Articles | Volume 17, issue 10
https://doi.org/10.5194/gmd-17-4229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 May 2024
Model description paper |
| 24 May 2024
| 24 May 2024
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
David Sandoval
CORRESPONDING AUTHOR
Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Ascot, UK
Iain Colin Prentice
Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Ascot, UK
Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia
Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, China
Rodolfo L. B. Nóbrega
School of Geographical Sciences, University of Bristol, Bristol, UK
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Joseph Ovwemuvwose, Ian Colin Prentice, and Heather Graven
EGUsphere, https://doi.org/10.5194/egusphere-2025-3785, https://doi.org/10.5194/egusphere-2025-3785, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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This work examines the role of cropland representation and the treatment of photosynthetic pathways in the uncertainties in the carbon flux simulations in Earth System Models (ESMs). Our results show that reducing these uncertainties will require improvement of the representation of C3 and C4 crops and natural vegetation area coverage as well as the theories underpinning the simulation of their carbon uptake and storage processes.
Amin Hassan, Iain Colin Prentice, and Xu Liang
EGUsphere, https://doi.org/10.5194/egusphere-2025-622, https://doi.org/10.5194/egusphere-2025-622, 2025
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Evapotranspiration (ET) is the evaporation occurring from plants, soil, and water bodies. Separating these components is challenging due to the lack of measurements and uncertainty of existing ET partitioning methods. We propose a method that utilizes hydrological measurements such as streamflow to determine the ratio of transpiration (evaporation from plants) to evapotranspiration. The results provide a better understanding of plant-water interactions and new perspective on a challenging topic.
Jierong Zhao, Boya Zhou, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2024-3897, https://doi.org/10.5194/egusphere-2024-3897, 2025
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We used eco-evolutionary optimality modelling to examine how climate and CO2 impacted vegetation at the Last Glacial Maximum (LGM, 21,000 years ago) and the mid-Holocene (MH, 6,000 years ago). Low CO2 at the LGM was as important as climate in reducing tree cover and productivity, and increasing C4 plant abundance. Climate had positive effects on MH vegetation, but the low CO2 was a constraint on plant growth. These results show it is important to consider changing CO2 to model ecosystem changes.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Mengmeng Liu, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-12, https://doi.org/10.5194/cp-2024-12, 2024
Preprint under review for CP
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Dansgaard-Oeschger events were large and rapid warming events that occurred multiple times during the last ice age. We show that changes in the northern extratropics and the southern extratropics were anti-phased, with warming over most of the north and cooling in the south. The reconstructions do not provide evidence for a change in seasonality in temperature. However, they do indicate that warming was generally accompanied by wetter conditions and cooling by drier conditions.
Huiying Xu, Han Wang, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 4511–4525, https://doi.org/10.5194/bg-20-4511-2023, https://doi.org/10.5194/bg-20-4511-2023, 2023
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Leaf carbon (C) and nitrogen (N) are crucial elements in leaf construction and physiological processes. This study reconciled the roles of phylogeny, species identity, and climate in stoichiometric traits at individual and community levels. The variations in community-level leaf N and C : N ratio were captured by optimality-based models using climate data. Our results provide an approach to improve the representation of leaf stoichiometry in vegetation models to better couple N with C cycling.
Esmeralda Cruz-Silva, Sandy P. Harrison, I. Colin Prentice, Elena Marinova, Patrick J. Bartlein, Hans Renssen, and Yurui Zhang
Clim. Past, 19, 2093–2108, https://doi.org/10.5194/cp-19-2093-2023, https://doi.org/10.5194/cp-19-2093-2023, 2023
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We examined 71 pollen records (12.3 ka to present) in the eastern Mediterranean, reconstructing climate changes. Over 9000 years, winters gradually warmed due to orbital factors. Summer temperatures peaked at 4.5–5 ka, likely declining because of ice sheets. Moisture increased post-11 kyr, remaining high from 10–6 kyr before a slow decrease. Climate models face challenges in replicating moisture transport.
Olivia Haas, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 3981–3995, https://doi.org/10.5194/bg-20-3981-2023, https://doi.org/10.5194/bg-20-3981-2023, 2023
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We quantify the impact of CO2 and climate on global patterns of burnt area, fire size, and intensity under Last Glacial Maximum (LGM) conditions using three climate scenarios. Climate change alone did not produce the observed LGM reduction in burnt area, but low CO2 did through reducing vegetation productivity. Fire intensity was sensitive to CO2 but strongly affected by changes in atmospheric dryness. Low CO2 caused smaller fires; climate had the opposite effect except in the driest scenario.
Giulia Mengoli, Sandy P. Harrison, and I. Colin Prentice
EGUsphere, https://doi.org/10.5194/egusphere-2023-1261, https://doi.org/10.5194/egusphere-2023-1261, 2023
Preprint archived
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Soil water availability affects plant carbon uptake by reducing leaf area and/or by closing stomata, which reduces its efficiency. We present a new formulation of how climatic dryness reduces both maximum carbon uptake and the soil-moisture threshold below which it declines further. This formulation illustrates how plants adapt their water conservation strategy to thrive in dry climates, and is step towards a better representation of soil-moisture effects in climate models.
Mengmeng Liu, Yicheng Shen, Penelope González-Sampériz, Graciela Gil-Romera, Cajo J. F. ter Braak, Iain Colin Prentice, and Sandy P. Harrison
Clim. Past, 19, 803–834, https://doi.org/10.5194/cp-19-803-2023, https://doi.org/10.5194/cp-19-803-2023, 2023
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We reconstructed the Holocene climates in the Iberian Peninsula using a large pollen data set and found that the west–east moisture gradient was much flatter than today. We also found that the winter was much colder, which can be expected from the low winter insolation during the Holocene. However, summer temperature did not follow the trend of summer insolation, instead, it was strongly correlated with moisture.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Yicheng Shen, Luke Sweeney, Mengmeng Liu, Jose Antonio Lopez Saez, Sebastián Pérez-Díaz, Reyes Luelmo-Lautenschlaeger, Graciela Gil-Romera, Dana Hoefer, Gonzalo Jiménez-Moreno, Heike Schneider, I. Colin Prentice, and Sandy P. Harrison
Clim. Past, 18, 1189–1201, https://doi.org/10.5194/cp-18-1189-2022, https://doi.org/10.5194/cp-18-1189-2022, 2022
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We present a method to reconstruct burnt area using a relationship between pollen and charcoal abundances and the calibration of charcoal abundance using modern observations of burnt area. We use this method to reconstruct changes in burnt area over the past 12 000 years from sites in Iberia. We show that regional changes in burnt area reflect known changes in climate, with a high burnt area during warming intervals and low burnt area when the climate was cooler and/or wetter than today.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
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Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Scientific Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018. a
Acosta, M., Pavelka, M., Montagnani, L., Kutsch, W., Lindroth, A., Juszczak, R., and Janouš, D.: Soil surface CO2 efflux measurements in Norway spruce forests: Comparison between four different sites across Europe – from boreal to alpine forest, Geoderma, 192, 295–303, https://doi.org/10.1016/j.geoderma.2012.08.027, 2013. a
Allen, R. G.: Assessing Integrity of Weather Data for Reference Evapotranspiration Estimation, J. Irrig. Drain. E., 122, 97–106, https://doi.org/10.1061/(ASCE)0733-9437(1996)122:2(97), 1996. a
Allen, R. G., Trezza, R., and Tasumi, M.: Analytical integrated functions for daily solar radiation on slopes, Agr. Forest Meteorol., 139, 55–73, https://doi.org/10.1016/j.agrformet.2006.05.012, 2006. a, b
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, Scientific Data, 5, 180040, https://doi.org/10.1038/sdata.2018.40, 2018. a
Amiro, B. D., Barr, A. G., Barr, J. G., Black, T. A., Bracho, R., Brown, M., Chen, J., Clark, K. L., Davis, K. J., Desai, A. R., Dore, S., Engel, V., Fuentes, J. D., Goldstein, A. H., Goulden, M. L., Kolb, T. E., Lavigne, M. B., Law, B. E., Margolis, H. A., Martin, T., McCaughey, J. H., Misson, L., Montes-Helu, M., Noormets, A., Randerson, J. T., Starr, G., and Xiao, J.: Ecosystem carbon dioxide fluxes after disturbance in forests of North America, J. Geophys. Res., 115, G00K02, https://doi.org/10.1029/2010JG001390, 2010. a, b
Ammann, C., Spirig, C., Leifeld, J., and Neftel, A.: Assessment of the nitrogen and carbon budget of two managed temperate grassland fields, Agr. Ecosyst. Environ., 133, 150–162, https://doi.org/10.1016/j.agee.2009.05.006, 2009. a
Anderson-Teixeira, K. J., Delong, J., Fox, A., Brese, D. A., and Litvak, M.: Differential responses of production and respiration to temperature and moisture drive the carbon balance across a climatic gradient in New Mexico, Glob. Change Biol., 17, 410–424, https://doi.org/10.1111/j.1365-2486.2010.02269.x, 2011. a, b
Anthoni, P. M., Law, B. E., and Unsworth, M. H.: Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem, Agr. Forest Meteorol., 95, 151–168, https://doi.org/10.1016/S0168-1923(99)00029-5, 1999. a
Anthoni, P. M., Unsworth, M. H., Law, B. E., Irvine, J., Baldocchi, D. D., Tuyl, S. V., and Moore, D.: Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystems, Agr. Forest Meteorol., 111, 203–222, https://doi.org/10.1016/S0168-1923(02)00021-7, 2002. a
Assouline, S.: Infiltration into soils: Conceptual approaches and solutions, Water Resour. Res., 49, 1755–1772, https://doi.org/10.1002/wrcr.20155, 2013. a, b
Bai, J., Zhang, G., Zhao, Q., Lu, Q., Jia, J., Cui, B., and Liu, X.: Depth-distribution patterns and control of soil organic carbon in coastal salt marshes with different plant covers, Scientific Reports, 6, 34835, https://doi.org/10.1038/srep34835, 2016. a
Baldocchi, D., Sturtevant, C., and Contributors, F.: Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration?, Agr. Forest Meteorol., 207, 117–126, https://doi.org/10.1016/j.agrformet.2015.03.010, 2015. a, b
Barr, A., Richardson, A., Hollinger, D., Papale, D., Arain, M., Black, T., Bohrer, G., Dragoni, D., Fischer, M., Gu, L., Law, B., Margolis, H., McCaughey, J., Munger, J., Oechel, W., and Schaeffer, K.: Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies, Agr. Forest Meteorol., 171-172, 31–45, https://doi.org/10.1016/j.agrformet.2012.11.023, 2013a. a
Barr, A., Richardson, A., Hollinger, D., Papale, D., Arain, M., Black, T., Bohrer, G., Dragoni, D., Fischer, M., Gu, L., Law, B., Margolis, H., McCaughey, J., Munger, J., Oechel, W., and Schaeffer, K.: Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies, Agr. Forest Meteorol., 171-172, 31–45, https://doi.org/10.1016/j.agrformet.2012.11.023, 2013b. a
Barron-Gafford, G. A., Scott, R. L., Jenerette, G. D., Hamerlynck, E. P., and Huxman, T. E.: Landscape and environmental controls over leaf and ecosystem carbon dioxide fluxes under woody plant expansion, J. Ecol., 101, 1471–1483, https://doi.org/10.1111/1365-2745.12161, 2013. a
Bastiaanssen, W., Menenti, M., Feddes, R., and Holtslag, A.: A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J. Hydrol., 212–213, 198–212, https://doi.org/10.1016/S0022-1694(98)00253-4, 1998a. a, b
Bastiaanssen, W., Pelgrum, H., Wang, J., Ma, Y., Moreno, J., Roerink, G., and van der Wal, T.: A remote sensing surface energy balance algorithm for land (SEBAL). 2. Validation, J. Hydrol., 212–213, 213–229, https://doi.org/10.1016/S0022-1694(98)00254-6, 1998b. a
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. a, b
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a
Beldring, S., Gottschalk, L., Seibert, J., and Tallaksen, L.: Distribution of soil moisture and groundwater levels at patch and catchment scales, Agr. Forest Meteorol., 98–99, 305–324, https://doi.org/10.1016/S0168-1923(99)00103-3, 1999. a
Belshe, E. F., Schuur, E. A. G., Bolker, B. M., and Bracho, R.: Incorporating spatial heterogeneity created by permafrost thaw into a landscape carbon estimate, J. Geophys. Res.-Biogeo., 117, G01026, https://doi.org/10.1029/2011JG001836, 2012. a
Bergström, S.: The HBV model, in: Computer Models of Watershed Hydrology, edited by: Singh, V., Water Resources Publications, Highlands Ranch, CO, 443–476, ISBN 0918334918, 1995. a
Beven, K. J. and Kirby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979. a
Bristow, M., Hutley, L. B., Beringer, J., Livesley, S. J., Edwards, A. C., and Arndt, S. K.: Quantifying the relative importance of greenhouse gas emissions from current and future savanna land use change across northern Australia, Biogeosciences, 13, 6285–6303, https://doi.org/10.5194/bg-13-6285-2016, 2016. a
Brooks, R. H. and Corey, A. T.: Hydraulic Properties of Porous Media, Transactions of the ASAE, 7, 0026–0028, https://doi.org/10.13031/2013.40684, 1964. a, b, c
Buytaert, W., De Bièvre, B., Wyseure, G., and Deckers, J.: The use of the linear reservoir concept to quantify the impact of changes in land use on the hydrology of catchments in the Andes, Hydrol. Earth Syst. Sci., 8, 108–114, https://doi.org/10.5194/hess-8-108-2004, 2004. a
Campbell, J. L., Sun, O. J., and Law, B. E.: Disturbance and net ecosystem production across three climatically distinct forest landscapes, Global Biogeochem. Cy., 18, GB4017, https://doi.org/10.1029/2004GB002236, 2004. a
Chapin, F. S. I., Matson, P. A., and Vitousek, P. M.: Principles of Terrestrial Ecosystem Ecology, Springer New York, New York, NY, ISBN 978-1-4419-9503-2, https://doi.org/10.1007/978-1-4419-9504-9, 2011. a, b
Chen, A., Li, W., Li, W., and Liu, X.: An observational study of snow aging and the seasonal variation of snow albedo by using data from Col de Porte, France, Chinese Sci. Bull., 59, 4881–4889, https://doi.org/10.1007/s11434-014-0429-9, 2014. a
Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., and Martin, T. A.: Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks, Geophys. Res. Lett., 45, 9275–9287, https://doi.org/10.1029/2018GL079306, 2018a. a
Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., and Martin, T. A.: Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks, Geophys. Res. Lett., 45, 9275–9287, https://doi.org/10.1029/2018GL079306, 2018b. a
Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., and Martin, T. A.: Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks, Geophys. Res. Lett., 45, 9275–9287, https://doi.org/10.1029/2018GL079306, 2018c. a
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet, R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017. a, b, c
Corps of Engineers: Summary report of the snow investigations, snow hydrology, Army Engineer Division, Portland, US, https://usace.contentdm.oclc.org/digital/collection/p266001coll1/id/4172/ (last access: 5 May 2021), 1956. a
Correa, A., Ochoa-Tocachi, B. F., Birkel, C., Ochoa-Sánchez, A., Zogheib, C., Tovar, C., and Buytaert, W.: A concerted research effort to advance the hydrological understanding of tropical páramos, Hydrol. Process., 34, 4609–4627, https://doi.org/10.1002/hyp.13904, 2020. a, b
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984. a, b, c
Cramer, W. and Prentice, I. C.: Simulation of regional soil moisture deficits on a European scale, Norsk Geogr. Tidsskr., 12, 149–151, https://doi.org/10.1080/00291958808552193, 1988. a
Crespo, P. J., Feyen, J., Buytaert, W., Bücker, A., Breuer, L., Frede, H.-G., and Ramírez, M.: Identifying controls of the rainfall–runoff response of small catchments in the tropical Andes (Ecuador), J. Hydrol., 407, 164–174, https://doi.org/10.1016/j.jhydrol.2011.07.021, 2011. a, b
Davis, T. W., Prentice, I. C., Stocker, B. D., Thomas, R. T., Whitley, R. J., Wang, H., Evans, B. J., Gallego-Sala, A. V., Sykes, M. T., and Cramer, W.: Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, 2017. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Dickinson, R. E., Henderson-Sellers, A., Kennedy, P. J., and Wilson, M. F.: Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model, https://doi.org/10.5065/D6668B58, 1986. a
Dietiker, D., Buchmann, N., and Eugster, W.: Testing the ability of the {DNDC} model to predict CO2 and water vapour fluxes of a {S}wiss cropland site, Agr. Ecosyst. Environ., 139, 396–401, https://doi.org/10.1016/j.agee.2010.09.002, 2010. a
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2: Multimodel analysis and implications for our perception of the land surface, B. Am. Meteorol. Soc., 87, 1381–1397, https://doi.org/10.1175/BAMS-87-10-1381, 2006. a
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018. a
Dunkerley, D.: Identifying individual rain events from pluviograph records: a review with analysis of data from an Australian dryland site, Hydrol. Process., 22, 5024–5036, https://doi.org/10.1002/hyp.7122, 2008. a
Etzold, S., Ruehr, N. K., Zweifel, R., Dobbertin, M., Zingg, A., Pluess, P., Häsler, R., Eugster, W., and Buchmann, N.: The Carbon Balance of Two Contrasting Mountain Forest Ecosystems in {S}witzerland: Similar Annual Trends, but Seasonal Differences, Ecosystems, 14, 1289–1309, https://doi.org/10.1007/s10021-011-9481-3, 2011. a
Euskirchen, E. S., Bret-Harte, M. S., Scott, G. J., Edgar, C., and Shaver, G. R.: Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska, Ecosphere, 3, 4, https://doi.org/10.1890/ES11-00202.1, 2012a. a
Euskirchen, E. S., Bret-Harte, M. S., Scott, G. J., Edgar, C., and Shaver, G. R.: Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska, Ecosphere, 3, 4, https://doi.org/10.1890/ES11-00202.1, 2012b. a
Euskirchen, E. S., Bret-Harte, M. S., Shaver, G. R., Edgar, C. W., and Romanovsky, V. E.: Long-Term Release of Carbon Dioxide from Arctic Tundra Ecosystems in Alaska, Ecosystems, 20, 960–974, https://doi.org/10.1007/s10021-016-0085-9, 2017. a
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006. a
Feddes, R. A. and Raats, P. A.: Parameterizing the soil – water – plant root system, in: Unsaturated-zone Modeling: Progress, Challenges and Applications, edited by: Feddes, R., de Rooij, G., and van Dam, J., Springer Netherlands, https://edepot.wur.nl/35358 (last access: 29 March 2019), 2004. a
Federer, C. A.: Spatial Variation of Net Radiation, Albedo and Surface Temperature of Forests, J. Appl. Meteorol., 7, 789–795, https://doi.org/10.1175/1520-0450(1968)007<0789:SVONRA>2.0.CO;2, 1968. a, b
Fellows, A. W., Flerchinger, G. N., Seyfried, M. S., Biederman, J. A., and Lohse, K. A.: Winter CO2 Efflux From Sagebrush Shrublands Distributed Across the Rain‐to‐Snow Transition Zone, J. Geophys. Res.-Biogeo., 125, e2019JG005325, https://doi.org/10.1029/2019JG005325, 2020. a, b, c
Frank, J. M., Massman, W. J., Ewers, B. E., Huckaby, L. S., and Negrõn, J. F.: Ecosystem
Friedl, M., Gray, J., and Sulla-Menashe, D.: MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q2.006, 2019. a, b, c, d
Gallego-Sala, A. V. and Prentice, I. C.: Blanket peat biome endangered by climate change, Nat. Clim. Change, 3, 152–155, https://doi.org/10.1038/nclimate1672, 2012. a
Galvagno, M., Wohlfahrt, G., Cremonese, E., Rossini, M., Colombo, R., Filippa, G., Julitta, T., Manca, G., Siniscalco, C., Morra Di Cella, U., and Migliavacca, M.: Phenology and carbon dioxide source/sink strength of a subalpine grassland in response to an exceptionally short snow season, Environ. Res. Lett., 8, 25008, https://doi.org/10.1088/1748-9326/8/2/025008, 2013. a
Gao, H., Tang, Q., Shi, X., Zhu, C., Bohn, T., Su, F., Sheffield, J., Pan, M., Lettenmaier, D., and Wood, E. F.: Water budget record from Variable Infiltration Capacity (VIC) model. Algorithm Theoretical Basis Document, Version 1.2, in: Algorithm theoretical basis document for terrestrial water cycle data records, Department of Civil and Environmental Engineering, University of Washington, 120–173, https://eprints.lancs.ac.uk/id/eprint/89407/1/Gao_et_al_VIC_2014.pdf (last access: 21 June 2021), 2009. a
Goldstein, A. H., Hultman, N. E., Fracheboud, J. M., Bauer, M. R., Panek, J. A., Xu, M., Qi, Y., Guenther, A. B., and Baugh, W.: Effects of climate variability on the carbon dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in the Sierra Nevada (CA), Agr. Forest Meteorol., 101, 113–129, https://doi.org/10.1016/S0168-1923(99)00168-9, 2000. a
Goulden, M.: AmeriFlux US-CZ2 Sierra Critical Zone, Sierra Transect, Ponderosa Pine Forest, Soaproot Saddle, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419510, 2018a. a
Goulden, M.: AmeriFlux US-CZ3 Sierra Critical Zone, Sierra Transect, Sierran Mixed Conifer, P301, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419512, 2018b. a
Goulden, M.: AmeriFlux US-CZ4 Sierra Critical Zone, Sierra Transect, Subalpine Forest, Shorthair, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419511, 2018c. a
Goulden, M.: AmeriFlux US-SCf Southern California Climate Gradient – Oak/Pine Forest, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419503, 2018d. a
Goulden, M.: AmeriFlux US-SCg Southern California Climate Gradient – Grassland, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419502, 2018e. a
Goulden, M.: AmeriFlux US-SCs Southern California Climate Gradient – Coastal Sage, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419501, 2018f. a
Goulden, M.: AmeriFlux US-SCw Southern California Climate Gradient – Pinyon/Juniper Woodland, AmeriFlux [data set], https://doi.org/10.17190/AMF/1419504, 2018g. a
Guo, X., Zha, T., Jia, X., Wu, B., Feng, W., Xie, J., Gong, J., Zhang, Y., and Peltola, H.: Dynamics of Dew in a Cold Desert-Shrub Ecosystem and Its Abiotic Controls, Atmosphere, 7, 32, https://doi.org/10.3390/atmos7030032, 2016. a
Gupta, H. V. and Kling, H.: On typical range, sensitivity, and normalization of Mean Squared Error and Nash-Sutcliffe Efficiency type metrics, Water Resour. Res., 47, 2–4, https://doi.org/10.1029/2011WR010962, 2011. a
Gurtz, J., Zappa, M., Jasper, K., Lang, H., Verbunt, M., Badoux, A., and Vitvar, T.: A comparative study in modelling runoff and its components in two mountainous catchments, Hydrol. Process., 17, 297–311, https://doi.org/10.1002/hyp.1125, 2003. a
Hall, D. K., Salomonson, V. V., and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m Grid. Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016. a
Harder, P. and Pomeroy, J. W.: Hydrological model uncertainty due to precipitation-phase partitioning methods, Hydrol. Process., 28, 4311–4327, https://doi.org/10.1002/hyp.10214, 2014. a
Harrison, S. P., Prentice, I. C., Barboni, D., Kohfeld, K. E., Ni, J., and Sutra, J.-P.: Ecophysiological and bioclimatic foundations for a global plant functional classification, J. Veg. Sci., 21, 300–317, https://doi.org/10.1111/j.1654-1103.2009.01144.x, 2010. a
Harrison, S. P., Cramer, W., Franklin, O., Prentice, I. C., Wang, H., Brännström, Å., Boer, H., Dieckmann, U., Joshi, J., Keenan, T. F., Lavergne, A., Manzoni, S., Mengoli, G., Morfopoulos, C., Peñuelas, J., Pietsch, S., Rebel, K. T., Ryu, Y., Smith, N. G., Stocker, B. D., and Wright, I. J.: Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models, New Phytol., 231, 2125–2141, https://doi.org/10.1111/nph.17558, 2021. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a, b
Hilberts, A. G. J., Troch, P. A., and Paniconi, C.: Storage-dependent drainable porosity for complex hillslopes, Water Resour. Res., 41, W06001, https://doi.org/10.1029/2004WR003725, 2005. a, b, c
Hillel, D.: Environmental Soil Physics: Fundamentals, Applications, and Environmental Considerations, Academic Press, ISBN 9780123485250, 1998. a
Hino, M., Odaka, Y., Nadaoka, K., and Sato, A.: Effect of initial soil moisture content on the vertical infiltration process – A guide to the problem of runoff-ratio and loss, J. Hydrol., 102, 267–284, https://doi.org/10.1016/0022-1694(88)90102-3, 1988. a
Hirschi, M., Michel, D., Lehner, I., and Seneviratne, S. I.: A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration, Hydrol. Earth Syst. Sci., 21, 1809–1825, https://doi.org/10.5194/hess-21-1809-2017, 2017. a
Hobley, E. U. and Wilson, B.: The depth distribution of organic carbon in the soils of eastern Australia, Ecosphere, 7, e01214, https://doi.org/10.1002/ecs2.1214, 2016. a
IFAS: Florida Soil Characterization Retrieval System, https://web.archive.org/web/20210415035753/https://soils.ifas.ufl.edu/flsoils/databaseintro.asp (last access: 25 August 2021), 2007. a
Imer, D., Merbold, L., Eugster, W., and Buchmann, N.: Temporal and spatial variations of soil CO2, CH4 and N2O fluxes at three differently managed grasslands, Biogeosciences, 10, 5931–5945, https://doi.org/10.5194/bg-10-5931-2013, 2013. a
Irvine, J., Law, B. E., and Hibbard, K. A.: Postfire carbon pools and fluxes in semiarid ponderosa pine in Central Oregon, Glob. Change Biol., 13, 1748–1760, https://doi.org/10.1111/j.1365-2486.2007.01368.x, 2007. a
Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database, http://srtm.csi.cgiar.org (last access: 25 August 2021), 2008. a
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial variation of the rain-snow temperature threshold across the Northern Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018. a, b, c, d
Jones, H. G.: Plants and Microclimate, Cambridge University Press, Cambridge, ISBN 9780511845727, https://doi.org/10.1017/CBO9780511845727, 2013. a
Kao, S.-C., Ashfaq, M., Rastogi, D., Gangrade, S., Uria Martinez, R., Fernandez, A., Konapala, G., Voisin, N., Zhou, T., Xu, W., Gao, H., Zhao, B., and Zhao, G.: The Third Assessment of the Effects of Climate Change on Federal Hydropower, Tech. rep., Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), ISBN 1800553684, https://doi.org/10.2172/1887712, 2022. a
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high resolution for the earth's land surface areas, Scientific Data, 4, 170122, https://doi.org/10.1038/sdata.2017.122, 2017. a
Kato, T., Tang, Y., Gu, S., Hirota, M., Du, M., Li, Y., and Zhao, X.: Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau, Glob. Change Biol., 12, 1285–1298, https://doi.org/10.1111/j.1365-2486.2006.01153.x, 2006. a
Kelsey, E. P. S. U. and Green, M. C. W. R. U.: AmeriFlux US-HBK Hubbard Brook Experimental Forest, AmeriFlux [data set], https://doi.org/10.17190/AMF/1634881, 2020. a
Kienzle, S. W.: A new temperature based method to separate rain and snow, Hydrol. Process., 22, 5067–5085, https://doi.org/10.1002/hyp.7131, 2008. a, b, c
Kilinc, M., Beringer, J., Hutley, L. B., Tapper, N. J., and McGuire, D. A.: Carbon and water exchange of the world's tallest angiosperm forest, Agr. Forest Meteorol., 182-183, 215–224, https://doi.org/10.1016/j.agrformet.2013.07.003, 2013. a
Kopp, G. and Lean, J. L.: A new, lower value of total solar irradiance: Evidence and climate significance, Geophys. Res. Lett., 38, L01706, https://doi.org/10.1029/2010GL045777, 2011. a
Körner, C.: Plant ecology at high elevations, Alpine Plant Life, 3rd edn., 1–7, https://doi.org/10.1007/978-3-030-59538-8_1, 2021. a
Körner, C.: Alpine Plant Life, Springer International Publishing, Cham, Switzerland, 3rd edn., ISBN 978-3-030-59537-1, https://doi.org/10.1007/978-3-030-59538-8, 2021. a, b
Kramer, C. and Gleixner, G.: Soil organic matter in soil depth profiles: Distinct carbon preferences of microbial groups during carbon transformation, Soil Biol. Biochem., 40, 425–433, https://doi.org/10.1016/j.soilbio.2007.09.016, 2008. a
Ladson, A. R., Brown, R., Neal, B., and Nathan, R.: A standard approach to baseflow separation using the Lyne and Hollick filter, Australasian Journal of Water Resources, 17, 25–34, https://www.tandfonline.com/doi/pdf/10.7158/13241583.2013.11465417 (last access: 23 January 2023), 2013. a
Laipelt, L., Henrique Bloedow Kayser, R., Santos Fleischmann, A., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., and Melton, F.: Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing, ISPRS J. Photogramm., 178, 81–96, https://doi.org/10.1016/j.isprsjprs.2021.05.018, 2021. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Leij, F., Alves, W., van Genuchten, M. T., and Williams, J.: Unsoda Unsaturated Soil Hydraulic Database, UNSODA 1.0 User's Manual, Report EPA/600/R-96/095, Tech. rep., US Environmental Protection Agency, Ada, Oklahoma, https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NRMRL&dirEntryId=124868 (last access: 14 June 2019), 1996. a
Leuning, R., Cleugh, H. A., Zegelin, S. J., and Hughes, D.: Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates, Agr. Forest Meteorol., 129, 151–173, https://doi.org/10.1016/j.agrformet.2004.12.004, 2005. a
Liang, X. and Xie, Z.: A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models, Adv. Water Resour., 24, 1173–1193, https://doi.org/10.1016/S0309-1708(01)00032-X, 2001. a, b
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface Soil Moisture Parameterization of the VIC-2L Model: Evaluation and Modifications, Journal of Global and Planetary Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1, 1996. a, b
Linacre, E. T.: Estimating the net-radiation flux, Agr. Meteorol., 5, 49–63, https://doi.org/10.1016/0002-1571(68)90022-8, 1968. a, b, c
Lindauer, M., Schmid, H., Grote, R., Mauder, M., Steinbrecher, R., and Wolpert, B.: Net ecosystem exchange over a non-cleared wind-throw-disturbed upland spruce forest–Measurements and simulations, Agr. Forest Meteorol., 197, 219–234, https://doi.org/10.1016/j.agrformet.2014.07.005, 2014. a
Livneh, B., Xia, Y., Mitchell, K. E., Ek, M. B., and Lettenmaier, D. P.: Noah LSM Snow Model Diagnostics and Enhancements, J. Hydrometeorol., 11, 721–738, https://doi.org/10.1175/2009JHM1174.1, 2010. a
López-Blanco, E., Lund, M., Williams, M., Tamstorf, M. P., Westergaard-Nielsen, A., Exbrayat, J.-F., Hansen, B. U., and Christensen, T. R.: Exchange of CO2 in Arctic tundra: impacts of meteorological variations and biological disturbance, Biogeosciences, 14, 4467–4483, https://doi.org/10.5194/bg-14-4467-2017, 2017. a
Kirkham, M. B.: Field Capacity, Wilting Point, Available Water, and the Non-Limiting Water Range, Principles of Soil and Plant Water Relations, 101–115, https://doi.org/10.1016/B978-0-12-420022-7.00010-0, 2005. a
Marcolla, B., Pitacco, A., and Cescatti, A.: Canopy architecture and turbulence structure in a coniferous forest, Bound.-Lay. Meteorol., 108, 39–59, https://doi.org/10.1023/A:1023027709805, 2003. a
Marcolla, B., Cescatti, A., Manca, G., Zorer, R., Cavagna, M., Fiora, A., Gianelle, D., Rodeghiero, M., Sottocornola, M., and Zampedri, R.: Climatic controls and ecosystem responses drive the inter-annual variability of the net ecosystem exchange of an alpine meadow, Agr. Forest Meteorol., 151, 1233–1243, https://doi.org/10.1016/j.agrformet.2011.04.015, 2011. a
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially distributed energy balance snowmelt model for application in mountain basins, Hydrol. Process., 13, 1935–1959, https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999. a
Mega, T., Ushio, T., Kubota, T., Kachi, M., Aonashi, K., and Shige, S.: Gauge adjusted global satellite mapping of precipitation (GSMaP_Gauge), in: 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), Beijing, China, 16–23 August 2014, ISBN 978-1-4673-5225-3, https://doi.org/10.1109/URSIGASS.2014.6929683, 2014. a, b
Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., and Buchmann, N.: Greenhouse gas budget (CO2, CH4 and N2O) of intensively managed grassland following restoration, Glob. Change Biol., 20, 1913–1928, https://doi.org/10.1111/gcb.12518, 2014. a
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. a, b, c
Meyers, T. N.: AmeriFlux US-CaV Canaan Valley, AmeriFlux [data set], https://doi.org/10.17190/AMF/1246042, 2016. a
Molina-Sanchis, I., Lázaro, R., Arnau-Rosalén, E., and Calvo-Cases, A.: Rainfall timing and runoff: The influence of the criterion for rain event separation, J. Hydrol. Hydromech., 64, 226–236, https://doi.org/10.1515/johh-2016-0024, 2016. a
Monson, R. K., Turnipseed, A. A., Sparks, J. P., Harley, P. C., Scott-Denton, L. E., Sparks, K., and Huxman, T. E.: Carbon sequestration in a high-elevation, subalpine forest, Glob. Change Biol., 8, 459–478, https://doi.org/10.1046/j.1365-2486.2002.00480.x, 2002. a
Montagnani, L., Manca, G., Canepa, E., Georgieva, E., Acosta, M., Feigenwinter, C., Janous, D., Kerschbaumer, G., Lindroth, A., Minach, L., Minerbi, S., Mölder, M., Pavelka, M., Seufert, G., Zeri, M., and Ziegler, W.: A new mass conservation approach to the study of CO2 advection in an alpine forest, J. Geophys. Res.-Atmos., 114, D07306, https://doi.org/10.1029/2008JD010650, 2009. a
Monteith, J. L. and Unsworth, M.: Principles of Environmental Physics, Elsevier, 4th edn., ISBN 9780123869104, https://doi.org/10.1016/C2010-0-66393-0, 1990. a, b, c, d
Morbidelli, R., Saltalippi, C., Flammini, A., and Govindaraju, R. S.: Role of slope on infiltration: A review, J. Hydrol., 557, 878–886, https://doi.org/10.1016/j.jhydrol.2018.01.019, 2018. a
Nash, J. and Sutcliffe, J.: 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. a
Niu, G. Y. and Yang, Z. L.: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins, J. Geophys. Res.-Atmos., 112, D21101, https://doi.org/10.1029/2007JD008674, 2007. a
Nobel, P. S.: Biophysical Plant Physiology and Ecology, W. H. Freeman, San Francisco/New York, 608 pp., ISBN 9780716714477, 1983. a
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes, Nat. Clim. Change, 6, 1023–1027, https://doi.org/10.1038/nclimate3114, 2016. a
Ochoa-Tocachi, B. F., Buytaert, W., Antiporta, J., Acosta, L., Bardales, J. D., Célleri, R., Crespo, P., Fuentes, P., Gil-Ríos, J., Guallpa, M., Llerena, C., Olaya, D., Pardo, P., Rojas, G., Villacís, M., Villazón, M., Viñas, P., and De Bièvre, B.: High-resolution hydrometeorological data from a network of headwater catchments in the tropical Andes, Scientific Data, 5, 180080, https://doi.org/10.1038/sdata.2018.80, 2018. a
Ottoni, M. V., Ottoni Filho, T. B., Schaap, M. G., Lopes-Assad, M. L. R., and Rotunno Filho, O. C.: Hydrophysical Database for Brazilian Soils (HYBRAS) and Pedotransfer Functions for Water Retention, Vadose Zone J., 17, 170095, https://doi.org/10.2136/vzj2017.05.0095, 2018. a
Pachepsky, Ya., Rajkai, K., and Tóth, B.: Pedotransfer in soil physics: trends and outlook – A review, Agrokém. Talajtan, 64, 339–360, https://doi.org/10.1556/0088.2015.64.2.3, 2015. a
Paschalis, A., Bonetti, S., Guo, Y., and Fatichi, S.: On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling, Water Resour. Res., 58, e2021WR031871, https://doi.org/10.1029/2021WR031871, 2022. a
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., de Grandcourt, A., Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., di Tommasi, P., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., Gitelson, A., Goded, I., Goeckede, M., Goldstein, A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening, C., Grünwald, T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson, C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera, N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt, J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch, W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J., Lion, M., Liska, A. J., Lohila, A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V., Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes, J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S., Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck, J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival, J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N., Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, Ü., Raz-Yaseef, N., Rebmann, C., Reed, D., de Dios, V. R., Restrepo-Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Cañete, E. P., Sanchez-Mejia, Z. M., Schmid, H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I., Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T., Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel, E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang, H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G., Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020. a
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Sy., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016. a
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.: Balancing the costs of carbon gain and water transport: Testing a new theoretical framework for plant functional ecology, Ecol. Lett., 17, 82–91, https://doi.org/10.1111/ele.12211, 2014. a
Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust and realistic: the three R's of next-generation land-surface modelling, Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, 2015. a, b
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a
Rahmati, M., Weihermüller, L., Vanderborght, J., Pachepsky, Y. A., Mao, L., Sadeghi, S. H., Moosavi, N., Kheirfam, H., Montzka, C., Van Looy, K., Toth, B., Hazbavi, Z., Al Yamani, W., Albalasmeh, A. A., Alghzawi, M. Z., Angulo-Jaramillo, R., Antonino, A. C. D., Arampatzis, G., Armindo, R. A., Asadi, H., Bamutaze, Y., Batlle-Aguilar, J., Béchet, B., Becker, F., Blöschl, G., Bohne, K., Braud, I., Castellano, C., Cerdà, A., Chalhoub, M., Cichota, R., Císlerová, M., Clothier, B., Coquet, Y., Cornelis, W., Corradini, C., Coutinho, A. P., de Oliveira, M. B., de Macedo, J. R., Durães, M. F., Emami, H., Eskandari, I., Farajnia, A., Flammini, A., Fodor, N., Gharaibeh, M., Ghavimipanah, M. H., Ghezzehei, T. A., Giertz, S., Hatzigiannakis, E. G., Horn, R., Jiménez, J. J., Jacques, D., Keesstra, S. D., Kelishadi, H., Kiani-Harchegani, M., Kouselou, M., Kumar Jha, M., Lassabatere, L., Li, X., Liebig, M. A., Lichner, L., López, M. V., Machiwal, D., Mallants, D., Mallmann, M. S., de Oliveira Marques, J. D., Marshall, M. R., Mertens, J., Meunier, F., Mohammadi, M. H., Mohanty, B. P., Pulido-Moncada, M., Montenegro, S., Morbidelli, R., Moret-Fernández, D., Moosavi, A. A., Mosaddeghi, M. R., Mousavi, S. B., Mozaffari, H., Nabiollahi, K., Neyshabouri, M. R., Ottoni, M. V., Ottoni Filho, T. B., Pahlavan-Rad, M. R., Panagopoulos, A., Peth, S., Peyneau, P.-E., Picciafuoco, T., Poesen, J., Pulido, M., Reinert, D. J., Reinsch, S., Rezaei, M., Roberts, F. P., Robinson, D., Rodrigo-Comino, J., Rotunno Filho, O. C., Saito, T., Suganuma, H., Saltalippi, C., Sándor, R., Schütt, B., Seeger, M., Sepehrnia, N., Sharifi Moghaddam, E., Shukla, M., Shutaro, S., Sorando, R., Stanley, A. A., Strauss, P., Su, Z., Taghizadeh-Mehrjardi, R., Taguas, E., Teixeira, W. G., Vaezi, A. R., Vafakhah, M., Vogel, T., Vogeler, I., Votrubova, J., Werner, S., Winarski, T., Yilmaz, D., Young, M. H., Zacharias, S., Zeng, Y., Zhao, Y., Zhao, H., and Vereecken, H.: Development and analysis of the Soil Water Infiltration Global database, Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, 2018. a
Remy, C. C., Krofcheck, D. J., Keyser, A. R., Litvak, M. E., Collins, S. L., and Hurteau, M. D.: Integrating Species‐Specific Information in Models Improves Regional Projections Under Climate Change, Geophys. Res. Lett., 46, 6554–6562, https://doi.org/10.1029/2019GL082762, 2019. a
Orth, R. and Seneviratne, S. I.: Introduction of a simple-model-based land surface dataset for Europe, Environ. Res. Lett., 10, 044012, https://doi.org/10.1088/1748-9326/10/4/044012, 2015. a
Reverter, B. R., Sánchez-Cañete, E. P., Resco, V., Serrano-Ortiz, P., Oyonarte, C., and Kowalski, A. S.: Analyzing the major drivers of NEE in a Mediterranean alpine shrubland, Biogeosciences, 7, 2601–2611, https://doi.org/10.5194/bg-7-2601-2010, 2010. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-j., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a
Roesch, A. and Roeckner, E.: Assessment of snow cover and surface albedo in the ECHAM5 general circulation model, J. Climate, 19, 3828–3843, https://doi.org/10.1175/JCLI3825.1, 2006. a
Romanov, P.: Mapping and monitoring of the snow cover fraction over North America, J. Geophys. Res., 108, 8619, https://doi.org/10.1029/2002JD003142, 2003. a
Ruehr, N. K., Martin, J. G., and Law, B. E.: Effects of water availability on carbon and water exchange in a young ponderosa pine forest: Above- and belowground responses, Agr. Forest Meteorol., 164, 136–148, https://doi.org/10.1016/j.agrformet.2012.05.015, 2012. a
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000, Remote Sens. Environ., 204, 812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018. a, b
Sandoval, D.: dsval/rsplash: Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes (GMD_preprint), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10047627, 2023. a
Sandoval, D.: dsval/splashTools: splashTools (splashTools), Zenodo [code], https://doi.org/10.5281/zenodo.11220559, 2024. a
Sarmiento, G.: Ecological features of climate in high tropical mountains, in: High Altitude Tropical Biogeography, edited by: Vuilleumier, F. and Monasterio, M., Oxford University Press, 11–45, ISBN 0-19-503625-5, 1986. a
Schaperow, J. and Li, D.: VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model (Version 1.6c), Zenodo [data set], https://doi.org/10.5281/zenodo.4059993, 2020. a
Scott, R. L., Biederman, J. A., Hamerlynck, E. P., and Barron-Gafford, G. A.: The carbon balance pivot point of southwestern U.S. semiarid ecosystems: Insights from the 21st century drought, J. Geophys. Res.-Biogeo., 120, 2612–2624, https://doi.org/10.1002/2015JG003181, 2015. a
Seneviratne, S. I., Lehner, I., Gurtz, J., Teuling, A. J., Lang, H., Moser, U., Grebner, D., Menzel, L., Schroff, K., Vitvar, T., and Zappa, M.: Swiss prealpine Rietholzbach research catchment and lysimeter: 32 year time series and 2003 drought event, Water Resour. Res., 48, W06526, https://doi.org/10.1029/2011WR011749, 2012. a, b
Serrano-Ortiz, P., Marañón-Jiménez, S., Reverter, B. R., Sánchez-Cañete, E. P., Castro, J., Zamora, R., and Kowalski, A. S.: Post-fire salvage logging reduces carbon sequestration in Mediterranean coniferous forest, Forest Ecol. Manag., 262, 2287–2296, https://doi.org/10.1016/j.foreco.2011.08.023, 2011. a
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, https://doi.org/10.1029/1999WR900090, 1999. a
Shi, P., Sun, X., Xu, L., Zhang, X., He, Y., Zhang, D., and Yu, G.: Net ecosystem CO2 exchange and controlling factors in a steppe–Kobresia meadow on the Tibetan Plateau, Sci. China Ser. D, 49, 207–218, https://doi.org/10.1007/s11430-006-8207-4, 2006. a
Sigut, L., Havrankova, K., Jocher, G., Pavelka, M., Janouš, D., Czerny, R., Stanik, K., and Trusina, J.: FLUXNET2015 CZ-BK2 Bily Kriz grassland, Fluxnet [data set], https://doi.org/10.18140/FLX/1440144. a
Smith, R. E. and Parlange, J. Y.: A parameter‐efficient hydrologic infiltration model, Water Resour. Res., 14, 533–538, https://doi.org/10.1029/WR014i003p00533, 1978. a
Stocker, B. D., Zscheischler, J., Keenan, T. F., Prentice, I. C., Peñuelas, J., and Seneviratne, S. I.: Quantifying soil moisture impacts on light use efficiency across biomes, New Phytol., 218, 1430–1449, https://doi.org/10.1111/nph.15123, 2018. a
Suehrcke, H., Bowden, R. S., and Hollands, K. G.: Relationship between sunshine duration and solar radiation, Sol. Energy, 92, 160–171, https://doi.org/10.1016/j.solener.2013.02.026, 2013. a, b, c
Tague, C. L. and Band, L. E.: Evaluating explicit and implicit routing for watersdhed hydro-ecological models of forest hydrology at the small catchment scale, Hydrol. Process., 15, 1415–1439, https://doi.org/10.1002/hyp.171, 2001. a
Tarboton, D. G.: Terrain analysis using digital elevation models in hydrology (TauDEM), https://web.archive.org/web/20131021162131/http://www.nrac.wvu.edu:80/classes/resm575/readings/tarboton.pdf (last access: 8 July 2018), 2016. a
Teuling, A. J., Lehner, I., Kirchner, J. W., and Seneviratne, S. I.: Catchments as simple dynamical systems: Experience from a Swiss prealpine catchment, Water Resour. Res., 46, W10502, https://doi.org/10.1029/2009WR008777, 2010. a
Thornton, M., Shrestha, R., Wei, Y., Thornton, P., Kao, S., and Wilson, B.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America. Version 4., ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1840, 2020. a
Thornton, P., Thornton, M., Mayer, B., Wei, Y., Devarakonda, R., Vose, R., and Cook., R.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1328, 2018. a, b
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and Tóth, G.: New generation of hydraulic pedotransfer functions for Europe, Eur. J. Soil Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015. a, b
Tromp-van Meerveld, H. J. and McDonnell, J. J.: On the interrelations between topography, soil depth, soil moisture, transpiration rates and species distribution at the hillslope scale, Adv. Water Resour., 29, 293–310, https://doi.org/10.1016/j.advwatres.2005.02.016, 2006. a
Ukkola, A. M., Prentice, I. C., Keenan, T. F., van Dijk, A. I. J. M., Viney, N. R., Myneni, R. B., and Bi, J.: Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation, Nat. Clim. Change, 6, 75–78, https://doi.org/10.1038/nclimate2831, 2015. a
Valentini, R., De Angelis, P., Matteucci, G., Monaco, R., Dore, S., and Scarascia Mugnozza, G. E.: Seasonal net carbon dioxide exchange of a beech forest with the atmosphere, Glob. Change Biol., 2, 199–207, https://doi.org/10.1111/j.1365-2486.1996.tb00072.x, 1996. a
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J., Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer Functions in Earth System Science: Challenges and Perspectives, Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017. a, b, c
Veihmeyer, F. U. o. C. and Hendrickson, A. U. o. C.: The moisture equivalent as a measure of the field capacity of soils, Soil Sci., 32, 181–194, 1931. a
Vereecken, H., Weihermüller, L., Assouline, S., Šimůnek, J., Verhoef, A., Herbst, M., Archer, N., Mohanty, B., Montzka, C., Vanderborght, J., Balsamo, G., Bechtold, M., Boone, A., Chadburn, S., Cuntz, M., Decharme, B., Ducharne, A., Ek, M., Garrigues, S., Goergen, K., Ingwersen, J., Kollet, S., Lawrence, D. M., Li, Q., Or, D., Swenson, S., Vrese, P., Walko, R., Wu, Y., and Xue, Y.: Infiltration from the Pedon to Global Grid Scales: An Overview and Outlook for Land Surface Modeling, Vadose Zone J., 18, 180191, https://doi.org/10.2136/vzj2018.10.0191, 2019. a, b, c
Vogel, R. M. and Kroll, C. N.: Estimation of baseflow recession constants, Water Resour. Manag., 10, 303–320, https://doi.org/10.1007/BF00508898, 1996. a
Von Freyberg, J., Radny, D., Gall, H. E., and Schirmer, M.: Implications of hydrologic connectivity between hillslopes and riparian zones on streamflow composition, J. Contam. Hydrol., 169, 62–74, https://doi.org/10.1016/j.jconhyd.2014.07.005, 2014. a
Wang, H., Prentice, I. C., and Davis, T. W.: Biophsyical constraints on gross primary production by the terrestrial biosphere, Biogeosciences, 11, 5987–6001, https://doi.org/10.5194/bg-11-5987-2014, 2014. a, b
Wang, Z. and Zeng, X.: Evaluation of snow albedo in land models for weather and climate studies, J. Appl. Meteorol. Clim., 49, 363–380, https://doi.org/10.1175/2009JAMC2134.1, 2010. a, b
Weizu, G. and Freer, J.: Patterns of surface and subsurface runoff generation, Tracer Technologies for Hydrological Systems, 229, 265–273, 1995. a
Westergaard-Nielsen, A., Lund, M., Hansen, B. U., and Tamstorf, M. P.: Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area, ISPRS J. Photogramm., 86, 89–99, https://doi.org/10.1016/j.isprsjprs.2013.09.006, 2013. a
Wohlfahrt, G., Hammerle, A., Haslwanter, A., Bahn, M., Tappeiner, U., and Cernusca, A.: Seasonal and inter-annual variability of the net ecosystem CO2 exchange of a temperate mountain grassland: Effects of weather and management, J. Geophys. Res., 113, D08110, https://doi.org/10.1029/2007JD009286, 2008. a
Wolf, S., Keenan, T. F., Fisher, J. B., Baldocchi, D. D., Desai, A. R., Richardson, A. D., Scott, R. L., Law, B. E., Litvak, M. E., Brunsell, N. A., Peters, W., and van der Laan-Luijkx, I. T.: Warm spring reduced carbon cycle impact of the 2012 US summer drought, P. Natl. Acad. Sci. USA, 113, 5880–5885, https://doi.org/10.1073/pnas.1519620113, 2016. a
Woods, R. a., Sivapalan, M., and Robinson, J. S.: Modeling the spatial variability of subsurface runoff using a topographic index, Water Resour. Res., 33, 1061–1073, https://doi.org/10.1029/97WR00232, 1997. a
Xiao, L., Che, T., Chen, L., Xie, H., and Dai, L.: Quantifying snow albedo radiative forcing and its feedback during 2003–2016, Remote Sensing, 9, 883, https://doi.org/10.3390/rs9090883, 2017. a
Yamamoto, M. K. and Shige, S.: Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers, Atmos. Res., 163, 36–47, https://doi.org/10.1016/j.atmosres.2014.07.024, 2015. a, b
Yang, H., Choi, H. T., and Lim, H.: Applicability assessment of estimation methods for baseflow recession constants in small forest catchments, Water, 10, 1074, https://doi.org/10.3390/w10081074, 2018. a
Yang, Y. and Roderick, M. L.: Radiation, surface temperature and evaporation over wet surfaces, Q. J. Roy. Meteor. Soc., 145, 1118–1129, https://doi.org/10.1002/qj.3481, 2019. a, b
Yu, R., Zhang, Z., Lu, X., Chang, I. S., and Liu, T.: Variations in dew moisture regimes in desert ecosystems and their influencing factors, Wiley Interdisciplinary Reviews: Water, 7, e1482, https://doi.org/10.1002/wat2.1482, 2020. a
Zeller, K. F. and Nikolov, N. T.: Quantifying simultaneous fluxes of ozone, carbon dioxide and water vapor above a subalpine forest ecosystem, Environ. Pollut., 107, 1–20, https://doi.org/10.1016/S0269-7491(99)00156-6, 2000. a
Zhang, Y. and Schaap, M. G.: Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3), J. Hydrol., 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017. a, b
Zielis, S., Etzold, S., Zweifel, R., Eugster, W., Haeni, M., and Buchmann, N.: NEP of a Swiss subalpine forest is significantly driven not only by current but also by previous year's weather, Biogeosciences, 11, 1627–1635, https://doi.org/10.5194/bg-11-1627-2014, 2014. a
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Numerous estimates of water and energy balances depend on empirical equations requiring...
