Ferguson, I. M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A., and Sulis, M.: Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 50, 1531–1549, https://doi.org/10.1002/2013WR013725, 2014.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
McNamara, J. P., Chandler, D., Seyfried, M., and Achet, S.: Soil moisture states, lateral flow, and streamflow generation in a semi-arid, snowmelt-driven catchment, Hydrol. Process., 19, 4023–4038, https://doi.org/10.1002/hyp.5869, 2005.
Miguez-Macho, G. and Fan, Y.: The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands, J. Geophys. Res.-Atmos., 117, D15113, https://doi.org/10.1029/2012JD017539, 2012.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nir, Y. K., Haibin, L., and Ying, F.: Groundwater flow across spatial scales: importance for climate modeling, Environ. Res. Lett., 9, 034003, https://doi.org/10.1088/1748-9326/9/3/034003, 2014.
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.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data, J. Geophys. Res.-Atmos., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007.
Niu, G.-Y., Paniconi, C., Troch, P. A., Scott, R. L., Durcik, M., Zeng, X., Huxman, T., and Goodrich, D. C.: An integrated modelling framework of catchment-scale ecohydrological processes: 1. Model description and tests over an energy-limited watershed, Ecohydrology, 7, 427–439, https://doi.org/10.1002/eco.1362, 2014.
Niu, J., Shen, C., Chambers, J. Q., Melack, J. M., and Riley, W. J.: Interannual Variation in Hydrologic Budgets in an Amazonian Watershed with a Coupled Subsurface–Land Surface Process Model, J. Hydrometeorol., 18, 2597–2617, https://doi.org/10.1175/JHM-D-17-0108.1, 2017.
Oleson, K. W., Keith, E. O., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Muszala, S., Ricciuto, D. M., Sacks, W., Tang, J., and Yang, Z.-L.: Technical Description of version 4.5 of the Community Land Model (CLM), Rep. Ncar Technical Note NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, CO, USA, 2013.
Pau, G. S. H., Bisht, G., and Riley, W. J.: A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape, Geosci. Model Dev., 7, 2091–2105, https://doi.org/10.5194/gmd-7-2091-2014, 2014.
Pau, G. S. H., Shen, C., Riley, W. J., and Liu, Y.: Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models, Water Resour. Res., 52, 791–812, https://doi.org/10.1002/2015WR017782, 2016.
Rahman, M., Sulis, M., and Kollet, S. J.: The subsurface-land surface-atmosphere connection under convective conditions, Adv. Water Resour., 83, 240–249, https://doi.org/10.1016/j.advwatres.2015.06.003, 2015.
Rihani, J. F., Chow, F. K., and Maxwell, R. M.: Isolating effects of terrain and soil moisture heterogeneity on the atmospheric boundary layer: Idealized simulations to diagnose land-atmosphere feedbacks, J. Adv. Model. Earth Sys., 7, 915–937, https://doi.org/10.1002/2014MS000371, 2015.
Riley, W. J. and Shen, C.: Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations, Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, 2014.
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5), J. Geophys. Res.-Atmos., 114, D01107, https://doi.org/10.1029/2008JD010834, 2009.
Schaller, M. F. and Fan, Y.: River basins as groundwater exporters and importers: Implications for water cycle and climate modeling, J. Geophys. Res.-Atmos., 114, D04103, https://doi.org/10.1029/2008JD010636, 2009.
Shen, C., Riley, W. J., Smithgall, K. M., Melack, J. M., and Fang, K.: The fan of influence of streams and channel feedbacks to simulated water and carbon fluxes, Water Resour. Res., 52, 880–902, https://doi.org/10.1002/2015WR018086, 2016.
Shi, Y., Davis, K. J., Duffy, C. J., and Yu, X.: Development of a Coupled Land Surface Hydrologic Model and Evaluation at a Critical Zone Observatory, J. Hydrometeorol., 14, 1401–1420, https://doi.org/10.1175/JHM-D-12-0145.1, 2013.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/mwr-d-14-00029.1, 2014.
Sulis, M., Williams, J. L., Shrestha, P., Diederich, M., Simmer, C., Kollet, S. J., and Maxwell, R. M.: Coupling Groundwater, Vegetation, and Atmospheric Processes: A Comparison of Two Integrated Models, J. Hydrometeorol., 18, 1489–1511, https://doi.org/10.1175/jhm-d-16-0159.1, 2017.
Swenson, S. C. and Lawrence, D. M.: Assessing a dry surface layer-based soil resistance parameterization for the Community Land Model using GRACE and FLUXNET-MTE data, J. Geophys. Res.-Atmos., 119, 10299–10312, https://doi.org/10.1002/2014JD022314, 2014.
Tang, G., Yuan, F., Bisht, G., Hammond, G. E., Lichtner, P. C., Kumar, J., Mills, R. T., Xu, X., Andre, B., Hoffman, F. M., Painter, S. L., and Thornton, P. E.: Addressing numerical challenges in introducing a reactive transport code into a land surface model: a biogeochemical modeling proof-of-concept with CLM–PFLOTRAN 1.0, Geosci. Model Dev., 9, 927–946, https://doi.org/10.5194/gmd-9-927-2016, 2016.
Tang, J. Y. and Riley, W. J.: Impacts of a new bare-soil evaporation formulation on site, regional, and global surface energy and water budgets in CLM4, J. Adv. Model. Earth Sys., 5, 558–571, https://doi.org/10.1002/jame.20034, 2013a.
Tang, J. Y. and Riley, W. J.: A new top boundary condition for modeling surface diffusive exchange of a generic volatile tracer: theoretical analysis and application to soil evaporation, Hydrol. Earth Syst. Sci., 17, 873–893, https://doi.org/10.5194/hess-17-873-2013, 2013b.
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow, L., Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., MacDonald, A., Fan, Y., Maxwell, R. M., Yechieli, Y., Gurdak, J. J., Allen, D. M., Shamsudduha, M., Hiscock, K., Yeh, P. J.-F., Holman, I., and Treidel, H.: Ground water and climate change, Nat. Clim. Change, 3, 322–329, 2013.
Thorne, P. D., Bergeron, M. P., Williams, M. D., and Freedman, V. L.: Groundwater Data Package for Hanford Assessments, Rep. PNNL-14753, Pacific Northwest National Laboratory, Richland, WA, USA, 2006.
Tiffan, K. F., Garland, R. D., and Rondorf, D. W.: Quantifying flow-dependent changes in subyearling fall chinook salmon rearing habitat using two-dimensional spatially explicit modeling, N. Am. J. Fish. Manage., 22, 713–726, https://doi.org/10.1577/1548-8675(2002)022<0713:Qfdcis>2.0.Co;2, 2002.
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Waichler, S. R., Perkins, W. A., and Richmond, M. C.: Hydrodynamic Simulation of the Columbia River, Hanford Reach, 1940-2004, Rep. PNNL-15226, Pacific Northwest National Laboratory, Richland, WA, USA, 2005.
Williams, M. D., Rockhold, M. L., Thorne, P. D., and Chen, Y.: Three-Dimensional Groundwater Models of the 300 Area at the Hanford Site, Washington State, Rep. PNNL-17708, Pacific Northwest National Laboratory, Richland, WA, USA, 2008.
Wood, E. F., Lettenmaier, D. P., and Zartarian, V. G.: A land-surface hydrology parameterization with subgrid variability for general circulation models, J. Geophys. Res.-Atmos., 97, 2717–2728, https://doi.org/10.1029/91JD01786, 1992.
Xu, X., Riley, W. J., Koven, C. D., Billesbach, D. P., Chang, R. Y.-W., Commane, R., Euskirchen, E. S., Hartery, S., Harazono, Y., Iwata, H., McDonald, K. C., Miller, C. E., Oechel, W. C., Poulter, B., Raz-Yaseef, N., Sweeney, C., Torn, M., Wofsy, S. C., Zhang, Z., and Zona, D.: A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands, Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, 2016.
Zachara, J. M., Chen, X., Murray, C., and Hammond, G.: River stage influences on uranium transport in a hydrologically dynamic groundwater-surface water transition zone, Water Resour. Res., 52, 1568–1590, https://doi.org/10.1002/2015WR018009, 2016.
Zeng, X., and Decker, M.: Improving the Numerical Solution of Soil Moisture-Based Richards Equation for Land Models with a Deep or Shallow Water Table, J. Hydrometeorol., 10, 308–319, https://doi.org/10.1175/2008JHM1011.1, 2009.
Zeng, X. and Wang, A.: Consistent Parameterization of Roughness Length and Displacement Height for Sparse and Dense Canopies in Land Models, J. Hydrometeorol., 8, 730–737, https://doi.org/10.1175/jhm607.1, 2007.
Zeng, X., Zhao, M., and Dickinson, R. E.: Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data, J. Climate, 11, 2628–2644, 1998.
Zeng, X., Dickinson, R. E., Barlage, M., Dai, Y., Wang, G., and Oleson, K.: Treatment of undercanopy turbulence in land models, J. Climate, 18, 5086–5094, https://doi.org/10.1175/JCLI3595.1, 2005.
Zhang, B., Tang, J. L., Gao, Ch., and Zepp, H.: Subsurface lateral flow from hillslope and its contribution to nitrate loading in streams through an agricultural catchment during subtropical rainstorm events, Hydrol. Earth Syst. Sci., 15, 3153–3170, https://doi.org/10.5194/hess-15-3153-2011, 2011.
Zhou, T., Nijssen, B., Gao, H. L., and Lettenmaier, D. P.: The Contribution of Reservoirs to Global Land Surface Water Storage Variations, J. Hydrometeorol., 17, 309–325, https://doi.org/10.1175/jhm-d-15-0002.1, 2016.
Short summary
A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to...