Articles | Volume 26, issue 17
https://doi.org/10.5194/hess-26-4515-2022
© 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-26-4515-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Sep 2022
Research article |
| 12 Sep 2022
| 12 Sep 2022
Scaling methods of leakage correction in GRACE mass change estimates revisited for the complex hydro-climatic setting of the Indus Basin
Vasaw Tripathi
Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
Andreas Groh
Institut für Planetare Geodäsie, Technische Universität
Dresden, Dresden, Germany
Martin Horwath
CORRESPONDING AUTHOR
Institut für Planetare Geodäsie, Technische Universität
Dresden, Dresden, Germany
Raaj Ramsankaran
Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
Related authors
No articles found.
Alejandro Blazquez, Benoit Meyssignac, Robin Fraudeau, Michael Ablain, Jonathan Bamber, Antonio Bonaduce, Marie Bouih, Anny Cazenave, Thorben Döhne, Ines Dussaillant, Ramiro Ferrari, Martin Horwath, Nicolas Kolodziejczyk, Hugo Lecomte, Stephanie Leroux, William Llovel, Daniele Melini, Erwan Oulhen, Thierry Penduff, Roshin P. Raj, Giorgio Spada, Marius Schlaak, Papasarafianou Stamatia, Andrea Storto, Chunxue Yang, and Sarah Connors
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-627, https://doi.org/10.5194/essd-2026-627, 2026
Preprint under review for ESSD
Short summary
Short summary
Closing the sea‑level budget on annual and longer time scales is a cornerstone of physical oceanography because sea‑level rise is one of the best indicators of climate change, and a closed budget shows we have identified and quantified all major drivers. We examined it from 1993 to 2022, finding an accelerated rise that matched ice melt and warm water until 2015. Afterwards an unexplained gap appears. Better deep‑ocean observations and refined gravity processing are needed to close the budget.
Tirthankar Ghosh, RAAJ Ramsankaran, Felicity S. McCormack, and Andrew N. Mackintosh
The Cryosphere, 20, 3415–3433, https://doi.org/10.5194/tc-20-3415-2026, https://doi.org/10.5194/tc-20-3415-2026, 2026
Short summary
Short summary
Our long-term analysis (1992–2023) shows that glaciers in the Zanskar Himalayas are undergoing sustained flow deceleration, closely associated with progressive thinning and the resulting reduction in driving stress. Glacier-specific factors, including geometry, topography, debris cover, and terminus type, further modulate individual glacier response. Overall, these results provide new insight into the long-term dynamic response of glaciers in the region to ongoing mass loss.
R. Chandra Prabha and RAAJ Ramsankaran
EGUsphere, https://doi.org/10.5194/egusphere-2026-1207, https://doi.org/10.5194/egusphere-2026-1207, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study demonstrates a simple, practical, and scalable method for measuring the depth of snow from satellite radar imagery that is not only easy to implement across large regions but also provides improved quantification compared to existing studies. In addition, AI techniques are applied to better understand the characteristics of radar signals associated with different snow types across diverse mountainous regions, highlighting variability in the underlying physical mechanisms.
Ajay Godara, P. Jayaraman Navinkumar, Tirthankar Ghosh, and RAAJ Ramsankaran
EGUsphere, https://doi.org/10.5194/egusphere-2025-5601, https://doi.org/10.5194/egusphere-2025-5601, 2026
Short summary
Short summary
Using drone we created ultra high resolution maps, allowing us to track where ice and snow increased or melted on the ice sheet near India’s Maitri Station in Antarctica over days, weeks, and a full year. This approach shows how drone based monitoring can improve our understanding of ice changes in remote polar regions and support climate monitoring. We also documented some challenges faced in drone surveying and provided few practical recommendations for future studies.
Maria T. Kappelsberger, Johan Nilsson, Martin Horwath, Veit Helm, and Alex S. Gardner
EGUsphere, https://doi.org/10.5194/egusphere-2025-5745, https://doi.org/10.5194/egusphere-2025-5745, 2025
Short summary
Short summary
We compare surface elevation changes of the Antarctic Ice Sheet interior as derived from satellite radar altimetry and laser altimetry between 2019 and 2024. Both methods indicate an increase in surface elevation across most of the study area, coinciding with an episode of excess snow accumulation as modelled. However, rates of surface elevation increase from radar altimetry are systematically lower than those from laser altimetry.
Torsten Kanzow, Angelika Humbert, Thomas Mölg, Mirko Scheinert, Matthias Braun, Hans Burchard, Francesca Doglioni, Philipp Hochreuther, Martin Horwath, Oliver Huhn, Maria Kappelsberger, Jürgen Kusche, Erik Loebel, Katrina Lutz, Ben Marzeion, Rebecca McPherson, Mahdi Mohammadi-Aragh, Marco Möller, Carolyne Pickler, Markus Reinert, Monika Rhein, Martin Rückamp, Janin Schaffer, Muhammad Shafeeque, Sophie Stolzenberger, Ralph Timmermann, Jenny Turton, Claudia Wekerle, and Ole Zeising
The Cryosphere, 19, 1789–1824, https://doi.org/10.5194/tc-19-1789-2025, https://doi.org/10.5194/tc-19-1789-2025, 2025
Short summary
Short summary
The Greenland Ice Sheet represents the second-largest contributor to global sea-level rise. We quantify atmosphere, ice and ocean processes related to the mass balance of glaciers in northeast Greenland, focusing on Greenland’s largest floating ice tongue, the 79° N Glacier. We find that together, the different in situ and remote sensing observations and model simulations reveal a consistent picture of a coupled atmosphere–ice sheet–ocean system that has entered a phase of major change.
Christoph Dahle, Eva Boergens, Ingo Sasgen, Thorben Döhne, Sven Reißland, Henryk Dobslaw, Volker Klemann, Michael Murböck, Rolf König, Robert Dill, Mike Sips, Ulrike Sylla, Andreas Groh, Martin Horwath, and Frank Flechtner
Earth Syst. Sci. Data, 17, 611–631, https://doi.org/10.5194/essd-17-611-2025, https://doi.org/10.5194/essd-17-611-2025, 2025
Short summary
Short summary
GRACE and GRACE-FO are unique observing systems to quantify mass changes at the Earth’s surface from space. Time series of these mass changes are of high value for various applications, e.g., in hydrology, glaciology, and oceanography. GravIS (Gravity Information Service) provides easy access to user-friendly, regularly updated mass anomaly products. The portal visualizes and describes these data, aiming to highlight their significance for understanding changes in the climate system.
Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath
Earth Syst. Sci. Data, 17, 65–78, https://doi.org/10.5194/essd-17-65-2025, https://doi.org/10.5194/essd-17-65-2025, 2025
Short summary
Short summary
Glacier calving front positions are important for understanding glacier dynamics and constraining ice modelling. We apply a deep-learning framework to multi-spectral Landsat imagery to create a calving front record for 42 key outlet glaciers of the Antarctic Peninsula Ice Sheet. The resulting data product includes 4817 calving front locations from 2013 to 2023 and achieves sub-seasonal temporal resolution.
Abelardo Romero, Andreas Richter, Amilcar Juarez, Federico Suad Corbetta, Eric Marderwald, Pedro Granovsky, Thorben Döhne, and Martin Horwath
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W6-2024, 51–58, https://doi.org/10.5194/isprs-archives-XLVIII-2-W6-2024-51-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W6-2024-51-2024, 2024
Sanjay Saifi and RAAJ Ramsankaran
Proc. IAHS, 387, 73–77, https://doi.org/10.5194/piahs-387-73-2024, https://doi.org/10.5194/piahs-387-73-2024, 2024
Short summary
Short summary
Visibility assessment is crucial for informed decision-making and disaster preparedness in mountainous regions due to snow-induced disasters. This paper presents an innovative approach using augmented reality (AR) to address this challenge. The Him-Drishti application harnesses the established correlation between snowfall intensity and visibility to create a predictive visibility simulation model. This study demonstrates the capacity of AR in disaster management and risk reduction.
Maria T. Kappelsberger, Martin Horwath, Eric Buchta, Matthias O. Willen, Ludwig Schröder, Sanne B. M. Veldhuijsen, Peter Kuipers Munneke, and Michiel R. van den Broeke
The Cryosphere, 18, 4355–4378, https://doi.org/10.5194/tc-18-4355-2024, https://doi.org/10.5194/tc-18-4355-2024, 2024
Short summary
Short summary
The interannual variations in the height of the Antarctic Ice Sheet (AIS) are mainly due to natural variations in snowfall. Precise knowledge of these variations is important for the detection of any long-term climatic trends in AIS surface elevation. We present a new product that spatially resolves these height variations over the period 1992–2017. The product combines the strengths of atmospheric modeling results and satellite altimetry measurements.
Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
Short summary
Short summary
We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere, 18, 3315–3332, https://doi.org/10.5194/tc-18-3315-2024, https://doi.org/10.5194/tc-18-3315-2024, 2024
Short summary
Short summary
Comprehensive datasets of calving-front changes are essential for studying and modeling outlet glaciers. Current records are limited in temporal resolution due to manual delineation. We use deep learning to automatically delineate calving fronts for 23 glaciers in Greenland. Resulting time series resolve long-term, seasonal, and subseasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.
Matthias O. Willen, Martin Horwath, Eric Buchta, Mirko Scheinert, Veit Helm, Bernd Uebbing, and Jürgen Kusche
The Cryosphere, 18, 775–790, https://doi.org/10.5194/tc-18-775-2024, https://doi.org/10.5194/tc-18-775-2024, 2024
Short summary
Short summary
Shrinkage of the Antarctic ice sheet (AIS) leads to sea level rise. Satellite gravimetry measures AIS mass changes. We apply a new method that overcomes two limitations: low spatial resolution and large uncertainties due to the Earth's interior mass changes. To do so, we additionally include data from satellite altimetry and climate and firn modelling, which are evaluated in a globally consistent way with thoroughly characterized errors. The results are in better agreement with independent data.
Imtiyaz Ahmad Bhat, Irfan Rashid, RAAJ Ramsankaran, Argha Banerjee, and Saurabh Vijay
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-522, https://doi.org/10.5194/essd-2023-522, 2024
Preprint withdrawn
Short summary
Short summary
A comprehensive rock glacier inventory (n = 5492) has been generated through manual delineation in a GIS environment for the western Himalayan region. The inventory has characterized each rock glacier with 22 attributes following the standard protocols. This inventory shall serve as a baseline for the future research related to rock glacier dynamics, their hydrological contribution and response to climate change.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
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.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
Short summary
Short summary
Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Cited articles
A, G., Wahr, J. and Zhong, S.: Computations of the viscoelastic response of
a 3-D compressible Earth to surface loading: an application to Glacial
Isostatic Adjustment in Antarctica and Canada, Geophys. J.
Int., 192, 557–572, https://doi.org/10.1093/gji/ggs030, 2013.
Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., and
Cazenave, A.: Exploring the uncertainty in GRACE estimates of the mass
redistributions at the Earth surface: implications for the global water and
sea level budgets, 215, 415–430, https://doi.org/10.1093/gji/ggy293, 2018.
Cáceres, D., Marzeion, B., Malles, J. H., Gutknecht, B. D., Müller Schmied, H., and Döll, P.: Assessing global water mass transfers from continents to oceans over the period 1948–2016, Hydrol. Earth Syst. Sci., 24, 4831–4851, https://doi.org/10.5194/hess-24-4831-2020, 2020.
Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., and Blewitt,
G.: GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science,
Geophys. Res. Lett., 45, 2203–2212, https://doi.org/10.1002/2017GL076644,
2018.
Cheema, M. J. M. and Bastiaanssen, W. G. M.: Land use and land cover
classification in the irrigated Indus Basin using growth phenology
information from satellite data to support water management analysis,
Agr. Water Manage., 97, 1541–1552,
https://doi.org/10.1016/j.agwat.2010.05.009, 2010.
Cheng, M. and Ries, J.: The unexpected signal in GRACE estimates of C20, J. Geod., 91, 897–914, https://doi.org/10.1007/s00190-016-0995-5, 2017.
Dahle, C., Flechtner, F., Murböck, M., Michalak, G., Neumayer, H.,
Abrykosov, O., Reinhold, A., and König, R.: GRACE Geopotential GSM
Coefficients GFZ RL06 (6.0), GRACE [data set], https://doi.org/10.5880/GFZ.GRACE_06_GSM, 2018.
Dimri, A. P.: Decoding the Karakoram Anomaly, Sci. Total
Environ., 788, 147864, https://doi.org/10.1016/j.scitotenv.2021.147864,
2021.
Dimri, A. P., Kumar, D., Chopra, S., and Choudhary, A.: Indus River Basin:
Future climate and water budget, Int. J. Climatol., 39, 395–406,
https://doi.org/10.1002/joc.5816, 2019.
Ditmar, P.: How to quantify the accuracy of mass anomaly time-series based
on GRACE data in the absence of knowledge about true signal?, J. Geod., 96,
54, https://doi.org/10.1007/s00190-022-01640-x, 2022.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for
deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4,
2003.
Farinotti, D., Immerzeel, W. W., de Kok, R. J., Quincey, D. J., and Dehecq,
A.: Manifestations and mechanisms of the Karakoram glacier Anomaly, Nat.
Geosci., 13, 8–16, https://doi.org/10.1038/s41561-019-0513-5, 2020.
Flechtner, F., Neumayer, K.-H., Dahle, C., Dobslaw, H., Fagiolini, E.,
Raimondo, J.-C., and Güntner, A.: What Can be Expected from the GRACE-FO
Laser Ranging Interferometer for Earth Science Applications?, Surv. Geophys.,
37, 453–470, https://doi.org/10.1007/s10712-015-9338-y, 2016.
Frenken, K.: Irrigation in Southern and Eastern Asia in Figures:
Aquastat survey, 2011, Food and Agriculture of the United Nations, FAO, Rome, 487 p., ISBN 9789251072820, 2012.
Garg, S., Motagh, M., Indu, J., and Karanam, V.: Tracking hidden crisis in
India's capital from space: implications of unsustainable groundwater use,
Sci. Rep., 12, 651, https://doi.org/10.1038/s41598-021-04193-9, 2022.
GRACE Tellus: Monthly Mass Grids – Land | Get Data – GRACE Tellus,
https://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land,
last access: 17 October 2019.
Groh, A., Horwath, M., Horvath, A., Meister, R., Sørensen, L. S.,
Barletta, V. R., Forsberg, R., Wouters, B., Ditmar, P., Ran, J., Klees, R.,
Su, X., Shang, K., Guo, J., Shum, C. K., Schrama, E., and Shepherd, A.:
Evaluating GRACE Mass Change Time Series for the Antarctic and Greenland Ice
Sheet – Methods and Results, Geosciences, 9, 415,
https://doi.org/10.3390/geosciences9100415, 2019.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset: Updated High-Resolution Grids Of Monthly Climatic Observations,
Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hsu, C.-W. and Velicogna, I.: Detection of sea level fingerprints derived
from GRACE gravity data: Detecting Sea Level Fingerprints, Geophys. Res.
Lett., 44, 8953–8961, https://doi.org/10.1002/2017GL074070, 2017.
Huang, Z., Jiao, J. J., Luo, X., Pan, Y., and Zhang, C.: Sensitivity
Analysis of Leakage Correction of GRACE Data in Southwest China Using
A-Priori Model Simulations: Inter-Comparison of Spherical Harmonics, Mass
Concentration and In Situ Observations, Sensors, 19, 3149,
https://doi.org/10.3390/s19143149, 2019.
Hussain, D., Kao, H.-C., Khan, A. A., Lan, W.-H., Imani, M., Lee, C.-M., and
Kuo, C.-Y.: Spatial and Temporal Variations of Terrestrial Water Storage in
Upper Indus Basin Using GRACE and Altimetry Data, IEEE Access., 8,
65327–65339, https://doi.org/10.1109/ACCESS.2020.2984794, 2020.
ICIMOD: Major River Basins in the Hindu Kush Himalaya (HKH) Region, ICIMOD, 22 April 2021, https://doi.org/10.26066/RDS.2732, 2021.
Jiang, D., Wang, J., Huang, Y., Zhou, K., Ding, X., and Fu, J.: The Review
of GRACE Data Applications in Terrestrial Hydrology Monitoring, Adv. Meteorol., 2014, 1–9, https://doi.org/10.1155/2014/725131, 2014.
Klees, R., Revtova, E. A., Gunter, B. C., Ditmar, P., Oudman, E., Winsemius,
H. C., and Savenije, H. H. G.: The design of an optimal filter for monthly
GRACE gravity models, Geophys. J. Int., 175, 417–432,
https://doi.org/10.1111/j.1365-246X.2008.03922.x, 2008.
Krakauer, N. Y.: Year-ahead predictability of South Asian Summer Monsoon
precipitation, Environ. Res. Lett., 14, 044006,
https://doi.org/10.1088/1748-9326/ab006a, 2019.
Kvas, A., Behzadpour, S., Ellmer, M., Klinger, B., Strasser, S., Zehentner,
N., and Mayer-Gürr, T.: ITSG-Grace2018: Overview and Evaluation of a New
GRACE-Only Gravity Field Time Series, J. Geophys. Res.-Solid Earth, 124,
9332–9344, https://doi.org/10.1029/2019JB017415, data available at https://www.tugraz.at/institute/ifg/downloads/gravity-field-models/itsg-grace2018 (last access: 1 April 2022), 2019.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates: ACCURACY OF GRACE-TWS, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete,
B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J.,
Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.:
High-resolution mapping of the world's reservoirs and dams for sustainable
river-flow management, Front. Ecol. Environ., 9,
494–502, https://doi.org/10.1890/100125, 2011.
Long, D., Longuevergne, L., and Scanlon, B. R.: Global analysis of
approaches for deriving total water storage changes from GRACE satellites,
Water Resour. Res., 51, 2574–2594, https://doi.org/10.1002/2014WR016853,
2015.
Marzeion, B., Jarosch, A. H., and Hofer, M.: Past and future sea-level change from the surface mass balance of glaciers, The Cryosphere, 6, 1295–1322, https://doi.org/10.5194/tc-6-1295-2012, 2012.
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, 2016.
NASA JPL (Jet Propulsion Laboratory): GRACE Static Field Geopotential
Coefficients JPL Release 6.0, GRACE [data set], https://doi.org/10.5067/GRGSM-20J06, 2018.
Peltier, W. R., Argus, D. F., and Drummond, R.: Comment on “An
Assessment of the ICE-6G_C (VM5a) Glacial Isostatic
Adjustment Model” by Purcell et al.: The ICE-6G_C (VM5a) GIA
model, J. Geophys. Res.-Solid Earth, 123, 2019–2028,
https://doi.org/10.1002/2016JB013844, 2018.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner,
A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S.,
Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B.
H., Rich, J., Sharp, M. J., and The Randolph Consortium: The Randolph
Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol.,
60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014.
Pradhan, G. P.: Understanding interannual groundwater variability in North
India using GRACE, Master's thesis, University of Twente, https://purl.utwente.nl/essays/84053 (last access: 1 July 2020), 2014.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates
of groundwater depletion in India, Nature, 460, 999–1002,
https://doi.org/10.1038/nature08238, 2009.
Sasgen, I., Martín-Español, A., Horvath, A., Klemann, V., Petrie,
E. J., Wouters, B., Horwath, M., Pail, R., Bamber, J. L., Clarke, P. J.,
Konrad, H., Wilson, T., and Drinkwater, M. R.: Altimetry, gravimetry, GPS
and viscoelastic modeling data for the joint inversion for glacial isostatic
adjustment in Antarctica (ESA STSE Project REGINA), Earth Syst. Sci. Data,
10, 493–523, https://doi.org/10.5194/essd-10-493-2018, 2018.
Save, H., Bettadpur, S., and Tapley, B. D.: High-resolution CSR GRACE RL05
mascons, J. Geophys. Res.-Solid Earth, 121, 7547–7569, https://doi.org/10.1002/2016JB013007, data available at http://www2.csr.utexas.edu/grace (last access: 1 July 2021), 2016.
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, Proc. Natl. Acad. Sci. USA, 115, E1080–E1089,
https://doi.org/10.1073/pnas.1704665115, 2018.
Scargle, J. D.: Studies in astronomical time series analysis. II –
Statistical aspects of spectral analysis of unevenly spaced data, ApJ, 263,
835, https://doi.org/10.1086/160554, 1982.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.: GPCC full data reanalysis version 7.0 at 0.50∘: monthly land-surface precipitation from rain-gauges built on GTS-based and historic data, https://doi.org/10.5676/dwd_gpcc/fd_m_v7_050, 2015.
Shrestha, A. B., Wagle, N., and Rajbhandari, R.: A Review on the Projected
Changes in Climate Over the Indus Basin, in: Indus River Basin, Water Sec. Sustain., 2019, 145–158, https://doi.org/10.1016/B978-0-12-812782-7.00007-2, 2019.
Sun, Y., Riva, R., and Ditmar, P.: Optimizing estimates of annual variations
and trends in geocenter motion and J2 from a combination of GRACE data
and geophysical models, J. Geophys. Res.-Solid Earth, 121, 8352–8370,
https://doi.org/10.1002/2016JB013073, 2016.
Swenson, S. and Wahr, J.: Post-processing removal of correlated errors in
GRACE data, Geophys. Res. Lett., 33, L08402,
https://doi.org/10.1029/2005GL025285, 2006.
Swenson, S., Chambers, D., and Wahr, J.: Estimating geocenter variations
from a combination of GRACE and ocean model output: Estimating Geocenter
Variations, J. Geophys. Res., 113, B08410, https://doi.org/10.1029/2007JB005338, 2008.
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S.,
Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D.
P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C.,
Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and
Velicogna, I.: Contributions of GRACE to understanding climate change, Nat.
Clim. Chang., 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019.
ul Hasson, S.: Future Water Availability from Hindukush-Karakoram-Himalaya upper Indus Basin under Conflicting Climate Change Scenarios, Climate, 4, 40, https://doi.org/10.3390/cli4030040, 2016.
UTCSR (University Of Texas Center For Space Research): Grace Static Field
Geopotential Coefficients Csr Release 6.0, GRACE [data set],
https://doi.org/10.5067/GRGSM-20C06, 2018.
Velicogna, I. and Wahr, J.: Time-variable gravity observations of ice sheet
mass balance: Precision and limitations of the GRACE satellite data:
Understanding Grace Ice Mass Estimates, Geophys. Res. Lett., 40, 3055–3063,
https://doi.org/10.1002/grl.50527, 2013.
Vishwakarma, B. D., Horwath, M., Devaraju, B., Groh, A., and Sneeuw, N.: A
Data-Driven Approach for Repairing the Hydrological Catchment Signal Damage
Due to Filtering of GRACE Products: Repairing Signal Damage Due To
Filtering, Water Resour. Res., 53, 9824–9844,
https://doi.org/10.1002/2017WR021150, 2017.
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., 103, 30205–30229,
https://doi.org/10.1029/98JB02844, 1998.
Willen, M. O., M. Horwath, A. Groh, V. Helm, B. Uebbing, and J. Kusche. :
Feasibility of a global inversion for spatially resolved glacial isostatic
adjustment and ice sheet mass changes proven in simulation experiments, J.
Geod., https://doi.org/10.1007/s00190-022-01651-8, accepted, 11 August 2022.
Zhang, L., Dobslaw, H., and Thomas, M.: Globally gridded terrestrial water
storage variations from GRACE satellite gravimetry for hydrometeorological
applications, Geophys. J. Int., 206, 368–378,
https://doi.org/10.1093/gji/ggw153, 2016.
Short summary
GRACE/GRACE-FO provided global observations of water storage change since 2002. Scaling is a common approach to compensate for the spatial filtering inherent to the results. However, for complex hydrological basins, the compatibility of scaling with the characteristics of regional hydrology has been rarely assessed. We assess traditional scaling approaches and a new scaling approach for the Indus Basin. Our results will help users with regional focus understand implications of scaling choices.
GRACE/GRACE-FO provided global observations of water storage change since 2002. Scaling is a...
