Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-453-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-453-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jan 2023
Research article |
| 20 Jan 2023
| 20 Jan 2023
A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia
Sarah Shannon
CORRESPONDING AUTHOR
Department of Geographical Science, University Road, University of Bristol, Bristol, BS8 1SS, UK
Cabot Institute, University of Bristol, Bristol, BS8 1UJ, UK
Anthony Payne
Department of Geographical Science, University Road, University of Bristol, Bristol, BS8 1SS, UK
Cabot Institute, University of Bristol, Bristol, BS8 1UJ, UK
Jim Freer
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, T1W 3G1, Canada
Cabot Institute, University of Bristol, Bristol, BS8 1UJ, UK
Department of Geographical Science, University Road, University of Bristol, Bristol, BS8 1SS, UK
Gemma Coxon
Department of Geographical Science, University Road, University of Bristol, Bristol, BS8 1SS, UK
Cabot Institute, University of Bristol, Bristol, BS8 1UJ, UK
Martina Kauzlaric
Universität Bern, Geographisches Institut, Climate Impact Research, Hallerstrasse 12, 3012 Bern, Switzerland
David Kriegel
Ingenieurbüro für Grundwasser GmbH, Leipzig, Germany
Stephan Harrison
Department of Geography, University of Exeter, Penryn, Cornwall TR10 9FE, UK
Related authors
No articles found.
Gemma Coxon, Yanchen Zheng, Rafael Barbedo, Hollie Cooper, Felipe Fileni, Hayley J. Fowler, Matt Fry, Amy Green, Tom Gribbin, Helen Harfoot, Elizabeth Lewis, Germano Gondim Ribeiro Neto, Xiaobin Qiu, Saskia Salwey, and Doris E. Wendt
Earth Syst. Sci. Data, 18, 4345–4371, https://doi.org/10.5194/essd-18-4345-2026, https://doi.org/10.5194/essd-18-4345-2026, 2026
Short summary
Short summary
We present the second version of a large-sample catchment hydrology dataset for Great Britain. The dataset collates (1) climate, river flow and groundwater timeseries at hourly to monthly timescales, (2) catchment attributes characterising topography, climate, streamflow, land cover, soils, hydrogeology and human influences, and (3) catchment boundaries for 671 catchments across Great Britain. The dataset is publicly available to use in a wide range of environmental and modelling analyses.
Ryoko Araki, Anne Holt, John C. Hammond, Admin Husic, Gemma Coxon, and Hilary K. McMillan
Hydrol. Earth Syst. Sci., 30, 3647–3673, https://doi.org/10.5194/hess-30-3647-2026, https://doi.org/10.5194/hess-30-3647-2026, 2026
Short summary
Short summary
We mapped dominant hydrologic processes across the United States by analyzing observed streamflow dynamics. Using random forest models and interpretable machine learning techniques, we predicted processes in data-scarce regions and identified key drivers such as climate, soil and geology, land cover, topography, and human influence. The resulting maps of dominant processes and their drivers reveal strong regional patterns that guide hydrologic model selection and water resource management.
Doris E. Wendt, Gemma Coxon, Saskia Salwey, and Francesca Pianosi
Hydrol. Earth Syst. Sci., 30, 2837–2857, https://doi.org/10.5194/hess-30-2837-2026, https://doi.org/10.5194/hess-30-2837-2026, 2026
Short summary
Short summary
Groundwater is a highly-used water source, which drought management is complicated. We introduce a socio-hydrological water resource model (SHOWER) to aid drought management in groundwater-rich managed environments. Results show which and when drought management interventions influence surface water and groundwater storage, with integrated interventions having most effect on reducing droughts. This encourages further exploration to reduce water shortages and improve future drought resilience.
Robin S. Smith, Tarkan A. Bilge, Thomas J. Bracegirdle, Paul R. Holland, Till Kuhlbrodt, Charlotte Lang, Spencer Liddicoat, Tom Mitcham, Jane Mulcahy, Kaitlin A. Naughten, Andrew Orr, Julien Palmieri, Antony J. Payne, Steven Rumbold, Marc Stringer, Ranjini Swaminathan, Sarah Taylor, Jeremy Walton, and Colin Jones
Earth Syst. Dynam., 17, 475–493, https://doi.org/10.5194/esd-17-475-2026, https://doi.org/10.5194/esd-17-475-2026, 2026
Short summary
Short summary
There is a dangerous amount of uncertainty in our predictions of climate change in polar regions because some of feedbacks that might lead to changes that are too rapid for us to adapt to, or that cannot be reversed. We have run a set of simulations with a state-of-the-art Earth System Model that helps improve our understanding of how climate in these regions might change. Some of the aspects we investigate are reversible but many are not, especially those affecting ice sheets and sea level.
Eleni Kritidou, Martina Kauzlaric, Maria Staudinger, Guillaume Evin, Marc Vis, and Daniel Viviroli
EGUsphere, https://doi.org/10.5194/egusphere-2026-2338, https://doi.org/10.5194/egusphere-2026-2338, 2026
Short summary
Short summary
In mountain regions, precipitation varies with elevation, but models often simplify this relationship. We used simulations combining a weather generator and a hydrological model to explore how different precipitation different lapse rates affect runoff and floods in Switzerland. We found that they impact yearly and seasonal river flow and flood behavior. Higher-elevation catchments were less sensitive, and higher lapse rates increased summer floods, while lower rates increased winter floods.
Felipe Fileni, Hayley J. Fowler, Elizabeth Lewis, Fiona McLay, Gemma Coxon, David Archer, Emma Bruce, Longzhi Yang, Matt Fry, Hollie Cooper, and Ollie Swain
EGUsphere, https://doi.org/10.5194/egusphere-2026-277, https://doi.org/10.5194/egusphere-2026-277, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study develops and applies a national framework to identify and flag issues in UK river flow data by combining systematic visual checks with automated tests. We demonstrate the importance of visual inspection, refine and sensitivity-test traditional quality-control methods, and develop new high-flow checks tailored to high-resolution data. We then quantify the frequency of these issues and demonstrate that, if overlooked, they can impact scientific results and lead to misleading conclusions
Felipe Fileni, Hayley J. Fowler, Elizabeth Lewis, Matt Fry, Hollie Cooper, Ollie Swain, Fiona McLay, Gemma Coxon, Emma Bruce, Longzhi Yang, and David Archer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-152, https://doi.org/10.5194/essd-2026-152, 2026
Preprint under review for ESSD
Short summary
Short summary
River flow data recorded at 15-minute resolution has been collected across the UK for over 70 years, but it has been difficult to access and use consistently. We brought these records together into a single national dataset, checked them for errors, harmonised different records, and clearly documented any issues and suspect values. The result is a reliable and transparent resource that supports better flood forecasting, water management, and research on climate change impacts on river flows.
Justine Berg, Pascal Horton, Martina Kauzlaric, Alexandra von der Esch, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2026-439, https://doi.org/10.5194/egusphere-2026-439, 2026
Short summary
Short summary
Snow and glacier melt are essential for water supply in mountain regions, but it is challenging to determine how much each source contributes to streamflow. To improve these estimates, we simulated 14 headwater catchments in Switzerland using a hydrological model modified to include snow redistribution and integrating glacier melt from a glacier evolution model. This allowed more accurate estimates of snow and glacier melt contributions, supporting future predictions of water availability.
William Veness, Alejandro Dussaillant, Gemma Coxon, Simon De Stercke, Gareth H. Old, Matthew Fry, Jonathan G. Evans, and Wouter Buytaert
Hydrol. Earth Syst. Sci., 29, 6201–6219, https://doi.org/10.5194/hess-29-6201-2025, https://doi.org/10.5194/hess-29-6201-2025, 2025
Short summary
Short summary
We investigated what users want from the next-generation of hydrological monitoring systems to better support science and innovation. Through literature review and interviews with UK experts, we found that beyond providing high-quality data, users particularly value additional support for collecting their own data, sharing it with others, and building collaborations with other data users. Designing systems with these needs in mind can greatly boost long-term engagement, data coverage and impact.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4069, https://doi.org/10.5194/egusphere-2025-4069, 2025
Short summary
Short summary
Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
Stephan Harrison, Adina Racoviteanu, Sarah Shannon, Darren Jones, Karen Anderson, Neil Glasser, Jasper Knight, Anna Ranger, Arindan Mandal, Bramha Dutt Vishwakarma, Jeffrey S. Kargel, Dan Shugar, Umesh Haritashya, Dongfeng Li, Aristeidis Koutroulis, Klaus Wyser, and Sam Inglis
The Cryosphere, 19, 4113–4124, https://doi.org/10.5194/tc-19-4113-2025, https://doi.org/10.5194/tc-19-4113-2025, 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 rapid 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.
Yanchen Zheng, Gemma Coxon, Mostaquimur Rahman, Ross Woods, Saskia Salwey, Youtong Rong, and Doris E. Wendt
Geosci. Model Dev., 18, 4247–4271, https://doi.org/10.5194/gmd-18-4247-2025, https://doi.org/10.5194/gmd-18-4247-2025, 2025
Short summary
Short summary
Groundwater is vital for people and ecosystems, but most physical models lack the representation of surface–groundwater interactions, leading to inaccurate streamflow predictions in groundwater-rich areas. This study presents DECIPHeR-GW v1, which links surface and groundwater systems to improve predictions of streamflow and groundwater levels. Tested across England and Wales, DECIPHeR-GW shows high accuracy, especially in southeast England, making it a valuable tool for large-scale water management.
Jing Jin, Antony J. Payne, and Christopher Y. S. Bull
The Cryosphere, 19, 1873–1896, https://doi.org/10.5194/tc-19-1873-2025, https://doi.org/10.5194/tc-19-1873-2025, 2025
Short summary
Short summary
The Amery Ice Shelf cavity is one of the largest cold cavities filled by relatively cold Dense Shelf Water. However, in this study, we show that warm intrusion of modified Circumpolar Deep Water flushes the Amery cavity, which changes it from a cold cavity to a warm cavity and leads to an abrupt increase in the basal melt rate in the 2060s. The shift to a warm cavity is attributed to a freshening-driven current reversal in front of the ice shelf.
Markus Mosimann, Martina Kauzlaric, Olivia Martius, and Andreas Paul Zischg
Abstr. Int. Cartogr. Assoc., 9, 26, https://doi.org/10.5194/ica-abs-9-26-2025, https://doi.org/10.5194/ica-abs-9-26-2025, 2025
Maria Staudinger, Martina Kauzlaric, Alexandre Mas, Guillaume Evin, Benoit Hingray, and Daniel Viviroli
Nat. Hazards Earth Syst. Sci., 25, 247–265, https://doi.org/10.5194/nhess-25-247-2025, https://doi.org/10.5194/nhess-25-247-2025, 2025
Short summary
Short summary
Various combinations of antecedent conditions and precipitation result in floods of varying degrees. Antecedent conditions played a crucial role in generating even large ones. The key predictors and spatial patterns of antecedent conditions leading to flooding at the basin's outlet were distinct. Precipitation and soil moisture from almost all sub-catchments were important for more frequent floods. For rarer events, only the predictors of specific sub-catchments were important.
Zhangyu Sun, Yan Hu, Adina Racoviteanu, Lin Liu, Stephan Harrison, Xiaowen Wang, Jiaxin Cai, Xin Guo, Yujun He, and Hailun Yuan
Earth Syst. Sci. Data, 16, 5703–5721, https://doi.org/10.5194/essd-16-5703-2024, https://doi.org/10.5194/essd-16-5703-2024, 2024
Short summary
Short summary
We propose a new dataset, TPRoGI (v1.0), encompassing rock glaciers in the entire Tibetan Plateau. We used a neural network, DeepLabv3+, and images from Planet Basemaps. The inventory identified 44 273 rock glaciers, covering 6 000 km2, mainly at elevations of 4000 to 5500 m a.s.l. The dataset, with details on distribution and characteristics, aids in understanding permafrost distribution, mountain hydrology, and climate impacts in High Mountain Asia, filling a knowledge gap.
Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut
Hydrol. Earth Syst. Sci., 28, 5011–5030, https://doi.org/10.5194/hess-28-5011-2024, https://doi.org/10.5194/hess-28-5011-2024, 2024
Short summary
Short summary
For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.
Matt Trevers, Antony J. Payne, and Stephen L. Cornford
The Cryosphere, 18, 5101–5115, https://doi.org/10.5194/tc-18-5101-2024, https://doi.org/10.5194/tc-18-5101-2024, 2024
Short summary
Short summary
The form of the friction law which determines the speed of ice sliding over the bedrock remains a major source of uncertainty in ice sheet model projections of future sea level rise. Jakobshavn Isbræ, the fastest-flowing glacier in Greenland, which has undergone significant changes in the last few decades, is an ideal case for testing sliding laws. We find that a regularised Coulomb friction law reproduces the large seasonal and inter-annual flow speed variations most accurately.
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024, https://doi.org/10.5194/hess-28-4203-2024, 2024
Short summary
Short summary
Reservoirs are essential for water resource management and can significantly impact downstream flow. However, representing reservoirs in hydrological models can be challenging, particularly across large scales. We design a new and simple method for simulating river flow downstream of water supply reservoirs using only open-access data. We demonstrate the approach in 264 reservoir catchments across Great Britain, where we can significantly improve the simulation of reservoir-impacted flow.
Yanchen Zheng, Gemma Coxon, Ross Woods, Daniel Power, Miguel Angel Rico-Ramirez, David McJannet, Rafael Rosolem, Jianzhu Li, and Ping Feng
Hydrol. Earth Syst. Sci., 28, 1999–2022, https://doi.org/10.5194/hess-28-1999-2024, https://doi.org/10.5194/hess-28-1999-2024, 2024
Short summary
Short summary
Reanalysis soil moisture products are a vital basis for hydrological and environmental research. Previous product evaluation is limited by the scale difference (point and grid scale). This paper adopts cosmic ray neutron sensor observations, a novel technique that provides root-zone soil moisture at field scale. In this paper, global harmonized CRNS observations were used to assess products. ERA5-Land, SMAPL4, CFSv2, CRA40 and GLEAM show better performance than MERRA2, GLDAS-Noah and JRA55.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
Short summary
Short summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Kathryn A. Leeming, John P. Bloomfield, Gemma Coxon, and Yanchen Zheng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-202, https://doi.org/10.5194/hess-2023-202, 2023
Preprint withdrawn
Short summary
Short summary
In this work we characterise annual patterns in baseflow, the component of streamflow that comes from subsurface storage. Our research identified early-, mid-, and late-seasonality of baseflow across catchments in Great Britain over two time blocks: 1976–1995 and 1996–2015, and found that many catchments have earlier seasonal patterns of baseflow in the second time period. These changes are linked to changes in climate signals: snow-melt in highland catchments and effective rainfall changes.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
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.
Louisa D. Oldham, Jim Freer, Gemma Coxon, Nicholas Howden, John P. Bloomfield, and Christopher Jackson
Hydrol. Earth Syst. Sci., 27, 761–781, https://doi.org/10.5194/hess-27-761-2023, https://doi.org/10.5194/hess-27-761-2023, 2023
Short summary
Short summary
Water can move between river catchments via the subsurface, termed intercatchment groundwater flow (IGF). We show how a perceptual model of IGF can be developed with relatively simple geological interpretation and data requirements. We find that IGF dynamics vary in space, correlated to the dominant underlying geology. We recommend that IGF
loss functionsmay be used in conceptual rainfall–runoff models but should be supported by perceptualisation of IGF processes and connectivities.
Rosanna A. Lane, Gemma Coxon, Jim Freer, Jan Seibert, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, https://doi.org/10.5194/hess-26-5535-2022, 2022
Short summary
Short summary
This study modelled the impact of climate change on river high flows across Great Britain (GB). Generally, results indicated an increase in the magnitude and frequency of high flows along the west coast of GB by 2050–2075. In contrast, average flows decreased across GB. All flow projections contained large uncertainties; the climate projections were the largest source of uncertainty overall but hydrological modelling uncertainties were considerable in some regions.
Antony Siahaan, Robin S. Smith, Paul R. Holland, Adrian Jenkins, Jonathan M. Gregory, Victoria Lee, Pierre Mathiot, Antony J. Payne, Jeff K. Ridley, and Colin G. Jones
The Cryosphere, 16, 4053–4086, https://doi.org/10.5194/tc-16-4053-2022, https://doi.org/10.5194/tc-16-4053-2022, 2022
Short summary
Short summary
The UK Earth System Model is the first to fully include interactions of the atmosphere and ocean with the Antarctic Ice Sheet. Under the low-greenhouse-gas SSP1–1.9 (Shared Socioeconomic Pathway) scenario, the ice sheet remains stable over the 21st century. Under the strong-greenhouse-gas SSP5–8.5 scenario, the model predicts strong increases in melting of large ice shelves and snow accumulation on the surface. The dominance of accumulation leads to a sea level fall at the end of the century.
Daniel Viviroli, Anna E. Sikorska-Senoner, Guillaume Evin, Maria Staudinger, Martina Kauzlaric, Jérémy Chardon, Anne-Catherine Favre, Benoit Hingray, Gilles Nicolet, Damien Raynaud, Jan Seibert, Rolf Weingartner, and Calvin Whealton
Nat. Hazards Earth Syst. Sci., 22, 2891–2920, https://doi.org/10.5194/nhess-22-2891-2022, https://doi.org/10.5194/nhess-22-2891-2022, 2022
Short summary
Short summary
Estimating the magnitude of rare to very rare floods is a challenging task due to a lack of sufficiently long observations. The challenge is even greater in large river basins, where precipitation patterns and amounts differ considerably between individual events and floods from different parts of the basin coincide. We show that a hydrometeorological model chain can provide plausible estimates in this setting and can thus inform flood risk and safety assessments for critical infrastructure.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
Short summary
Short summary
We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
John P. Bloomfield, Mengyi Gong, Benjamin P. Marchant, Gemma Coxon, and Nans Addor
Hydrol. Earth Syst. Sci., 25, 5355–5379, https://doi.org/10.5194/hess-25-5355-2021, https://doi.org/10.5194/hess-25-5355-2021, 2021
Short summary
Short summary
Groundwater provides flow, known as baseflow, to surface streams and rivers. It is important as it sustains the flow of many rivers at times of water stress. However, it may be affected by water management practices. Statistical models have been used to show that abstraction of groundwater may influence baseflow. Consequently, it is recommended that information on groundwater abstraction is included in future assessments and predictions of baseflow.
Cited articles
Aizen, V. B., Aizen, E. M., and Melack, J. M.: Climate, Snow Cover, Glaciers,
and Runoff in the Tien Shan, central Asia, Am. J. Water Resour. Assoc., 31,
1113001129, https://doi.org/10.1111/j.1752-1688.1995.tb03426.x, 1995. a
Allen, R., Pereira, L., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, FAO Irrigation and drainage paper 56, Fao, Rome, https://www.fao.org/3/X0490E/X0490E00.htm (last access: 19 January 2023), 1998. a
Ambroise, B., Freer, J., and Beven, K.: Application of a generalized TOPMODEL to the small Ringelbach catchment, Vosges, France, Water Resour. Res., 32, 2147–2159, https://doi.org/10.1029/95WR03715, 1996. a
APHRODITE: APHRODITE's Water Resources, http://aphrodite.st.hirosaki-u.ac.jp/download, last access: 5 May 2021. a
Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S. J. S., Raup, B., Hill, A. F., Khan, A. L., Wilson, A. M.,
Kayastha, R. B., Fetterer, F., and Armstrong, B.: Runoff from glacier ice and
seasonal snow in High Asia: separating melt water sources in river flow, Reg. Environ. Change, 19, 1249–1261, https://doi.org/10.1007/s10113-018-1429-0, 2019. a, b, c
Barandun, M., Fiddes, J., Scherler, M., Mathys, T., Saks, T., Petrakov, D., and Hoelzle, M.: The state and future of the cryosphere in Central Asia, Water Secur., 11, 100072, https://doi.org/10.1016/j.wasec.2020.100072, 2020. a, b
Bernauer, T. and Siegfried, T.: Climate change and international water conflict in Central Asia, J. Peace Res., 49, 227–239,
https://doi.org/10.1177/0022343311425843, 2012. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Beven, K. and Binley, A.: The Future Of Distributed Models – Model Calibration And Uncertainty Prediction, Hydrol. Process., 6, 279–298,
https://doi.org/10.1002/hyp.3360060305, 1992. a
Beven, K. and Freer, J.: A dynamic TOPMODEL, Hydrol. Process., 15, 1993–2011, https://doi.org/10.1002/hyp.252, 2001. a
CDS: ERA5 hourly data on single levels from 1959 to present, https://doi.org/10.24381/cds.adbb2d47, last access: 5 February 2021. a
Chen, Y. N., Li, W. H., Deng, H. J., Fang, G. H., and Li, Z.: Changes in Central Asia's Water Tower: Past, Present and Future, Scient. Rep., 6, 35458, https://doi.org/10.1038/srep39364, 2016. a
Copernicus: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), https://cds.climate.copernicus.eu (last access: 5 February 2021), 2017. a
Coxon, G., Freer, J., Lane, R., Dunne, T., Knoben, W. J. M., Howden, N. J. K., Quinn, N., Wagener, T., and Woods, R.: DECIPHeR v1: Dynamic fluxEs and
ConnectIvity for Predictions of HydRology, Geosci. Model Dev., 12, 2285–2306, https://doi.org/10.5194/gmd-12-2285-2019, 2019. a, b, c, d, e, f, g, h, i, j
Duethmann, D., Peters, J., Blume, T., Vorogushyn, S., and Guentner, A.: The
value of satellite- derived snow cover images for calibrating a hydrological
model in snow- dominated catchments in Central Asia, Water Resour. Res., 50, 2002–2021, https://doi.org/10.1002/2013WR014382, 2014. a
Engel, Z., Šobr, M., and Yerokhin, S. A.: Changes of Petrov glacier and its proglacial lake in the Akshiirak massif, central Tien Shan, since 1977,
J. Glaciol., 58, 388–398, https://doi.org/10.3189/2012JoG11J085, 2012. a
Farinotti, D., Usselmann, S., Huss, M., Bauder, A., and Funk, M.: Runoff
evolution in the Swiss Alps: projections for selected high-alpine catchments
based on ENSEMBLES scenarios, Hydrol. Process., 26, 1909–1924,
https://doi.org/10.1002/hyp.8276, 2012. a
Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P.: The
value of glacier mass balance, satellite snow cover images, and hourly
discharge for improving the performance of a physically based distributed
hydrological model, Water Resour. Res., 47, W07519, https://doi.org/10.1029/2010WR009824, 2011. a
Finger, D., Vis, M., Huss, M., and Seibert, J.: The value of multiple data set calibration versus model complexity for improving the performance of
hydrological models in mountain catchments, Water Resour. Res., 51,
1939–1958, https://doi.org/10.1002/2014WR015712, 2015. a
Fontaine, T., Cruickshank, T., Arnold, J., and Hotchkiss, R.: Development of a snowfall-snowmelt routine for mountainous terrain for the soil water
assessment tool (SWAT), J. Hydrol., 262, 209–223,
https://doi.org/10.1016/S0022-1694(02)00029-X, 2002. a
Frans, C., Istanbulluoglu, E., Lettenmaier, D. P., Fountain, A. G., and Riedel, J.: Glacier Recession and the Response of Summer Streamflow in the Pacific Northwest United States, 1960–2099, Water Resour. Res., 54, 6202–6225, https://doi.org/10.1029/2017WR021764, 2018. a
Freer, J., Beven, K., and Ambroise, B.: Bayesian estimation of uncertainty in
runoff prediction and the value of data: An application of the GLUE approach,
Water Resour. Res., 32, 2161–2173, https://doi.org/10.1029/95wr03723, 1996. a
Freer, J., McMillan, H., McDonnell, J., and Beven, K.: Constraining dynamic
TOPMODEL responses for imprecise water table information using fuzzy rule
based performance measures, J. Hydrol., 291, 254–277,
https://doi.org/10.1016/j.jhydrol.2003.12.037, 2004. a
Freudiger, D., Kohn, I., Seibert, J., Stahl, K., and Weiler, M.: Snow
redistribution for the hydrological modeling of alpine catchments, Wiley Interdisciplin. Rev.-Water, 4, e1232, https://doi.org/10.1002/wat2.1232, 2017. a
Frey, H., Machguth, H., Huss, M., Huggel, C., Bajracharya, S., Bolch, T., Kulkarni, A., Linsbauer, A., Salzmann, N., and Stoffel, M.: Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods, The Cryosphere, 8, 2313–2333, https://doi.org/10.5194/tc-8-2313-2014, 2014. a, b
Fyffe, C., Brock, B., Kirkbride, M., Mair, D., Arnold, N., Smiraglia, C.,
Diolaiuti, G., and Diotri, F.: Do debris-covered glaciers demonstrate
distinctive hydrological behaviour compared to clean glaciers?, J. Hydrol., 570, 584–597, https://doi.org/10.1016/j.jhydrol.2018.12.069, 2019. a
Gan, R., Luo, Y., Zuo, Q. T., and Sun, L.: Effects of projected climate change on the glacier and runoff generation in the Naryn River Basin, Central Asia, J. Hydrol., 523, 240–251, https://doi.org/10.1016/j.jhydrol.2015.01.057, 2015. a
Gardelle, J., Berthier, E., and Arnaud, Y.: Slight mass gain of Karakoram
glaciers in the early twenty-first century, Nat. Geosci., 5, 322–325,
https://doi.org/10.1038/ngeo1450, 2012. a
Gardelle, J., Berthier, E., Arnaud, Y., and Kaab, A.: Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011, The Cryosphere, 7, 1263–1286, https://doi.org/10.5194/tc-7-1263-2013, 2013. a
Gharehchahi, S., James, W. H. M., Bhardwaj, A., Jensen, J. L. R., Sam, L., Ballinger, T. J., and Butler, D. R.: Glacier Ice Thickness Estimation and Future Lake Formation in Swiss Southwestern Alps – The Upper Rhone Catchment: A VOLTA Application, Rem. Sens., 12, 22,
https://doi.org/10.3390/rs12223709, 2020. a
GRDC: Data Download, https://portal.grdc.bafg.de, last access: 14 September 2018. a
Hagg, W., Mayer, C., Lambrecht, A., Kriegel, D., and Azizov, E.: Glacier
changes in the Big Naryn basin, Central Tian Shan, Global Planet. Change, 110, 40–50, https://doi.org/10.1016/j.gloplacha.2012.07.010, 2013. a
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover 8-Day L3 Global 500 m SIN Grid, Version 6, NSIDC [data set], https://doi.org/10.5067/MODIS/MOD10A2.006, 2016. a, b
Herreid, S. and Pellicciotti, F.: The state of rock debris covering Earth's
glaciers, Nat. Geosci., 13, 621–627, https://doi.org/10.1038/s41561-020-0615-0, 2020. a
Hewitt, K.: The Karakoram anomaly? Glacier expansion and the `elevation
effect', Karakoram Himalaya, Mt. Res. Dev., 25, 332–340,
https://doi.org/10.1659/0276-4741(2005)025[0332:tkagea]2.0.co;2, 2005. a
Hong, L., Stein, B., Xu, C.-Y., Huss, M., Kjetil, M., and Jain, S. K.: Integrating a glacier retreat model into a hydrological model – Case
studies of three glacierised catchments in Norway and Himalayan region,
J. Hydrol. 527, 656–667, https://doi.org/10.1016/j.jhydrol.2015.05.017, 2015. a
Horton, P., Schaefli, B., and Kauzlaric, M.: Why do we have so many different
hydrological models? A review based on the case of Switzerland, WIREs Water,
9, e1574, https://doi.org/10.1002/wat2.1574, 2022. a
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L.,
Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kaab, A.:
Accelerated global glacier mass loss in the early twenty-first century,
Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a
Hydro: MERIT DEM: Multi-Error-Removed Improved-Terrain DEM, http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM, last access: 7 March 2018. a
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate Change
Will Affect the Asian Water Towers, Science, 328, 1382–1385,
https://doi.org/10.1126/science.1183188, 2010. a
Immerzeel, W. W., van Beek, L. P. H., Konz, M., Shrestha, A. B., and Bierkens, M. F. P.: Hydrological response to climate change in a glacierized catchment in the Himalayas, Climatic Change, 110, 721–736,
https://doi.org/10.1007/s10584-011-0143-4, 2012. a
Immerzeel, W. W., Pellicciotti, F., and Bierkens, M. F. P.: Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds, Nat. Geosci., 3, 742–745, https://doi.org/10.1038/ngeo1896, 2013. a, b
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T.,
Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernandez, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a
Janský, B., Šobr, M., and Engel, Z.: Outburst flood hazard: Case studies from the Tien-Shan Mountains, Kyrgyzstan, Limnologica, 40, 358–364,
https://doi.org/10.1016/j.limno.2009.11.013, 2010. a
Jones, D., Harrison, S., Anderson, K., Shannon, S., and Betts, R.: Rock
glaciers represent hidden water stores in the Himalaya, Sci. Total Environ., 793, 145368, https://doi.org/10.1016/j.scitotenv.2021.145368, 2021. a
Kaser, G., Großhauser, M., and Marzeion, B.: Contribution potential of
glaciers to water availability in different climate regimes, P. Natl. Acad. Sci. USA, 107, 20223–20227, https://doi.org/10.1073/pnas.1008162107, 2010. a
Koboltschnig, G. R., Schöner, W., Zappa, M., Kroisleitner, C., and Holzmann, H.: Runoff modelling of the glacierized Alpine Upper Salzach basin (Austria): multi-criteria result validation, Hydrol. Process., 22, 3950–3964, https://doi.org/10.1002/hyp.7112, 2008. a
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W. W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's
glaciers, Nature, 549, 257–260, https://doi.org/10.1038/nature23878, 2017. a
Kriegel, D., Mayer, C., Hagg, W., Vorogushyn, S., Duethmann, D., Gafurov, A.,
and Farinotti, D.: Changes in glacierisation, climate and runoff in the
second half of the 20th century in the Naryn basin, Central Asia, Global Planet. Change, 110, 51–61, https://doi.org/10.1016/j.gloplacha.2013.05.014, 2013. a, b, c, d, e, f
Lambert, L. and Chitrakar, B. D.: Variation of potential evapotranspiration
with elevation in Nepal, Mt. Res. Dev., 9, 145–152–50, 1989. a
Lane, R. A., Freer, J. E., Coxon, G., and Wagener, T.: Incorporating
Uncertainty Into Multiscale Parameter Regionalization to Evaluate the
Performance of Nationally Consistent Parameter Fields for a Hydrological
Model, Water Resour. Res., 57, 10, https://doi.org/10.1029/2020WR028393, 2021. a
Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.:
Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997. a
Liu, Q. and Liu, S.: Response of glacier mass balance to climate change in the Tianshan Mountains during the second half of the twentieth century, Cllim. Dynam., 46, 303–316, https://doi.org/10.1007/s00382-015-2585-2, 2016. a
Luo, Y., Arnold, J., Liu, S. Y., Wang, X. Y., and Chen, X.: Inclusion of
glacier processes for distributed hydrological modeling at basin scale with
application to a watershed in Tianshan Mountains, northwest China, J. Hydrol., 477, 72–85, https://doi.org/10.1016/j.jhydrol.2012.11.005, 2013a. a
Luo, Y., Arnold, J., Liu, S. Y., Wang, X. Y., and Chen, X.: Inclusion of
glacier processes for distributed hydrological modeling at basin scale with
application to a watershed in Tianshan Mountains, northwest China, J.
Hydrol., 477, 72–85, https://doi.org/10.1016/j.jhydrol.2012.11.005, 2013b. a
Lutz, A., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.: Consistent increase in High Asia's runoff due to increasing glacier melt and
precipitation, Nat. Clim.Change, 4, 587–592, https://doi.org/10.1038/nclimate2237, 2014. a
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: Evaluating snow models with varying process representations for hydrological applications, Water Resour. Res., 51, 2707–2723,
https://doi.org/10.1002/2014WR016498, 2015. a
Malsy, M., Beek, T. A. D., and Florke, M.: Evaluation of large-scale
precipitation data sets for water resources modelling in Central Asia,
Environ. Earth Sci., 73, 787–799, https://doi.org/10.1007/s12665-014-3107-y, 2015. a
Marzeion, B., Hock, R., Anderson, B., Bliss, A., Champollion, N., Fujita, K., Huss, M., Immerzeel, W. W., Kraaijenbrink, P., Malles, J. H., Maussion, F., Radic, V., Rounce, D. R., Sakai, A., Shannon, S., van deWal, R., and Zekollari, H.: Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change, Earths Future, 8, e2019EF001470, https://doi.org/10.1029/2019ef001470, 2020. a
Mayr, E., Hagg, W., Mayer, C., and Braun, L.: Calibrating a spatially
distributed conceptual hydrological model using runoff, annual mass balance
and winter mass balance, J. Hydrol., 478, 40–49,
https://doi.org/10.1016/j.jhydrol.2012.11.035, 2013. a, b
McKay, M. D., Beckman, R. J., and Conover, W. J.: Comparison of Three Methods
for Selecting Values of Input Variables in the Analysis of Output from a
Computer Code, Technometrics, 21, 239–245, 1979. a
Meier, J., Zabel, F., and Mauser, W.: A global approach to estimate irrigated
areas – a comparison between different data and statistics, Hydrol. Earth Syst. Sci., 22, 1119–1133, https://doi.org/10.5194/hess-22-1119-2018, 2018. a
Metcalfe, P., Beven, K., and Freer, J.: Dynamic TOPMODEL: A new implementation in R and its sensitivity to time and space steps, Environ. Model. Softw., 72, 155–172, https://doi.org/10.1016/j.envsoft.2015.06.010, 2015. a
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
Water Assessment Tool Theoretical Documentation, Version 2005, Grassland,
Soil and Water Research Laboratory, http://swat.tamu.edu/documentation (last access: 30 January 2019), 2005. a
Omani, N., Srinivasan, R., Karthikeyan, R., and Smith, P. K.: Hydrological
Modeling of Highly Glacierized Basins (Andes, Alps, and Central Asia), Water,
9, 11, https://doi.org/10.3390/w9020111, 2017. a, b
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.,
and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? Part 2 – Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol., 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Parajka, J. and Bloschl, G.: The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models, J. Hydrol., 358, 240–258, https://doi.org/10.1016/j.jhydrol.2008.06.006, 2008. a
Paterson, W.: The physics of glaciers, in: 3rd Edn., Pergamon Press, Oxford, New York, Tokyo, https://doi.org/10.1016/C2009-0-14802-X, 1994. a
Pellicciotti, F., Buergi, C., Immerzeel, W. W., Konz, M., and Shrestha, A. B.: Challenges and Uncertainties in Hydrological Modeling of Remote Hindu
Kush-Karakoram-Himalayan (HKH) Basins: Suggestions for Calibration
Strategies, Mt. Res. Dev., 32, 39–50, https://doi.org/10.1659/MRD-JOURNAL-D-11-00092.1, 2012. a
Peters, N., Freer, J., and Beven, K.: Modelling hydrologic responses in a
small forested catchment (Panola Mountain, Georgia, USA): a comparison of the
original and a new dynamic TOPMODEL, Hydrol. Process., 17, 345–362, https://doi.org/10.1002/hyp.1128, 2003. a
Pieczonka, T. and Bolch, T.: Region-wide glacier mass budgets and area changes for the Central Tien Shan between 1975 and 1999 using Hexagon KH-9 imagery, Global Planet. Change, 128, 1–13, https://doi.org/10.1016/j.gloplacha.2014.11.014, 2015. a
Pu, Z. X., Xu, L., and Salomonson, V. V.: MODIS/Terra observed seasonal
variations of snow cover over the Tibetan Plateau, Geophys. Res. Lett., 34, L06706, https://doi.org/10.1029/2007gl029262, 2007. a
Radchenko, I., Dernedde, Y., Mannig, B., Frede, H.-G., and Breuer, L.: Climate Change Impacts on Runoff in the Ferghana Valley (Central Asia), Water
Resour., 44, 707–730, https://doi.org/10.1134/S0097807817050098, 2017. a, b
Ragettli, S. and Pellicciotti, F.: Calibration of a physically based,
spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model
parameters, Water Resour. Res., 48, W03509, https://doi.org/10.1029/2011WR010559, 2012. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Theriault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu,
R., Hall, M., Ikeda, K., and Gutmann, E.: How well are we measusing snow? The
NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/bams-d-11-00052.1, 2012. a, b
Ren, Z., Su, F., Xu, B., Xie, Y., and Kan, B.: A Coupled Glacier-Hydrology
Model and Its Application in Eastern Pamir, J. Geophys. Res.-Atmos., 123, 13692–13713, https://doi.org/10.1029/2018JD028572, 2018. a
RGI: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines:
Version 6.0, Technical Report, Global Land Ice Measurements from Space, Colorado, USA, https://doi.org/10.7265/N5-RGI-60, 2017. a
Saks, T., Pohl, E., Machguth, H., Dehecq, A., Barandun, M., Kenzhebaev, R.,
Kalashnikova, O., and Hoelzle, M.: Glacier Runoff Variation Since 1981 in the
Upper Naryn River Catchments, Central Tien Shan, Front. Environ. Sci., 9, 780466 , https://doi.org/10.3389/fenvs.2021.780466, 2022. a, b
Schaner, N., Voisin, N., Nijssen, B., and Lettenmaier, D. P.: The contribution of glacier melt to streamflow, Environ. Res. Lett., 7, 034029, https://doi.org/10.1088/1748-9326/7/3/034029, 2012.
a
Scherler, D., Wulf, H., and Gorelick, N.: Global Assessment of Supraglacial
Debris-Cover Extents, Geophys. Res. Lett., 45, 11798–11805, https://doi.org/10.1029/2018gl080158, 2018. a
Shannon, S.: sarahshannon/DECIPHeR-glacier: DECIPHeR-glacier (v1.1), Zenodo [code], https://doi.org/10.5281/zenodo.7544529, 2023. a
Sorg, A., Bolch, T., Stoffel, M., Solomina, O., and Beniston, M.: Climate
change impacts on glaciers and runoff in Tien Shan (Central Asia), Nat. Clim.Change, 2, 725–731, https://doi.org/10.1038/nclimate1592, 2012. a
van Tiel, M., Stahl, K., Freudiger, D., and Seibert, J.: Glacio-hydrological
model calibration and evaluation, Wiley Interdisciplin. Rev.-Water, 7, e1483, https://doi.org/10.1002/wat2.1483, 2020. a, b, c
Wing, O. E. J., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and Erickson, T. A.: Validation of a 30 m resolution flood hazard model of the conterminous United States, Water Resour. Res., 53, 7968–7986,
https://doi.org/10.1002/2017WR020917, 2017. a
Wortmann, M., Bolch, T., Buda, S., and Krysanova, V.: An efficient
representation of glacier dynamics in a semi-distributed hydrological model
to bridge glacier and river catchment scales, J. Hydrol., 573, 136–152, https://doi.org/10.1016/j.jhydrol.2019.03.006, 2019. a
Xie, R. H. and Wang, A. H.: Comparison of Ten Potential Evapotranspiration
Models and Their Attribution Analyses for Ten Chinese Drainage Basins, Adv. Atmos. Sci., 37, 959–974, https://doi.org/10.1007/s00376-020-2105-0, 2020. a
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017gl072874, 2017. a, b
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh,
A.: APHRODITE Constructing a Long-Term Daily Gridded Precipitation Dataset
for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/bams-d-11-00122.1, 2012. a, b
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic
approach to model evaluation: Application to the NWS distributed hydrologic
model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007wr006716, 2008. a
Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J., Barandun,
M., Machguth, H., Nussbaumer, S. U., Gartner-Roer, I., Thomson, L., Paul, F.,
Maussion, F., Kutuzov, S., and Cogley, J. G.: Global glacier mass changes
and their contributions to sea-level rise from 1961 to 2016, Nature, 568, 382–386, https://doi.org/10.1038/s41586-019-1071-0, 2019. a
Zhang, L. L., Su, F. G., Yang, D. Q., Hao, Z. C., and Tong, K.: Discharge
regime and simulation for the upstream of major rivers over Tibetan Plateau,
J. Geophys. Res.-Atmos., 118, 8500–8518, https://doi.org/10.1002/jgrd.50665, 2013. a
Zou, S., Jilili, A., Duan, W. L., De Maeyer, P., and Van de Voorde, T.: Human
and Natural Impacts on the Water Resources in the Syr Darya River Basin,
Central Asia, Sustainability, 11, 3084, https://doi.org/10.3390/su11113084, 2019. a
Short summary
Climate change poses a potential threat to water supply in glaciated river catchments. In this study, we added a snowmelt and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology model (DECIPHeR). The model is applied to the Naryn River catchment in central Asia and is found to reproduce past change discharge and the spatial extent of seasonal snow cover well.
Climate change poses a potential threat to water supply in glaciated river catchments. In this...
