Articles | Volume 20, issue 7
https://doi.org/10.5194/hess-20-2737-2016
© 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-20-2737-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
Hannah Cloke
Department of Geography and Environmental Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts, Reading, UK
School of Geographical Sciences, Bristol University, Bristol, UK
Antje Weisheimer
European Centre for Medium-Range Weather Forecasts, Reading, UK
Department of Physics, National Centre for Atmospheric Science (NCAS), University of Oxford, Oxford, UK
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Nat. Hazards Earth Syst. Sci., 21, 261–277, https://doi.org/10.5194/nhess-21-261-2021, https://doi.org/10.5194/nhess-21-261-2021, 2021
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Christopher H. O'Reilly, Daniel J. Befort, and Antje Weisheimer
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Shaun Harrigan, Ervin Zsoter, Lorenzo Alfieri, Christel Prudhomme, Peter Salamon, Fredrik Wetterhall, Christopher Barnard, Hannah Cloke, and Florian Pappenberger
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Louise Arnal, Liz Anspoks, Susan Manson, Jessica Neumann, Tim Norton, Elisabeth Stephens, Louise Wolfenden, and Hannah Louise Cloke
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Kristian Strommen, Hannah M. Christensen, Dave MacLeod, Stephan Juricke, and Tim N. Palmer
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Jamie Towner, Hannah L. Cloke, Ervin Zsoter, Zachary Flamig, Jannis M. Hoch, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, https://doi.org/10.5194/hess-23-3057-2019, 2019
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Sazzad Hossain, Hannah L. Cloke, Andrea Ficchì, Andrew G. Turner, and Elisabeth Stephens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-286, https://doi.org/10.5194/hess-2019-286, 2019
Manuscript not accepted for further review
Seshagiri Rao Kolusu, Mohammad Shamsudduha, Martin C. Todd, Richard G. Taylor, David Seddon, Japhet J. Kashaigili, Girma Y. Ebrahim, Mark O. Cuthbert, James P. R. Sorensen, Karen G. Villholth, Alan M. MacDonald, and Dave A. MacLeod
Hydrol. Earth Syst. Sci., 23, 1751–1762, https://doi.org/10.5194/hess-23-1751-2019, https://doi.org/10.5194/hess-23-1751-2019, 2019
Hylke E. Beck, Ming Pan, Tirthankar Roy, Graham P. Weedon, Florian Pappenberger, Albert I. J. M. van Dijk, George J. Huffman, Robert F. Adler, and Eric F. Wood
Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, https://doi.org/10.5194/hess-23-207-2019, 2019
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Christophe Lavaysse, Jürgen Vogt, Andrea Toreti, Marco L. Carrera, and Florian Pappenberger
Nat. Hazards Earth Syst. Sci., 18, 3297–3309, https://doi.org/10.5194/nhess-18-3297-2018, https://doi.org/10.5194/nhess-18-3297-2018, 2018
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Forecasting droughts in Europe 1 month in advance would provide valuable information for decision makers. However, these extreme events are still difficult to predict. In this study, we develop forecasts based on predictors using the geopotential anomalies, generally more predictable than precipitation, derived from the ECMWF model. Results show that this approach outperforms the prediction using precipitation, especially in winter and in northern Europe, where 65 % of droughts are predicted.
Jessica L. Neumann, Louise Arnal, Rebecca E. Emerton, Helen Griffith, Stuart Hyslop, Sofia Theofanidi, and Hannah L. Cloke
Geosci. Commun., 1, 35–57, https://doi.org/10.5194/gc-1-35-2018, https://doi.org/10.5194/gc-1-35-2018, 2018
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Seasonal hydrological forecasts (SHF) can predict floods, droughts, and water use in the coming months, but little is known about how SHF are used for decision-making. We asked 11 water sector participants what decisions they would make when faced with a possible flood event in 6 weeks' time. Flood forecasters and groundwater hydrologists responded to the flood risk more than water supply managers. SHF need to be tailored for use and communicated more clearly if they are to aid decision-making.
Rebecca Emerton, Ervin Zsoter, Louise Arnal, Hannah L. Cloke, Davide Muraro, Christel Prudhomme, Elisabeth M. Stephens, Peter Salamon, and Florian Pappenberger
Geosci. Model Dev., 11, 3327–3346, https://doi.org/10.5194/gmd-11-3327-2018, https://doi.org/10.5194/gmd-11-3327-2018, 2018
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Global overviews of upcoming flood and drought events are key for many applications from agriculture to disaster risk reduction. Seasonal forecasts are designed to provide early indications of such events weeks or even months in advance. This paper introduces GloFAS-Seasonal, the first operational global-scale seasonal hydro-meteorological forecasting system producing openly available forecasts of high and low river flow out to 4 months ahead.
Louise Arnal, Hannah L. Cloke, Elisabeth Stephens, Fredrik Wetterhall, Christel Prudhomme, Jessica Neumann, Blazej Krzeminski, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, https://doi.org/10.5194/hess-22-2057-2018, 2018
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This paper presents a new operational forecasting system (driven by atmospheric forecasts), predicting river flow in European rivers for the next 7 months. For the first month only, these river flow forecasts are, on average, better than predictions that do not make use of atmospheric forecasts. Overall, this forecasting system can predict whether abnormally high or low river flows will occur in the next 7 months in many parts of Europe, and could be valuable for various applications.
Hylke E. Beck, Noemi Vergopolan, Ming Pan, Vincenzo Levizzani, Albert I. J. M. van Dijk, Graham P. Weedon, Luca Brocca, Florian Pappenberger, George J. Huffman, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, https://doi.org/10.5194/hess-21-6201-2017, 2017
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This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.
Erin Coughlan de Perez, Elisabeth Stephens, Konstantinos Bischiniotis, Maarten van Aalst, Bart van den Hurk, Simon Mason, Hannah Nissan, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 21, 4517–4524, https://doi.org/10.5194/hess-21-4517-2017, https://doi.org/10.5194/hess-21-4517-2017, 2017
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Disaster managers would like to use seasonal forecasts to anticipate flooding months in advance. However, current seasonal forecasts give information on rainfall instead of flooding. Here, we find that the number of extreme events, rather than total rainfall, is most related to flooding in different regions of Africa. We recommend several forecast adjustments and research opportunities that would improve flood information at the seasonal timescale in different regions.
Paolo Davini, Jost von Hardenberg, Susanna Corti, Hannah M. Christensen, Stephan Juricke, Aneesh Subramanian, Peter A. G. Watson, Antje Weisheimer, and Tim N. Palmer
Geosci. Model Dev., 10, 1383–1402, https://doi.org/10.5194/gmd-10-1383-2017, https://doi.org/10.5194/gmd-10-1383-2017, 2017
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The Climate SPHINX project is a large set of more than 120 climate simulations run with the EC-Earth global climate. It explores the sensitivity of present-day and future climate to the model horizontal resolution (from 150 km up to 16 km) and to the introduction of two stochastic physics parameterisations. Results shows that the the stochastic schemes can represent a cheaper alternative to a resolution increase, especially for the representation of the tropical climate variability.
Louise Crochemore, Maria-Helena Ramos, Florian Pappenberger, and Charles Perrin
Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, https://doi.org/10.5194/hess-21-1573-2017, 2017
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Louise Crochemore, Maria-Helena Ramos, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, https://doi.org/10.5194/hess-20-3601-2016, 2016
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This study investigates the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. Eight variants of bias correction approaches based on the linear scaling and the distribution mapping methods are applied to the precipitation forecasts prior to generating the streamflow forecasts. One of the main results of the study is that distribution mapping of daily values is successful in improving forecast reliability.
Erin Coughlan de Perez, Bart van den Hurk, Maarten K. van Aalst, Irene Amuron, Deus Bamanya, Tristan Hauser, Brenden Jongma, Ana Lopez, Simon Mason, Janot Mendler de Suarez, Florian Pappenberger, Alexandra Rueth, Elisabeth Stephens, Pablo Suarez, Jurjen Wagemaker, and Ervin Zsoter
Hydrol. Earth Syst. Sci., 20, 3549–3560, https://doi.org/10.5194/hess-20-3549-2016, https://doi.org/10.5194/hess-20-3549-2016, 2016
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Many flood disaster impacts could be avoided by preventative action; however, early action is not guaranteed. This article demonstrates the design of a new system of forecast-based financing, which automatically triggers action when a flood forecast arrives, before a potential disaster. We establish "action triggers" for northern Uganda based on a global flood forecasting system, verifying these forecasts and assessing the uncertainties inherent in setting a trigger in a data-scarce location.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
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Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
Jon Olav Skøien, Konrad Bogner, Peter Salamon, Paul Smith, and Florian Pappenberger
Proc. IAHS, 373, 109–114, https://doi.org/10.5194/piahs-373-109-2016, https://doi.org/10.5194/piahs-373-109-2016, 2016
V. Thiemig, B. Bisselink, F. Pappenberger, and J. Thielen
Hydrol. Earth Syst. Sci., 19, 3365–3385, https://doi.org/10.5194/hess-19-3365-2015, https://doi.org/10.5194/hess-19-3365-2015, 2015
C. Lavaysse, J. Vogt, and F. Pappenberger
Hydrol. Earth Syst. Sci., 19, 3273–3286, https://doi.org/10.5194/hess-19-3273-2015, https://doi.org/10.5194/hess-19-3273-2015, 2015
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This paper assesses the predictability of meteorological droughts over Europe 1 month in advance using ensemble prediction systems.
It has been shown that, on average and using the most relevant method, 40 % of droughts in Europe are correctly forecasted, with less than 25 % false alarms.
This study is a reference for other studies that are motivated to improving the drought forecasting.
R. D. Field, A. C. Spessa, N. A. Aziz, A. Camia, A. Cantin, R. Carr, W. J. de Groot, A. J. Dowdy, M. D. Flannigan, K. Manomaiphiboon, F. Pappenberger, V. Tanpipat, and X. Wang
Nat. Hazards Earth Syst. Sci., 15, 1407–1423, https://doi.org/10.5194/nhess-15-1407-2015, https://doi.org/10.5194/nhess-15-1407-2015, 2015
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We have developed a global database of daily, gridded Fire Weather Index System calculations beginning in 1980. Input data and two different estimates of precipitation from rain gauges were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications. This data set can be used for analyzing historical relationships between fire weather and fire activity, and in identifying large-scale atmosphere–ocean controls on fire weather.
F. Wetterhall, H. C. Winsemius, E. Dutra, M. Werner, and E. Pappenberger
Hydrol. Earth Syst. Sci., 19, 2577–2586, https://doi.org/10.5194/hess-19-2577-2015, https://doi.org/10.5194/hess-19-2577-2015, 2015
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Dry spells can have a devastating impact on agricuture in areas where irrigation is not available. Forecasting these dry spells could enhance preparedness in sensitive regions and avoid economic loss due to harvest failure. In this study, ECMWF seasonal forecasts are applied in the Limpopo basin in southeastern Africa to forecast dry spells in the seasonal rains. The results indicate skill in the forecast which is further improved by post-processing of the precipitation forecasts.
A. C. Spessa, R. D. Field, F. Pappenberger, A. Langner, S. Englhart, U. Weber, T. Stockdale, F. Siegert, J. W. Kaiser, and J. Moore
Nat. Hazards Earth Syst. Sci., 15, 429–442, https://doi.org/10.5194/nhess-15-429-2015, https://doi.org/10.5194/nhess-15-429-2015, 2015
G. Balsamo, C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, E. Dutra, J. Muñoz-Sabater, F. Pappenberger, P. de Rosnay, T. Stockdale, and F. Vitart
Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, https://doi.org/10.5194/hess-19-389-2015, 2015
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ERA-Interim/Land is a global land surface reanalysis covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim and a precipitation bias correction based on GPCP. A selection of verification results show the added value in representing the terrestrial water cycle and its main land surface storages and fluxes.
P. Trambauer, S. Maskey, M. Werner, F. Pappenberger, L. P. H. van Beek, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 18, 2925–2942, https://doi.org/10.5194/hess-18-2925-2014, https://doi.org/10.5194/hess-18-2925-2014, 2014
E. Dutra, F. Wetterhall, F. Di Giuseppe, G. Naumann, P. Barbosa, J. Vogt, W. Pozzi, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 2657–2667, https://doi.org/10.5194/hess-18-2657-2014, https://doi.org/10.5194/hess-18-2657-2014, 2014
E. Dutra, W. Pozzi, F. Wetterhall, F. Di Giuseppe, L. Magnusson, G. Naumann, P. Barbosa, J. Vogt, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 2669–2678, https://doi.org/10.5194/hess-18-2669-2014, https://doi.org/10.5194/hess-18-2669-2014, 2014
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
L. Alfieri, F. Pappenberger, and F. Wetterhall
Nat. Hazards Earth Syst. Sci., 14, 1505–1515, https://doi.org/10.5194/nhess-14-1505-2014, https://doi.org/10.5194/nhess-14-1505-2014, 2014
G. Naumann, E. Dutra, P. Barbosa, F. Pappenberger, F. Wetterhall, and J. V. Vogt
Hydrol. Earth Syst. Sci., 18, 1625–1640, https://doi.org/10.5194/hess-18-1625-2014, https://doi.org/10.5194/hess-18-1625-2014, 2014
H. C. Winsemius, E. Dutra, F. A. Engelbrecht, E. Archer Van Garderen, F. Wetterhall, F. Pappenberger, and M. G. F. Werner
Hydrol. Earth Syst. Sci., 18, 1525–1538, https://doi.org/10.5194/hess-18-1525-2014, https://doi.org/10.5194/hess-18-1525-2014, 2014
E. Mwangi, F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 611–620, https://doi.org/10.5194/hess-18-611-2014, https://doi.org/10.5194/hess-18-611-2014, 2014
P. Trambauer, E. Dutra, S. Maskey, M. Werner, F. Pappenberger, L. P. H. van Beek, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 18, 193–212, https://doi.org/10.5194/hess-18-193-2014, https://doi.org/10.5194/hess-18-193-2014, 2014
F. Wetterhall, F. Pappenberger, L. Alfieri, H. L. Cloke, J. Thielen-del Pozo, S. Balabanova, J. Daňhelka, A. Vogelbacher, P. Salamon, I. Carrasco, A. J. Cabrera-Tordera, M. Corzo-Toscano, M. Garcia-Padilla, R. J. Garcia-Sanchez, C. Ardilouze, S. Jurela, B. Terek, A. Csik, J. Casey, G. Stankūnavičius, V. Ceres, E. Sprokkereef, J. Stam, E. Anghel, D. Vladikovic, C. Alionte Eklund, N. Hjerdt, H. Djerv, F. Holmberg, J. Nilsson, K. Nyström, M. Sušnik, M. Hazlinger, and M. Holubecka
Hydrol. Earth Syst. Sci., 17, 4389–4399, https://doi.org/10.5194/hess-17-4389-2013, https://doi.org/10.5194/hess-17-4389-2013, 2013
E. Dutra, F. Di Giuseppe, F. Wetterhall, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 2359–2373, https://doi.org/10.5194/hess-17-2359-2013, https://doi.org/10.5194/hess-17-2359-2013, 2013
M. H. Ramos, S. J. van Andel, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 2219–2232, https://doi.org/10.5194/hess-17-2219-2013, https://doi.org/10.5194/hess-17-2219-2013, 2013
L. Alfieri, P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, https://doi.org/10.5194/hess-17-1161-2013, 2013
Related subject area
Subject: Global hydrology | Techniques and Approaches: Uncertainty analysis
Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin
Information content of soil hydrology in a west Amazon watershed as informed by GRACE
Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports
The 63-year changes in annual streamflow volumes across Europe with a focus on the Mediterranean basin
Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model
Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates
Historical and future changes in global flood magnitude – evidence from a model–observation investigation
A global-scale evaluation of extreme event uncertainty in the eartH2Observe project
Assessment of precipitation error propagation in multi-model global water resource reanalysis
The potential of global reanalysis datasets in identifying flood events in Southern Africa
Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model
Global change in streamflow extremes under climate change over the 21st century
Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
Sensitivity of future continental United States water deficit projections to general circulation models, the evapotranspiration estimation method, and the greenhouse gas emission scenario
Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use
Flood and drought hydrologic monitoring: the role of model parameter uncertainty
Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration
Climate change impacts on runoff in West Africa: a review
Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis
Disinformative data in large-scale hydrological modelling
The impact of climate mitigation on projections of future drought
Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data
Monitoring and quantifying future climate projections of dryness and wetness extremes: SPI bias
Improving runoff estimates from regional climate models: a performance analysis in Spain
A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models
Error characterisation of global active and passive microwave soil moisture datasets
Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
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The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Hiroki Mizuochi, Agnès Ducharne, Frédérique Cheruy, Josefine Ghattas, Amen Al-Yaari, Jean-Pierre Wigneron, Vladislav Bastrikov, Philippe Peylin, Fabienne Maignan, and Nicolas Vuichard
Hydrol. Earth Syst. Sci., 25, 2199–2221, https://doi.org/10.5194/hess-25-2199-2021, https://doi.org/10.5194/hess-25-2199-2021, 2021
Samuel Saxe, William Farmer, Jessica Driscoll, and Terri S. Hogue
Hydrol. Earth Syst. Sci., 25, 1529–1568, https://doi.org/10.5194/hess-25-1529-2021, https://doi.org/10.5194/hess-25-1529-2021, 2021
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We compare simulated values from 47 models estimating surface water over the USA. Results show that model uncertainty is substantial over much of the conterminous USA and especially high in the west. Applying the studied models to a simple water accounting equation shows that model selection can significantly affect research results. This paper concludes that multimodel ensembles help to best represent uncertainty in conclusions and suggest targeted research efforts in arid regions.
Hong Xuan Do, Fang Zhao, Seth Westra, Michael Leonard, Lukas Gudmundsson, Julien Eric Stanislas Boulange, Jinfeng Chang, Philippe Ciais, Dieter Gerten, Simon N. Gosling, Hannes Müller Schmied, Tobias Stacke, Camelia-Eliza Telteu, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 24, 1543–1564, https://doi.org/10.5194/hess-24-1543-2020, https://doi.org/10.5194/hess-24-1543-2020, 2020
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We presented a global comparison between observed and simulated trends in a flood index over the 1971–2005 period using the Global Streamflow Indices and Metadata archive and six global hydrological models available through The Inter-Sectoral Impact Model Intercomparison Project. Streamflow simulations over 2006–2099 period robustly project high flood hazard in several regions. These high-flood-risk areas, however, are under-sampled by the current global streamflow databases.
Toby R. Marthews, Eleanor M. Blyth, Alberto Martínez-de la Torre, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 24, 75–92, https://doi.org/10.5194/hess-24-75-2020, https://doi.org/10.5194/hess-24-75-2020, 2020
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Climate change impact modellers can only act on predictions of the occurrence of an extreme event in the Earth system if they know the uncertainty in that prediction and how uncertainty is attributable to different model components. Using eartH2Observe data, we quantify the balance between different sources of uncertainty in global evapotranspiration and runoff, making a crucial contribution to understanding the spatial distribution of water resources allocation deficiencies.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Jan Polcher, Clément Albergel, Emanuel Dutra, Gabriel Fink, Alberto Martínez-de la Torre, and Simon Munier
Hydrol. Earth Syst. Sci., 23, 1973–1994, https://doi.org/10.5194/hess-23-1973-2019, https://doi.org/10.5194/hess-23-1973-2019, 2019
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This study investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period of 11 years based on six global hydrologic models and five precipitation datasets. Results show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure.
Gaby J. Gründemann, Micha Werner, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 22, 4667–4683, https://doi.org/10.5194/hess-22-4667-2018, https://doi.org/10.5194/hess-22-4667-2018, 2018
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Flooding in vulnerable and data-sparse regions such as the Limpopo basin in Southern Africa is a key concern. Data available to local flood managers are often limited, inconsistent or asymmetrically distributed. We demonstrate that freely available global datasets are well suited to provide essential information. Despite the poor performance of simulated discharges, these datasets hold potential in identifying damaging flood events, particularly for higher-resolution datasets and larger basins.
Emiliano Gelati, Bertrand Decharme, Jean-Christophe Calvet, Marie Minvielle, Jan Polcher, David Fairbairn, and Graham P. Weedon
Hydrol. Earth Syst. Sci., 22, 2091–2115, https://doi.org/10.5194/hess-22-2091-2018, https://doi.org/10.5194/hess-22-2091-2018, 2018
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We compared land surface model simulations forced by several meteorological datasets with observations over the Euro-Mediterranean area, for the 1979–2012 period. Precipitation was the most uncertain forcing variable. The impacts of forcing uncertainty were larger on the mean and standard deviation rather than the timing, shape and inter-annual variability of simulated discharge. Simulated leaf area index and surface soil moisture were relatively insensitive to these uncertainties.
Behzad Asadieh and Nir Y. Krakauer
Hydrol. Earth Syst. Sci., 21, 5863–5874, https://doi.org/10.5194/hess-21-5863-2017, https://doi.org/10.5194/hess-21-5863-2017, 2017
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Multi-model analysis of global streamflow extremes for the 20th and 21st centuries under two warming scenarios is performed. About 37 and 43 % of global land areas show potential for increases in flood and drought events. Nearly 10 % of global land areas, holding around 30 % of world’s population, reflect a potentially worsening hazard of flood and drought. A significant increase in streamflow of the regions near and above the Arctic Circle, and decrease in subtropical arid areas, is projected.
Sebastian Sippel, Jakob Zscheischler, Martin Heimann, Holger Lange, Miguel D. Mahecha, Geert Jan van Oldenborgh, Friederike E. L. Otto, and Markus Reichstein
Hydrol. Earth Syst. Sci., 21, 441–458, https://doi.org/10.5194/hess-21-441-2017, https://doi.org/10.5194/hess-21-441-2017, 2017
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The paper re-investigates the question whether observed precipitation extremes and annual totals have been increasing in the world's dry regions over the last 60 years. Despite recently postulated increasing trends, we demonstrate that large uncertainties prevail due to (1) the choice of dryness definition and (2) statistical data processing. In fact, we find only minor (and only some significant) increases if (1) dryness is based on aridity and (2) statistical artefacts are accounted for.
Seungwoo Chang, Wendy D. Graham, Syewoon Hwang, and Rafael Muñoz-Carpena
Hydrol. Earth Syst. Sci., 20, 3245–3261, https://doi.org/10.5194/hess-20-3245-2016, https://doi.org/10.5194/hess-20-3245-2016, 2016
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Projecting water deficit depends on how researchers combine possible future climate scenarios such as general circulation models (GCMs), evapotranspiration estimation method (ET), and greenhouse gas emission scenarios. Using global sensitivity analysis, we found the relative contribution of each of these factors to projecting future water deficit and the choice of ET estimation method are as important as the choice of GCM, and greenhouse gas emission scenario is less influential than the others.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, https://doi.org/10.5194/hess-20-2877-2016, 2016
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The assessment of water balance components of the global land surface by means of hydrological models is affected by large uncertainties, in particular related to meteorological forcing. We analyze the effect of five state-of-the-art forcings on water balance components at different spatial and temporal scales modeled with WaterGAP. Furthermore, the dominant effect (precipitation/human alteration) for long-term changes in river discharge is assessed.
N. W. Chaney, J. D. Herman, P. M. Reed, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, https://doi.org/10.5194/hess-19-3239-2015, 2015
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Land surface modeling is playing an increasing role in global monitoring and prediction of extreme hydrologic events. However, uncertainties in parameter identifiability limit the reliability of model predictions. This study makes use of petascale computing to perform a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
H. Müller Schmied, S. Eisner, D. Franz, M. Wattenbach, F. T. Portmann, M. Flörke, and P. Döll
Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, https://doi.org/10.5194/hess-18-3511-2014, 2014
P. Roudier, A. Ducharne, and L. Feyen
Hydrol. Earth Syst. Sci., 18, 2789–2801, https://doi.org/10.5194/hess-18-2789-2014, https://doi.org/10.5194/hess-18-2789-2014, 2014
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
A. Kauffeldt, S. Halldin, A. Rodhe, C.-Y. Xu, and I. K. Westerberg
Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, https://doi.org/10.5194/hess-17-2845-2013, 2013
I. H. Taylor, E. Burke, L. McColl, P. D. Falloon, G. R. Harris, and D. McNeall
Hydrol. Earth Syst. Sci., 17, 2339–2358, https://doi.org/10.5194/hess-17-2339-2013, https://doi.org/10.5194/hess-17-2339-2013, 2013
H. Xie, L. Longuevergne, C. Ringler, and B. R. Scanlon
Hydrol. Earth Syst. Sci., 16, 3083–3099, https://doi.org/10.5194/hess-16-3083-2012, https://doi.org/10.5194/hess-16-3083-2012, 2012
F. Sienz, O. Bothe, and K. Fraedrich
Hydrol. Earth Syst. Sci., 16, 2143–2157, https://doi.org/10.5194/hess-16-2143-2012, https://doi.org/10.5194/hess-16-2143-2012, 2012
D. González-Zeas, L. Garrote, A. Iglesias, and A. Sordo-Ward
Hydrol. Earth Syst. Sci., 16, 1709–1723, https://doi.org/10.5194/hess-16-1709-2012, https://doi.org/10.5194/hess-16-1709-2012, 2012
S. N. Gosling, R. G. Taylor, N. W. Arnell, and M. C. Todd
Hydrol. Earth Syst. Sci., 15, 279–294, https://doi.org/10.5194/hess-15-279-2011, https://doi.org/10.5194/hess-15-279-2011, 2011
W. A. Dorigo, K. Scipal, R. M. Parinussa, Y. Y. Liu, W. Wagner, R. A. M. de Jeu, and V. Naeimi
Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, https://doi.org/10.5194/hess-14-2605-2010, 2010
G. Schumann, D. J. Lunt, P. J. Valdes, R. A. M. de Jeu, K. Scipal, and P. D. Bates
Hydrol. Earth Syst. Sci., 13, 1545–1553, https://doi.org/10.5194/hess-13-1545-2009, https://doi.org/10.5194/hess-13-1545-2009, 2009
Cited articles
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Barrios, M. and Francés, F.: Spatial scale effect on the upper soil effective parameters of a distributed hydrological model, Hydrological Processes, 26, 1022–1033, https://doi.org/10.1002/hyp.8193, 2012.
Bonan, G. B. and Stillwell-Soller, L. M.: Soil water and the persistence of floods and droughts in the Mississippi River Basin, Water Resour. Res., 34, 2693, https://doi.org/10.1029/98WR02073, 1998.
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions of soil water retention characteristics, Water Resour. Res., 24, 755–769, https://doi.org/10.1029/WR024i005p00755, 1988.
Cloke, H., Weisheimer, A., and Pappenberger, F.: Representing uncertainty in land surface hydrology : fully coupled simulations with the ECMWF land surface scheme, in: ECMWF Workshop on Model Uncertainty, June, 20–24, 2011.
Corti, S., Palmer, T., Balmaseda, M., Weisheimer, A., Drijfhout, S., Dunstone, N., Hazeleger, W., Kröger, J., Pohlmann, H., Smith, D., von Storch, J.-S., and Wouters, B.: Impact of Initial Conditions versus External Forcing in Decadal Climate Predictions: A Sensitivity Experiment, J. Climate, 28, 4454–4470, https://doi.org/10.1175/JCLI-D-14-00671.1, 2015.
Delworth, T. and Manaba, S.: Climate variability and land-surface processes, Adv. Water Resour., 16, 3–20, https://doi.org/10.1016/0309-1708(93)90026-C, 1993.
Delworth, T. and Manabe, S.: The Influence of Soil Wetness on Near-Surface Atmospheric Variability, J. Climate, 2, 1447–1462, https://doi.org/10.1175/1520-0442(1989)002<1447:TIOSWO>2.0.CO;2, 1989.
Douville, H.: Relevance of soil moisture for seasonal atmospheric predictions: Is it an initial value problem?, Clim. Dynam., 22, 429–446, https://doi.org/10.1007/s00382-003-0386-5, 2004.
Fischer, E. M., Seneviratne, S. I., Lüthi, D., and Schär, C.: Contribution of land-atmosphere coupling to recent European summer heat waves, Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006GL029068, 2007.
Hagemann, S. and Stacke, T.: Impact of the soil hydrology scheme on simulated soil moisture memory, Clim. Dynam., 44, 1731–1750, https://doi.org/10.1007/s00382-014-2221-6, 2014.
Hong, S. Y. and Kalnay, E.: Role of sea surface temperature and soil-moisture feedback in the 1998 Oklahoma-Texas drought, Nature, 408, 842–844, https://doi.org/10.1038/35048548, 2000.
Koster, R. D. and Suarez, M. J.: Relative contributions of land and ocean processes to precipitation variability, J. Geophys. Res., 100, 13775, https://doi.org/10.1029/95JD00176, 1995.
Koster, R. D. and Suarez, M. J.: Soil Moisture Memory in Climate Models, J. Hydrometeorol., 2, 558–570, https://doi.org/10.1175/1525-7541(2001)002<0558:SMMICM>2.0.CO;2, 2001.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C.-H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.: Regions of strong coupling between soil moisture and precipitation., Science, New York, NY, 305, 1138–1140, https://doi.org/10.1126/science.1100217, 2004.
Koster, R. D., Mahanama, S. P. P., Livneh, B., Lettenmaier, D. P., and Reichle, R. H.: Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow, Nat. Geosci., 3, 613–616, https://doi.org/10.1038/ngeo944, 2010.
Liu, D., Wang, G., Mei, R., Yu, Z., and Yu, M.: Impact of initial soil moisture anomalies on climate mean and extremes over Asia, J. Geophys. Res.-Atmos., 119, 529–545, https://doi.org/10.1002/2013JD020890, 2014.
Lorenz, R., Jaeger, E. B., and Seneviratne, S. I.: Persistence of heat waves and its link to soil moisture memory, Geophys. Res. Lett., 37, 1–5, https://doi.org/10.1029/2010GL042764, 2010.
MacLeod, D. A., Cloke, H. L., Pappenberger, F., and Weisheimer, A.: Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface, Q. J. Roy. Meteor. Soc., 42, 79–90, https://doi.org/10.1002/qj.2631, 2015.
Nicholson, S.: Land surface processes and Sahel climate, Rev. Geophys., 38, 117–139, https://doi.org/10.1029/1999RG000014, 2000.
Orth, R., Koster, R. D., and Seneviratne, S. I.: Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model, J. Hydrometeorol., 14, 1773–1790, https://doi.org/10.1175/JHM-D-12-099.1, 2013.
Orth, R., Dutra, E., and Pappenberger, F.: Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty, Mon. Weather Rev., 144, 1551–1569, https://doi.org/10.1175/MWR-D-15-0283.1, 2016.
Pal, J. S. and Eltahir, E. A. B.: Pathways relating soil moisture conditions to future summer rainfall within a model of the land-atmosphere system, J. Climate, 14, 1227–1242, https://doi.org/10.1175/1520-0442(2001)014<1227:PRSMCT>2.0.CO;2, 2001.
Rahman, M., Sulis, M., and Kollet, S.: The subsurface–land surface–atmosphere connection under convective conditions, Adv. Water Resour., 83, 240–249, https://doi.org/10.1016/j.advwatres.2015.06.003, 2015.
Seneviratne, S. I. and Koster, R. D.: A Revised Framework for Analyzing Soil Moisture Memory in Climate Data: Derivation and Interpretation, J. Hydrometeorol., 13, 404–412, https://doi.org/10.1175/JHM-D-11-044.1, 2012.
Seneviratne, S. I., Koster, R. D., Guo, Z., Dirmeyer, P. A., Kowalczyk, E., Lawrence, D., Liu, P., Mocko, D., Lu, C.-H., Oleson, K. W., and Verseghy, D.: Soil Moisture Memory in AGCM Simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) Data, J. Hydrometeorol., 7, 1090–1112, https://doi.org/10.1175/JHM533.1, 2006.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture-climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1, Soil Sci. Soc. Ame. J., 44, 892, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Wang, W. and Kumar, A.: A GCM assessment of atmospheric seasonal predictability America, J. Geophys. Res., 103, 28637–28646, https://doi.org/10.1029/1998JD200010, 1998.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014.
Weisheimer, A., Corti, S., Palmer, T., and Vitart, F.: Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system, Phil. Trans. Roy. Soc., 372, https://doi.org/10.1098/rsta.2013.0290, 2014.
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of hydrologic prediction uncertainty, Geophys. Res. Lett., 35, L14401, https://doi.org/10.1029/2008GL034648, 2008.
Wu, W. and Dickinson, R. E.: Time Scales of Layered Soil Moisture Memory in the Context of Land–Atmosphere Interaction, J. Climate, 17, 2752–2764, https://doi.org/10.1175/1520-0442(2004)017<2752:TSOLSM>2.0.CO;2, 2004.
Short summary
Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.
Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between...