Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1929-2023
© Author(s) 2023. 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-27-1929-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 May 2023
Research article |
| 17 May 2023
| 17 May 2023
Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups
Yuchan Chen
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Xiuzhi Chen
CORRESPONDING AUTHOR
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Meimei Xue
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Chuanxun Yang
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences,
Guangzhou, 510640, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Wei Zheng
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Jun Cao
Institute of Ecological Civilization and Green Development, Guangdong Provincial Academy of Environmental Science, Guangzhou, 510635, China
Wenting Yan
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Wenping Yuan
Guangdong Province Data Center of Terrestrial and Marine Ecosystems
Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and
Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen
University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Related authors
No articles found.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-147, https://doi.org/10.5194/essd-2024-147, 2024
Preprint under review for ESSD
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice mapping method using to address the challenges of cloud contamination and missing data in optical remote sensing observations. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed strong correlation with statistical data.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Preprint under review for ESSD
Short summary
Short summary
This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
Short summary
Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
Short summary
Short summary
We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2851–2864, https://doi.org/10.5194/essd-14-2851-2022, https://doi.org/10.5194/essd-14-2851-2022, 2022
Short summary
Short summary
In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
Short summary
Short summary
In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
Short summary
Short summary
For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
Short summary
Short summary
We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
HESS Opinions: A few camels or a whole caravan?
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability has minor effects on the hydrological response in the Neckar basin, Germany
Toward interpretable LSTM-based modeling of hydrological systems
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
What controls the tail behaviour of flood series: rainfall or runoff generation?
Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California
Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau
Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings
Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool
Evolution of river regime in the Mekong River basin over eight decades and role of dams in recent hydrologic extremes
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Projected future changes in cryosphere and hydrology of a mountainous catchment in the Upper Heihe River, China
Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes
Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling
Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use
Direct integration of reservoirs' operations in a hydrological model for streamflow estimation: coupling a CLSTM model with MOHID-Land
Skill of seasonal flow forecasts at catchment-scale: an assessment across South Korea
Modelling the regional sensitivity of snowmelt, soil moisture, and streamflow generation to climate over the Canadian Prairies using a basin classification approach
To what extent does river routing matter in hydrological modeling?
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
airGRteaching: an open-source tool for teaching hydrological modeling with R
To What Extent Do Extreme Storm Events Change Future Flood Hazards?
Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Short summary
Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Short summary
Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
Short summary
A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
Short summary
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
Short summary
Short summary
In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
Short summary
Short summary
Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Short summary
Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
Short summary
Short summary
Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
Short summary
Short summary
We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
Short summary
Short summary
Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
Short summary
Short summary
We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Franziska Maria Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
EGUsphere, https://doi.org/10.5194/egusphere-2024-864, https://doi.org/10.5194/egusphere-2024-864, 2024
Short summary
Short summary
We compare the catchment forcing data provided in large-sample datasets, namely the Caravan dataset and three of the original CAMELS datasets (US, BR, GB). We show that the differences affect hydrological model performance and that the data quality in the Caravan dataset is lower than the one in the CAMELS datasets, both for precipitation and potential evapotranspiration. We want to raise awareness of the lower data quality in Caravan and we suggest possible improvements for the Caravan dataset.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
Short summary
Short summary
Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
Short summary
Short summary
This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
Short summary
Short summary
To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
EGUsphere, https://doi.org/10.5194/egusphere-2024-629, https://doi.org/10.5194/egusphere-2024-629, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
Short summary
Short summary
Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-62, https://doi.org/10.5194/hess-2024-62, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Root zone storage capacity (Sumax) significantly changes over multiple decades reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
Short summary
Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
Short summary
Short summary
Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
Short summary
Short summary
In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
Short summary
Short summary
In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
Short summary
Short summary
This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
Short summary
Short summary
Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
Short summary
Short summary
We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
Short summary
Short summary
Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
Short summary
Short summary
How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Huy Dang and Yadu Pokhrel
EGUsphere, https://doi.org/10.5194/egusphere-2023-3158, https://doi.org/10.5194/egusphere-2023-3158, 2024
Short summary
Short summary
By examining basin-wide simulations of the river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River Basin (MRB) over the long-term; however, dams have largely altered the seasonality of Mekong’s flow regime and annual flooding patterns at major downstream areas in recent years. These findings could help rethink the planning of future dams and water resource management in the MRB.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
Short summary
Short summary
Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
Short summary
Short summary
We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
Short summary
Short summary
Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2023-3043, https://doi.org/10.5194/egusphere-2023-3043, 2023
Short summary
Short summary
An integrated cryospheric-hydrologic model, FLEX-Cryo, was developed, considered glaciers, snow cover, frozen soil, and their dynamic impacts on hydrology. We utilized the model to simulate the future changes in cryosphere and hydrology in Hulu catchment. Our projections showed that the two glaciers will melt out around 2050; snow cover will reduce; and permafrost will degrade. For hydrology, runoff will decrease after glacier melt out; and permafrost degradation will increase baseflow.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
Short summary
Short summary
Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
Short summary
Short summary
Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
Short summary
Short summary
An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
Short summary
Short summary
This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
EGUsphere, https://doi.org/10.5194/egusphere-2023-2169, https://doi.org/10.5194/egusphere-2023-2169, 2023
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (one or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
Short summary
Short summary
This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
Short summary
Short summary
This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
Short summary
Short summary
The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
Short summary
Short summary
A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
Short summary
Short summary
Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
EGUsphere, https://doi.org/10.5194/egusphere-2023-1969, https://doi.org/10.5194/egusphere-2023-1969, 2023
Short summary
Short summary
Due to climate change, flooding is expected to become more frequent globally in the coming decades. Locally, storm-induced channel geometry changes can drastically affect flood hazards, yet rivers are mostly treated as static elements in flood studies. This study tried to gain an understanding of the effects of major storm events on future flood hazards, promoting a framework for incorporating channel conveyance adjustments into flood hazard assessment.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
Short summary
Short summary
This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Cited articles
Ahmed, K., Shahid, S., Wang, X., Nawaz, N., and Khan, N.: Evaluation of
gridded precipitation datasets over arid regions of Pakistan, Water, 11,
210, https://doi.org/10.3390/w11020210, 2019.
Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model L4 monthly 0.25 × 0.25 degree V2.1, GES DISC [data set], https://doi.org/10.5067/SXAVCZFAQLNO, 2020.
Bierhuizen, J.: Some observations on the relation between transpiration and
soil moisture, Neth. J. Agr. Sci., 6, 94–98,
https://doi.org/10.18174/njas.v6i2.17713, 1958.
Budyko, M. I.: Climate and life, Academic Press, New York, 508 pp., ISBN 0121394506, 1974.
Caracciolo, D., Pumo, D., and Viola, F.: Budyko's based method for annual
runoff characterization across different climatic areas: an application to
United States, Water Resour. Manag., 32, 3189–3202, https://doi.org/10.1007/s11269-018-1984-7, 2018.
Cavanaugh, M. L., Kurc, S. A., and Scott, R. L.: Evapotranspiration
partitioning in semiarid shrubland ecosystems: a two-site evaluation of soil
moisture control on transpiration, Ecohydrology, 4, 671–681,
https://doi.org/10.1002/eco.157, 2011.
Chen, X. and Sivapalan, M.: Hydrological basis of the Budyko curve:
Data-guided exploration of the mediating role of soil moisture,
Water Resour. Res., 56, e2020WR028221,
https://doi.org/10.1029/2020WR028221, 2020.
Choudhury, B.: Evaluation of an empirical equation for annual evaporation
using field observations and results from a biophysical model,
J. Hydrol., 216, 99–110, https://doi.org/10.1016/S0022-1694(98)00293-5, 1999.
Degefu, M. A., Bewket, W., and Amha, Y.: Evaluating performance of 20 global
and quasi-global precipitation products in representing drought events in
Ethiopia I: Visual and correlation analysis, Weather and Climate Extremes,
35, 100416, https://doi.org/10.1016/j.wace.2022.100416, 2022.
de Lavenne, A. and Andréassian, V.: Impact of climate seasonality on
catchment yield: A parameterization for commonly-used water balance
formulas, J. Hydrol., 558, 266–274,
https://doi.org/10.1016/j.jhydrol.2018.01.009, 2018.
Du, X., Silwal, G., and Faramarzi, M.: Investigating the impacts of glacier
melt on stream temperature in a cold-region watershed: Coupling a glacier
melt model with a hydrological model, J. Hydrol., 605, 127303, https://doi.org/10.1016/j.jhydrol.2021.127303, 2022.
Ducharne, A., Laval, K., and Polcher, J.: Sensitivity of the hydrological
cycle to the parametrization of soil hydrology in a GCM, Clim. Dynam.,
14, 307–327, https://doi.org/10.1007/s003820050226, 1998.
Feng, X.: Global maps of seasonality indices, HydroShare [data set], http://www.hydroshare.org/resource/ff287c90c9e947a78e351c8d07d9d3f3 (last access: 11 April 2022), 2019.
Fiedler, K. and Döll, P.: Global modelling of continental water storage changes – sensitivity to different climate data sets, Adv. Geosci., 11, 63–68, https://doi.org/10.5194/adgeo-11-63-2007, 2007.
Frölicher, T. L., Fischer, E. M., and Gruber, N.: Marine heatwaves under
global warming, Nature, 560, 360–364, https://doi.org/10.1038/s41586-018-0383-9, 2018.
Fu, B.: On the calculation of the evaporation from land surface, Chinese
Journal of Atmospheric Sciences, 5, 23–31, https://doi.org/10.3878/j.issn.1006-9895.1981.01.03, 1981.
Gan, G., Liu, Y., and Sun, G.: Understanding interactions among climate,
water, and vegetation with the Budyko framework, Earth-Sci. Rev., 212,
103451, https://doi.org/10.1016/j.earscirev.2020.103451, 2021.
Gao, M., Chen, X., Liu, J., and Zhang, Z.: Regionalization of annual runoff
characteristics and its indication of co-dependence among
hydro-climate–landscape factors in Jinghe River Basin, China,
Stoch. Env. Res. Risk. A, 32, 1613–1630,
10.1007/s00477-017-1494-9, 2018.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019.
Goswami, M. and O'Connor, K. M.: A “monster” that made the SMAR conceptual
model “right for the wrong reasons”, Hydrolog. Sci. J., 55,
913–927, https://doi.org/10.1080/02626667.2010.505170, 2010.
Goswami, U. P. and Goyal, M. K.: Relative Contribution of Climate Variables
on Long-Term Runoff Using Budyko Framework, in: Water Resources Management
and Sustainability, Springer, 147–159, https://doi.org/10.1007/978-981-16-6573-8_7, 2022.
GRDC: Watershed Boundaries of GRDC Stations/Global Runoff Data Centre, Federal Institute of Hydrology (BfG), GRDC [data set], https://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/222_WSB/watershedBoundaries.html (last access: 16 April 2021), 2011.
GRDC: GRDC discharge data, GRDC [data set], https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html (last access: 16 April 2021), 2021,
Guan, X., Zhang, J., Yang, Q., and Wang, G.: Quantifying the effects of
climate and watershed structure changes on runoff variations in the Tao
River basin by using three different methods under the Budyko framework,
Theor. Appl. Climatol., 151, 1–14, https://doi.org/10.1007/s00704-021-03894-5, 2022.
Guo, A., Chang, J., Wang, Y., Huang, Q., Guo, Z., and Li, Y.: Uncertainty
analysis of water availability assessment through the Budyko framework,
J. Hydrol., 576, 396–407,
https://doi.org/10.1016/j.jhydrol.2019.06.033, 2019.
Havranek, W. M. and Benecke, U.: The influence of soil moisture on water
potential, transpiration and photosynthesis of conifer seedlings,
Plant and Soil, 49, 91–103, https://doi.org/10.1007/BF02149911, 1978.
Hu, Z., Zhou, Q., Chen, X., Li, J., Li, Q., Chen, D., Liu, W., and Yin, G.:
Evaluation of three global gridded precipitation data sets in central Asia
based on rain gauge observations, Int. J. Climatol., 38,
3475–3493, https://doi.org/10.1002/joc.5510, 2018.
Jiao, L., Lu, N., Fang, W., Li, Z., Wang, J., and Jin, Z.: Determining the
independent impact of soil water on forest transpiration: a case study of a
black locust plantation in the Loess Plateau, China, J. Hydrol.,
572, 671–681, https://doi.org/10.1016/j.jhydrol.2019.03.045,
2019.
Jin, Y., Liu, J., Lin, L., Wang, A., and Chen, X.: Exploring hydrologically
similar catchments in terms of the physical characteristics of upstream
regions, Hydrol. Res., 49, 1467–1483,
https://doi.org/10.2166/nh.2017.191, 2017.
Kanishka, G. and Eldho, T.: Watershed classification using isomap technique
and hydrometeorological attributes, J. Hydrol. Eng., 22,
04017040, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001562,
2017.
Kanishka, G. and Eldho, T.: Streamflow estimation in ungauged basins using
watershed classification and regionalization techniques,
J. Earth Syst. Sci., 129, 1–18,
https://doi.org/10.1007/s12040-020-01451-8, 2020.
Kim, D. and Chun, J. A.: Revisiting a Two-Parameter Budyko Equation With the
Complementary Evaporation Principle for Proper Consideration of Surface
Energy Balance, Water Resour. Res., 57, e2021WR030838,
https://doi.org/10.1029/2021WR030838, 2021.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R.
A.: Grouped Response Units for Distributed Hydrologic Modeling,
J. Water Res. Pl., 119, 289–305,
https://doi.org/10.1061/(ASCE)0733-9496(1993)119:3(289), 1993.
Lei, H., Yang, D., and Huang, M.: Impacts of climate change and vegetation
dynamics on runoff in the mountainous region of the Haihe River basin in the
past five decades, J. Hydrol., 511, 786–799,
https://doi.org/10.1016/j.jhydrol.2014.02.029, 2014.
Li, D., Pan, M., Cong, Z., Zhang, L., and Wood, E.: Vegetation control on
water and energy balance within the Budyko framework, Water Resour. Res., 49, 969–976, https://doi.org/10.1002/wrcr.20107,
2013.
Li, Y., Li, F., Shangguan, D., and Ding, Y.: A new global gridded glacier
dataset based on the Randolph Glacier Inventory version 6.0,
J. Glaciol., 67, 773–776, https://doi.org/10.1017/jog.2021.28,
2021.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., and Zhao, X.: The global land surface satellite (GLASS)
product suite, B. Am. Meteorol. Soc., 102,
E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liang, W., Bai, D., Wang, F., Fu, B., Yan, J., Wang, S., Yang, Y., Long, D.,
and Feng, M.: Quantifying the impacts of climate change and ecological
restoration on streamflow changes based on a Budyko hydrological model in
China's Loess Plateau, Water Resour. Res., 51, 6500–6519,
https://doi.org/10.1002/2014WR016589, 2015.
Liu, Q. and Liang, L.: Impacts of climate change on the water balance of a
large nonhumid natural basin in China, Theor. Appl. Climatol.,
121, 489–497, https://doi.org/10.1007/s00704-014-1255-3, 2015.
Liu, J., You, Y., Zhang, Q., and Gu, X.: Attribution of streamflow changes
across the globe based on the Budyko framework, Sci. Total Environ., 794, 148662,
https://doi.org/10.1016/j.scitotenv.2021.148662, 2021.
Liu, J., Long, A., Deng, X., Yin, Z., Deng, M., An, Q., Gu, X., Li, S., and
Liu, G.: The Impact of Climate Change on Hydrological Processes of the
Glacierized Watershed and Projections, Remote Sens., 14, 1314,
https://doi.org/10.3390/rs14061314, 2022.
Liu, S., Wang, X., Zhang, L., Kong, W., Gao, H., and Xiao, C.: Effect of
glaciers on the annual catchment water balance within Budyko framework,
Advances in Climate Change Research, 13, 51–62,
https://doi.org/10.1016/j.accre.2021.10.004, 2022.
Metselaar, K. and de Jong van Lier, Q.: The shape of the transpiration
reduction function under plant water stress, Vadose Zone J., 6,
124–139, https://doi.org/10.2136/vzj2006.0086, 2007.
Mezentsev, V.: Back to the computation of total evaporation, Meteorologia i
Gidrologia, 5, 24–26, 1955.
Milly, P. and Shmakin, A.: Global modeling of land water and energy
balances. Part II: Land-characteristic contributions to spatial variability,
J. Hydrometeorol., 3, 301–310,
https://doi.org/10.1175/1525-7541(2002)003<0301:Gmolwa>2.0.Co;2, 2002.
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.
National Earth System Science Data: The GLASS FVC product, National Earth System Science Data [data set], http://www.glass.umd.edu/05D/FVC/ (last access: 22 December 2021), 2020.
Ning, T., Li, Z., and Liu, W.: Vegetation dynamics and climate seasonality jointly control the interannual catchment water balance in the Loess Plateau under the Budyko framework, Hydrol. Earth Syst. Sci., 21, 1515–1526, https://doi.org/10.5194/hess-21-1515-2017, 2017.
NOAANCEI: Monthly National Climate Report for July 2011, NOAA National Centers for Environmental Information, https://www.ncei.noaa.gov/access/monitoring/monthly-report/national/201107 (last access: 8 June 2022), 2011.
Padrón, R. S., Gudmundsson, L., Greve, P., and Seneviratne, S. I.:
Large-scale controls of the surface water balance over land: Insights from a
systematic review and meta-analysis, Water Resour. Res., 53,
9659–9678, https://doi.org/10.1002/2017WR021215, 2017.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn:
Machine learning in Python, J. Mach. Learn. Res., 12,
2825–2830, 2011.
Rau, P., Bourrel, L., Labat, D., Frappart, F., Ruelland, D., Lavado, W.,
Dewitte, B., and Felipe, O.: Hydroclimatic change disparity of Peruvian
Pacific drainage catchments, Theor. Appl. Climatol., 134,
139-153, https://doi.org/10.1007/s00704-017-2263-x, 2018.
Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., and Bosilovich, M.: The
global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Roderick, M. L. and Farquhar, G. D.: A simple framework for relating
variations in runoff to variations in climatic conditions and catchment
properties, Water Resour. Res., 47, W00G07, https://doi.org/10.1029/2010wr009826, 2011.
Salaudeen, A., Ismail, A., Adeogun, B. K., Ajibike, M. A., and Zubairu, I.:
Evaluation of ground-based, daily, gridded precipitation products for Upper
Benue River basin, Nigeria, Engineering and Applied Science Research, 48,
397–405, https://doi.org/10.14456/easr.2021.42, 2021.
Santra, P., Das, B. S., and Chakravarty, D.: Delineation of hydrologically
similar units in a watershed based on fuzzy classification of soil hydraulic
properties, Hydrol. Process., 25, 64–79,
https://doi.org/10.1002/hyp.7820, 2011.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., and Ziese, M.: GPCC Full Data Monthly Product Version 2018 at 0.5∘: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data, DWD [data set], https://doi.org/10.5676/DWD_GPCC/FD_M_V2018_050, 2018.
Schwarzel, K., Zhang, L., Montanarella, L., Wang, Y., and Sun, G.: How
afforestation affects the water cycle in drylands: A process-based
comparative analysis, Glob. Change Biol., 26, 944–959, https://doi.org/10.1111/gcb.14875, 2020.
Scientific Data Curation Team: Metadata record for: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, figshare [data set], https://doi.org/10.6084/m9.figshare.11980500.v1, 2020.
Sinha, J., Jha, S., and Goyal, M. K.: Influences of watershed
characteristics on long-term annual and intra-annual water balances over
India, J. Hydrol., 577, 123970,
https://doi.org/10.1016/j.jhydrol.2019.123970, 2019.
Sivapalan, M.: Process complexity at hillslope scale, process simplicity at
the watershed scale: is there a connection?, Hydrol. Process., 17,
1037–1041, https://doi.org/10.1002/hyp.5109, 2003.
Tixeront, J.: Prévision des apports des cours d'eau, in: Symposium, surface waters: hold at the occasion of the General Assembly of Berkeley of the I.U.G.G./World Meteorological Organization and International Association of Scientific Hydrology, International Association of Scientific Hydrology, Gentbrugge, Belgique, 118–126, LCCN 66038288, 1964.
Turc, L.: The water balance of soils: relation between precipitation,
evaporation and flow, Ann. Agron, 5, 491–569, 1954.
UNEP: World atlas of desertification, United Nations Environment Programme, UNEP [data set], https://wedocs.unep.org/20.500.11822/30300 (last access: 16 April 2021), 1997.
Verhoef, A. and Egea, G.: Modeling plant transpiration under limited soil
water: Comparison of different plant and soil hydraulic parameterizations
and preliminary implications for their use in land surface models,
Agr. Forest Meteorology, 191, 22–32,
https://doi.org/10.1016/j.agrformet.2014.02.009, 2014.
Vora, A. and Singh, R.: Satellite based Budyko framework reveals the human
imprint on long-term surface water partitioning across India, J. Hydrol., 602, 126770,
https://doi.org/10.1016/j.jhydrol.2021.126770, 2021.
Walsh, R. and Lawler, D.: Rainfall seasonality: description, spatial
patterns and change through time, Weather, 36, 201–208,
https://doi.org/10.1002/j.1477-8696.1981.tb05400.x, 1981.
Wang, D. and Tang, Y.: A one-parameter Budyko model for water balance
captures emergent behavior in Darwinian hydrologic models, Geophys.
Res. Lett., 41, 4569–4577,
https://doi.org/10.1002/2014gl060509, 2014.
Wang, F., Xia, J., Zou, L., Zhan, C., and Liang, W.: Estimation of
time-varying parameter in Budyko framework using long short-term memory
network over the Loess Plateau, China, J. Hydrol., 607, 127571, https://doi.org/10.1016/j.jhydrol.2022.127571, 2022.
Wang, H., Lv, X., and Zhang, M.: Sensitivity and attribution analysis of
vegetation changes on evapotranspiration with the Budyko framework in the
Baiyangdian catchment, China, Ecol. Indic., 120, 106963,
https://doi.org/10.1016/j.ecolind.2020.106963, 2021.
Wang, Y., Bredemeier, M., Bonell, M., Yu, P., Feger, K.-H., Xiong, W., and Xu, L.: Comparison between a statistical approach and paired catchment study in estimating water yield response to afforestation, in: Revisiting Experimental Catchment Studies in Forest Hydrology: Proceedings of a Workshop held during the XXV IUGG General Assembly in Melbourne, June–July 2011, edited by: Webb, A. A., Bonell, M., Bren, L., Lane, P. N. J., McGuire, D., Neary, D. G., Nettles, J., Scott, D. F., Stednick, J., and Wang, Y., International Association of Hydrological Sciences (IAHS), 3–11, ISBN 9781907161315, 2012.
Xu, X., Liu, W., Scanlon, B. R., Zhang, L., and Pan, M.: Local and global
factors controlling water-energy balances within the Budyko framework,
Geophys. Res. Lett., 40, 6123–6129,
https://doi.org/10.1002/2013gl058324, 2013.
Yang, D., Shao, W., Yeh, P. J. F., Yang, H., Kanae, S., and Oki, T.: Impact
of vegetation coverage on regional water balance in the nonhumid regions of
China, Water Resour. Res., 45, W00A14, https://doi.org/10.1029/2008WR006948, 2009.
Yang, H., Yang, D., Lei, Z., and Sun, F.: New analytical derivation of the
mean annual water-energy balance equation, Water Resour. Res., 44, W03410,
https://doi.org/10.1029/2007wr006135, 2008.
Yang, H., Qi, J., Xu, X., Yang, D., and Lv, H.: The regional variation in
climate elasticity and climate contribution to runoff across China, J. Hydrol., 517, 607–616,
https://doi.org/10.1016/j.jhydrol.2014.05.062, 2014.
Yao, J., Mao, W., Yang, Q., Xu, X., and Liu, Z.: Annual actual
evapotranspiration in inland river catchments of China based on the Budyko
framework, Stoch. Env. Res. Risk. A, 31,
1409–1421, https://doi.org/10.1007/s00477-016-1271-1, 2017.
Yao, W., Xiao, P., Shen, Z., Wang, J., and Jiao, P.: Analysis of the
contribution of multiple factors to the recent decrease in discharge and
sediment yield in the Yellow River Basin, China, J. Geogr. Sci., 26, 1289–1304,
https://doi.org/10.1007/s11442-016-1227-7, 2016.
Yu, K., Zhang, X., Xu, B., Li, P., Zhang, X., Li, Z., and Zhao, Y.:
Evaluating the impact of ecological construction measures on water balance
in the Loess Plateau region of China within the Budyko framework, J. Hydrol., 601, 126596, https://doi.org/10.1016/j.jhydrol.2021.126596, 2021.
Zhang, L., Dawes, W., and Walker, G.: Response of mean annual
evapotranspiration to vegetation changes at catchment scale, Water Resour. Res., 37, 701–708, https://doi.org/10.1029/2000WR900325,
2001.
Zhang, S., Yang, Y., McVicar, T. R., and Yang, D.: An analytical solution
for the impact of vegetation changes on hydrological partitioning within the
Budyko framework, Water Resour. Res., 54, 519–537,
https://doi.org/10.1002/2017wr022028, 2018.
Zhou, G., Wei, X., Chen, X., Zhou, P., Liu, X., Xiao, Y., Sun, G., Scott, D.
F., Zhou, S., and Han, L.: Global pattern for the effect of climate and land
cover on water yield, Nat. Commun., 6, 1–9,
https://doi.org/10.1038/ncomms6918, 2015.
Short summary
This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
This study addresses the quantification and estimation of the watershed-characteristic-related...
