Articles | Volume 28, issue 21
https://doi.org/10.5194/hess-28-4819-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4819-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Nov 2024
Research article |
| 06 Nov 2024
| 06 Nov 2024
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
Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, United Kingdom
Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, United Kingdom
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Nicholas Cowan, Toby Roberts, Mark Hanlon, Aurelia Bezanger, Galina Toteva, Alex Tweedie, Karen Yeung, Ajinkya Deshpande, Peter Levy, Ute Skiba, Eiko Nemitz, and Julia Drewer
Biogeosciences, 22, 3449–3461, https://doi.org/10.5194/bg-22-3449-2025, https://doi.org/10.5194/bg-22-3449-2025, 2025
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We measured soil hydrogen (H2) fluxes from two field sites, a managed grassland and a planted deciduous woodland, with flux measurements of H2 covering full seasonal cycles. We estimate annual H2 uptake of −3.1 ± 0.1 and −12.0 ± 0.4 kg H2 ha−1 yr−1 for the grassland and woodland sites, respectively. Soil moisture was found to be the primary driver of H2 uptake, with the silt/clay content of the soils providing a physical barrier which limited H2 uptake.
Peter Levy, Laura Bentley, Peter Danks, Bridget Emmett, Angus Garbutt, Stephen Heming, Peter Henrys, Aidan Keith, Inma Lebron, Niall McNamara, Richard Pywell, John Redhead, David Robinson, and Alexander Wickenden
Biogeosciences, 21, 4301–4315, https://doi.org/10.5194/bg-21-4301-2024, https://doi.org/10.5194/bg-21-4301-2024, 2024
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We collated a large data set (15 790 soil cores) on soil carbon stock in different land uses. Soil carbon stocks were highest in woodlands and lowest in croplands. The variability in the effects was large. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use because the effect of a given intervention is very hard to verify.
Hannah Chawner, Eric Saboya, Karina E. Adcock, Tim Arnold, Yuri Artioli, Caroline Dylag, Grant L. Forster, Anita Ganesan, Heather Graven, Gennadi Lessin, Peter Levy, Ingrid T. Luijkx, Alistair Manning, Penelope A. Pickers, Chris Rennick, Christian Rödenbeck, and Matthew Rigby
Atmos. Chem. Phys., 24, 4231–4252, https://doi.org/10.5194/acp-24-4231-2024, https://doi.org/10.5194/acp-24-4231-2024, 2024
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The quantity of atmospheric potential oxygen (APO), derived from coincident measurements of carbon dioxide (CO2) and oxygen (O2), has been proposed as a tracer for fossil fuel CO2 emissions. In this model sensitivity study, we examine the use of APO for this purpose in the UK and compare our model to observations. We find that our model simulations are most sensitive to uncertainties relating to ocean fluxes and boundary conditions.
Alice E. Ramsden, Anita L. Ganesan, Luke M. Western, Matthew Rigby, Alistair J. Manning, Amy Foulds, James L. France, Patrick Barker, Peter Levy, Daniel Say, Adam Wisher, Tim Arnold, Chris Rennick, Kieran M. Stanley, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, https://doi.org/10.5194/acp-22-3911-2022, 2022
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Quantifying methane emissions from different sources is a key focus of current research. We present a method for estimating sectoral methane emissions that uses ethane as a tracer for fossil fuel methane. By incorporating variable ethane : methane emission ratios into this model, we produce emissions estimates with improved uncertainty characterisation. This method will be particularly useful for studying methane emissions in areas with complex distributions of sources.
Julia Drewer, Melissa M. Leduning, Robert I. Griffiths, Tim Goodall, Peter E. Levy, Nicholas Cowan, Edward Comynn-Platt, Garry Hayman, Justin Sentian, Noreen Majalap, and Ute M. Skiba
Biogeosciences, 18, 1559–1575, https://doi.org/10.5194/bg-18-1559-2021, https://doi.org/10.5194/bg-18-1559-2021, 2021
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In Southeast Asia, oil palm plantations have largely replaced tropical forests. The impact of this shift in land use on greenhouse gas fluxes and soil microbial communities remains uncertain. We have found emission rates of the potent greenhouse gas nitrous oxide on mineral soil to be higher from oil palm plantations than logged forest over a 2-year study and concluded that emissions have increased over the last 42 years in Sabah, with the proportion of emissions from plantations increasing.
Nicholas Cowan, Peter Levy, Andrea Moring, Ivan Simmons, Colin Bache, Amy Stephens, Joana Marinheiro, Jocelyn Brichet, Ling Song, Amy Pickard, Connie McNeill, Roseanne McDonald, Juliette Maire, Benjamin Loubet, Polina Voylokov, Mark Sutton, and Ute Skiba
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Commonly used nitrogen fertilisers, ammonium nitrate, urea and urea coated with a urease inhibitor, were applied to experimental plots. Fertilisation with ammonium nitrate supported the largest yields but also resulted in the largest nitrous oxide emissions. Urea was the largest emitter of ammonia. The coated urea did not significantly increase yields; however, ammonia emissions were substantially smaller than urea. The coated urea was the best environmentally but is economically unattractive.
Emily D. White, Matthew Rigby, Mark F. Lunt, T. Luke Smallman, Edward Comyn-Platt, Alistair J. Manning, Anita L. Ganesan, Simon O'Doherty, Ann R. Stavert, Kieran Stanley, Mathew Williams, Peter Levy, Michel Ramonet, Grant L. Forster, Andrew C. Manning, and Paul I. Palmer
Atmos. Chem. Phys., 19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019, https://doi.org/10.5194/acp-19-4345-2019, 2019
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Understanding carbon dioxide (CO2) fluxes from the terrestrial biosphere on a national scale is important for evaluating land use strategies to mitigate climate change. We estimate emissions of CO2 from the UK biosphere using atmospheric data in a top-down approach. Our findings show that bottom-up estimates from models of biospheric fluxes overestimate the amount of CO2 uptake in summer. This suggests these models wrongly estimate or omit key processes, e.g. land disturbance due to harvest.
Peter Levy, Marcel van Oijen, Gwen Buys, and Sam Tomlinson
Biogeosciences, 15, 1497–1513, https://doi.org/10.5194/bg-15-1497-2018, https://doi.org/10.5194/bg-15-1497-2018, 2018
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We present a new method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with reliable national-scale census data whilst retaining the information available from several other sources. This includes detailed spatial data; further data sources, such as new satellites, could easily be added in future. Uncertainty is propagated appropriately into the output.
Sarah R. Leeson, Peter E. Levy, Netty van Dijk, Julia Drewer, Sophie Robinson, Matthew R. Jones, John Kentisbeer, Ian Washbourne, Mark A. Sutton, and Lucy J. Sheppard
Biogeosciences, 14, 5753–5764, https://doi.org/10.5194/bg-14-5753-2017, https://doi.org/10.5194/bg-14-5753-2017, 2017
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Nitrogen deposition was experimentally increased on a Scottish peat bog over a period of 13 years (2002–2015), simulating pollution from agricultural and fossil-fuel sources. We measured emissions of the greenhouse gas, nitrous oxide (N2O), in response to the increased nitrogen input. In the plots treated with ammonium and nitrate solution, no response was seen. Areas subjected to high ammonia emitted more N2O than expected. Differences were related to impacts on the vegetation.
Stephanie K. Jones, Carole Helfter, Margaret Anderson, Mhairi Coyle, Claire Campbell, Daniela Famulari, Chiara Di Marco, Netty van Dijk, Y. Sim Tang, Cairistiona F. E. Topp, Ralf Kiese, Reimo Kindler, Jan Siemens, Marion Schrumpf, Klaus Kaiser, Eiko Nemitz, Peter E. Levy, Robert M. Rees, Mark A. Sutton, and Ute M. Skiba
Biogeosciences, 14, 2069–2088, https://doi.org/10.5194/bg-14-2069-2017, https://doi.org/10.5194/bg-14-2069-2017, 2017
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We assessed the nitrogen (N), carbon (C) and greenhouse gas (GHG) budget from an intensively managed grassland in southern Scotland using flux budget calculations as well as changes in soil N and C pools over time. Estimates from flux budget calculations indicated that N and C were sequestered, whereas soil stock measurements indicated a smaller N storage and a loss of C from the ecosystem. The GHG sink strength of the net CO2 ecosystem exchange was strongly affected by CH4 and N2O emissions.
Kerry J. Dinsmore, Julia Drewer, Peter E. Levy, Charles George, Annalea Lohila, Mika Aurela, and Ute M. Skiba
Biogeosciences, 14, 799–815, https://doi.org/10.5194/bg-14-799-2017, https://doi.org/10.5194/bg-14-799-2017, 2017
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Release of greenhouse gases from northern soils contributes significantly to the global atmosphere and plays an important role in regulating climate. This study, based in N. Finland, aimed to measure and understand release of CH4 and N2O, and using satellite imagery, upscale our results to a 2 × 2 km area. Wetlands released large amounts of CH4, with emissions linked to temperature and the presence of Sphagnum; landscape emissions were 2.05 mg C m−2 hr−1. N2O fluxes were consistently near-zero.
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Biogeosciences, 13, 4811–4821, https://doi.org/10.5194/bg-13-4811-2016, https://doi.org/10.5194/bg-13-4811-2016, 2016
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Using a quantum cascade laser we measured N2O fluxes before and after a tillage event on a long-term grazed grassland field using the flux chamber and eddy covariance methods. The measurements were gap-filled using a generalised additive model which used meteorological data at the site. Results suggest that tillage of soils containing plant material (crop residues) releases a relatively large amount of N2O-N, similar in magnitude to approximately 0.9 % of the nitrogen in the plant materials.
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The spatial variability of N2O fluxes measured at the field scale were investigated using a high-precision closed loop dynamic chamber. The results highlight the large and often unpredictable variation in N2O flux magnitude measured from agricultural soils at a relatively small scale. Available nitrogen content of the soils accounts for some of the variability in N2O emissions. The study suggests animal waste is an important source of N2O emissions released from livestock farms.
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
N. J. Cowan, D. Famulari, P. E. Levy, M. Anderson, D. S. Reay, and U. M. Skiba
Atmos. Meas. Tech., 7, 4455–4462, https://doi.org/10.5194/amt-7-4455-2014, https://doi.org/10.5194/amt-7-4455-2014, 2014
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
L. J. Sheppard, I. D. Leith, S. R. Leeson, N. van Dijk, C. Field, and P. Levy
Biogeosciences, 10, 149–160, https://doi.org/10.5194/bg-10-149-2013, https://doi.org/10.5194/bg-10-149-2013, 2013
Nicholas Cowan, Toby Roberts, Mark Hanlon, Aurelia Bezanger, Galina Toteva, Alex Tweedie, Karen Yeung, Ajinkya Deshpande, Peter Levy, Ute Skiba, Eiko Nemitz, and Julia Drewer
Biogeosciences, 22, 3449–3461, https://doi.org/10.5194/bg-22-3449-2025, https://doi.org/10.5194/bg-22-3449-2025, 2025
Short summary
Short summary
We measured soil hydrogen (H2) fluxes from two field sites, a managed grassland and a planted deciduous woodland, with flux measurements of H2 covering full seasonal cycles. We estimate annual H2 uptake of −3.1 ± 0.1 and −12.0 ± 0.4 kg H2 ha−1 yr−1 for the grassland and woodland sites, respectively. Soil moisture was found to be the primary driver of H2 uptake, with the silt/clay content of the soils providing a physical barrier which limited H2 uptake.
Peter Levy, Laura Bentley, Peter Danks, Bridget Emmett, Angus Garbutt, Stephen Heming, Peter Henrys, Aidan Keith, Inma Lebron, Niall McNamara, Richard Pywell, John Redhead, David Robinson, and Alexander Wickenden
Biogeosciences, 21, 4301–4315, https://doi.org/10.5194/bg-21-4301-2024, https://doi.org/10.5194/bg-21-4301-2024, 2024
Short summary
Short summary
We collated a large data set (15 790 soil cores) on soil carbon stock in different land uses. Soil carbon stocks were highest in woodlands and lowest in croplands. The variability in the effects was large. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use because the effect of a given intervention is very hard to verify.
Hannah Chawner, Eric Saboya, Karina E. Adcock, Tim Arnold, Yuri Artioli, Caroline Dylag, Grant L. Forster, Anita Ganesan, Heather Graven, Gennadi Lessin, Peter Levy, Ingrid T. Luijkx, Alistair Manning, Penelope A. Pickers, Chris Rennick, Christian Rödenbeck, and Matthew Rigby
Atmos. Chem. Phys., 24, 4231–4252, https://doi.org/10.5194/acp-24-4231-2024, https://doi.org/10.5194/acp-24-4231-2024, 2024
Short summary
Short summary
The quantity of atmospheric potential oxygen (APO), derived from coincident measurements of carbon dioxide (CO2) and oxygen (O2), has been proposed as a tracer for fossil fuel CO2 emissions. In this model sensitivity study, we examine the use of APO for this purpose in the UK and compare our model to observations. We find that our model simulations are most sensitive to uncertainties relating to ocean fluxes and boundary conditions.
Alice E. Ramsden, Anita L. Ganesan, Luke M. Western, Matthew Rigby, Alistair J. Manning, Amy Foulds, James L. France, Patrick Barker, Peter Levy, Daniel Say, Adam Wisher, Tim Arnold, Chris Rennick, Kieran M. Stanley, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, https://doi.org/10.5194/acp-22-3911-2022, 2022
Short summary
Short summary
Quantifying methane emissions from different sources is a key focus of current research. We present a method for estimating sectoral methane emissions that uses ethane as a tracer for fossil fuel methane. By incorporating variable ethane : methane emission ratios into this model, we produce emissions estimates with improved uncertainty characterisation. This method will be particularly useful for studying methane emissions in areas with complex distributions of sources.
Julia Drewer, Melissa M. Leduning, Robert I. Griffiths, Tim Goodall, Peter E. Levy, Nicholas Cowan, Edward Comynn-Platt, Garry Hayman, Justin Sentian, Noreen Majalap, and Ute M. Skiba
Biogeosciences, 18, 1559–1575, https://doi.org/10.5194/bg-18-1559-2021, https://doi.org/10.5194/bg-18-1559-2021, 2021
Short summary
Short summary
In Southeast Asia, oil palm plantations have largely replaced tropical forests. The impact of this shift in land use on greenhouse gas fluxes and soil microbial communities remains uncertain. We have found emission rates of the potent greenhouse gas nitrous oxide on mineral soil to be higher from oil palm plantations than logged forest over a 2-year study and concluded that emissions have increased over the last 42 years in Sabah, with the proportion of emissions from plantations increasing.
Nicholas Cowan, Peter Levy, Andrea Moring, Ivan Simmons, Colin Bache, Amy Stephens, Joana Marinheiro, Jocelyn Brichet, Ling Song, Amy Pickard, Connie McNeill, Roseanne McDonald, Juliette Maire, Benjamin Loubet, Polina Voylokov, Mark Sutton, and Ute Skiba
Biogeosciences, 16, 4731–4745, https://doi.org/10.5194/bg-16-4731-2019, https://doi.org/10.5194/bg-16-4731-2019, 2019
Short summary
Short summary
Commonly used nitrogen fertilisers, ammonium nitrate, urea and urea coated with a urease inhibitor, were applied to experimental plots. Fertilisation with ammonium nitrate supported the largest yields but also resulted in the largest nitrous oxide emissions. Urea was the largest emitter of ammonia. The coated urea did not significantly increase yields; however, ammonia emissions were substantially smaller than urea. The coated urea was the best environmentally but is economically unattractive.
Emily D. White, Matthew Rigby, Mark F. Lunt, T. Luke Smallman, Edward Comyn-Platt, Alistair J. Manning, Anita L. Ganesan, Simon O'Doherty, Ann R. Stavert, Kieran Stanley, Mathew Williams, Peter Levy, Michel Ramonet, Grant L. Forster, Andrew C. Manning, and Paul I. Palmer
Atmos. Chem. Phys., 19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019, https://doi.org/10.5194/acp-19-4345-2019, 2019
Short summary
Short summary
Understanding carbon dioxide (CO2) fluxes from the terrestrial biosphere on a national scale is important for evaluating land use strategies to mitigate climate change. We estimate emissions of CO2 from the UK biosphere using atmospheric data in a top-down approach. Our findings show that bottom-up estimates from models of biospheric fluxes overestimate the amount of CO2 uptake in summer. This suggests these models wrongly estimate or omit key processes, e.g. land disturbance due to harvest.
Peter Levy, Marcel van Oijen, Gwen Buys, and Sam Tomlinson
Biogeosciences, 15, 1497–1513, https://doi.org/10.5194/bg-15-1497-2018, https://doi.org/10.5194/bg-15-1497-2018, 2018
Short summary
Short summary
We present a new method for estimating land-use change using a Bayesian data assimilation approach. This allows us to constrain estimates of gross land-use change with reliable national-scale census data whilst retaining the information available from several other sources. This includes detailed spatial data; further data sources, such as new satellites, could easily be added in future. Uncertainty is propagated appropriately into the output.
Sarah R. Leeson, Peter E. Levy, Netty van Dijk, Julia Drewer, Sophie Robinson, Matthew R. Jones, John Kentisbeer, Ian Washbourne, Mark A. Sutton, and Lucy J. Sheppard
Biogeosciences, 14, 5753–5764, https://doi.org/10.5194/bg-14-5753-2017, https://doi.org/10.5194/bg-14-5753-2017, 2017
Short summary
Short summary
Nitrogen deposition was experimentally increased on a Scottish peat bog over a period of 13 years (2002–2015), simulating pollution from agricultural and fossil-fuel sources. We measured emissions of the greenhouse gas, nitrous oxide (N2O), in response to the increased nitrogen input. In the plots treated with ammonium and nitrate solution, no response was seen. Areas subjected to high ammonia emitted more N2O than expected. Differences were related to impacts on the vegetation.
Stephanie K. Jones, Carole Helfter, Margaret Anderson, Mhairi Coyle, Claire Campbell, Daniela Famulari, Chiara Di Marco, Netty van Dijk, Y. Sim Tang, Cairistiona F. E. Topp, Ralf Kiese, Reimo Kindler, Jan Siemens, Marion Schrumpf, Klaus Kaiser, Eiko Nemitz, Peter E. Levy, Robert M. Rees, Mark A. Sutton, and Ute M. Skiba
Biogeosciences, 14, 2069–2088, https://doi.org/10.5194/bg-14-2069-2017, https://doi.org/10.5194/bg-14-2069-2017, 2017
Short summary
Short summary
We assessed the nitrogen (N), carbon (C) and greenhouse gas (GHG) budget from an intensively managed grassland in southern Scotland using flux budget calculations as well as changes in soil N and C pools over time. Estimates from flux budget calculations indicated that N and C were sequestered, whereas soil stock measurements indicated a smaller N storage and a loss of C from the ecosystem. The GHG sink strength of the net CO2 ecosystem exchange was strongly affected by CH4 and N2O emissions.
Kerry J. Dinsmore, Julia Drewer, Peter E. Levy, Charles George, Annalea Lohila, Mika Aurela, and Ute M. Skiba
Biogeosciences, 14, 799–815, https://doi.org/10.5194/bg-14-799-2017, https://doi.org/10.5194/bg-14-799-2017, 2017
Short summary
Short summary
Release of greenhouse gases from northern soils contributes significantly to the global atmosphere and plays an important role in regulating climate. This study, based in N. Finland, aimed to measure and understand release of CH4 and N2O, and using satellite imagery, upscale our results to a 2 × 2 km area. Wetlands released large amounts of CH4, with emissions linked to temperature and the presence of Sphagnum; landscape emissions were 2.05 mg C m−2 hr−1. N2O fluxes were consistently near-zero.
Nicholas J. Cowan, Peter E. Levy, Daniela Famulari, Margaret Anderson, Julia Drewer, Marco Carozzi, David S. Reay, and Ute M. Skiba
Biogeosciences, 13, 4811–4821, https://doi.org/10.5194/bg-13-4811-2016, https://doi.org/10.5194/bg-13-4811-2016, 2016
Short summary
Short summary
Using a quantum cascade laser we measured N2O fluxes before and after a tillage event on a long-term grazed grassland field using the flux chamber and eddy covariance methods. The measurements were gap-filled using a generalised additive model which used meteorological data at the site. Results suggest that tillage of soils containing plant material (crop residues) releases a relatively large amount of N2O-N, similar in magnitude to approximately 0.9 % of the nitrogen in the plant materials.
N. J. Cowan, P. Norman, D. Famulari, P. E. Levy, D. S. Reay, and U. M. Skiba
Biogeosciences, 12, 1585–1596, https://doi.org/10.5194/bg-12-1585-2015, https://doi.org/10.5194/bg-12-1585-2015, 2015
Short summary
Short summary
The spatial variability of N2O fluxes measured at the field scale were investigated using a high-precision closed loop dynamic chamber. The results highlight the large and often unpredictable variation in N2O flux magnitude measured from agricultural soils at a relatively small scale. Available nitrogen content of the soils accounts for some of the variability in N2O emissions. The study suggests animal waste is an important source of N2O emissions released from livestock farms.
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
N. J. Cowan, D. Famulari, P. E. Levy, M. Anderson, D. S. Reay, and U. M. Skiba
Atmos. Meas. Tech., 7, 4455–4462, https://doi.org/10.5194/amt-7-4455-2014, https://doi.org/10.5194/amt-7-4455-2014, 2014
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
L. J. Sheppard, I. D. Leith, S. R. Leeson, N. van Dijk, C. Field, and P. Levy
Biogeosciences, 10, 149–160, https://doi.org/10.5194/bg-10-149-2013, https://doi.org/10.5194/bg-10-149-2013, 2013
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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
Saudi Rainfall (SaRa): Hourly 0.1° Gridded Rainfall (1979–Present) for Saudi Arabia via Machine Learning Fusion of Satellite and Model Data
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
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.
Xuetong Wang, Raied S. Alharbi, Oscar M. Baez-Villanueva, Amy Green, Matthew F. McCabe, Yoshihide Wada, Albert I. J. M. Van Dijk, Muhammad A. Abid, and Hylke Beck
EGUsphere, https://doi.org/10.5194/egusphere-2025-254, https://doi.org/10.5194/egusphere-2025-254, 2025
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Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.
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.
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.
Cited articles
Ahlmer, A.-K., Cavalli, M., Hansson, K., Koutsouris, A. J., Crema, S., and Kalantari, Z.: Soil Moisture Remote-Sensing Applications for Identification of Flood-Prone Areas along Transport Infrastructure, Environ. Earth Sci., 77, 533, https://doi.org/10.1007/s12665-018-7704-z, 2018. a
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008. a
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping Topsoil Physical Properties at European Scale Using the LUCAS Database, Geoderma, 261, 110–123, https://doi.org/10.1016/j.geoderma.2015.07.006, 2016. a, b
Ballabio, C., Lugato, E., Fernández-Ugalde, O., Orgiazzi, A., Jones, A., Borrelli, P., Montanarella, L., and Panagos, P.: Mapping LUCAS Topsoil Chemical Properties at European Scale Using Gaussian Process Regression, Geoderma, 355, 113912, https://doi.org/10.1016/j.geoderma.2019.113912, 2019. a
Bates, D., Mächler, M., Bolker, B., and Walker, S.: Fitting Linear Mixed-Effects Models Using Lme4, J. Stat. Softw., 67, 1–48, https://doi.org/10.18637/jss.v067.i01, 2015. a
Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L., and Wagner, W.: Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering, Remote Sensing, 10, 1030, https://doi.org/10.3390/rs10071030, 2018. a, b, c
Bell, V. A., Davies, H. N., Fry, M., Zhang, T., Murphy, H., Hitt, O., Hewitt, E. J., Chapman, R., and Black, K. B.: Collated neutron probe measurements and derived soil moisture data, UK, 1966–2013, NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/450bb14b-c711-47af-8792-f9bd88482cd4, 2022. a
Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., and Reynard, N. S.: Use of Soil Data in a Grid-Based Hydrological Model to Estimate Spatial Variation in Changing Flood Risk across the UK, J. Hydrol., 377, 335–350, https://doi.org/10.1016/j.jhydrol.2009.08.031, 2009. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Betancourt, M.: A Conceptual Introduction to Hamiltonian Monte Carlo, arXiv [preprint], https://doi.org/10.48550/arXiv.1701.02434, 2017. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrolog. Sci. Bull., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979. a
Bogena, H. R., Huisman, J. A., Baatz, R., Hendricks Franssen, H.-J., and Vereecken, H.: Accuracy of the Cosmic-Ray Soil Water Content Probe in Humid Forest Ecosystems: The Worst Case Scenario, Water Resour. Res., 49, 5778–5791, https://doi.org/10.1002/wrcr.20463, 2013. a
Bogena, H. R., Schrön, M., Jakobi, J., Ney, P., Zacharias, S., Andreasen, M., Baatz, R., Boorman, D., Duygu, M. B., Eguibar-Galán, M. A., Fersch, B., Franke, T., Geris, J., González Sanchis, M., Kerr, Y., Korf, T., Mengistu, Z., Mialon, A., Nasta, P., Nitychoruk, J., Pisinaras, V., Rasche, D., Rosolem, R., Said, H., Schattan, P., Zreda, M., Achleitner, S., Albentosa-Hernández, E., Akyürek, Z., Blume, T., del Campo, A., Canone, D., Dimitrova-Petrova, K., Evans, J. G., Ferraris, S., Frances, F., Gisolo, D., Güntner, A., Herrmann, F., Iwema, J., Jensen, K. H., Kunstmann, H., Lidón, A., Looms, M. C., Oswald, S., Panagopoulos, A., Patil, A., Power, D., Rebmann, C., Romano, N., Scheiffele, L., Seneviratne, S., Weltin, G., and Vereecken, H.: COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors, Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, 2022. a
Bürkner, P.-C.: Advanced Bayesian Multilevel Modeling with the R Package Brms, R J., 10, 395–411, 2018. a
Caubet, M., Román Dobarco, M., Arrouays, D., Minasny, B., and Saby, N. P. A.: Merging Country, Continental and Global Predictions of Soil Texture: Lessons from Ensemble Modelling in France, Geoderma, 337, 99–110, https://doi.org/10.1016/j.geoderma.2018.09.007, 2019. a
Chifflard, P., Kranl, J., zur Strassen, G., and Zepp, H.: The Significance of Soil Moisture in Forecasting Characteristics of Flood Events. A Statistical Analysis in Two Nested Catchments, J. Hydrol. Hydromech., 66, 1–11, https://doi.org/10.1515/johh-2017-0037, 2018. a
Cressie, N.: The Origins of Kriging, Math. Geol., 22, 239–252, https://doi.org/10.1007/BF00889887, 1990. a
Davidson, E. A.: Sources of Nitric Oxide and Nitrous Oxide Following Wetting of Dry Soil, Soil Sci. Soc. Am. J., 56, 95–102, https://doi.org/10.2136/sssaj1992.03615995005600010015x, 1992. a
de Brogniez, D., Ballabio, C., Stevens, A., Jones, R. J. A., Montanarella, L., and van Wesemael, B.: A Map of the Topsoil Organic Carbon Content of Europe Generated by a Generalized Additive Model, Eur. J. Soil Sci., 66, 121–134, https://doi.org/10.1111/ejss.12193, 2015. a
Deng, Y., Wang, S., Bai, X., Wu, L., Cao, Y., Li, H., Wang, M., Li, C., Yang, Y., Hu, Z., Tian, S., and Lu, Q.: Comparison of Soil Moisture Products from Microwave Remote Sensing, Land Model, and Reanalysis Using Global Ground Observations, Hydrol. Process., 34, 836–851, https://doi.org/10.1002/hyp.13636, 2020. a
Dobson, M., Ulaby, F., Hallikainen, M., and El-Rayes, M.: Microwave Dielectric Behavior of Wet Soil – Part II: Dielectric Mixing Models, IEEE T. Geosci. Remote, GE-23, 35–46, https://doi.org/10.1109/TGRS.1985.289498, 1985. a
Emmett, B. A., Reynolds, B., Chamberlain, P. M., Rowe, E., Spurgeon, D., Brittain, S. A., Frogbrook, Z., Hughes, S., Lawlor, A. J., Poskitt, J., Potter, E., Robinson, D. A., Scott, A., Wood, C., and Woods, C.: Countryside Survey: Soils Report from 2007, NERC/Centre for Ecology and Hydrology, 192 pp., https://nora.nerc.ac.uk/id/eprint/9354/ (last access: 25 October 2024), 2010. a
Escorihuela, M. J. and Quintana-Seguí, P.: Comparison of Remote Sensing and Simulated Soil Moisture Datasets in Mediterranean Landscapes, Remote Sens. Environ., 180, 99–114, https://doi.org/10.1016/j.rse.2016.02.046, 2016. a
Evans, J. G., Ward, H. C., Blake, J. R., Hewitt, E. J., Morrison, R., Fry, M., Ball, L. A., Doughty, L. C., Libre, J. W., Hitt, O. E., Rylett, D., Ellis, R. J., Warwick, A. C., Brooks, M., Parkes, M. A., Wright, G. M. H., Singer, A. C., Boorman, D. B., and Jenkins, A.: Soil Water Content in Southern England Derived from a Cosmic-Ray Soil Moisture Observing System – COSMOS-UK, Hydrol. Process., 30, 4987–4999, https://doi.org/10.1002/hyp.10929, 2016. a, b
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian Data Analysis, Third Edition, Chapman and Hall/CRC, Boca Raton, ISBN 978-1-4398-4095-5, 2013. a
Gelman, A., Hill, J., and Vehtari, A.: Regression and Other Stories, Cambridge University Press, Cambridge, 548 pp., https://doi.org/10.1017/9781139161879 2020. a
Hallema, D. W., Périard, Y., Lafond, J. A., Gumiere, S. J., and Caron, J.: Characterization of Water Retention Curves for a Series of Cultivated Histosols, Vadose Zone J., 14, vzj2014.10.0148, https://doi.org/10.2136/vzj2014.10.0148, 2015. a
Harrison, D. L., Driscoll, S. J., and Kitchen, M.: Improving Precipitation Estimates from Weather Radar Using Quality Control and Correction Techniques, Meteorol. Appl., 7, 135–144, https://doi.org/10.1017/S1350482700001468, 2000. a
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global Gridded Soil Information Based on Machine Learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a
Hoffman, M. D. and Gelman, A.: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15, 1593–1623, 2014. a
Johnson, P. C. D.: Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models, Methods Ecol. Evol., 5, 944–946, https://doi.org/10.1111/2041-210X.12225, 2014. a
Jury, W. A. and Horton, R.: Soil Physics, John Wiley & Sons, ISBN 978-0-471-05965-3, 2004. a
Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and Zacharias, S.: Footprint Characteristics Revised for Field-Scale Soil Moisture Monitoring with Cosmic-Ray Neutrons, Water Resour. Res., 51, 5772–5790, https://doi.org/10.1002/2015WR017169, 2015. a
Kolassa, J., Gentine, P., Prigent, C., Aires, F., and Alemohammad, S. H.: Soil Moisture Retrieval from AMSR-E and ASCAT Microwave Observation Synergy. Part 2: Product Evaluation, Remote Sens. Environ., 195, 202–217, https://doi.org/10.1016/j.rse.2017.04.020, 2017. a
Kosugi, K.: Lognormal Distribution Model for Unsaturated Soil Hydraulic Properties, Water Resour. Res., 32, 2697–2703, https://doi.org/10.1029/96WR01776, 1996. a
Lal, R.: Soil Organic Matter and Water Retention, Agron. J., 112, 3265–3277, https://doi.org/10.1002/agj2.20282, 2020. a
Levy, P. E.: Daily soil moisture maps for the UK (2016–2023) at 2 km resolution, NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/5aa8c5b4-4485-4954-b5c3-18d937a418f7, 2024a. a
Levy, P. E.: Daily soil moisture maps for the UK (2016–present) at 2 km resolution, [data set], https://gws-access.jasmin.ac.uk/public/dare_uk/smuk/ (last access: 25 October 2024), 2024b. a
Liu, H. and Lennartz, B.: Hydraulic Properties of Peat Soils along a Bulk Density Gradient – A Meta Study, Hydrol. Process., 33, 101–114, https://doi.org/10.1002/hyp.13314, 2019. a
Met Office: 1-Km Resolution UK Composite Rainfall Data from the Met Office Nimrod System, https://catalogue.ceda.ac.uk/uuid/27dd6ffba67f667a18c62de5c3456350 (last access: 14 August 2022), 2003. a
Met Office: NWP-UKV: Met Office UK Atmospheric High Resolution Model Data, https://catalogue.ceda.ac.uk/uuid/f47bc62786394626b665e23b658d385f (last access: 14 August 2022), 2016. a
Milan, M., Macpherson, B., Tubbs, R., Dow, G., Inverarity, G., Mittermaier, M., Halloran, G., Kelly, G., Li, D., Maycock, A., Payne, T., Piccolo, C., Stewart, L., and Wlasak, M.: Hourly 4D-Var in the Met Office UKV Operational Forecast Model, Q. J. Roy. Meteor. Soc., 146, 1281–1301, https://doi.org/10.1002/qj.3737, 2020. a
Minasny, B. and McBratney, A. B.: Limited Effect of Organic Matter on Soil Available Water Capacity, Eur. J. Soil Sci., 69, 39–47, https://doi.org/10.1111/ejss.12475, 2018. a
Nakagawa, S., Johnson, P. C. D., and Schielzeth, H.: The Coefficient of Determination R2 and Intra-Class Correlation Coefficient from Generalized Linear Mixed-Effects Models Revisited and Expanded, J. R. Soc. Interface, 14, 20170213, https://doi.org/10.1098/rsif.2017.0213, 2017. a
Pan, F., Peters-Lidard, C. D., and Sale, M. J.: An Analytical Method for Predicting Surface Soil Moisture from Rainfall Observations, Water Resour. Res., 39, 1314, https://doi.org/10.1029/2003WR002142, 2003. a
Pardo-Iguzquiza, E. and Chica-Olmo, M.: Geostatistics with the Matern Semivariogram Model: A Library of Computer Programs for Inference, Kriging and Simulation, Comput. Geosci., 34, 1073–1079, https://doi.org/10.1016/j.cageo.2007.09.020, 2008. a
Peng, J., Tanguy, M., Robinson, E. L., Pinnington, E., Evans, J., Ellis, R., Cooper, E., Hannaford, J., Blyth, E., and Dadson, S.: Estimation and Evaluation of High-Resolution Soil Moisture from Merged Model and Earth Observation Data in the Great Britain, Remote Sens. Environ., 264, 112610, https://doi.org/10.1016/j.rse.2021.112610, 2021. a, b, c
Pezij, M., Augustijn, D. C. M., Hendriks, D. M. D., and Hulscher, S. J. M. H.: Transfer Function‐Noise Modeling Using Remote Sensing Data to Characterize Soil Moisture Dynamics: a Data-driven Approach, AGU Fall Meeting 2019, https://research.utwente.nl/en/publications/transfer-functionnoise-modeling-using-remote-sensing-data-to-char (last access: 31 July 2023), 2019. a
Pinnington, E., Amezcua, J., Cooper, E., Dadson, S., Ellis, R., Peng, J., Robinson, E., Morrison, R., Osborne, S., and Quaife, T.: Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data, Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, 2021. a, b
Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S., and Thompson, J.: Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution, Soil Sci. Soc. Am. J., 82, 186–201, https://doi.org/10.2136/sssaj2017.04.0122, 2018. a
Räsänen, A., Tolvanen, A., and Kareksela, S.: Monitoring Peatland Water Table Depth with Optical and Radar Satellite Imagery, Int. J. Appl. Earth Obs., 112, 102866, https://doi.org/10.1016/j.jag.2022.102866, 2022. a, b, c
Rasche, D., Köhli, M., Schrön, M., Blume, T., and Güntner, A.: Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints, Hydrol. Earth Syst. Sci., 25, 6547–6566, https://doi.org/10.5194/hess-25-6547-2021, 2021. a
Ribeiro Jr., P. and Diggle, P.: geoR: A Package for Geostatistical Analysis, R-NEWS, 1, 15–18, 2001. a
Scheiffele, L. M., Baroni, G., Franz, T. E., Jakobi, J., and Oswald, S. E.: A Profile Shape Correction to Reduce the Vertical Sensitivity of Cosmic-Ray Neutron Sensing of Soil Moisture, Vadose Zone J., 19, e20083, https://doi.org/10.1002/vzj2.20083, 2020. a
Stanley, S., Antoniou, V., Ball, L., Bennett, E., Blake, J., Boorman, D., Brooks, M., Clarke, M., Cooper, H., Cowan, N., Evans, J., Farrand, P., Fry, M., Hitt, O., Jenkins, A., Kral, F., Lord, W., Morrison, R., Nash, G., Rylett, D., Scarlett, P., Swain, O., Thornton, J., Trill, E., Warwick, A., and Winterbourn, J.: Daily and Sub-Daily Hydrometeorological and Soil Data (2013–2019) [COSMOS-UK], NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185, 2019. a, b
Thomas, A. R. C., Cosby, B. J., Henrys, P. A., and Emmett, B. A.: Topsoil carbon concentration estimates from the Countryside Survey of Great Britain, 2007 using a generalized additive model, NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/3aaa52d3-918a-4f95-b065-32f33e45d4f6, 2020. a
van Genuchten, M. Th.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a, b
van Oijen, M.: Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models, Current Forestry Reports, 3, 269–280, https://doi.org/10.1007/s40725-017-0069-9, 2017. a
van Oijen, M.: Bayesian Compendium, Springer International Publishing, ISBN 978-3-030-55896-3, https://doi.org/10.1007/978-3-030-55897-0, 2020. a
Vitolo, C., Fry, M., and Buytaert, W.: Rnrfa: An R Package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive, R J., 8, 102–116, 2016. a
Walczak, R., Rovdan, E., and Witkowska-Walczak, B.: Water Retention Characteristics of Peat and Sand Mixtures, Int. Agrophys., 16, 161–165, 2002. a
Wang, Y., Ma, R., and Zhu, G.: Improved Prediction of Hydraulic Conductivity With a Soil Water Retention Curve That Accounts for Both Capillary and Adsorption Forces, Water Resour. Res., 58, e2021WR031297, https://doi.org/10.1029/2021WR031297, 2022. a, b
Weiss, R., Alm, J., Laiho, R., and Laine, J.: Modeling Moisture Retention in Peat Soils, Soil Sci. Soc. Am. J., 62, 305, https://doi.org/10.2136/sssaj1998.03615995006200020002x, 1998. a, b
Wigneron, J. P., Calvet, J. C., Pellarin, T., Van de Griend, A. A., Berger, M., and Ferrazzoli, P.: Retrieving Near-Surface Soil Moisture from Microwave Radiometric Observations: Current Status and Future Plans, Remote Sens. Environ., 85, 489–506, https://doi.org/10.1016/S0034-4257(03)00051-8, 2003. a
Yu, J., Li, X.-F., Lewis, E., Blenkinsop, S., and Fowler, H. J.: UKGrsHP: A UK High-Resolution Gauge–Radar–Satellite Merged Hourly Precipitation Analysis Dataset, Clim. Dynam., 54, 2919–2940, https://doi.org/10.1007/s00382-020-05144-2, 2020. a
Zou, J., Ziegler, A. D., Chen, D., McNicol, G., Ciais, P., Jiang, X., Zheng, C., Wu, J., Wu, J., Lin, Z., He, X., Brown, L. E., Holden, J., Zhang, Z., Ramchunder, S. J., Chen, A., and Zeng, Z.: Rewetting Global Wetlands Effectively Reduces Major Greenhouse Gas Emissions, Nat. Geosci., 15, 627–632, https://doi.org/10.1038/s41561-022-00989-0, 2022. a
Zreda, M., Desilets, D., Ferré, T. P. A., and Scott, R. L.: Measuring Soil Moisture Content Non-Invasively at Intermediate Spatial Scale Using Cosmic-Ray Neutrons, Geophys. Res. Lett., 35, L21402, https://doi.org/10.1029/2008GL035655, 2008. a
Short summary
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.
Having accurate up-to-date maps of soil moisture is important for many purposes. However,...
