Articles | Volume 20, issue 6
https://doi.org/10.5194/hess-20-2403-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2403-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Jun 2016
Research article |
| 21 Jun 2016
Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model
Anna M. Ukkola
CORRESPONDING AUTHOR
ARC Centre of Excellence for Climate System Science, University of
New South Wales, Kensington, NSW 2052, Australia
Andy J. Pitman
ARC Centre of Excellence for Climate System Science, University of
New South Wales, Kensington, NSW 2052, Australia
Climate Change
Research Centre, University of New South Wales, Kensington, NSW 2052,
Australia
Mark Decker
ARC Centre of Excellence for Climate System Science, University of
New South Wales, Kensington, NSW 2052, Australia
Climate Change
Research Centre, University of New South Wales, Kensington, NSW 2052,
Australia
Martin G. De Kauwe
Department of Biological Sciences, Macquarie University,
Balaclava Road, North Ryde, NSW 2109, Australia
Gab Abramowitz
ARC Centre of Excellence for Climate System Science, University of
New South Wales, Kensington, NSW 2052, Australia
Climate Change
Research Centre, University of New South Wales, Kensington, NSW 2052,
Australia
Jatin Kala
ARC Centre of Excellence for Climate System Science, University of
New South Wales, Kensington, NSW 2052, Australia
Murdoch
University, School of Veterinary and Life Sciences, Environmental and
Conservation Sciences, Murdoch, WA 6150, Australia
Ying-Ping Wang
CSIRO Ocean and Atmosphere, Aspendale, VIC 3195, Australia
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Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
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We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, https://doi.org/10.5194/gmd-11-3159-2018, 2018
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Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides a formal framework for quantification of model hypothesis uncertainty.
Ned Haughton, Gab Abramowitz, Martin G. De Kauwe, and Andy J. Pitman
Biogeosciences, 15, 4495–4513, https://doi.org/10.5194/bg-15-4495-2018, https://doi.org/10.5194/bg-15-4495-2018, 2018
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This project explores predictability in energy, water, and carbon fluxes in the free-use Tier 1 of the FLUXNET 2015 dataset using a uniqueness metric based on comparison of locally and globally trained models. While there is broad spread in predictability between sites, we found strikingly few strong patterns. Nevertheless, these results can contribute to the standardisation of site selection for land surface model evaluation and help pinpoint regions that are ripe for further FLUXNET research.
Kashif Mahmud, Belinda E. Medlyn, Remko A. Duursma, Courtney Campany, and Martin G. De Kauwe
Biogeosciences, 15, 4003–4018, https://doi.org/10.5194/bg-15-4003-2018, https://doi.org/10.5194/bg-15-4003-2018, 2018
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A major limitation of current terrestrial vegetation models is that we do not know how to model C balance processes under sink-limited conditions. To address this limitation, we applied data assimilation of a simple C balance model to a manipulative experiment in which sink limitation was induced with low rooting volume. Our analysis framework allowed us to infer that, in addition to a feedback on photosynthetic rates, the reduction in growth was effected by other C balance processes.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-270, https://doi.org/10.5194/bg-2018-270, 2018
Revised manuscript has not been submitted
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This study presents a global estimate of land carbon uptake through photosynthesis. We make use satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and then use the optimized model to estimate photosynthetic carbon uptake. This provides a new tool that can combine measurements and observations in a systematic way and maximise the use of chlorophyll fluorescence to improve our understanding of the land carbon cycle.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
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Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Natthachet Tangdamrongsub, Shin-Chan Han, Mark Decker, In-Young Yeo, and Hyungjun Kim
Hydrol. Earth Syst. Sci., 22, 1811–1829, https://doi.org/10.5194/hess-22-1811-2018, https://doi.org/10.5194/hess-22-1811-2018, 2018
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We present a new approach to improve the water storage estimate. Our approach combines GRACE's raw data (least-squares normal equation) with the results from the Community Atmosphere Land Exchange (CABLE) model. No post-processing filter is applied to GRACE data, and the full GRACE signal and error information are exploited. The approach is applied over 10 Australian river basins, and the evident improvement of the water storage estimate, particularly groundwater component, is clearly observed.
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Anna Ukkola
Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
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We present a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009 and 0.5 grid cell size. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. We confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also show that the weighted product performs better than 10 different existing global ET datasets in a range of metrics.
Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson
Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, https://doi.org/10.5194/esd-9-135-2018, 2018
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Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Ned Haughton, Gab Abramowitz, and Andy J. Pitman
Geosci. Model Dev., 11, 195–212, https://doi.org/10.5194/gmd-11-195-2018, https://doi.org/10.5194/gmd-11-195-2018, 2018
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Previous studies indicate that fluxes of heat, water, and carbon between the land surface and atmosphere are substantially more predictable than the performance of the current crop of land surface models would indicate. This study uses simple empirical models to estimate the amount of useful information in meteorological forcings that is available for predicting land surface fluxes. These models can be used as benchmarks for land surface models and may help identify areas ripe for improvement.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
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This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Martin G. De Kauwe, Belinda E. Medlyn, Jürgen Knauer, and Christopher A. Williams
Biogeosciences, 14, 4435–4453, https://doi.org/10.5194/bg-14-4435-2017, https://doi.org/10.5194/bg-14-4435-2017, 2017
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Understanding the sensitivity of transpiration to stomatal conductance is critical to simulating the water cycle. This sensitivity is a function of the degree of coupling between the vegetation and the atmosphere. We combined an extensive literature summary with estimates of coupling derived from FLUXNET data. We found notable departures from the values previously reported. These data form a model benchmarking metric to test existing coupling assumptions.
Richard Wartenburger, Martin Hirschi, Markus G. Donat, Peter Greve, Andy J. Pitman, and Sonia I. Seneviratne
Geosci. Model Dev., 10, 3609–3634, https://doi.org/10.5194/gmd-10-3609-2017, https://doi.org/10.5194/gmd-10-3609-2017, 2017
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This article analyses regional changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. Readers are encouraged to use the online tool for visualization of specific indices of interest, e.g. to assess their response to 1.5 or 2 °C global warming.
Anna M. Ukkola, Ned Haughton, Martin G. De Kauwe, Gab Abramowitz, and Andy J. Pitman
Geosci. Model Dev., 10, 3379–3390, https://doi.org/10.5194/gmd-10-3379-2017, https://doi.org/10.5194/gmd-10-3379-2017, 2017
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Flux towers measure energy, carbon dioxide and water vapour fluxes. These data have become essential for evaluating land surface models (LSMs) – key tools for projecting future climate change. However, these data as released are not immediately usable with LSMs and must be post-processed to change units, screened for missing data and gap-filling. We present an open-source R package that transforms flux tower measurements into a format directly usable by LSMs.
Rachel M. Law, Tilo Ziehn, Richard J. Matear, Andrew Lenton, Matthew A. Chamberlain, Lauren E. Stevens, Ying-Ping Wang, Jhan Srbinovsky, Daohua Bi, Hailin Yan, and Peter F. Vohralik
Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, https://doi.org/10.5194/gmd-10-2567-2017, 2017
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The paper describes a version of the Australian Community Climate and Earth System Simulator that has been enabled to simulate the carbon cycle, which is designated ACCESS-ESM1. The model performance for pre-industrial conditions is assessed and land and ocean carbon fluxes are found to be simulated realistically.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
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Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Eva A. Kowalczyk, Lauren E. Stevens, Rachel M. Law, Ian N. Harman, Martin Dix, Charmaine N. Franklin, and Ying-Ping Wang
Geosci. Model Dev., 9, 2771–2791, https://doi.org/10.5194/gmd-9-2771-2016, https://doi.org/10.5194/gmd-9-2771-2016, 2016
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This paper compares two ACCESS model versions that differ only in their land surface scheme. Differences in the simulated present-day climate are attributed to differences in the representation of various land surface processes.
Rashid Rafique, Jianyang Xia, Oleksandra Hararuk, Ghassem R. Asrar, Guoyong Leng, Yingping Wang, and Yiqi Luo
Earth Syst. Dynam., 7, 649–658, https://doi.org/10.5194/esd-7-649-2016, https://doi.org/10.5194/esd-7-649-2016, 2016
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Traceability analysis was used to diagnose the causes of differences in simulating ecosystem carbon storage capacity between two land models: CLMA-CASA and CABLE. Results showed that the simulated ecosystem carbon storage capacity is largely influenced by the photosynthesis parameterization, residence time and organic matter decomposition.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
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In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
Y. P. Wang, J. Jiang, B. Chen-Charpentier, F. B. Agusto, A. Hastings, F. Hoffman, M. Rasmussen, M. J. Smith, K. Todd-Brown, Y. Wang, X. Xu, and Y. Q. Luo
Biogeosciences, 13, 887–902, https://doi.org/10.5194/bg-13-887-2016, https://doi.org/10.5194/bg-13-887-2016, 2016
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Comparing two nonlinear microbial models, we found that,
in response to warming, soil C decreases in one model but can increase or decrease in the other model, and sensitivity of priming response to carbon input increases with soil T in one model but decreases in the other model
Significance: these differences in the responses can be used to discern which model is more realistic, which will improve our understanding of the significance of soil microbial processes in the terrestrial C cycle.
M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, https://doi.org/10.5194/bg-12-7503-2015, 2015
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Future climate change has the potential to increase drought in many regions of the globe. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art models currently assume the same drought sensitivity for all vegetation. Our results indicate that models will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
J. Kala, M. G. De Kauwe, A. J. Pitman, R. Lorenz, B. E. Medlyn, Y.-P Wang, Y.-S Lin, and G. Abramowitz
Geosci. Model Dev., 8, 3877–3889, https://doi.org/10.5194/gmd-8-3877-2015, https://doi.org/10.5194/gmd-8-3877-2015, 2015
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We implement a new stomatal conductance scheme within a land surface model coupled to a global climate model. The new model differs from the default in that it allows model parameters to vary by the different plant functional types, derived from global synthesis of observations. We show that the new scheme results in improvements in the model climatology and improves existing biases in warm temperature extremes by up to 10-20% over the boreal forests during summer.
M. Decker, A. Pitman, and J. Evans
Hydrol. Earth Syst. Sci., 19, 3433–3447, https://doi.org/10.5194/hess-19-3433-2015, https://doi.org/10.5194/hess-19-3433-2015, 2015
I. C. Prentice, X. Liang, B. E. Medlyn, and Y.-P. Wang
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, https://doi.org/10.5194/acp-15-5987-2015, 2015
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Land surface models (LSMs) describe how carbon and water fluxes react to environmental change. They are key component of climate models, yet they differ enormously. Many perform poorly, despite having many parameters. We outline a development strategy emphasizing robustness, reliability and realism, none of which is guaranteed by complexity alone. We propose multiple constraints, benchmarking and data assimilation, and representing unresolved processes stochastically, as tools in this endeavour.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
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Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
M. G. De Kauwe, J. Kala, Y.-S. Lin, A. J. Pitman, B. E. Medlyn, R. A. Duursma, G. Abramowitz, Y.-P. Wang, and D. G. Miralles
Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, https://doi.org/10.5194/gmd-8-431-2015, 2015
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Stomatal conductance affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the CABLE land surface model (LSM). The new implementation resulted in a large reduction in the annual fluxes of transpiration across evergreen needleleaf, tundra and C4 grass regions. We conclude that optimisation theory can yield a tractable approach to predicting stomatal conductance in LSMs.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Biogeosciences, 11, 6999–7008, https://doi.org/10.5194/bg-11-6999-2014, https://doi.org/10.5194/bg-11-6999-2014, 2014
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We use a reduced complexity soil organic carbon (SOC) model to address the influence of two parameters on the response of SOC stocks to climate change: baseline turnover time (k) and temperature sensitivity of decomposition (Q10). In our model, k determines SOC stocks and the magnitude of the response to climate change (from 1850 to 2100 under RCP 8.5) while Q10 drives its sign. We dismiss unlikely simulations using global SOC data to reduce the uncertainty in projections and parameter values.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Geosci. Model Dev., 7, 2683–2692, https://doi.org/10.5194/gmd-7-2683-2014, https://doi.org/10.5194/gmd-7-2683-2014, 2014
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Pre-industrial soil organic carbon (SOC) stocks vary 6-fold in models used in the 5th IPCC Assessment Report. This paper shows that this range is largely determined by model-specific responses of microbal decomposition during the equilibration procedure. As SOC stocks are maintained through the present and to 2100 almost unchanged, we propose that current SOC observations could be used to constrain this equilibration procedure and thereby reduce the uncertainty in climate change projections.
J. Kala, J. P. Evans, A. J. Pitman, C. B. Schaaf, M. Decker, C. Carouge, D. Mocko, and Q. Sun
Geosci. Model Dev., 7, 2121–2140, https://doi.org/10.5194/gmd-7-2121-2014, https://doi.org/10.5194/gmd-7-2121-2014, 2014
Y. P. Wang, B. C. Chen, W. R. Wieder, M. Leite, B. E. Medlyn, M. Rasmussen, M. J. Smith, F. B. Agusto, F. Hoffman, and Y. Q. Luo
Biogeosciences, 11, 1817–1831, https://doi.org/10.5194/bg-11-1817-2014, https://doi.org/10.5194/bg-11-1817-2014, 2014
R. Lorenz, A. J. Pitman, M. G. Donat, A. L. Hirsch, J. Kala, E. A. Kowalczyk, R. M. Law, and J. Srbinovsky
Geosci. Model Dev., 7, 545–567, https://doi.org/10.5194/gmd-7-545-2014, https://doi.org/10.5194/gmd-7-545-2014, 2014
J.-F. Exbrayat, A. J. Pitman, Q. Zhang, G. Abramowitz, and Y.-P. Wang
Biogeosciences, 10, 7095–7108, https://doi.org/10.5194/bg-10-7095-2013, https://doi.org/10.5194/bg-10-7095-2013, 2013
Q. Zhang, A. J. Pitman, Y. P. Wang, Y. J. Dai, and P. J. Lawrence
Earth Syst. Dynam., 4, 333–345, https://doi.org/10.5194/esd-4-333-2013, https://doi.org/10.5194/esd-4-333-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts
Towards a robust hydrologic data assimilation system for hurricane-induced river flow forecasting
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
High-resolution land surface modelling over Africa: the role of uncertain soil properties in combination with forcing temporal resolution
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
Distribution, trends, and drivers of flash droughts in the United Kingdom
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada
Assessment of seasonal soil moisture forecasts over the Central Mediterranean
Do land models miss key soil hydrological processes controlling soil moisture memory?
Observation-driven model for calculating water-harvesting potential from advective fog in (semi-)arid coastal regions
Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets
Barriers of urban hydro-meteorological simulation: a review
Downscaling the probability of heavy rainfall over the Nordic countries
Modelling convective cell life cycles with a copula-based approach
Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Assessing rainfall radar errors with an inverse stochastic modelling framework
Enhanced hydrological modelling with the WRF-Hydro lake/reservoir module at Convection-Permitting scale: a case study of the Tana River basin in East Africa
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Karakoram Himalaya
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Storyline Analytical Framework for Understanding Future Severe Low-Water Episodes and Their Consequences
The role of land-atmosphere coupling in subseasonal surface air temperature prediction
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and VIC models
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Global catalog of soil moisture droughts over the past four decades
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Potential for historically unprecedented Australian droughts from natural variability and climate change
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025, https://doi.org/10.5194/hess-29-2551-2025, 2025
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Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025, https://doi.org/10.5194/hess-29-2407-2025, 2025
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three southeastern US watersheds during hurricanes, HEAVEN assimilates real-time United States Geological Survey (USGS) streamflow data, boosting forecast accuracy.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
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Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
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We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
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The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
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Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Lorenzo Silvestri, Miriam Saraceni, Bruno Brunone, Silvia Meniconi, Giulia Passadore, and Paolina Bongioannini Cerlini
Hydrol. Earth Syst. Sci., 29, 925–946, https://doi.org/10.5194/hess-29-925-2025, https://doi.org/10.5194/hess-29-925-2025, 2025
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This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in the areas of the Central Mediterranean where longer memory timescales are found. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance, and, in general, dry events are better captured than wet events.
Mohammad A. Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, https://doi.org/10.5194/hess-29-547-2025, 2025
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Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo del Río
Hydrol. Earth Syst. Sci., 29, 109–125, https://doi.org/10.5194/hess-29-109-2025, https://doi.org/10.5194/hess-29-109-2025, 2025
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Water resources are fundamental for the social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligate us to find new water resources. Fog harvesting (FH) emerges as a complementary resource in regions where it is abundant but untapped. This research proposes a model to estimate FH potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where FH could be a viable water source.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci., 29, 85–108, https://doi.org/10.5194/hess-29-85-2025, https://doi.org/10.5194/hess-29-85-2025, 2025
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We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Xuan Chen, Job Augustijn van der Werf, Arjan Droste, Miriam Coenders-Gerrits, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-3988, https://doi.org/10.5194/egusphere-2024-3988, 2025
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Our research highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate disasters in cities. We find that inadequate representation of water surfaces, hydraulic systems, and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.
Rasmus E. Benestad, Kajsa M. Parding, and Andreas Dobler
Hydrol. Earth Syst. Sci., 29, 45–65, https://doi.org/10.5194/hess-29-45-2025, https://doi.org/10.5194/hess-29-45-2025, 2025
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We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
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This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
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This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024, https://doi.org/10.5194/hess-28-4819-2024, 2024
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Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
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Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-278, https://doi.org/10.5194/hess-2024-278, 2024
Revised manuscript accepted for HESS
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To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
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We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
Marc Girona-Mata, Andrew Orr, Martin Widmann, Daniel Bannister, Ghulam Hussain Dars, Scott Hosking, Jesse Norris, David Ocio, Tony Phillips, Jakob Steiner, and Richard E. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2805, https://doi.org/10.5194/egusphere-2024-2805, 2024
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We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Karakoram Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical / machine learning techniques. Our results show this approach outperforms traditional methods, especially in remote, ungauged areas, suggesting it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
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Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Gabriel Rondeau-Genesse, Louis-Philippe Caron, Kristelle Audet, Laurent Da Silva, Daniel Tarte, Rachel Parent, Élise Comeau, and Dominic Matte
EGUsphere, https://doi.org/10.5194/egusphere-2024-2595, https://doi.org/10.5194/egusphere-2024-2595, 2024
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The 2021 drought in Quebec showcased the province’s potential vulnerability. This study uses a storyline approach to explore impacts of future extreme droughts under +2 °C and +3 °C warming scenarios. By combining regional climate and hydrological simulations, it highlights the potential for severe water deficits. Collaboration with water management experts helped project the impacts of those future extreme droughts and their consequences for ecosystems and human activities.
Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
EGUsphere, https://doi.org/10.5194/egusphere-2024-2312, https://doi.org/10.5194/egusphere-2024-2312, 2024
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To better utilize a given set of predictions, identifying “forecasts of opportunity” has great value. It can help anticipate when prediction skill will be higher. This study reveals that when strong L-A coupling is detected 3–4 weeks into a forecast, the prediction skill for surface air temperature at this lead increases across the Midwest and northern Great Plains. Regions experiencing strong L-A coupling exhibit warm and dry anomalies, leading to improved predictions of abnormally warm events.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
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The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
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We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
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Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
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The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
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The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1434, https://doi.org/10.5194/egusphere-2024-1434, 2024
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
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Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
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Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
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This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
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This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
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A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
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Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
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We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
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We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
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Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
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Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
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Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
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Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
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Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
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High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
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