Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4971-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4971-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed
Haifan Liu
School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China
Heng Dai
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
Jie Niu
CORRESPONDING AUTHOR
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
Bill X. Hu
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
Dongwei Gui
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
Ming Ye
Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Chuanhao Wu
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
Jin Zhang
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
William Riley
Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Manuscript not accepted for further review
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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
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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
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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
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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
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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
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Jing Tao, Qing Zhu, William J. Riley, and Rebecca B. Neumann
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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
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Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, and Alberto Guadagnini
Hydrol. Earth Syst. Sci., 25, 1689–1709, https://doi.org/10.5194/hess-25-1689-2021, https://doi.org/10.5194/hess-25-1689-2021, 2021
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Our study shows that (i) monitoring wells installed with packers provide the (overall) best conductivity estimates; (ii) conductivity estimates anchored on information from partially and fully screened wells are of similar quality; (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when a simplified flow model is adopted; and (iv) when compared to the MC-based EnKF, the MEs-based EnKF can efficiently and accurately estimate conductivity and head fields.
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
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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
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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.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
Geosci. Model Dev., 13, 3553–3569, https://doi.org/10.5194/gmd-13-3553-2020, https://doi.org/10.5194/gmd-13-3553-2020, 2020
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Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Wei Mao, Yan Zhu, Heng Dai, Ming Ye, Jinzhong Yang, and Jingwei Wu
Hydrol. Earth Syst. Sci., 23, 3481–3502, https://doi.org/10.5194/hess-23-3481-2019, https://doi.org/10.5194/hess-23-3481-2019, 2019
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A new quasi-3-D model was developed by coupling a soil water balance model with MODFLOW iteratively for regional-scale water flow modeling. The model was tested to be effective and efficient with well-maintained mass balance. A modeling framework was developed to organize the coupling scheme and to handle the pre- and post-processing information. The model is then used to evaluate groundwater recharge in a real-world application, which shows the model practicability in regional-scale problems.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Han Qiu, Dongwei Gui, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-246, https://doi.org/10.5194/hess-2019-246, 2019
Manuscript not accepted for further review
Guoxiao Wei, Xiaoying Zhang, Ming Ye, Ning Yue, and Fei Kan
Hydrol. Earth Syst. Sci., 23, 2877–2895, https://doi.org/10.5194/hess-23-2877-2019, https://doi.org/10.5194/hess-23-2877-2019, 2019
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Accurately evaluating evapotranspiration (ET) is a critical challenge in improving hydrological process modeling. Here we evaluated four ET models (PM, SW, PT–FC, and AA) under the Bayesian framework. Our results reveal that the SW model has the best performance. This is in part because the SW model captures the main physical mechanism in ET; the other part is that the key parameters, such as the extinction factor, could be well constrained with observation data.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
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The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Ahmed S. Elshall, Ming Ye, Guo-Yue Niu, and Greg A. Barron-Gafford
Geosci. Model Dev., 12, 2009–2032, https://doi.org/10.5194/gmd-12-2009-2019, https://doi.org/10.5194/gmd-12-2009-2019, 2019
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The assumptions that the residuals are independent, identically distributed, and have constant variance tend to simplify the underlying mathematics of data models for Bayesian inference. We relax these three assumptions step-wise, resulting in eight data models. Using three mechanistic soil respiration models with different levels of model discrepancy, we discuss the impacts of data models on parameter estimation and predictive performance, and provide recommendations for data model selection.
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, Virginia I. Rich, and Scott R. Saleska
The Cryosphere, 13, 647–663, https://doi.org/10.5194/tc-13-647-2019, https://doi.org/10.5194/tc-13-647-2019, 2019
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Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition under changing climate. We estimated effects of climate forcing biases on carbon cycling at a thawing permafrost peatland in subarctic Sweden. Our results indicate that many climate reanalysis products are cold and wet biased in our study region, leading to erroneous active layer depth and carbon budget estimates. Future studies should recognize the effects of climate forcing uncertainty on carbon cycling.
Guoping Lu and Bill X. Hu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-624, https://doi.org/10.5194/hess-2018-624, 2019
Manuscript not accepted for further review
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It has been postulated that deep faults are well channeled and networked in the crust. The Xinzhou geothermal field presents a deep fault zone with dome-shaped surface of equilibrium hydraulic heads. Thermal fluid flows are strongly regulated by gravity, buoyancy and viscosity as well. This paper showed that the deep fault is as permeable as clean sands and lower end of gravels. Fluid-flowing faults implicate propagation of pressure/porosity waves and lower limit of groundwater circulations.
Gautam Bisht, William J. Riley, Glenn E. Hammond, and David M. Lorenzetti
Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, https://doi.org/10.5194/gmd-11-4085-2018, 2018
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Most existing global land surface models used to study impacts of climate change on water resources routinely use different models for near-surface unsaturated soil and the deeper groundwater table. We developed a model that uses a unified treatment of soil hydrologic processes throughout the entire soil column. Using a calibrated drainage parameter, the new model is able to correctly predict deep water table depth as reported in an observationally constrained global dataset.
Dan Yu, Ping Xie, Xiaohua Dong, Xiaonong Hu, Ji Liu, Yinghai Li, Tao Peng, Haibo Ma, Kai Wang, and Shijin Xu
Hydrol. Earth Syst. Sci., 22, 5001–5019, https://doi.org/10.5194/hess-22-5001-2018, https://doi.org/10.5194/hess-22-5001-2018, 2018
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, https://doi.org/10.5194/gmd-11-3159-2018, 2018
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Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides a formal framework for quantification of model hypothesis uncertainty.
Xiyan Xu, William J. Riley, Charles D. Koven, and Gensuo Jia
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-257, https://doi.org/10.5194/bg-2018-257, 2018
Preprint withdrawn
Peng-Fei Han, Xu-Sheng Wang, Xiaomei Jin, and Bill X. Hu
Proc. IAHS, 379, 433–442, https://doi.org/10.5194/piahs-379-433-2018, https://doi.org/10.5194/piahs-379-433-2018, 2018
Chuanhao Wu, Bill X. Hu, Guoru Huang, Peng Wang, and Kai Xu
Hydrol. Earth Syst. Sci., 22, 1971–1991, https://doi.org/10.5194/hess-22-1971-2018, https://doi.org/10.5194/hess-22-1971-2018, 2018
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China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal–spatial patterns of water resources. In this paper, the Budyko-based elasticity method was used to investigate the responses of runoff to historical and future climate variability over China at both grid and catchment scales. The results help to better understand the hydrological effects of climate change and adapt to a changing environment.
Ming Wu, Jianfeng Wu, Jichun Wu, and Bill X. Hu
Hydrol. Earth Syst. Sci., 22, 1001–1015, https://doi.org/10.5194/hess-22-1001-2018, https://doi.org/10.5194/hess-22-1001-2018, 2018
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Fractal models of regular triangle arrangement (RTA) and square pitch arrangement (SPA) are developed in this study. Results suggest RTA can cause more groundwater contamination and make remediation more difficult. In contrast, the cleanup of contaminants in aquifers with SPA is easier. This study demonstrates how microscale arrangements control contaminant migration and remediation, which is helpful in designing successful remediation schemes for subsurface contamination.
Zexuan Xu, Bill X. Hu, and Ming Ye
Hydrol. Earth Syst. Sci., 22, 221–239, https://doi.org/10.5194/hess-22-221-2018, https://doi.org/10.5194/hess-22-221-2018, 2018
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This study helps hydrologists better understand the parameters in modeling seawater intrusion in a coastal karst aquifer. Local and global sensitivity studies are conducted to evaluate a density-dependent numerical model of seawater intrusion. The sensitivity analysis indicates that karst features are critical for seawater intrusion modeling, and the evaluation of hydraulic conductivity is biased in continuum SEAWAT model. Dispervisity is no longer important in the advection-dominated aquifer.
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
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The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
Geosci. Model Dev., 10, 4539–4562, https://doi.org/10.5194/gmd-10-4539-2017, https://doi.org/10.5194/gmd-10-4539-2017, 2017
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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.
James C. Stegen, Carolyn G. Anderson, Ben Bond-Lamberty, Alex R. Crump, Xingyuan Chen, and Nancy Hess
Biogeosciences, 14, 4341–4354, https://doi.org/10.5194/bg-14-4341-2017, https://doi.org/10.5194/bg-14-4341-2017, 2017
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CO2 loss from soil to the atmosphere (
soil respiration) is a key ecosystem function, especially in systems with permafrost. We find that soil respiration shows a non-linear threshold at permafrost depths > 140 cm and that the number of large trees governs soil respiration. This suggests that remote sensing could be used to estimate spatial variation in soil respiration and (with knowledge of key thresholds) empirically constrain models that predict ecosystem responses to permafrost thaw.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Xiujie Wu, Xu-Sheng Wang, Yang Wang, and Bill X. Hu
Hydrol. Earth Syst. Sci., 21, 4419–4431, https://doi.org/10.5194/hess-21-4419-2017, https://doi.org/10.5194/hess-21-4419-2017, 2017
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It is critical to identify the origins of water in arid and semiarid regions for management and protection of the water resources. The D, 18O, 3H and 14C in water samples from the Badain Jaran Desert, China, were analyzed. The results show that groundwater supplies the lakes and originates from local precipitation and adjacent mountains. Negative d-excess values of water in the area were the result of evaporation. The 14C ages do not represent the residence time of local groundwater.
Jin-Yun Tang and William J. Riley
Geosci. Model Dev., 10, 3277–3295, https://doi.org/10.5194/gmd-10-3277-2017, https://doi.org/10.5194/gmd-10-3277-2017, 2017
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We proposed the SUPECA kinetics to scale from single biogeochemical reactions to a network of mixed substrates and consumers. The framework for the first time represents single-substrate reactions, two-substrate reactions, and mineral surface sorption reactions in a scaling consistent manner. This new theory is theoretically solid and outperforms existing theories, particularly for substrate-limiting systems. The test with aerobic soil respiration showed its strengths for pragmatic application.
Chuanhao Wu, Pat J.-F. Yeh, Kai Xu, Bill X. Hu, Guoru Huang, and Peng Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-441, https://doi.org/10.5194/hess-2017-441, 2017
Manuscript not accepted for further review
Dongwei Gui, Jie Xue, Yi Liu, Jiaqiang Lei, and Fanjiang Zeng
Solid Earth Discuss., https://doi.org/10.5194/se-2017-59, https://doi.org/10.5194/se-2017-59, 2017
Revised manuscript not accepted
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This paper clarifies the dialectical relationship between oasification and desertification in arid regions, and it also elucidates the significance of oasification research in Northwest China. Furthermore, the study points out the key point in the oasification research.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
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Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Kathrin M. Keller, Sebastian Lienert, Anil Bozbiyik, Thomas F. Stocker, Olga V. Churakova (Sidorova), David C. Frank, Stefan Klesse, Charles D. Koven, Markus Leuenberger, William J. Riley, Matthias Saurer, Rolf Siegwolf, Rosemarie B. Weigt, and Fortunat Joos
Biogeosciences, 14, 2641–2673, https://doi.org/10.5194/bg-14-2641-2017, https://doi.org/10.5194/bg-14-2641-2017, 2017
Hui Wan, Kai Zhang, Philip J. Rasch, Balwinder Singh, Xingyuan Chen, and Jim Edwards
Geosci. Model Dev., 10, 537–552, https://doi.org/10.5194/gmd-10-537-2017, https://doi.org/10.5194/gmd-10-537-2017, 2017
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Solution reproductibility testing is an important task for assuring the software quality of a climate model. A new method is developed using the concept of numerical convergence with respect to temporal resolution. The method is objective, easy to implement, and computationally efficient. This paper describes the new test and demonstrates its utility in the Community Atmosphere Model version 5 (CAM5).
Jie Xue, Dongwei Gui, Jiaqiang Lei, Fanjiang Zeng, Rong Huang, and Donglei Mao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-618, https://doi.org/10.5194/hess-2016-618, 2016
Preprint retracted
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There is an increasing consensus on the importance of integrating ecosystem services into integrated water resource management due to a wide range of benefits to human from the ecosystem services. This paper develops a participatory Bayesian network model to perform an ecosystem services-based water management framework under public participation. The participatory Bayesian network effectively provides the support of transdisciplinary water management.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Xiyan Xu, William J. Riley, Charles D. Koven, Dave P. Billesbach, Rachel Y.-W. Chang, Róisín Commane, Eugénie S. Euskirchen, Sean Hartery, Yoshinobu Harazono, Hiroki Iwata, Kyle C. McDonald, Charles E. Miller, Walter C. Oechel, Benjamin Poulter, Naama Raz-Yaseef, Colm Sweeney, Margaret Torn, Steven C. Wofsy, Zhen Zhang, and Donatella Zona
Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, https://doi.org/10.5194/bg-13-5043-2016, 2016
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Wetlands are the largest global natural methane source. Peat-rich bogs and fens lying between 50°N and 70°N contribute 10–30% to this source. The predictive capability of the seasonal methane cycle can directly affect the estimation of global methane budget. We present multiscale methane seasonal emission by observations and modeling and find that the uncertainties in predicting the seasonal methane emissions are from the wetland extent, cold-season CH4 production and CH4 transport processes.
Xiaofeng Xu, Fengming Yuan, Paul J. Hanson, Stan D. Wullschleger, Peter E. Thornton, William J. Riley, Xia Song, David E. Graham, Changchun Song, and Hanqin Tian
Biogeosciences, 13, 3735–3755, https://doi.org/10.5194/bg-13-3735-2016, https://doi.org/10.5194/bg-13-3735-2016, 2016
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Accurately projecting future climate change requires a good methane modeling. However, how good the current models are and what are the key improvements needed remain unclear. This paper reviews the 40 published methane models to characterize the strengths and weakness of current methane models and further lay out the roadmap for future model improvements.
Jinyun Tang and William J. Riley
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-233, https://doi.org/10.5194/bg-2016-233, 2016
Preprint retracted
J. Y. Tang and W. J. Riley
Biogeosciences, 13, 723–735, https://doi.org/10.5194/bg-13-723-2016, https://doi.org/10.5194/bg-13-723-2016, 2016
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We present a generic flux-limiting approach to simultaneously handle the availability limitation from many substrates, a problem common in all biogeochemical models. Our approach does not have the ordering problem like a few existing ad hoc approaches, and is straightforward to implement. Our results imply that significant uncertainties could have occurred in many biogeochemical models because of the improper handling of the substrate co-limitation problem.
Q. Zhu, W. J. Riley, J. Tang, and C. D. Koven
Biogeosciences, 13, 341–363, https://doi.org/10.5194/bg-13-341-2016, https://doi.org/10.5194/bg-13-341-2016, 2016
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Here we develop, calibrate, and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers based on enzyme kinetics theory. Our model provides an ecologically consistent representation of nutrient competition appropriate for land biogeochemical models integrated in Earth system models.
C. D. Koven, J. Q. Chambers, K. Georgiou, R. Knox, R. Negron-Juarez, W. J. Riley, V. K. Arora, V. Brovkin, P. Friedlingstein, and C. D. Jones
Biogeosciences, 12, 5211–5228, https://doi.org/10.5194/bg-12-5211-2015, https://doi.org/10.5194/bg-12-5211-2015, 2015
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Terrestrial carbon feedbacks are a large uncertainty in climate change. We separate modeled feedback responses into those governed by changed carbon inputs (productivity) and changed outputs (turnover). The disaggregated responses show that both are important in controlling inter-model uncertainty. Interactions between productivity and turnover are also important, and research must focus on these interactions for more accurate projections of carbon cycle feedbacks.
U. Mishra and W. J. Riley
Biogeosciences, 12, 3993–4004, https://doi.org/10.5194/bg-12-3993-2015, https://doi.org/10.5194/bg-12-3993-2015, 2015
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
C. H. Wu, G. R. Huang, and H. J. Yu
Hydrol. Earth Syst. Sci., 19, 1385–1399, https://doi.org/10.5194/hess-19-1385-2015, https://doi.org/10.5194/hess-19-1385-2015, 2015
N. J. Bouskill, W. J. Riley, and J. Y. Tang
Biogeosciences, 11, 6969–6983, https://doi.org/10.5194/bg-11-6969-2014, https://doi.org/10.5194/bg-11-6969-2014, 2014
G. Bisht and W. J. Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-12833-2014, https://doi.org/10.5194/hessd-11-12833-2014, 2014
Revised manuscript has not been submitted
G. S. H. Pau, G. Bisht, and W. J. Riley
Geosci. Model Dev., 7, 2091–2105, https://doi.org/10.5194/gmd-7-2091-2014, https://doi.org/10.5194/gmd-7-2091-2014, 2014
J. Y. Tang and W. J. Riley
Biogeosciences, 11, 3721–3728, https://doi.org/10.5194/bg-11-3721-2014, https://doi.org/10.5194/bg-11-3721-2014, 2014
W. J. Riley, F. Maggi, M. Kleber, M. S. Torn, J. Y. Tang, D. Dwivedi, and N. Guerry
Geosci. Model Dev., 7, 1335–1355, https://doi.org/10.5194/gmd-7-1335-2014, https://doi.org/10.5194/gmd-7-1335-2014, 2014
W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, https://doi.org/10.5194/hess-18-2463-2014, 2014
I. N. Williams, W. J. Riley, M. S. Torn, S. C. Biraud, and M. L. Fischer
Atmos. Chem. Phys., 14, 1571–1585, https://doi.org/10.5194/acp-14-1571-2014, https://doi.org/10.5194/acp-14-1571-2014, 2014
J. Y. Tang and W. J. Riley
Biogeosciences, 10, 8329–8351, https://doi.org/10.5194/bg-10-8329-2013, https://doi.org/10.5194/bg-10-8329-2013, 2013
C. D. Koven, W. J. Riley, Z. M. Subin, J. Y. Tang, M. S. Torn, W. D. Collins, G. B. Bonan, D. M. Lawrence, and S. C. Swenson
Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, https://doi.org/10.5194/bg-10-7109-2013, 2013
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
J. H. Shim, H. H. Powers, C. W. Meyer, A. Knohl, T. E. Dawson, W. J. Riley, W. T. Pockman, and N. McDowell
Biogeosciences, 10, 4937–4956, https://doi.org/10.5194/bg-10-4937-2013, https://doi.org/10.5194/bg-10-4937-2013, 2013
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
S. C. Biraud, M. S. Torn, J. R. Smith, C. Sweeney, W. J. Riley, and P. P. Tans
Atmos. Meas. Tech., 6, 751–763, https://doi.org/10.5194/amt-6-751-2013, https://doi.org/10.5194/amt-6-751-2013, 2013
W. J. Riley
Geosci. Model Dev., 6, 345–352, https://doi.org/10.5194/gmd-6-345-2013, https://doi.org/10.5194/gmd-6-345-2013, 2013
J. Y. Tang and W. J. Riley
Hydrol. Earth Syst. Sci., 17, 873–893, https://doi.org/10.5194/hess-17-873-2013, https://doi.org/10.5194/hess-17-873-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
J. Y. Tang, W. J. Riley, C. D. Koven, and Z. M. Subin
Geosci. Model Dev., 6, 127–140, https://doi.org/10.5194/gmd-6-127-2013, https://doi.org/10.5194/gmd-6-127-2013, 2013
W. Tian, X. Li, G.-D. Cheng, X.-S. Wang, and B. X. Hu
Hydrol. Earth Syst. Sci., 16, 4707–4723, https://doi.org/10.5194/hess-16-4707-2012, https://doi.org/10.5194/hess-16-4707-2012, 2012
Related subject area
Subject: Groundwater hydrology | Techniques and Approaches: Uncertainty analysis
Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties
Interpretation of multi-scale permeability data through an information theory perspective
Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization
Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios
Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions
Numerical modeling and sensitivity analysis of seawater intrusion in a dual-permeability coastal karst aquifer with conduit networks
On the efficiency of the hybrid and the exact second-order sampling formulations of the EnKF: a reality-inspired 3-D test case for estimating biodegradation rates of chlorinated hydrocarbons at the port of Rotterdam
Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models
Groundwater flow processes and mixing in active volcanic systems: the case of Guadalajara (Mexico)
Analyses of uncertainties and scaling of groundwater level fluctuations
Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time
Interpolation of groundwater quality parameters with some values below the detection limit
An approach to identify urban groundwater recharge
Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile
Falk Heße, Sebastian Müller, and Sabine Attinger
Hydrol. Earth Syst. Sci., 28, 357–374, https://doi.org/10.5194/hess-28-357-2024, https://doi.org/10.5194/hess-28-357-2024, 2024
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In this study, we have presented two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.
Aronne Dell'Oca, Alberto Guadagnini, and Monica Riva
Hydrol. Earth Syst. Sci., 24, 3097–3109, https://doi.org/10.5194/hess-24-3097-2020, https://doi.org/10.5194/hess-24-3097-2020, 2020
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Permeability of natural systems exhibits heterogeneous spatial variations linked with the size of the measurement support scale. As the latter becomes coarser, the system appearance is less heterogeneous. As such, sets of permeability data associated with differing support scales provide diverse amounts of information. In this contribution, we leverage information theory to quantify the information content of gas permeability datasets collected with four diverse measurement support scales.
Robert Reinecke, Laura Foglia, Steffen Mehl, Jonathan D. Herman, Alexander Wachholz, Tim Trautmann, and Petra Döll
Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, https://doi.org/10.5194/hess-23-4561-2019, 2019
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Recently, the first global groundwater models were developed to better understand surface-water–groundwater interactions and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. In this study we present the first global maps of model sensitivity according to their parameterization and build a foundation to improve datasets, model design, and model understanding.
Syed M. Touhidul Mustafa, M. Moudud Hasan, Ajoy Kumar Saha, Rahena Parvin Rannu, Els Van Uytven, Patrick Willems, and Marijke Huysmans
Hydrol. Earth Syst. Sci., 23, 2279–2303, https://doi.org/10.5194/hess-23-2279-2019, https://doi.org/10.5194/hess-23-2279-2019, 2019
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This study evaluates the effect of conceptual hydro(geo)logical model (CHM) structure, climate change and groundwater abstraction on future groundwater-level prediction uncertainty. If the current groundwater abstraction trend continues, groundwater level is predicted to decline quickly. Groundwater abstraction in NW Bangladesh should decrease by 60 % to ensure sustainable use. Abstraction scenarios are the dominant uncertainty source, followed by CHM uncertainty and climate model uncertainty.
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, https://doi.org/10.5194/hess-23-171-2019, 2019
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We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
Zexuan Xu, Bill X. Hu, and Ming Ye
Hydrol. Earth Syst. Sci., 22, 221–239, https://doi.org/10.5194/hess-22-221-2018, https://doi.org/10.5194/hess-22-221-2018, 2018
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This study helps hydrologists better understand the parameters in modeling seawater intrusion in a coastal karst aquifer. Local and global sensitivity studies are conducted to evaluate a density-dependent numerical model of seawater intrusion. The sensitivity analysis indicates that karst features are critical for seawater intrusion modeling, and the evaluation of hydraulic conductivity is biased in continuum SEAWAT model. Dispervisity is no longer important in the advection-dominated aquifer.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
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The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Nikolaj Kruse Christensen, Steen Christensen, and Ty Paul A. Ferre
Hydrol. Earth Syst. Sci., 20, 1925–1946, https://doi.org/10.5194/hess-20-1925-2016, https://doi.org/10.5194/hess-20-1925-2016, 2016
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Our primary objective in this study is to provide a virtual environment that allows users to determine the value of geophysical data and, furthermore, to investigate how best to use those data to develop groundwater models and to reduce their prediction errors. When this has been carried through for alternative data sampling, parameterization and inversion approaches, the best alternative can be chosen by comparison of prediction results between the alternatives.
A. Hernández-Antonio, J. Mahlknecht, C. Tamez-Meléndez, J. Ramos-Leal, A. Ramírez-Orozco, R. Parra, N. Ornelas-Soto, and C. J. Eastoe
Hydrol. Earth Syst. Sci., 19, 3937–3950, https://doi.org/10.5194/hess-19-3937-2015, https://doi.org/10.5194/hess-19-3937-2015, 2015
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A conceptual model of groundwater flow processes and mixing was developed using a combination of hydrogeochemistry, isotopes and multivariate analysis. The implementation to the case of Guadalajara showed that groundwater was classified into four groups: cold groundwater, hydrothermal water, polluted groundwater and mixed groundwater. A multivariate mixing model was used to calculate the proportion of different fluids in sampled well water. The result helps authorities in decision making.
X. Y. Liang and Y.-K. Zhang
Hydrol. Earth Syst. Sci., 19, 2971–2979, https://doi.org/10.5194/hess-19-2971-2015, https://doi.org/10.5194/hess-19-2971-2015, 2015
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The error or uncertainty in head, obtained with an analytical or numerical solution, at an early time is mainly caused by the random initial condition. The error reduces with time, later reaching a constant error. The constant error at a later time is mainly due to the effects of the uncertain source/sink. The error caused by the uncertain boundary is limited to a narrow zone. Temporal scaling of head exists in most parts of a low permeable aquifer, mainly caused by recharge fluctuation.
X. He, T. O. Sonnenborg, F. Jørgensen, A.-S. Høyer, R. R. Møller, and K. H. Jensen
Hydrol. Earth Syst. Sci., 17, 3245–3260, https://doi.org/10.5194/hess-17-3245-2013, https://doi.org/10.5194/hess-17-3245-2013, 2013
A. Bárdossy
Hydrol. Earth Syst. Sci., 15, 2763–2775, https://doi.org/10.5194/hess-15-2763-2011, https://doi.org/10.5194/hess-15-2763-2011, 2011
E. Vázquez-Suñé, J. Carrera, I. Tubau, X. Sánchez-Vila, and A. Soler
Hydrol. Earth Syst. Sci., 14, 2085–2097, https://doi.org/10.5194/hess-14-2085-2010, https://doi.org/10.5194/hess-14-2085-2010, 2010
R. Rojas, O. Batelaan, L. Feyen, and A. Dassargues
Hydrol. Earth Syst. Sci., 14, 171–192, https://doi.org/10.5194/hess-14-171-2010, https://doi.org/10.5194/hess-14-171-2010, 2010
Cited articles
Ba, S., Myers, W. R., and Brenneman, W. A.:
Optimal sliced Latin hypercube designs,
Technometrics,
57, 479–487, 2015.
Baroni, G. and Tarantola, S.:
A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: a hydrological case study,
Environ. Modell. Softw.,
51, 26–34, https://doi.org/10.1016/j.envsoft.2013.09.022, 2014.
Beven, K.:
Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system,
Hydrol. Process.,
16, 189–206, https://doi.org/10.1002/hyp.343, 2002.
Bixio, A., Gambolati, G., Paniconi, C., Putti, M., Shestopalov, V., Bublias, V., Bohuslavsky, A., Kasteltseva, N., and Rudenko, Y.:
Modeling groundwater-surface water interactions including effects of morphogenetic depressions in the Chernobyl exclusion zone,
Environ. Geol.,
42, 162–177, https://doi.org/10.1007/s00254-001-0486-7, 2002.
Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P., Lawrence, D. M., Leung, L. R., Niu, G.-Y., Troch, P. A., and Zeng, X.:
Implementing and evaluating variable soil thickness in the community land model, Version 4.5 (CLM4.5),
J. Climate,
29, 3441–3461, https://doi.org/10.1175/jcli-d-15-0307.1, 2016.
Caflisch, R. E.:
Monte carlo and quasi-monte carlo methods,
Acta numer.,
1998, 1–49, 1998.
Celia, M. A., Bouloutas, E. T., and Zarba, R. L.:
A general mass-conservative numerical solution for the unsaturated flow equation,
Water Resour. Res.,
26, 1483–1496, https://doi.org/10.1029/WR026i007p01483, 1990.
Chávarri, E., Crave, A., Bonnet, M.-P., Mejía, A., Santos Da Silva, J., and Guyot, J. L.:
Hydrodynamic modelling of the Amazon River: factors of uncertainty,
J. S. Am. Earth Sci.,
44, 94–103, https://doi.org/10.1016/j.jsames.2012.10.010, 2013.
Christoffersen, B. O., Restrepo-Coupe, N., Arain, M. A., Baker, I. T., Cestaro, B. P., Ciais, P., Fisher, J. B., Galbraith, D., Guan, X., Gulden, L., van den Hurk, B., Ichii, K., Imbuzeiro, H., Jain, A., Levine, N., Miguez-Macho, G., Poulter, B., Roberti, D. R., Sakaguchi, K., Sahoo, A., Schaefer, K., Shi, M., Verbeeck, H., Yang, Z.-L., Araújo, A. C., Kruijt, B., Manzi, A. O., da Rocha, H. R., von Randow, C., Muza, M. N., Borak, J., Costa, M. H., Gonçalves de Gonçalves, L. G., Zeng, X., and Saleska, S. R.:
Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado,
Agr. Forest Meteorol.,
191, 33–50, https://doi.org/10.1016/j.agrformet.2014.02.008, 2014.
Crombecq, K., Laermans, E., and Dhaene, T.:
Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling,
Eur. J. Oper. Res.,
214, 683–696, 2011.
Cuartas, L. A., Tomasella, J., Nobre, A. D., Nobre, C. A., Hodnett, M. G., Waterloo, M. J., de Oliveira, S. M., von Randow, R. D. C., Trancoso, R., and Ferreira, M.:
Distributed hydrological modeling of a micro-scale rainforest watershed in Amazonia: Model evaluation and advances in calibration using the new HAND terrain model,
J. Hydrol.,
462–463, 15–27, https://doi.org/10.1016/j.jhydrol.2011.12.047, 2012.
Dai, H. and Ye, M.:
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation, J. Hydrol., 528, 286–300, https://doi.org/10.1016/j.jhydrol.2015.06.034, 2015.
Dai, H., Chen, X., Ye, M., Song, X., and Zachara, J. M.:
A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling,
Water Resour. Res.,
53, 4327–4343, https://doi.org/10.1002/2016wr019756, 2017a.
Dai, H., Ye, M., Walker, A. P., and Chen, X.:
A new process sensitivity index to identify important system processes under process model and parametric uncertainty,
Water Resour. Res.,
53, 3476–3490, https://doi.org/10.1002/2016wr019715, 2017b.
Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., and Zachara, J. M.:
Using Bayesian networks for sensitivity analysis of complex biogeochemical models,
Water Resour. Res.,
55, 3541–3555, 2019.
Damblin, G., Couplet, M., and Iooss, B.:
Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties,
J. Simul,
7, 276–289, https://doi.org/10.1057/jos.2013.16, 2013.
de Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M.-P., Frappart, F., Calmant, S., and Bulhões Mendes, C. A.:
Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin,
Water Resour. Res.,
49, 1226–1243, https://doi.org/10.1002/wrcr.20067, 2013.
do Rosário, F. F., Custodio, E., and da Silva, G. C.:
Hydrogeology of the Western Amazon Aquifer System (WAAS),
J. S. Am. Earth Sci.,
72, 375–386, https://doi.org/10.1016/j.jsames.2016.10.004, 2016.
Emery, C. M., Biancamaria, S., Boone, A., Garambois, P.-A., Ricci, S., Rochoux, M. C., and Decharme, B.:
Temporal variance-based sensitivity analysis of the river-routing component of the large-scale hydrological model ISBA–TRIP: application on the Amazon Basin,
J. Hydrometeorol.,
17, 3007–3027, https://doi.org/10.1175/jhm-d-16-0050.1, 2016.
Fisher, R. A., Williams, M., de Lourdes Ruivo, M., de Costa, A. L., and Meir, P.:
Evaluating climatic and soil water controls on evapotranspiration at two Amazonian rainforest sites,
Agr. Forest Meteorol.,
148, 850–861, https://doi.org/10.1016/j.agrformet.2007.12.001, 2008.
Freeze, R. A. and Harlan, R.:
Blueprint for a physically-based, digitally-simulated hydrologic response model,
J. Hydrol.,
9, 237–258, 1969.
Gedeon, M. and Mallants, D.:
Sensitivity analysis of a combined groundwater flow and solute transport model using local-grid refinement: a case study,
Math. Geosci.,
44, 881–899, https://doi.org/10.1007/s11004-012-9416-3, 2012.
Grosso, A., Jamali, A., and Locatelli, M.:
Finding maximin latin hypercube designs by iterated local search heuristics,
Eur. J. Oper. Res.,
197, 541–547, 2009.
Hamby, D. M.:
A review of techniques for parameter sensitivity analysis of environmental models,
Environ. Monit. Assess.,
32, 135–154, https://doi.org/10.1007/bf00547132, 1994.
Helton, J. C. and Davis, F. J.:
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems,
Reliab. Eng. Syst. Safe.,
81, 23–69, https://doi.org/10.1016/s0951-8320(03)00058-9, 2003.
Husslage, B. G. M., Rennen, G., van Dam, E. R., and den Hertog, D.:
Space-filling Latin hypercube designs for computer experiments,
Optim. Eng.,
12, 611–630, https://doi.org/10.1007/s11081-010-9129-8, 2011.
Iman, R. L. and Conover, W.:
Small sample sensitivity analysis techniques for computer models. with an application to risk assessment,
Commun. Stat. Theory,
9, 1749–1842, 1980.
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.
Kanso, A., Chebbo, G., and Tassin, B.:
Application of MCMC–GSA model calibration method to urban runoff quality modeling,
Reliab. Eng. Syst. Safe.,
91, 1398–1405, https://doi.org/10.1016/j.ress.2005.11.051, 2006.
King, D. M. and Perera, B. J. C.:
Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield – A case study,
J. Hydrol.,
477, 17–32, https://doi.org/10.1016/j.jhydrol.2012.10.017, 2013.
Lu, D., Ye, M., and Hill, M. C.:
Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification,
Water Resour. Res.,
48, W09521, https://doi.org/10.1029/2011wr011289, 2012.
Makler-Pick, V., Gal, G., Gorfine, M., Hipsey, M. R., and Carmel, Y.:
Sensitivity analysis for complex ecological models – A new approach,
Environ. Modell. Softw.,
26, 124–134, https://doi.org/10.1016/j.envsoft.2010.06.010, 2011.
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.
McKay, M. D., Beckman, R. J., and Conover, W. J.:
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,
Technometrics,
21, 239–245, https://doi.org/10.2307/1268522, 1979.
Meyer, P. D., Ye, M., Rockhold, M. L., Neuman, S. P., and Cantrell, K. J.:
Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area,
Geosciences, Office of Scientific and Technical Information (OSTI), Richland, WA, 2007.
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, 2012a.
Miguez-Macho, G. and Fan, Y.:
The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration,
J. Geophys. Res.-Atmos.,
117, D15114, https://doi.org/10.1029/2012jd017540, 2012b.
Mualem, Y.:
A new model for predicting the hydraulic conductivity of unsaturated porous media,
Water Resour. Res.,
12, 513–522, https://doi.org/10.1029/wr012i003p00513, 1976.
NASA Orbital Debris Program Office:
Re-entry and Risk Assessment for the Tropical Rainfall Measuring Mission (TRMM), available at: https://trmm.gsfc.nasa.gov/publications_dir/TRMM_Reentry_Risk_Assessment_FINAL_20150604.pdf (last access: 27 July 2020), 2015.
Neuman, S. P.:
Maximum likelihood Bayesian averaging of uncertain model predictions,
Stoch. Env. Res. Risk A.,
17, 291–305, https://doi.org/10.1007/s00477-003-0151-7, 2003.
Neumann, M. B.:
Comparison of sensitivity analysis methods for pollutant degradation modelling: a case study from drinking water treatment,
Sci. Total Environ.,
433, 530–537, https://doi.org/10.1016/j.scitotenv.2012.06.026, 2012.
Nijssen, B., O'Donnell, G. M., Hamlet, A. F., and Lettenmaier, D. P.:
Hydrologic sensitivity of global rivers to climate change,
Climatic Change,
50, 143–175, https://doi.org/10.1023/a:1010616428763, 2001.
Niu, J., Shen, C., Li, S.-G., and Phanikumar, M. S.:
Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface-land surface process model and GRACE, MODIS products,
Water Resour. Res.,
50, 7359–7377, https://doi.org/10.1002/2014wr015589, 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., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S., Dai, A., and Qian, T.:
Improvements to the Community Land Model and their impact on the hydrological cycle,
J. Geophys. Res.-Biogeo.,
113, G01021 https://doi.org/10.1029/2007jg000563, 2008.
Oogathoo, S., Prasher, S. O., Rudra, R. P., and Patel, R. M.:
Evaluation of the MIKE SHE Model in a Cold Region,
Journal of Agricultural Engineering,
48, 26–37, 2011.
Owen, A. B.:
Latin supercube sampling for very high-dimensional simulations,
ACM T. Model. Comput. S.,
8, 71–102, https://doi.org/10.1145/272991.273010, 1998.
Pan, F., Zhu, J., Ye, M., Pachepsky, Y. A., and Wu, Y.-S.:
Sensitivity analysis of unsaturated flow and contaminant transport with correlated parameters,
J. Hydrol.,
397, 238–249, https://doi.org/10.1016/j.jhydrol.2010.11.045, 2011.
Pan, Y., Zeng, X., Xu, H., Sun, Y., Wang, D., and Wu, J.:
Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty,
J. Hydrol.,
584, 124690, https://doi.org/10.1016/j.jhydrol.2020.124690, 2020.
Parkin, G., O'Donnell, G., Ewen, J., Bathurst, J. C., O'Connell, P. E., and Lavabre, J.:
Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment,
J. Hydrol.,
175, 595–613, https://doi.org/10.1016/S0022-1694(96)80027-8, 1996.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G. Y., Williams, Z., Brunke, M. A., and Gochis, D.:
A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling,
J. Adv. Model. Earth Sy.,
8, 41–65, https://doi.org/10.1002/2015ms000526, 2016.
Piao, S. L., Ito, A., Li, S. G., Huang, Y., Ciais, P., Wang, X. H., Peng, S. S., Nan, H. J., Zhao, C., Ahlström, A., Andres, R. J., Chevallier, F., Fang, J. Y., Hartmann, J., Huntingford, C., Jeong, S., Levis, S., Levy, P. E., Li, J. S., Lomas, M. R., Mao, J. F., Mayorga, E., Mohammat, A., Muraoka, H., Peng, C. H., Peylin, P., Poulter, B., Shen, Z. H., Shi, X., Sitch, S., Tao, S., Tian, H. Q., Wu, X. P., Xu, M., Yu, G. R., Viovy, N., Zaehle, S., Zeng, N., and Zhu, B.: The carbon budget of terrestrial ecosystems in East Asia over the last two decades, Biogeosciences, 9, 3571–3586, https://doi.org/10.5194/bg-9-3571-2012, 2012.
Pokhrel, Y. N., Fan, Y., and Miguez-Macho, G.:
Potential hydrologic changes in the Amazon by the end of the 21st century and the groundwater buffer,
Environ. Res. Lett.,
9, 084004, https://doi.org/10.1088/1748-9326/9/8/084004, 2014.
Qian, P. Z. G.:
Sliced Latin Hypercube Designs,
J. Am. Stat. Assoc.,
107, 393–399, https://doi.org/10.1080/01621459.2011.644132, 2012.
Qiu, H., Niu, J., and Phanikumar, M. S.:
Quantifying the space – time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework,
Sci. Total Environ.,
676, 176–189, https://doi.org/10.1016/j.scitotenv.2019.04.147, 2019.
Razavi, S. and Gupta, H. V.:
What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models,
Water Resour. Res.,
51, 3070–3092, https://doi.org/10.1002/2014wr016527, 2015.
Razavi, S. and Gupta, H. V.:
A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory,
Water Resour. Res.,
52, 423–439, https://doi.org/10.1002/2015wr017559, 2016.
Refsgaard, J. C. and Knudsen, J.:
Operational validation and intercomparison of different types of hydrological models,
Water Resour. Res.,
32, 2189–2202, 1996.
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.
Rojas, R., Kahunde, S., Peeters, L., Batelaan, O., Feyen, L., and Dassargues, A.:
Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modelling,
J. Hydrol.,
394, 416–435, https://doi.org/10.1016/j.jhydrol.2010.09.016, 2010.
Saltelli, A. and Sobol, I. M.:
About the use of rank transformation in sensitivity analysis of model output,
Reliab. Eng. Syst. Safe.,
50, 225–239, https://doi.org/10.1016/0951-8320(95)00099-2, 1995.
Saltelli, A., Chan, K., and Scott, E. M.:
Sensitivity Analysis,
John Wiley & Sons Ltd, Chichester, NY, 2000.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S.:
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index,
Comput. Phys. Commun.,
181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010.
Schöniger, A., Wöhling, T., Samaniego, L., and Nowak, W.:
Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian model evidence,
Water Resour. Res.,
50, 9484–9513, https://doi.org/10.1002/2014wr016062, 2014.
Shen, C. and Phanikumar, M. S.:
A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling,
Adv. Water Resour.,
33, 1524–1541, https://doi.org/10.1016/j.advwatres.2010.09.002, 2010.
Shen, C., Niu, J., and Phanikumar, M. S.:
Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model,
Water Resour. Res.,
49, 2552–2572, https://doi.org/10.1002/wrcr.20189, 2013.
Shen, C., Niu, J., and Fang, K.:
Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model,
Environ. Modell. Softw.,
59, 146–161, https://doi.org/10.1016/j.envsoft.2014.05.006, 2014.
Shen, C., Riley, W. J., Smithgall, K. R., Melack, J. M., and Fang, K.:
The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics,
Water Resour. Res.,
52, 880–902, https://doi.org/10.1002/2015wr018086, 2016.
Singh, V. P. and Woolhiser, D. A.:
Mathematical modeling of watershed hydrology,
J. Hydrol. Eng.,
7, 270–292, 2002.
Song, X., Kong, F., Zhan, C., Han, J., and Zhang, X.:
Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach,
Water Science and Engineering,
6, 1–17, 2013.
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.:
Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications,
J. Hydrol.,
523, 739–757, https://doi.org/10.1016/j.jhydrol.2015.02.013, 2015.
Sulis, M., Paniconi, C., Rivard, C., Harvey, R., and Chaumont, D.:
Assessment of climate change impacts at the catchment scale with a detailed hydrological model of surface-subsurface interactions and comparison with a land surface model,
Water Resour. Res.,
47, W01513, https://doi.org/10.1029/2010wr009167, 2011.
Teixeira, W. G., Schroth, G., Marques, J. D., and Huwe, B.:
Unsaturated Soil Hydraulic Conductivity in the Central Amazon: Field Evaluations,
Springer International Publishing, 283–305, https://doi.org/10.1007/978-3-319-06013-2_13, 2014.
van Dam, J. C. and Feddes, R. A.:
Numerical simulation of infiltration, evaporation and shallow groundwater levels with the Richards equation,
J. Hydrol.,
233, 72–85, https://doi.org/10.1016/s0022-1694(00)00227-4, 2000.
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.
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and Srinivasan, R.:
A global sensitivity analysis tool for the parameters of multi-variable catchment models,
J. Hydrol.,
324, 10–23, https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006.
Vertessy, R. A., Hatton, T. J., O'Shaughnessy, P. J., and Jayasuriya, M. D. A.:
Predicting water yield from a mountain ash forest catchment using a terrain analysis based catchment model,
J. Hydrol.,
150, 665–700, https://doi.org/10.1016/0022-1694(93)90131-R, 1993.
Wainwright, H. M., Finsterle, S., Jung, Y., Zhou, Q., and Birkholzer, J. T.:
Making sense of global sensitivity analyses,
Comput. Geosci.,
65, 84–94, https://doi.org/10.1016/j.cageo.2013.06.006, 2014.
Weill, S., Mazzia, A., Putti, M., and Paniconi, C.:
Coupling water flow and solute transport into a physically-based surface–subsurface hydrological model,
Adv. Water Resour.,
34, 128–136, 2011.
Ye, M., Neuman, S. P., Meyer, P. D., and Pohlmann, K.:
Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff,
Water Resour. Res.,
41, W12429, https://doi.org/10.1029/2005wr004260, 2005.
Zeng, X., Wang, D., and Wu, J.:
Sensitivity analysis of the probability distribution of groundwater level series based on information entropy,
Stoch. Env. Res. Risk A.,
26, 345–356, 2012.
Zeng, X., Ye, M., Wu, J., Wang, D., and Zhu, X.:
Improved nested sampling and surrogate-enabled comparison with other marginal likelihood estimators,
Water Resour. Res.,
54, 797–826, https://doi.org/10.1002/2017wr020782, 2018.
Zhang, C., Chu, J., and Fu, G.:
Sobol's sensitivity analysis for a distributed hydrological model of Yichun River Basin, China,
J. Hydrol.,
480, 58–68, https://doi.org/10.1016/j.jhydrol.2012.12.005, 2013.
Zhang, Y. and Pinder, G.:
Latin hypercube lattice sample selection strategy for correlated random hydraulic conductivity fields,
Water Resour. Res.,
39, 1226 https://doi.org/10.1029/2002wr001822, 2003.
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.
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis...