Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-35-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-35-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How well are we able to close the water budget at the global scale?
Fanny Lehmann
CORRESPONDING AUTHOR
School of Geographical Sciences, University of Bristol, UK
Bramha Dutt Vishwakarma
School of Geographical Sciences, University of Bristol, UK
Interdisciplinary Centre for Water Research, Indian Institute of Science, Bengaluru, India
Jonathan Bamber
School of Geographical Sciences, University of Bristol, UK
Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Munich, Germany
Related authors
Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau
Earth Syst. Sci. Data, 16, 3949–3972, https://doi.org/10.5194/essd-16-3949-2024, https://doi.org/10.5194/essd-16-3949-2024, 2024
Short summary
Short summary
Numerical simulations are a promising approach to characterizing the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of 3D simulations can limit their usability. We present the first database of seismic-induced ground motion generated by an earthquake simulator for a collection of 30 000 heterogeneous geologies. The HEMEWS-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
Maya Raghunath Suryawanshi, K. Satish Kumar, Amin Shakya, Shard Chander, Bhaskar Nikam, Nagesh Kumar Dasika, and Bramha Dutt Vishwakarma
EGUsphere, https://doi.org/10.5194/egusphere-2025-2888, https://doi.org/10.5194/egusphere-2025-2888, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Several GRACE downscaling products already available for regional analysis owing to its coarse resolution (~3°). But they lack validation (both original GRACE and downscaled product). Here for the first time, we have validated both, Original GRACE and downscaled product. Along with developed a new product which is outperforming the existing products over most of the parts of India. Thereby, we are offering a valuable tool for regional water resource analysis.
Adam Igneczi and Jonathan Louis Bamber
Earth Syst. Sci. Data, 17, 3203–3218, https://doi.org/10.5194/essd-17-3203-2025, https://doi.org/10.5194/essd-17-3203-2025, 2025
Short summary
Short summary
Freshwater from Arctic land ice loss strongly affects the Arctic and North Atlantic oceans. Datasets describing this freshwater discharge have low resolution and do not cover the entire Arctic. We statistically enhanced coarse-resolution climate model data – from approximately 6 km to 250 m – and routed meltwater towards the coastlines to provide high-resolution data covering all Arctic regions. This approach has far fewer computational requirements than running climate models at high resolution.
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anne Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1940, https://doi.org/10.5194/egusphere-2025-1940, 2025
Short summary
Short summary
The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu
The Cryosphere, 19, 645–661, https://doi.org/10.5194/tc-19-645-2025, https://doi.org/10.5194/tc-19-645-2025, 2025
Short summary
Short summary
Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
Stephan Harrison, Adina Racoviteanu, Sarah Shannon, Darren Jones, Karen Anderson, Neil Glasser, Jasper Knight, Anna Ranger, Arindan Mandal, Brahma Dutt Vishwakarma, Jeffrey Kargel, Dan Shugar, Umesh Haritishaya, Dongfeng Li, Aristeidis Koutroulis, Klaus Wyser, and Sam Inglis
EGUsphere, https://doi.org/10.5194/egusphere-2024-4033, https://doi.org/10.5194/egusphere-2024-4033, 2025
Short summary
Short summary
Climate change is leading to a global recession of mountain glaciers, and numerical modelling suggests that this will result in the eventual disappearance of many glaciers, impacting water supplies. However, an alternative scenario suggests that increased rock fall and debris flows to valley bottoms will cover glaciers with thick rock debris, slowing melting and transforming glaciers into rock-ice mixtures called rock glaciers. This paper explores these scenarios.
Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau
Earth Syst. Sci. Data, 16, 3949–3972, https://doi.org/10.5194/essd-16-3949-2024, https://doi.org/10.5194/essd-16-3949-2024, 2024
Short summary
Short summary
Numerical simulations are a promising approach to characterizing the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of 3D simulations can limit their usability. We present the first database of seismic-induced ground motion generated by an earthquake simulator for a collection of 30 000 heterogeneous geologies. The HEMEWS-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data, 16, 919–939, https://doi.org/10.5194/essd-16-919-2024, https://doi.org/10.5194/essd-16-919-2024, 2024
Short summary
Short summary
Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
Short summary
Short summary
To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
Benoit S. Lecavalier, Lev Tarasov, Greg Balco, Perry Spector, Claus-Dieter Hillenbrand, Christo Buizert, Catherine Ritz, Marion Leduc-Leballeur, Robert Mulvaney, Pippa L. Whitehouse, Michael J. Bentley, and Jonathan Bamber
Earth Syst. Sci. Data, 15, 3573–3596, https://doi.org/10.5194/essd-15-3573-2023, https://doi.org/10.5194/essd-15-3573-2023, 2023
Short summary
Short summary
The Antarctic Ice Sheet Evolution constraint database version 2 (AntICE2) consists of a large variety of observations that constrain the evolution of the Antarctic Ice Sheet over the last glacial cycle. This includes observations of past ice sheet extent, past ice thickness, past relative sea level, borehole temperature profiles, and present-day bedrock displacement rates. The database is intended to improve our understanding of past Antarctic changes and for ice sheet model calibrations.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 17, 1003–1022, https://doi.org/10.5194/tc-17-1003-2023, https://doi.org/10.5194/tc-17-1003-2023, 2023
Short summary
Short summary
The Totten and Moscow University glaciers in East Antarctica have the potential to make a significant contribution to future sea-level rise. We used a combination of different satellite measurements to show that the grounding lines have been retreating along the fast-flowing ice streams across these two glaciers. We also found two tide-modulated ocean channels that might open new pathways for the warm ocean water to enter the ice shelf cavity.
Sam Royston, Rory J. Bingham, and Jonathan L. Bamber
Ocean Sci., 18, 1093–1107, https://doi.org/10.5194/os-18-1093-2022, https://doi.org/10.5194/os-18-1093-2022, 2022
Short summary
Short summary
Decadal sea-level variability masks longer-term changes and increases uncertainty in observed trend and acceleration estimates. We use numerical ocean models to determine the magnitude of decadal variability we might expect in sea-level trends at coastal locations around the world, resulting from natural, internal variability. A proportion of that variability can be replicated from known climate modes, giving a range to add to short- to mid-term projections of regional sea-level trends.
Stephen J. Chuter, Andrew Zammit-Mangion, Jonathan Rougier, Geoffrey Dawson, and Jonathan L. Bamber
The Cryosphere, 16, 1349–1367, https://doi.org/10.5194/tc-16-1349-2022, https://doi.org/10.5194/tc-16-1349-2022, 2022
Short summary
Short summary
We find the Antarctic Peninsula to have a mean mass loss of 19 ± 1.1 Gt yr−1 over the 2003–2019 period, driven predominantly by changes in ice dynamic flow like due to changes in ocean forcing. This long-term record is crucial to ascertaining the region’s present-day contribution to sea level rise, with the understanding of driving processes enabling better future predictions. Our statistical approach enables us to estimate this previously poorly surveyed regions mass balance more accurately.
Tom Mitcham, G. Hilmar Gudmundsson, and Jonathan L. Bamber
The Cryosphere, 16, 883–901, https://doi.org/10.5194/tc-16-883-2022, https://doi.org/10.5194/tc-16-883-2022, 2022
Short summary
Short summary
We modelled the response of the Larsen C Ice Shelf (LCIS) and its tributary glaciers to the calving of the A68 iceberg and validated our results with observations. We found that the impact was limited, confirming that mostly passive ice was calved. Through further calving experiments we quantified the total buttressing provided by the LCIS and found that over 80 % of the buttressing capacity is generated in the first 5 km of the ice shelf downstream of the grounding line.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
Earth Syst. Sci. Data, 14, 535–557, https://doi.org/10.5194/essd-14-535-2022, https://doi.org/10.5194/essd-14-535-2022, 2022
Short summary
Short summary
Accurate knowledge of the Antarctic grounding zone is important for mass balance calculation, ice sheet stability assessment, and ice sheet model projections. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries. This new data product will provide more comprehensive insights into ice sheet instability, which is valuable for both the cryosphere and sea level science communities.
Tian Li, Geoffrey J. Dawson, Stephen J. Chuter, and Jonathan L. Bamber
The Cryosphere, 14, 3629–3643, https://doi.org/10.5194/tc-14-3629-2020, https://doi.org/10.5194/tc-14-3629-2020, 2020
Short summary
Short summary
Accurate knowledge of the Antarctic grounding zone is critical for the understanding of ice sheet instability and the evaluation of mass balance. We present a new, fully automated method to map the grounding zone from ICESat-2 laser altimetry. Our results of Larsen C Ice Shelf demonstrate the efficiency, density, and high spatial accuracy with which ICESat-2 can image complex grounding zones.
Cited articles
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin,
D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R.,
and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b
Armanios, D. E. and Fisher, J. B.: Measuring water availability with limited
ground data: assessing the feasibility of an entirely remote-sensing-based
hydrologic budget of the Rufiji Basin, Tanzania, using TRMM, GRACE, MODIS, SRB, and AIRS, Hydrol. Process., 28, 853–867, https://doi.org/10.1002/hyp.9611, 2014. a
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
Bhattarai, N., Mallick, K., Stuart, J., Vishwakarma, B. D., Niraula, R., Sen,
S., and Jain, M.: An automated multi-model evapotranspiration mapping
framework using remotely sensed and reanalysis data, Remote Sens. Environ., 229, 69–92, https://doi.org/10.1016/j.rse.2019.04.026, 2019. a, b, c
Blöschl, G., Bierkens, M. F., Chambel, A., Cudennec, C., Destouni, G., Fiori, A., Kirchner, J. W., McDonnell, J. J., Savenije, H. H., Sivapalan, M.,
Stumpp, C., Toth, E., Volpi, E., Carr, G., Lupton, C., Salinas, J., Széles, B., Viglione, A., Aksoy, H., Allen, S. T., Amin, A., Andréassian, V., Arheimer, B., Aryal, S. K., Baker, V., Bardsley, E., Barendrecht, M. H., Bartosova, A., Batelaan, O., Berghuijs, W. R., Beven, K., Blume, T., Bogaard, T., Borges de Amorim, P., Böttcher, M. E., Boulet, G., Breinl, K., Brilly, M., Brocca, L., Buytaert, W., Castellarin, A., Castelletti, A., Chen, X., Chen, Y., Chen, Y., Chifflard, P., Claps, P., Clark, M. P., Collins, A. L., Croke, B., Dathe, A., David, P. C., de Barros, F. P. J., de Rooij, G., Di Baldassarre, G., Driscoll, J. M., Duethmann, D., Dwivedi, R., Eris, E., Farmer, W. H., Feiccabrino, J., Ferguson, G., Ferrari, E., Ferraris, S., Fersch, B., Finger, D., Foglia, L., Fowler, K., Gartsman, B., Gascoin, S., Gaume, E., Gelfan, A., Geris, J., Gharari, S., Gleeson, T., Glendell, M., Gonzalez Bevacqua, A., González-Dugo, M. P., Grimaldi, S., Gupta, A. B., Guse, B., Han, D., Hannah, D., Harpold, A., Haun, S., Heal, K., Helfricht, K., Herrnegger, M., Hipsey, M., Hlaváčiková, H., Hohmann, C., Holko, L., Hopkinson, C., Hrachowitz, M., Illangasekare, T. H., Inam, A., Innocente, C., Istanbulluoglu, E., Jarihani, B., Kalantari, Z., Kalvans, A., Khanal, S., Khatami, S., Kiesel, J., Kirkby, M., Knoben, W., Kochanek, K., Kohnová, S., Kolechkina, A., Krause, S., Kreamer, D., Kreibich, H., Kunstmann, H., Lange, H., Liberato, M. L. R., Lindquist, E., Link, T., Liu, J., Loucks, D. P., Luce, C., Mahé, G., Makarieva, O., Malard, J., Mashtayeva, S., Maskey, S., Mas-Pla, J., Mavrova-Guirguinova, M., Mazzoleni, M., Mernild, S., Misstear, B. D., Montanari, A., Müller-Thomy, H., Nabizadeh, A., Nardi, F., Neale, C., Nesterova, N., Nurtaev, B., Odongo, V. O., Panda, S., Pande, S., Pang, Z., Papacharalampous, G., Perrin, C., Pfister, L., Pimentel, R., Polo, M. J., Post, D., Prieto Sierra, C., Ramos, M.-H., Renner, M., Reynolds, J. E., Ridolfi, E., Rigon, R., Riva, M., Robertson, D. E., Rosso, R., Roy, T., Sá, J. H., Salvadori, G., Sandells, M., Schaefli, B., Schumann, A., Scolobig, A., Seibert, J., Servat, E., Shafiei, M., Sharma, A., Sidibe, M., Sidle, R. C., Skaugen, T., Smith, H., Spiessl, S. M., Stein, L., Steinsland, I., Strasser, U., Su, B., Szolgay, J., Tarboton, D., Tauro, F., Thirel, G., Tian, F., Tong, R., Tussupova, K., Tyralis, H., Uijlenhoet, R., van Beek, R., van der Ent, R. J., van der Ploeg, M., Van Loon, A. F., van Meerveld, I., van Nooijen, R., van Oel, P. R., Vidal, J.-P., von Freyberg, J., Vorogushyn, S., Wachniew, P., Wade, A. J., Ward, P., Westerberg, I. K., White, C., Wood, E. F., Woods, R., Xu, Z., Yilmaz, K. K., and Zhang, Y.: Twenty-three unsolved problems in hydrology (UPH) – a community perspective, Hydrolog. Sci. J., 64, 1141–1158, https://doi.org/10.1080/02626667.2019.1620507, 2019. a
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H.-L., Koren, V., Duan, Q. Y., Ek, M., and Betts, A.: Modeling of land surface evaporation by four schemes and comparison with FIFE observations, J. Geophys. Res.-Atmos., 101, 7251–7268, https://doi.org/10.1029/95JD02165, 1996. a
Chen, J., Tapley, B., Rodell, M., Seo, K., Wilson, C., Scanlon, B. R., and
Pokhrel, Y.: Basin‐Scale River Runoff Estimation From GRACE Gravity Satellites, Climate Models, and In Situ Observations: A Case Study in the Amazon Basin, Water Resour. Res., 56, e2020WR028032, https://doi.org/10.1029/2020WR028032, 2020. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R.,
McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K.,
Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J.,
French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.:
The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water
resources, Water Resour. Res., 53, 2618–2626, https://doi.org/10.1002/2016WR020175, 2017. a
Gao, H., Tang, Q., Ferguson, C. R., Wood, E. F., and Lettenmaier, D. P.:
Estimating the water budget of major US river basins via remote sensing, Int. J. Remote Sens., 31, 3955–3978, https://doi.org/10.1080/01431161.2010.483488, 2010. a, b, c, d
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an
observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019. a
GRDC: Major River Basins of the World – Global Runoff Data Centre, available at: https://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/221_MRB/riverbasins_node.html, last access: 3 December 2020. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Scient. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a, b
Henriksen, H. J., Troldborg, L., Nyegaard, P., Sonnenborg, T. O., Refsgaard,
J. C., and Madsen, B.: Methodology for construction, calibration and validation of a national hydrological model for Denmark, J. Hydrol., 280, 52–71, https://doi.org/10.1016/S0022-1694(03)00186-0, 2003. a, b
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007. a
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Nelkin, E. J.: The TRMM
Multi-Satellite Precipitation Analysis (TMPA), in: Satellite rainfall Applications for Surface Hydrology, edited by: Gebremichael, M. and Hossain, F., Springer, Dordrecht, 3–22, https://doi.org/10.1007/978-90-481-2915-7_1, 2010. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C.,
Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), National Aeronautics and Space Administration, p. 38, https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06, 2019. a
Jain, S. K. and Sudheer, K. P.: Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index, J. Hydrol. Eng., 13, 981–986, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:10(981), 2008. a, b
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling
of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013,
https://doi.org/10.5194/bg-6-2001-2009, 2009. a
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scient. Data, 6, 74,
https://doi.org/10.1038/s41597-019-0076-8, 2019. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015. a
Koren, V., Schaake, J., Mitchell, K., Duan, Q.-Y., Chen, F., and Baker, J. M.: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models, J. Geophys. Res.-Atmos., 104, 19569–19585, https://doi.org/10.1029/1999JD900232, 1999. a
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A
catchment-based approach to modeling land surface processes in a general
circulation model: 1. Model structure, J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of
the Köppen–Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Landerer, F. W., Dickey, J. O., and Güntner, A.: Terrestrial water budget of the Eurasian pan-Arctic from GRACE satellite measurements during 2003–2009, J. Geophys. Res., 115, D23115, https://doi.org/10.1029/2010JD014584, 2010. a, b
Lehmann, F.: lehmannfa/water_budget_closure, GitHub [code], https://github.com/lehmannfa/water_budget_closure, last access: 2 January 2022. a
Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., Goncalves, L. G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S.,
Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I. B., Daira, D., Bila, M., Lannoy, G., Mocko, D., Steele‐Dunne, S. C., Save, H., and Bettadpur, S.: Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges, Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018WR024618, 2019. a, b
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14415, https://doi.org/10.1029/94JD00483, 1994. a
Liu, W., Wang, L., Zhou, J., Li, Y., Sun, F., Fu, G., Li, X., and Sang, Y.-F.: A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method, J. Hydrol., 538, 82–95,
https://doi.org/10.1016/j.jhydrol.2016.04.006, 2016. a, b
Long, D., Longuevergne, L., and Scanlon, B. R.: Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE
satellites, Water Resour. Res., 50, 1131–1151, https://doi.org/10.1002/2013WR014581, 2014. a, b
Long, D., Yang, Y., Wada, Y., Hong, Y., Liang, W., Chen, Y., Yong, B., Hou, A., Wei, J., and Chen, L.: Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River Basin, Remote Sens. Environ., 168, 177–193,
https://doi.org/10.1016/j.rse.2015.07.003, 2015. a, b
Longuevergne, L., Scanlon, B. R., and Wilson, C. R.: GRACE Hydrological
estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA, Water Resour. Res., 46, 11517, https://doi.org/10.1029/2009WR008564, 2010. a
Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., and
Riegger, J.: Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances, J. Hydrometeorol., 15, 2111–2139, https://doi.org/10.1175/JHM-D-13-0157.1, 2014. a, b, c, d, e, f, g, h, i
Lorenz, C., Tourian, M. J., Devaraju, B., Sneeuw, N., and Kunstmann, H.:
Basin‐scale runoff prediction: An Ensemble Kalman filter framework based on global hydrometeorological data sets, Water Resour. Res., 51, 8450–8475, https://doi.org/10.1002/2014WR016794, 2015. a
Lv, M., Ma, Z., Yuan, X., Lv, M., Li, M., and Zheng, Z.: Water budget closure
based on GRACE measurements and reconstructed evapotranspiration using GLDAS and water use data for two large densely-populated mid-latitude basins, J. Hydrol., 547, 585–599, https://doi.org/10.1016/j.jhydrol.2017.02.027, 2017. a, b
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R.
A. M., Fernández-Prieto, D., Beck, H E., Dorigo, W. A., and Verhoest, N.
E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a
Monteith, J. L.: Evaporation and Environment, Symposia of the Society for
Experimental Biology, 205–234, available at:
https://repository.rothamsted.ac.uk/item/8v5v7/evaporation-and-environment (last access: 9 December 2020), 1965. a
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011. a
Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M., Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M., Maignan, F., McCabe, M. F., Reichle, R., Reichstein, M., Rodell, M., Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations: global land evapotranspiration datasets, Geophys. Res. Lett., 38, 06402, https://doi.org/10.1029/2010GL046230, 2011. a, b
Muñoz‐Sabater, J.: ERA5-Land monthly averaged data from 2001 to present,
ECMWF [dat aset], https://doi.org/10.24381/CDS.68D2BB30, 2019. 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
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006. a
Oliveira, P. T. S., Nearing, M. A., Moran, M. S., Goodrich, D. C., Wendland,
E., and Gupta, H. V.: Trends in water balance components across the Brazilian Cerrado, Water Resour. Res., 50, 7100–7114, https://doi.org/10.1002/2013WR015202, 2014. a, b
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Multisource Estimation of Long-Term Terrestrial Water Budget for Major Global River Basins, J. Climate, 25, 3191–3206, https://doi.org/10.1175/JCLI-D-11-00300.1, 2012. a, b
Pascolini-Campbell, M. A., Reager, J. T., and Fisher, J. B.: GRACE-based
Mass Conservation as a Validation Target for Basin-Scale Evapotranspiration in the Contiguous United States, Water Resour. Res., 56, e2019WR026594, https://doi.org/10.1029/2019WR026594, 2020. a, b, c
Penatti, N. C., d. Almeida, T. I. R., Ferreira, L. G., Arantes, A. E., and Coe, M. T.: Satellite-based hydrological dynamics of the world's largest
continuous wetland, Remote Sens. Environ., 170, 1–13, https://doi.org/10.1016/j.rse.2015.08.031, 2015. a
Penman, H. L.: Natural evaporation from open water, bare soil and grass,
P. Roy. Soc. Lond. A, 193, 120–145, https://doi.org/10.1098/rspa.1948.0037, 1948. a
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Touré, A.: Assessment and Enhancement of MERRA
Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338,
https://doi.org/10.1175/JCLI-D-10-05033.1, 2011. a
Reichle, R. H., Liu, Q., Koster, R. D., Draper, C. S., Mahanama, S. P. P., and Partyka, G. S.: Land Surface Precipitation in MERRA-2, J. Climate, 30, 1643–1664, https://doi.org/10.1175/JCLI-D-16-0570.1, 2017. a
Rodell, M. and Famiglietti, J. S.: Detectability of variations in continental
water storage from satellite observations of the time dependent gravity
field, Water Resour. Res., 35, 2705–2723, https://doi.org/10.1029/1999WR900141, 1999. 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
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J.,
Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A., Sheffield, J., and Wood, E. F.: The Observed State of the Water Cycle in the Early Twenty-First Century, J. Climate, 28, 8289–8318, https://doi.org/10.1175/JCLI-D-14-00555.1, 2015. a
Saemian, P., Elmi, O., Vishwakarma, B., Tourian, M., and Sneeuw, N.: Analyzing the Lake Urmia restoration progress using ground-based and spaceborne observations, Sci. Total Environ., 739, 139857,
https://doi.org/10.1016/j.scitotenv.2020.139857, 2020. a
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Reconciling the global terrestrial water budget using satellite remote sensing, Remote Sens. Environ., 115, 1850–1865,
https://doi.org/10.1016/j.rse.2011.03.009, 2011. a, b, c, d
Samuelsen, A., Hansen, C., and Wehde, H.: Tuning and assessment of the
HYCOM-NORWECOM V2.1 biogeochemical modeling system for the North Atlantic and Arctic oceans, Geosci. Model Dev., 8, 2187–2202, https://doi.org/10.5194/gmd-8-2187-2015, 2015. a, b
Save, H.: CSR GRACE and GRACE-FO RL06 Mascon Solutions v02, available at:
http://www2.csr.utexas.edu/grace, last access: 27 June 2021. a
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., van Beek, L. P. H., Wiese, D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Döll, P., and Bierkens, M. F. P.: Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data, P. Natl. Acad. Sci. USA, 115, E1080–E1089,
https://doi.org/10.1073/pnas.1704665115, 2018. a
Schneider, U., Becker, A., Finger, P., Rustemeier, E., and Ziese, M.: GPCC
Full Data Monthly Version 2020 at 0.5∘, Global Precipitation Climatology Centre at Deutscher Wetterdienst,
https://doi.org/10.5676/DWD_GPCC/FD_M_V2020_050, 2020. a
Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., and Verdin, J. P.: Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach, J. Am. Water Resour. Assoc., 49, 577–591, https://doi.org/10.1111/jawr.12057, 2013. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year
High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006. a
Sheffield, J., Ferguson, C. R., Troy, T. J., Wood, E. F., and McCabe, M. F.:
Closing the terrestrial water budget from satellite remote sensing, Geophys. Res. Lett., 36, 07403, https://doi.org/10.1029/2009GL037338, 2009. a
Sneeuw, N., Lorenz, C., Devaraju, B., Tourian, M. J., Riegger, J., Kunstmann,
H., and Bárdossy, A.: Estimating Runoff Using Hydro-Geodetic Approaches, Surv. Geophys., 35, 1333–1359, https://doi.org/10.1007/s10712-014-9300-4, 2014. a, b
Swann, A. L. S. and Koven, C. D.: A Direct Estimate of the Seasonal Cycle of Evapotranspiration over the Amazon Basin, J. Hydrometeorol., 18, 2173–2185, https://doi.org/10.1175/JHM-D-17-0004.1, 2017. a, b
Tapley, B. D.: GRACE Measurements of Mass Variability in the Earth System, Science, 305, 503–505, https://doi.org/10.1126/science.1099192, 2004. a
Thor, R.: Least-Squares prediction of runoff, Stuttgart University, Stuttgart, 2013. a
Tourian, M., Schwatke, C., and Sneeuw, N.: River discharge estimation at daily resolution from satellite altimetry over an entire river basin, J. Hydrol., 546, 230–247, https://doi.org/10.1016/j.jhydrol.2017.01.009, 2017. a
Vishwakarma, B., Devaraju, B., and Sneeuw, N.: What Is the Spatial Resolution of GRACE Satellite Products for Hydrology?, Remote Sens., 10, 852, https://doi.org/10.3390/rs10060852, 2018. a, b, c
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res.-Solid, 103, 30205–30229, https://doi.org/10.1029/98JB02844, 1998. a
Wahr, J., Swenson, S., and Velicogna, I.: Accuracy of GRACE mass estimates,
Geophys. Res. Lett., 33, L06401, https://doi.org/10.1029/2005GL025305, 2006. a
Wan, Z., Zhang, K., Xue, X., Hong, Z., Hong, Y., and Gourley, J. J.: Water
balance-based actual evapotranspiration reconstruction from ground and satellite observations over the conterminous United States: water
balance-based observational ET reconstruction, Water Resour. Res., 51, 6485–6499, https://doi.org/10.1002/2015WR017311, 2015. a, b
Wang, H., Guan, H., Gutiérrez-Jurado, H. A., and Simmons, C. T.: Examination of water budget using satellite products over Australia, J.
Hydrol., 511, 546–554, https://doi.org/10.1016/j.jhydrol.2014.01.076, 2014. a, b
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., and Landerer, F. W.:
Improved methods for observing Earth's time variable mass distribution with
GRACE using spherical cap mascons: Improved Gravity Observations from GRACE, J. Geophys. Res.-Solid, 120, 2648–2671, https://doi.org/10.1002/2014JB011547, 2015. a
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W., and Watkins, M. M.:
JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height Release 06 Coastal Resolution Improvement (CRI) Filtered Version 1.0, NASA [data set], https://doi.org/10.5067/TEMSC-3MJC6, 2018. a, b
Xie, J., Xu, Y., Gao, C., Xuan, W., and Bai, Z.: Total Basin Discharge From GRACE and Water Balance Method for the Yarlung Tsangpo River Basin, Southwestern China, J. Geophys. Res.-Atmos., 124, 7617–7632, https://doi.org/10.1029/2018JD030025, 2019. a
Zaitchik, B. F., Rodell, M., and Olivera, F.: Evaluation of the Global Land
Data Assimilation System using global river discharge data and a source-to-sink routing scheme: source to sink routing for global models,
Water Resour. Res., 46, 06507, https://doi.org/10.1029/2009WR007811, 2010. a
Zhang, J.: Assessing the statistical relations of terrestrial water mass
changewith hydrological variables and climate variability, PhD thesis,
Universität Stuttgart, München, available at: https://publikationen.badw.de/de/046188119/pdf/CC BY (last access: 13 April 2020), 2019.
a
Zhang, Y., Pan, M., Sheffield, J., Siemann, A. L., Fisher, C. K., Liang, M.,
Beck, H. E., Wanders, N., MacCracken, R. F., Houser, P. R., Zhou, T.,
Lettenmaier, D. P., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood,
E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263,
https://doi.org/10.5194/hess-22-241-2018, 2018. a, b
Short summary
Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.
Many data sources are available to evaluate components of the water cycle (precipitation,...