Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0)
Yunfei Wang
College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, China
Institute of Water Saving Agriculture in Arid Regions of China (IWSA), Northwest Agriculture and Forestry University, Yangling, China
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Northwest Agriculture and Forestry University, Yangling, China
Yijian Zeng
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
Lianyu Yu
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
Peiqi Yang
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
Christiaan Van der Tol
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
Qiang Yu
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Northwest Agriculture and Forestry University, Yangling, China
Xiaoliang Lü
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Water and Soil Conservation, Northwest Agriculture and Forestry University, Yangling, China
Huanjie Cai
CORRESPONDING AUTHOR
College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, China
Institute of Water Saving Agriculture in Arid Regions of China (IWSA), Northwest Agriculture and Forestry University, Yangling, China
Zhongbo Su
CORRESPONDING AUTHOR
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, School of Water and Environment, Chang'an University, Xi'an, China
Related authors
No articles found.
Qianqian Han, Yijian Zeng, Yunfei Wang, Fakhereh Sarah Alidoost, Francesco Nattino, Yang Liu, and Bob Su
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-183, https://doi.org/10.5194/essd-2025-183, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Understanding how land interacts with the atmosphere is crucial for studying climate change, yet global high-resolution data on energy, water, and carbon exchanges remain limited. This study introduces a new dataset that estimates these exchanges hourly from 2000 to 2020 by combining physical process model, field measurements, and machine learning with satellite and meteorological data. Our dataset provides valuable insights into how ecosystems respond to climate extremes worldwide.
Xuetong Wang, Liang He, Peng Li, Jiageng Ma, Yu Shi, Qi Tian, Gang Zhao, Jianqiang He, Hao Feng, Hao Shi, and Qiang Yu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-192, https://doi.org/10.5194/essd-2025-192, 2025
Preprint under review for ESSD
Short summary
Short summary
This study developed a high-resolution daily soil temperature dataset across China from 2010 to 2020. By combining ground measurements, satellite observations, and weather data with a machine learning method, we accurately captured the spatial and temporal variations of soil temperature at different depths. The dataset offers a scientific basis for agricultural management and ecological research.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Zengjing Song, Yijian Zeng, Yunfei Wang, Enting Tang, Danyang Yu, Fakhereh Alidoost, Mingguo Ma, Xujun Han, Xuguang Tang, Zhongjing Zhu, Yao Xiao, Debing Kong, and Zhongbo Su
EGUsphere, https://doi.org/10.5194/egusphere-2024-2940, https://doi.org/10.5194/egusphere-2024-2940, 2024
Preprint archived
Short summary
Short summary
The exchange of water and carbon between the plant and the atmosphere is affected under water stress conditions. In this study, a leaf-water-potential-based water stress factor is considered in the STEMMUS-SCOPE (hereafter STEMMUS-SCOPE-PHS), to replace the conventional soil-moisture-based water stress factor. The results show that leaf water potential reflects the plant water stress well, and the STEMMUS-SCOPE-PHS outperforms STEMMUS-SCOPE in the dynamics of the water, energy and carbon fluxes.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Yunfei Wang, Yijian Zeng, Zengjing Song, Danyang Yu, Qianqian Han, Enting Tang, Henk de Bruin, and Zhongbo Su
EGUsphere, https://doi.org/10.5194/egusphere-2024-1321, https://doi.org/10.5194/egusphere-2024-1321, 2024
Preprint archived
Short summary
Short summary
Various methods were proposed to estimate irrigation water requirements (IWR). However, the simulated IWR exhibits large differences. This study evaluates six potential evapotranspiration (PET) methods and proposes a practical approach to estimate IWR. The radiation-based methods show promise in approximating daily PET accurately, and the STEMMUS-SCOPE model can reliably estimate IWR. This research enhances our understanding of different PET methods and their implications for water management.
Enting Tang, Yijian Zeng, Yunfei Wang, Zengjing Song, Danyang Yu, Hongyue Wu, Chenglong Qiao, Christiaan van der Tol, Lingtong Du, and Zhongbo Su
Biogeosciences, 21, 893–909, https://doi.org/10.5194/bg-21-893-2024, https://doi.org/10.5194/bg-21-893-2024, 2024
Short summary
Short summary
Our study shows that planting shrubs in a semiarid grassland reduced the soil moisture and increased plant water uptake and transpiration. Notably, the water used by the ecosystem exceeded the rainfall received during the growing seasons, indicating an imbalance in the water cycle. The findings demonstrate the effectiveness of the STEMMUS–SCOPE model as a tool to represent ecohydrological processes and highlight the need to consider energy and water budgets for future revegetation projects.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, and Yijian Zeng
Hydrol. Earth Syst. Sci., 27, 2579–2590, https://doi.org/10.5194/hess-27-2579-2023, https://doi.org/10.5194/hess-27-2579-2023, 2023
Short summary
Short summary
Our knowledge on sources and dynamics of rock moisture is limited. By using frequency domain reflectometry (FDR), we monitored rock moisture in a cave. The results of an explainable deep learning model reveal that the direct source of rock moisture responsible for weathering in the studied cave is vapour, not infiltrating precipitation. A physics-informed deep learning model, which uses variables controlling vapor condensation as model inputs, leads to accurate rock water content predictions.
Lianyu Yu, Yijian Zeng, Huanjie Cai, Mengna Li, Yuanyuan Zha, Jicai Zeng, Hui Qian, and Zhongbo Su
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-221, https://doi.org/10.5194/gmd-2022-221, 2023
Revised manuscript not accepted
Short summary
Short summary
We developed a coupled soil water-groundwater (SW-GW) model, which is verified as physically accurate and applicable in large-scale groundwater problems. The role of vadose zone processes, coupling approach, and spatiotemporal heterogeneity of SW-GW interactions were highlighted as essential to represent the SW-GW system. Given the relevant dataset, the developed SW-GW modeling framework has the potential to portray the processes "from bedrock to atmosphere" in a physically consistent manner.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yaoming Ma, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 14, 5513–5542, https://doi.org/10.5194/essd-14-5513-2022, https://doi.org/10.5194/essd-14-5513-2022, 2022
Short summary
Short summary
Soil moisture and soil temperature (SMST) are important state variables for quantifying the heat–water exchange between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements at multiple depths are scarce on the Tibetan Plateau (TP). The presented dataset would be valuable for the evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change.
Hong Zhao, Yijian Zeng, Jan G. Hofste, Ting Duan, Jun Wen, and Zhongbo Su
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-333, https://doi.org/10.5194/hess-2022-333, 2022
Revised manuscript not accepted
Short summary
Short summary
This paper demonstrated the capability of our developed platform for simulating microwave emission and backscatter signals at multi-frequency. The results of associated investigations on impacts of vegetation water (VW) and temperature (T) imply the need to first disentangle the impact of T for the use of high-frequency signals as its variation is more due to dynamic T. Estimated vegetation optical depth is frequency-dependent, while its diurnal variation depends on that of VW despite frequency.
Alby Duarte Rocha, Stenka Vulova, Christiaan van der Tol, Michael Förster, and Birgit Kleinschmit
Hydrol. Earth Syst. Sci., 26, 1111–1129, https://doi.org/10.5194/hess-26-1111-2022, https://doi.org/10.5194/hess-26-1111-2022, 2022
Short summary
Short summary
Evapotranspiration (ET) is a sum of soil evaporation and plant transpiration. ET produces a cooling effect to mitigate heat waves in urban areas. Our method uses a physical model with remote sensing and meteorological data to predict hourly ET. Designed for uniform vegetation, it overestimated urban ET. To correct it, we create a factor using vegetation fraction that proved efficient for reducing bias and improving accuracy. This approach was tested on two Berlin sites and can be used to map ET.
P. E. K. Campbell, K. F. Huemmrich, E. M. Middleton, J. Alfieri, C. van der Tol, and C. S. R. Neigh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 1–8, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, 2022
Shaoning Lv, Clemens Simmer, Yijian Zeng, Jun Wen, Yuanyuan Guo, and Zhongbo Su
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-369, https://doi.org/10.5194/tc-2021-369, 2022
Preprint withdrawn
Short summary
Short summary
The freeze-thaw of the ground is an interesting topic to climatology, hydrology, and other earth sciences. The global freeze-thaw distribution is available by passive microwave remote sensing technique. However, the remote sensing technique indirectly detects freeze-thaw states by measuring the brightness temperature difference between frozen and unfrozen soil. Thus, we present different interprets of the brightness signals to the FT-state by using its sub-daily character.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
Short summary
Short summary
We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
Short summary
Short summary
The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Hong-Yu Xie, Xiao-Wei Jiang, Shu-Cong Tan, Li Wan, Xu-Sheng Wang, Si-Hai Liang, and Yijian Zeng
Hydrol. Earth Syst. Sci., 25, 4243–4257, https://doi.org/10.5194/hess-25-4243-2021, https://doi.org/10.5194/hess-25-4243-2021, 2021
Short summary
Short summary
Freezing-induced groundwater migration and water table decline are widely observed, but quantitative understanding of these processes is lacking. By considering wintertime atmospheric conditions and occurrence of lateral groundwater inflow, a model coupling soil water and groundwater reproduced field observations of soil temperature, soil water content, and groundwater level well. The model results led to a clear understanding of the balance of the water budget during the freezing–thawing cycle.
Peiqi Yang, Egor Prikaziuk, Wout Verhoef, and Christiaan van der Tol
Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, https://doi.org/10.5194/gmd-14-4697-2021, 2021
Short summary
Short summary
Since the first publication 12 years ago, the SCOPE model has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
Short summary
Short summary
Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3075–3102, https://doi.org/10.5194/essd-13-3075-2021, https://doi.org/10.5194/essd-13-3075-2021, 2021
Short summary
Short summary
This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface soil moisture (SM) dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs. This surface SM dataset includes the original 15 min in situ measurements collected by multiple SM monitoring sites of three networks (i.e. the Maqu, Naqu, and Ngari networks) and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
Jan G. Hofste, Rogier van der Velde, Jun Wen, Xin Wang, Zuoliang Wang, Donghai Zheng, Christiaan van der Tol, and Zhongbo Su
Earth Syst. Sci. Data, 13, 2819–2856, https://doi.org/10.5194/essd-13-2819-2021, https://doi.org/10.5194/essd-13-2819-2021, 2021
Short summary
Short summary
The dataset reported in this paper concerns the measurement of microwave reflections from an alpine meadow over the Tibetan Plateau. These microwave reflections were measured continuously over 1 year. With it, variations in soil water content due to evaporation, precipitation, drainage, and soil freezing/thawing can be seen. A better understanding of the effects aforementioned processes have on microwave reflections may improve methods for estimating soil water content used by satellites.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
María P. González-Dugo, Xuelong Chen, Ana Andreu, Elisabet Carpintero, Pedro J. Gómez-Giraldez, Arnaud Carrara, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, https://doi.org/10.5194/hess-25-755-2021, 2021
Short summary
Short summary
Drought is a devastating natural hazard and difficult to define, detect and quantify. Global meteorological data and remote-sensing products present new opportunities to characterize drought in an objective way. In this paper, we applied the surface energy balance model SEBS to estimate monthly evapotranspiration (ET) from 2001 to 2018 over the dehesa area of the Iberian Peninsula. ET anomalies were used to identify the main drought events and analyze their impacts on dehesa vegetation.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
Short summary
Short summary
NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Peiqi Yang, Christiaan van der Tol, Petya K. E. Campbell, and Elizabeth M. Middleton
Biogeosciences, 18, 441–465, https://doi.org/10.5194/bg-18-441-2021, https://doi.org/10.5194/bg-18-441-2021, 2021
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) has the potential to facilitate the monitoring of photosynthesis from space. This study presents a systematic analysis of the physical and physiological meaning of the relationship between fluorescence and photosynthesis at both leaf and canopy levels. We unravel the individual effects of incoming light, vegetation structure and leaf physiology and highlight their joint effects on the relationship between canopy fluorescence and photosynthesis.
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
Short summary
Short summary
The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Bart Schilperoort, Miriam Coenders-Gerrits, César Jiménez Rodríguez, Christiaan van der Tol, Bas van de Wiel, and Hubert Savenije
Biogeosciences, 17, 6423–6439, https://doi.org/10.5194/bg-17-6423-2020, https://doi.org/10.5194/bg-17-6423-2020, 2020
Short summary
Short summary
With distributed temperature sensing (DTS) we measured a vertical temperature profile in a forest, from the forest floor to above the treetops. Using this temperature profile we can see which parts of the forest canopy are colder (thus more dense) or warmer (and less dense) and study the effect this has on the suppression of turbulent mixing. This can be used to improve our knowledge of the interaction between the atmosphere and forests and improve carbon dioxide flux measurements over forests.
Xu Yuan, Xiaolong Yu, and Zhongbo Su
Ocean Sci., 16, 1285–1296, https://doi.org/10.5194/os-16-1285-2020, https://doi.org/10.5194/os-16-1285-2020, 2020
Short summary
Short summary
This work investigates the variabilities of the barrier layer thickness (BLT) in the tropical Indian Ocean with the Simple Ocean Data Assimilation version 3 ocean reanalysis data. Our results show that the seasonal variation of the BLT is in relation to the changes of thermocline and sea surface salinity. In terms of the interannual timescale, BLT presents a clear seasonal phase locking dominated by different drivers during the Indian Dipole and El Niño–Southern Oscillation events.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Hydrol. Earth Syst. Sci., 24, 4813–4830, https://doi.org/10.5194/hess-24-4813-2020, https://doi.org/10.5194/hess-24-4813-2020, 2020
Short summary
Short summary
Soil mass and heat transfer processes were represented in three levels of model complexities to understand soil freeze–thaw mechanisms. Results indicate that coupled mass and heat transfer models considerably improved simulations of the soil hydrothermal regime. Vapor flow and thermal effects on water flow are the main mechanisms for the improvements. Given the explicit consideration of airflow, vapor flow and its effects on heat transfer were enhanced during the freeze–thaw transition period.
Cited articles
Amato, M. and Ritchie, J. T.: Spatial distribution of roots and water uptake of maize (Zea mays L.) as affected by soil structure, Crop Sci., 42, 773–780, https://doi.org/10.2135/cropsci2002.7730, 2002.
Amenu, G. G. and Kumar, P.: A model for hydraulic redistribution incorporating coupled soil-root moisture transport, Hydrol. Earth Syst. Sci., 12, 55–74, https://doi.org/10.5194/hess-12-55-2008, 2008.
Aston, M. and Lawlor, D. W.: The relationship between transpiration, root water uptake, and leaf water potential, J. Exp. Bot., 30, 169-181, https://doi.org/10.1093/jxb/30.1.169, 1979.
Bayat, B., Van der Tol, C., and Verhoef, W.: Integrating satellite optical and thermal infrared observations for improving daily ecosystem functioning estimations during a drought episode, Remote Sens. Environ., 209, 375–394, https://doi.org/10.1016/j.rse.2018.02.027, 2018.
Bayat, B., Van der Tol, C., Yang, P., and Verhoef, W.: Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions, Remote Sens. Environ., 221, 286–301, https://doi.org/10.1016/j.rse.2018.11.021, 2019.
Beaudoin, N., Mary, B., Launay, M., and Brisson, N.: Conceptual basis, formalisations and parameterization of the STICS crop model, Editions Quae, Versailles Cedex, France, 2009.
Bingham, I. J. and Wu, L.: Simulation of wheat growth using the 3D root architecture model SPACSYS: validation and sensitivity analysis, Eur. J. Agron., 34, 181–189, https://doi.org/10.1016/j.eja.2011.01.003, 2011.
Caldwell, M. M., Dawson, T. E., and Richards, J. H.: Hydraulic lift: consequences of water efflux from the roots of plants, Oecologia, 113, 151–161, https://doi.org/10.1007/s004420050363, 1998.
Camargo, G. and Kemanian, A.: Six crop models differ in their simulation of water uptake, Agr. Forest Meteorol., 220, 116–129, https://doi.org/10.1016/j.agrformet.2016.01.013, 2016.
Chassot, A., Stamp, P., and Richner, W.: Root distribution and morphology of maize seedlings as affected by tillage and fertilizer placement, Plant Soil, 231, 123–135, https://doi.org/10.1023/A:1010335229111, 2001.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration, Agr. Forest Meteorol. 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Collatz, G. J., Ribas-Carbo, and M., Berry, J. A.: Coupled photosynthesis-stomatal conductance model for leaves of C4 plants, Aust. J. Plant Physiol., 19, 519–538, 1992.
Couvreur, V., Vanderborght, J., and Javaux, M.: A simple three-dimensional macroscopic root water uptake model based on the hydraulic architecture approach, Hydrol. Earth Syst. Sci., 16, 2957–2971, https://doi.org/10.5194/hess-16-2957-2012, 2012.
De Kauwe, M. G., Zhou, S.-X., Medlyn, B. E., Pitman, A. J., Wang, Y.-P., Duursma, R. A., and Prentice, I. C.: Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe, Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, 2015.
De Kauwe, M. G., Medlyn, B. E., Ukkola, A. M., Mu, M., Sabot, M. E., Pitman, A. J., Meir, P., Cernusak, L., Rifai, S. W., Choat, B., Tissue, D. T., Blackman, C. J., Li, X., Roderick, M., and Briggs, P. R.: Identifying areas at risk of drought-induced tree mortality across SouthEastern Australia, Glob. Change Biol., 26, 5716–5733, https://doi.org/10.1111/gcb.15215, 2020.
Deng, Z., Guan, H., Hutson, J., Forster, M. A., Wang, Y., and Simmons, C. T.: A vegetation focused soil-plant-atmospheric continuum model to study hydrodynamic soil-plant water relations, Water Resour. Res., 53, 4965–4983, https://doi.org/10.1002/2017WR020467, 2017.
Elfving, D. C., Kaufmann, M. R., and Hall, A. E.: Interpreting leaf water potential measurements with a model of the soil-plant-atmosphere continuum, Physiol. Plant., 27, 161–168, https://doi.org/10.1111/j.1399-3054.1972.tb03594.x, 1972.
Eller, C. B., Rowland, L., Mencuccini, M., Rosas, T., Williams, K., Harper, A., Medlyn, B., Wagner, Y., Klein, T., Teodoro, G., Oliveira, R., Matos, I., Rosado, B. H. P., Fuchs, K., Wohlfahrt, G., Montagnani, L., Meir, P., Sitch, S., and Cox, P.: Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate, New Phytol., 226, 1622–1637, https://doi.org/10.1111/nph.16419, 2020.
Espeleta, J., West, J., and Donovan, L.: Species-specific patterns of hydraulic lift in co-occurring adult trees and grasses in a sandhill community, Oecologia, 138, 341–349, https://doi.org/10.1007/s00442-004-1539-x, 2004.
Fan, X., Hu, H., Huang, G., Huang, F., Li, Y., and Palta, J.: Soil inoculation with Burkholderia sp. LD-11 has positive effect on water-use efficiency in inbred lines of maize, Plant Soil, 390, 337–349, https://doi.org/10.1007/s11104-015-2410-z, 2015.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/bf00386231, 1980.
Fu, C., Wang, G., Goulden, M. L., Scott, R. L., Bible, K., and G. Cardon, Z.: Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites, Hydrol. Earth Syst. Sci., 20, 2001–2018, https://doi.org/10.5194/hess-20-2001-2016, 2016.
Guo, Y.: Simulation of water transport in the soil-plant-atmosphere system, Iowa State University, USA, https://doi.org/10.31274/rtd-180813-9473, 1992.
Jarvis, N. J.: Simple physics-based models of compensatory plant water uptake: concepts and eco-hydrological consequences, Hydrol. Earth Syst. Sci., 15, 3431–3446, https://doi.org/10.5194/hess-15-3431-2011, 2011.
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L., Wilkens, P. W., Singh, U., Gijsman, A. J., and Ritchie, J. T.: The DSSAT cropping system model, Eur. J. Agron., 18, 235–265, https://doi.org/10.1016/s1161-0301(02)00107-7, 2003.
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N., Meinke, H., and Hochman, Z.: An overview of APSIM, a model designed for farming systems simulation, Eur. J. Agron., 18, 267–288, https://doi.org/10.1016/s1161-0301(02)00108-9, 2003.
Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R. A., da Costa, A., and Gentine, P.: Implementing plant hydraulics in the community land model, version 5, J. Adv. Model. Earth Sy., 11, 485–513, https://doi.org/10.1029/2018MS001500, 2019.
Klepper, B., Rickman, R. W., and Taylor, H. M.: Farm management and the function of field crop root systems, Agric. Water Manage., 7, 115–141, https://doi.org/10.1016/0378-3774(83)90078-1, 1983.
Krinner, G., N. Viovy, de Noblet-Ducoudre, N., Ogee, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice. I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Lawrence, D., Fisher, R., Koven, C., Oleson, K., Swenson, S., and Vertenstein, M.: Technical Description of version 5.0 of the Community LandModel (CLM), 2020.
Leitner, D., Klepsch, S., Bodner, G., and Schnepf, A.: A dynamic root system growth model based on L-Systems, Plant Soil, 332, 177–192, https://doi.org/10.1007/s11104-010-0284-7, 2010.
Mackay, D. S., Savoy, P. R., Grossiord, C., Tai, X., Oleban, J., Wang, D., McDowell, N., Adams, H., Sperry, J. S.: Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics, New Phytol., 225, 679–692, https://doi.org/10.1111/nph.16043, 2019.
Martineau, E., Domec, J.-C., Bosc, A., Denoroy, P., Fandino, V. r.A., Lavres Jr., J., and Jordan-Meille, L.: The effects of potassium nutrition on water use in field-grown maize (Zea mays L.), Environ. Exp. Bot., 134, 62–71, https://doi.org/10.1016/j.envexpbot.2016.11.004, 2017.
Medlyn, B. E., Kauwe, M. G. D., and Duursma, R. A.: New developments in the effort to model ecosystems under water stress, New Phytol., 212, 5–7, https://doi.org/10.1111/nph.14082, 2016.
Mcdowell, N. G., Brodribb, T. J., and Nardini, A.: Hydraulics in the 21st century, New Phytol., 224, 537–542, https://doi.org/10.1111/nph.16151, 2019.
Mohammed, G. H., Colombo, R., Middleton, E. M., Rascher, U., Van der Tol, C., Nedbal, L., Goulas Y., Perez-Priego, O., Damm A., Meroni M., Joiner J., Cogliati S., Verhoef W., Malenovsky Z., Gastellu-Etchegorry J., Miller, J., Guanter, L., Moreno, J., Berry, J., Frankenberg, C., Zarco-Tejada, P. J.: Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress, Remote Sens. Environ., 231, 111177, https://doi.org/10.1016/j.rse.2019.04.030, 2019.
Ning, P., Li, S., White, P. J., and Li, C.: Maize varieties released in different eras have similar root length density distributions in the soil, which are negatively correlated with local concentrations of soil mineral nitrogen, PLOS ONE, 10, e0121892, https://doi.org/10.1371/journal.pone.0121892, 2015.
Niu, G., Fang, Y., Chang, L., Jin, J., Yuan, H., and Zeng, X.: Enhancing the Noah-MP ecosystem response to droughts with an explicit representation of plant water storage supplied by dynamic root water uptake, J. Adv. Model. Earth Syst., 12, e2020MS002062, https://doi.org/10.1029/2020MS002062, 2020.
O'Toole, J. C. and Cruz, R. T.: Response of leaf water potential, stomatal resistance, and leaf rolling to water stress, Plant Physiol., 65, 428–432, https://doi.org/10.1104/pp.65.3.428, 1980.
Oikeh, S., Kling, J., Horst, W., Chude, V., and Carsky, R.: Growth and distribution of maize roots under nitrogen fertilization in plinthite soil, Field Crop. Res., 62, 1–13, https://doi.org/10.1016/s0378-4290(98)00169-5, 1999.
Peng, Y., Yu, P., Zhang, Y., Sun, G., Ning, P., Li, X., and Li, C.: Temporal and spatial dynamics in root length density of field-grown maize and NPK in the soil profile, Field Crop. Res., 131, 9–16, https://doi.org/10.1016/j.fcr.2012.03.003, 2012.
Qin, R., Stamp, P., and Richner, W.: Impact of tillage on maize rooting in a Cambisol and Luvisol in Switzerland, Soil Till. Res., 85, 50–61, https://doi.org/10.1016/j.still.2004.12.003, 2006.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., and Granier, A.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Reid, J. B. and Huck, M. G.: Diurnal variation of crop hydraulic resistance: a new analysis, Agron. J., 82, 827–834. https://doi.org/10.1016/0378-3774(90)90029-X, 1990.
Richards, J. H. and Caldwell, M. M.: Hydraulic lift: substantial nocturnal water transport between soil layers by Artemisia tridentata roots, Oecologia, 73, 486–489, https://doi.org/10.1007/bf00379405, 1987.
Robertson, M., Fukai, S., Hammer, G., and Ludlow, M.: Modelling root growth of grain sorghum using the CERES approach, Field Crop. Res., 33, 113–130, https://doi.org/10.1016/0378-4290(93)90097-7, 1993.
Ryel, R., Caldwell, M., Yoder, C., Or, D., and Leffler, A.: Hydraulic redistribution in a stand of Artemisia tridentata: evaluation of benefits to transpiration assessed with a simulation model, Oecologia, 130, 173–184, https://doi.org/10.1007/s004420100794, 2002.
Schroder, J., Groenwold, J., and Zaharieva, T.: Soil mineral nitrogen availability to young maize plants as related to root length density distribution and fertilizer application method, NJAS-Wagen, J. Life Sci., 44, 209–225, 1996.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture-climate interactions in a changing climate: a review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Shan, N., Ju, W., Migliavacca, M., Martini, D., Guanter, L., and Chen, J. M., Goulas, Y., and Zhang, Y.: Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence, Agr. Forest Meteorol., 268, 189–201, https://doi.org/10.1016/j.agrformet.2019.01.031, 2019.
Stöckle, C. O., Donatelli, M., and Nelson, R.: CropSyst, a cropping systems simulation model, Eur. J. Agron., 18, 289–307, https://doi.org/10.1016/s1161-0301(02)00109-0, 2003.
Sulis, M., Couvreur, V., Keune, J., Cai, G., Trebs, I., Junk, J. , Junk, J., Shrestha, P., Simmer, C., Kollet, S. J., Vereecken, H., Vanderborght, J.: Incorporating a root water uptake model based on the hydraulic architecture approach in terrestrial systems simulations, Agr. Forest Meteorol., 269270, 28–45, https://doi.org/10.1016/j.agrformet.2019.01.034, 2019.
Sun, Y., Fu, R., Dickinson, R., Joiner, J., Frankenberg, C., Gu, L., Xia, Y., and Fernando, N.: Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: insights from two contrasting extreme events, J. Geophys. Res.-Biogeo., 120, 2427–2440, https://doi.org/10.1002/2015JG003150, 2016.
Supit, I., Hooijer, A., and Van Diepen, C.: System description of the WOFOST 6.0 crop simulation model implemented in CGMS, vol. 1: Theory and Algorithms, Joint Research Centre, Commission of the European Communities, 146, EUR 15956, 1994.
Van Dam, J. C.: Field-Scale Water Flow and Solute Transport. SWAP ModelConcepts, Parameter Estimation and Case Studies, Wageningen University, Wageningen, the Netherlands, 167 pp., 2000.
van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., and Su, Z.: An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance, Biogeosciences, 6, 3109–3129, https://doi.org/10.5194/bg-6-3109-2009, 2009.
Van der Tol, C., Berry, J., Campbell, P., Rascher, U.: Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence, J. Geophys. Res.-Biogeo., 119, 2312–2327, https://doi.org/10.1002/2014JG002713, 2014.
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.
Wang, E. and Smith, C. J.: Modelling the growth and water uptake function of plant root systems: a review, Aust. J. Agr. Res., 55, 501–523, https://doi.org/10.1071/ar03201, 2004.
Wang, Y., Cai, H. J., Zeng, Y. J., Su, Z., and Yu, L. Y.: Data underlying the research on Seasonal and interannual variation in evapotranspiration, energy flux, and Bowen ratio over a dry semi-humid cropland in Northwest China, 4TU, Centre for Research Data, Dataset, https://doi.org/10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7, 2019.
Wang, Y., Cai, H. J., Yu, L. Y., Peng, X. B., Xu, J. T., and Wang, X. W.: Evapotranspiration partitioning and crop coefficient of maize in dry semi-humid climate regime, Agric. Water Manag., 236, 106164, https://doi.org/10.1016/j.agwat.2020.106164, [preprint], 2020a.
Wang, Y., Zeng, Y. J., Yu, L. Y., Yang, P., Van der Tol, C., Su, Z., and Cai, H. J.: Integrated Modeling of Photosynthesis and Transfer of Energy, Mass and Momentum (SCOPE_STEMMUS v1.0), Zenodo, https://doi.org/10.5281/zenodo.3839092, 2020b.
Wiesler, F. and Horst, W.: Root growth and nitrate utilization of maize cultivars under field conditions, Plant Soil, 163, 267–277, https://doi.org/10.1007/bf00007976, 1994.
Williams, J., Jones, C., Kiniry, J., and Spanel, D. A.: The EPIC crop growth model, T. ASAE, 32, 497–511, https://doi.org/10.13031/2013.31032, 1989.
Williams, J. R., Gerik, T., Francis, L., Greiner, J., Magre, M., Meinardus, A., Steglich, E., and Taylor, R.: EPIC – Environmental Policy Integrated Climate Model, UsersManual version 0810, Blackland Research and Extension Center, Texas A&M AgriLife, Temple, TX, 2014.
Williams, M., Rastetter, E. B., Fernandes, D. N., Goulden, M. L., Wofsy, S. C., Shaver G. R., Melillo J. M., Munger, J. W., Fan, S. M., and Nadelhoffer, K. J.: Modelling the soil-plant-atmosphere continuum in a Quercus-Acer stand at Harvard Forest: the regulation of stomatal conductance by light, nitrogen and soil/plant hydraulic properties, Plant Cell Environ., 19, 911–927, https://doi.org/10.1111/j.1365-3040.1996.tb00456.x, 1996.
Wu, L., McGechan, M., Watson, C., and Baddeley, J.: Developing existing plant root system architecture models to meet future agricultural challenges, Adv. Agron., 85, 85004–85001, https://doi.org/10.1016/s0065-2113(04)85004-1, 2005.
Xu, L. and Baldocchi, D. D.: Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature, Tree Physiol., 23, 865–877, https://doi.org/10.1093/treephys/23.13.865, 2003.
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M., and Guan, K.: Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests, New Phytol., 212, 80–95, https://doi.org/10.1111/nph.14009, 2016.
Yu, L., Zeng, Y., Su, Z., Cai, H., and Zheng, Z.: The effect of different evapotranspiration methods on portraying soil water dynamics and ET partitioning in a semi-arid environment in Northwest China, Hydrol. Earth Syst. Sci., 20, 975–990, https://doi.org/10.5194/hess-20-975-2016, 2016.
Yu, L., Zeng, Y., Wen, J., and Su, Z.: Liquid-Vapor-Air Flow in the Frozen Soil, J. Geophys. Res.-Atmos., 123, 7393–7415, https://doi.org/10.1029/2018jd028502, 2018.
Zeng, Y. and Su, Z.: STEMMUS: Simultaneous Transfer of Engery, Mass and Momentum in Unsaturated Soil, University of Twente, Faculty of Geo-Information and Earth Observation (ITC), Enschede, 2013.
Zeng, Y., Su, Z., Wan, L., and Wen, J.: A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model, Water Resour. Res., 47, W10529, https://doi.org/10.1029/2011wr010701, 2011a.
Zeng, Y., Su, Z., Wan, L., and Wen, J.: Numerical analysis of air-water-heat flow in unsaturated soil: Is it necessary to consider airflow in land surface models?, J. Geophys. Res.-Atmos., 116, D20107, https://doi.org/10.1029/2011jd015835, 2011b.
Zhang, Y., Guanter, L., Joiner, J., Song, L., and Guan, K.: Spatially-explicit monitoring of crop photosynthetic capacity through space-based chlorophyll fluorescence data, Remote Sens. Environ., 210, 362–374, https://doi.org/10.1016/j.rse.2018.03.031, 2018.
Zhang, Z., Zhang, Y. G., Porcar-Castell, A., Joiner, J., Guanter, L., Yang, X., Migliavacca, M., Ju, W., Sun, Z., Chen, S., Martini, D., Zhang, Q., Li, Z., Cleverly, J., Wang, H., and Goulas, Y.: Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence, Remote Sens. Environ., 240, 111722, https://doi.org/10.1016/j.rse.2020.111722, 2020.
Zheng, Z. and Wang, G.: Modelling the dynamic root water uptake and its hydrological impact at the Reserva Jaru site in Amazonia, J. Geophys. Res.-Biogeo., 112, G04012, https://doi.org/10.1029/2007jg000413, 2007.
Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W., and Prentice, I. C.: How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress, Agric. For. Meteorol., 182, 204–214, https://doi.org/10.1016/j.agrformet.2013.05.009, 2013.
Zhou, S., Yu, B., Huang, Y., and Wang, G.: The effect of vapor pressure deficit on water use efficiency at the subdaily time scale, Geophys. Res. Lett., 41, 5005–5013, https://doi.org/10.1002/2014GL060741, 2014.
Zhou, S., Yu, B., Zhang, Y., Huang, Y., and Wang, G.: Partitioning evapotranspiration based on the concept of underlying water use efficiency, Water Resour. Res., 52, 1160–1175, https://doi.org/10.1002/2015wr017766, 2016.
Zhu, S., Chen, H., Zhang, X., Wei, N., Shangguan, W., Yuan, H., Zhang, S., Wang, L., Zhou, L., and Dai, Y.: Incorporating root hydraulic redistribution and compensatory water uptake in the Common Land Model: Effects on site level and global land modelling, J. Geophys. Res.-Atmos., 122, 7308–7322, https://doi.org/10.1002/2016jd025744, 2017.
Zhuang, J., Nakayama, K., Yu, G.-R., and Urushisaki, T.: Estimation of root water uptake of maize: an ecophysiological perspective, Field Crop. Res., 69, 201–213, https://doi.org/10.1016/s0378-4290(00)00142-8, 2001a.
Zhuang, J., Yu, G., and Nakayama, K.: Scaling of root length density of maize in the field profile, Plant Soil, 235, 135–142, https://doi.org/10.1023/A:1011972019617, 2001b.
Short summary
This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
This study integrates photosynthesis and transfer of energy, mass, and momentum in the...