Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2319-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2319-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2022
Research article |
| 03 May 2022
| 03 May 2022
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
Jorn Van de Velde
CORRESPONDING AUTHOR
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Matthias Demuzere
Department of Geography, Ruhr University Bochum, Bochum, Germany
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
Bernard De Baets
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Niko E. C. Verhoest
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
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Jorn Van de Velde, Bernard De Baets, Matthias Demuzere, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-83, https://doi.org/10.5194/hess-2020-83, 2020
Revised manuscript not accepted
Short summary
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Though climate models have different types of biases in comparison to the observations, most research is focused on adjusting the intensity. Yet, variables like precipitation are also biased in the occurrence: there are too many days with rainfall. We compared four methods for adjusting the occurrence, with the goal of improving flood representation. From this comparison, we concluded that more advanced methods do not necessarily add value, especially in multivariate settings.
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416, https://doi.org/10.5194/essd-2024-416, 2024
Preprint under review for ESSD
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Absence of globally consistent and spatially continuous urban surface properties have long prevented large-scale high-resolution urban climate modeling. We developed the U-Surf data, a 1km-resolution dataset that provides key urban surface properties worldwide. U-Surf enhances urban representation in models, enables city-to-city comparison, and supports kilometer-scale Earth system modeling. Its broader applications can be extended to machine learning and many other non-climatic practices.
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.
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304, https://doi.org/10.5194/hess-2023-304, 2024
Preprint under review for HESS
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The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
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Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Dailé Avila-Alonso, Jan M. Baetens, Rolando Cardenas, and Bernard De Baets
Nat. Hazards Earth Syst. Sci., 21, 837–859, https://doi.org/10.5194/nhess-21-837-2021, https://doi.org/10.5194/nhess-21-837-2021, 2021
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Hurricanes are extreme storms that induce substantial biophysical changes on oceans. We investigated the effects induced by consecutive Hurricanes Dorian and Humberto over the western Sargasso Sea in 2019 using satellite remote sensing and modelled data. These hurricanes superimposed effects on the upper-ocean response because of the strong induced mixing and upwelling. The sea surface cooling and phytoplankton bloom induced by these hurricanes were higher compared to climatological records.
Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen
Hydrol. Earth Syst. Sci., 24, 4793–4812, https://doi.org/10.5194/hess-24-4793-2020, https://doi.org/10.5194/hess-24-4793-2020, 2020
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Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and Ocean Salinity (SMOS), may help to reduce errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated a long time series of SMOS observations into a hydro-meteorological model and showed that this helps to improve model predictions. This work therefore contributes to the development of faster and more accurate drought prediction tools.
Brecht Martens, Dominik L. Schumacher, Hendrik Wouters, Joaquín Muñoz-Sabater, Niko E. C. Verhoest, and Diego G. Miralles
Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020, https://doi.org/10.5194/gmd-13-4159-2020, 2020
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Climate reanalyses are widely used in different fields and an in-depth evaluation of the different variables provided by reanalyses is a necessary means to provide feedback on the quality to their users and the operational centres producing these data sets. In this study, we show the improvements of ECMWF's latest climate reanalysis (ERA5) upon its predecessor (ERA-Interim) in partitioning the available energy at the land surface.
Jorn Van de Velde, Bernard De Baets, Matthias Demuzere, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-83, https://doi.org/10.5194/hess-2020-83, 2020
Revised manuscript not accepted
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Though climate models have different types of biases in comparison to the observations, most research is focused on adjusting the intensity. Yet, variables like precipitation are also biased in the occurrence: there are too many days with rainfall. We compared four methods for adjusting the occurrence, with the goal of improving flood representation. From this comparison, we concluded that more advanced methods do not necessarily add value, especially in multivariate settings.
Arthur Depicker, Bernard De Baets, and Jan Marcel Baetens
Nat. Hazards Earth Syst. Sci., 20, 363–376, https://doi.org/10.5194/nhess-20-363-2020, https://doi.org/10.5194/nhess-20-363-2020, 2020
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In recent years, several valuable nature reserves in Belgium have been severely damaged by wildfires. In order to optimize risk management and prepare for a possibly increasing number of such events, the first wildfire ignition probability map is developed for Belgium, based on data that were obtained from the government and newspaper articles. We find that most ignitions occur in the provinces of Limburg and Antwerp and that most causes are of anthropogenic nature (such as military exercises).
Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019, https://doi.org/10.5194/bg-16-4851-2019, 2019
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Bidirectional interactions between vegetation and climate are unraveled over short (monthly) and long (inter-annual) temporal scales. Analyses use a novel causal inference method based on wavelet theory. The performance of climate models at representing these interactions is benchmarked against satellite data. Climate models can reproduce the overall climate controls on vegetation at all temporal scales, while their performance at representing biophysical feedbacks on climate is less adequate.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
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APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Ashley M. Broadbent, Andrew M. Coutts, Kerry A. Nice, Matthias Demuzere, E. Scott Krayenhoff, Nigel J. Tapper, and Hendrik Wouters
Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, https://doi.org/10.5194/gmd-12-785-2019, 2019
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We present a simple model for assessing the cooling impacts of vegetation and water features (green and blue infrastructure) in urban environments. This model is designed to be computationally efficient so that those without technical knowledge or access to high-performance computers can use it. TARGET can be used to model average street-level air temperature at canyon to block scales (e.g. 100 m resolution). The model is carefully designed to provide reliable and accurate cooling estimates.
Wouter H. Maes, Pierre Gentine, Niko E. C. Verhoest, and Diego G. Miralles
Hydrol. Earth Syst. Sci., 23, 925–948, https://doi.org/10.5194/hess-23-925-2019, https://doi.org/10.5194/hess-23-925-2019, 2019
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Potential evaporation (Ep) is the amount of water an ecosystem would consume if it were not limited by water availability or other stress factors. In this study, we compared several methods to estimate Ep using a global dataset of 107 FLUXNET sites. A simple radiation-driven method calibrated per biome consistently outperformed more complex approaches and makes a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Minh Tu Pham, Hilde Vernieuwe, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 22, 1263–1283, https://doi.org/10.5194/hess-22-1263-2018, https://doi.org/10.5194/hess-22-1263-2018, 2018
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In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Still, the developed model has great potential for hydrological impact analysis.
Wouter H. Maes, Pierre Gentine, Niko E. C. Verhoest, and Diego G. Miralles
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-682, https://doi.org/10.5194/hess-2017-682, 2018
Revised manuscript not accepted
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Potential evaporation is a key parameter in numerous models used for assessing water use and drought severity. Yet, multiple incompatible methods have been proposed, thus estimates of potential evaporation remain uncertain. Based on the largest available dataset of FLUXNET data, we identify the best method to calculate potential evaporation globally. A simple radiation-driven method calibrated per biome consistently performed best; more complex models did not perform as good.
Dominik Rains, Xujun Han, Hans Lievens, Carsten Montzka, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5929–5951, https://doi.org/10.5194/hess-21-5929-2017, https://doi.org/10.5194/hess-21-5929-2017, 2017
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We have assimilated 6 years of satellite-observed passive microwave data into a state-of-the-art land surface model to improve surface soil moisture as well as root-zone soil moisture simulations. Long-term assimilation effects/biases are identified, and they are especially dependent on model perturbations, applied to simulate model uncertainty. The implications are put into context of using such assimilation-improved data for classifying extremes within hydrological monitoring systems.
Katrien Van Eerdenbrugh, Stijn Van Hoey, Gemma Coxon, Jim Freer, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5315–5337, https://doi.org/10.5194/hess-21-5315-2017, https://doi.org/10.5194/hess-21-5315-2017, 2017
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Consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach). Various measurement stations in the UK, New Zealand and Belgium are selected based on their historical ratings information and their characteristics related to data consistency. When applying a BReach analysis on them, the methodology provides results that appear consistent with the available knowledge and thus facilitates a reliable assessment of (in)consistency in stage–discharge data.
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, https://doi.org/10.5194/hess-21-3879-2017, 2017
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We examine the opportunities and challenges that technological advances in Earth observation will present to the hydrological community. From advanced space-based sensors to unmanned aerial vehicles and ground-based distributed networks, these emergent systems are set to revolutionize our understanding and interpretation of hydrological and related processes.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
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In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps, Daniël Tielemans, and Nicole P. M. van Lipzig
Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, https://doi.org/10.5194/gmd-9-3027-2016, 2016
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A methodology is presented for translating three-dimensional information of urban areas into land-surface parameters that can be easily employed in atmospheric modelling. As demonstrated with the COSMO-CLM model for a Belgian summer, it enables them to represent urban heat islands and their dependency on urban design with a low computational cost. It allows for efficiently incorporating urban information systems (e.g., WUDAPT) into climate change assessment and numerical weather prediction.
Benedikt Gräler, Andrea Petroselli, Salvatore Grimaldi, Bernard De Baets, and Niko Verhoest
Proc. IAHS, 373, 175–178, https://doi.org/10.5194/piahs-373-175-2016, https://doi.org/10.5194/piahs-373-175-2016, 2016
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Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.
H. Vernieuwe, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, https://doi.org/10.5194/hess-19-2685-2015, 2015
M. J. van den Berg, L. Delobbe, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 18, 5331–5344, https://doi.org/10.5194/hess-18-5331-2014, https://doi.org/10.5194/hess-18-5331-2014, 2014
M. Dessie, N. E. C. Verhoest, V. R. N. Pauwels, T. Admasu, J. Poesen, E. Adgo, J. Deckers, and J. Nyssen
Hydrol. Earth Syst. Sci., 18, 5149–5167, https://doi.org/10.5194/hess-18-5149-2014, https://doi.org/10.5194/hess-18-5149-2014, 2014
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In this study, topography is considered as a proxy for the variability of most of the catchment characteristics. The model study suggests that classifying the catchments into different runoff production areas based on topography and including the impermeable rocky areas separately in the modeling process mimics the rainfall–runoff process in the Upper Blue Nile basin well and yields a useful result for operational management of water resources in this data-scarce region.
M. T. Pham, W. J. Vanhaute, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 5167–5183, https://doi.org/10.5194/hess-17-5167-2013, https://doi.org/10.5194/hess-17-5167-2013, 2013
H. Wouters, K. De Ridder, M. Demuzere, D. Lauwaet, and N. P. M. van Lipzig
Atmos. Chem. Phys., 13, 8525–8541, https://doi.org/10.5194/acp-13-8525-2013, https://doi.org/10.5194/acp-13-8525-2013, 2013
J. Minet, N. E. C. Verhoest, S. Lambot, and M. Vanclooster
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-4063-2013, https://doi.org/10.5194/hessd-10-4063-2013, 2013
Revised manuscript has not been submitted
B. Gräler, M. J. van den Berg, S. Vandenberghe, A. Petroselli, S. Grimaldi, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 1281–1296, https://doi.org/10.5194/hess-17-1281-2013, https://doi.org/10.5194/hess-17-1281-2013, 2013
L. Loosvelt, H. Vernieuwe, V. R. N. Pauwels, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 461–478, https://doi.org/10.5194/hess-17-461-2013, https://doi.org/10.5194/hess-17-461-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
Monthly new water fractions and their relationships with climate and catchment properties across Alpine rivers
Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments
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On the regional-scale variability in flow duration curves in Peninsular India
Towards a conceptualization of the hydrological processes behind changes of young water fraction with elevation: a focus on mountainous alpine catchments
A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework
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Significant regime shifts in historical water yield in the Upper Brahmaputra River basin
A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records
Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Parsimonious statistical learning models for low-flow estimation
Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling
Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences
Effects of climate anomalies on warm-season low flows in Switzerland
Histogram via entropy reduction (HER): an information-theoretic alternative for geostatistics
Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework
A methodology to estimate flow duration curves at partially ungauged basins
The role of flood wave superposition in the severity of large floods
Contribution of low-frequency climatic–oceanic oscillations to streamflow variability in small, coastal rivers of the Sierra Nevada de Santa Marta (Colombia)
Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling
An assessment of trends and potential future changes in groundwater-baseflow drought based on catchment response times
More frequent flooding? Changes in flood frequency in the Pearl River basin, China, since 1951 and over the past 1000 years
Topography significantly influencing low flows in snow-dominated watersheds
A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data
Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China
Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information
On the probability distribution of daily streamflow in the United States
The European 2015 drought from a hydrological perspective
Heterogeneity measures in hydrological frequency analysis: review and new developments
ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction
Ordinary kriging as a tool to estimate historical daily streamflow records
Trends in floods in West Africa: analysis based on 11 catchments in the region
Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment
Spatial controls on groundwater response dynamics in a snowmelt-dominated montane catchment
Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?
Data compression to define information content of hydrological time series
Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?
Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain
Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses
Land cover and water yield: inference problems when comparing catchments with mixed land cover
An elusive search for regional flood frequency estimates in the River Nile basin
Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment
Contrasting trends in floods for two sub-arctic catchments in northern Sweden – does glacier presence matter?
Long-range forecasting of intermittent streamflow
Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization
Low-frequency variability of European runoff
Comparison of catchment grouping methods for flow duration curve estimation at ungauged sites in France
Regional flow duration curves for ungauged sites in Sicily
Marius G. Floriancic, Michael P. Stockinger, James W. Kirchner, and Christine Stumpp
Hydrol. Earth Syst. Sci., 28, 3675–3694, https://doi.org/10.5194/hess-28-3675-2024, https://doi.org/10.5194/hess-28-3675-2024, 2024
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The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. To assess water availability in streams, we need to understand how much streamflow is derived from old water stored in the subsurface versus more recent precipitation. We use tracer data from 32 Alpine streams and statistical tools to assess how much recent precipitation can be found in Alpine rivers and how this amount is related to catchment properties and climate.
Alessio Gentile, Jana von Freyberg, Davide Gisolo, Davide Canone, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 28, 1915–1934, https://doi.org/10.5194/hess-28-1915-2024, https://doi.org/10.5194/hess-28-1915-2024, 2024
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Can we leverage high-resolution and low-cost EC measurements and biweekly δ18O data to estimate the young water fraction at higher temporal resolution? Here, we present the EXPECT method that combines two widespread techniques: EC-based hydrograph separation and sine-wave models of the seasonal isotope cycles. The method is not without its limitations, but its application in three small Swiss catchments is promising for future applications in catchments with different characteristics.
Anne Bartens, Bora Shehu, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 28, 1687–1709, https://doi.org/10.5194/hess-28-1687-2024, https://doi.org/10.5194/hess-28-1687-2024, 2024
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River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Pankaj Dey, Jeenu Mathai, Murugesu Sivapalan, and Pradeep P. Mujumdar
Hydrol. Earth Syst. Sci., 28, 1493–1514, https://doi.org/10.5194/hess-28-1493-2024, https://doi.org/10.5194/hess-28-1493-2024, 2024
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This study explores the regional streamflow variability in Peninsular India. This variability is governed by monsoons, mountainous systems, and geologic gradients. A linkage between these influencing factors and streamflow variability is established using a Wegenerian approach and flow duration curves.
Alessio Gentile, Davide Canone, Natalie Ceperley, Davide Gisolo, Maurizio Previati, Giulia Zuecco, Bettina Schaefli, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 27, 2301–2323, https://doi.org/10.5194/hess-27-2301-2023, https://doi.org/10.5194/hess-27-2301-2023, 2023
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What drives young water fraction, F*yw (i.e., the fraction of water in streamflow younger than 2–3 months), variations with elevation? Why is F*yw counterintuitively low in high-elevation catchments, in spite of steeper topography? In this paper, we present a perceptual model explaining how the longer low-flow duration at high elevations, driven by the persistence of winter snowpacks, increases the proportion of stored (old) water contributing to the stream, thus reducing F*yw.
Gregor Laaha
Hydrol. Earth Syst. Sci., 27, 2019–2034, https://doi.org/10.5194/hess-27-2019-2023, https://doi.org/10.5194/hess-27-2019-2023, 2023
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In seasonal climates with a warm and a cold season, low flows are generated by different processes so that return periods used as a measure of event severity will be inaccurate. We propose a novel mixed copula estimator that is shown to outperform previous calculation methods. The new method is highly relevant for a wide range of European river flow regimes and should be used by default.
Gregor Laaha
Hydrol. Earth Syst. Sci., 27, 689–701, https://doi.org/10.5194/hess-27-689-2023, https://doi.org/10.5194/hess-27-689-2023, 2023
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Knowing the severity of an extreme event is of particular importance to hydrology and water policies. In this paper we propose a mixed distribution approach for low flows. It provides one consistent approach to quantify the severity of summer, winter, and annual low flows based on their respective annualities (or return periods). We show that the new method is much more accurate than existing methods and should therefore be used by engineers and water agencies.
Hao Li, Baoying Shan, Liu Liu, Lei Wang, Akash Koppa, Feng Zhong, Dongfeng Li, Xuanxuan Wang, Wenfeng Liu, Xiuping Li, and Zongxue Xu
Hydrol. Earth Syst. Sci., 26, 6399–6412, https://doi.org/10.5194/hess-26-6399-2022, https://doi.org/10.5194/hess-26-6399-2022, 2022
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This study examines changes in water yield by determining turning points in the direction of yield changes and highlights that regime shifts in historical water yield occurred in the Upper Brahmaputra River basin, both the climate and cryosphere affect the magnitude of water yield increases, climate determined the declining trends in water yield, and meltwater has the potential to alleviate the water shortage. A repository for all source files is made available.
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 26, 5391–5410, https://doi.org/10.5194/hess-26-5391-2022, https://doi.org/10.5194/hess-26-5391-2022, 2022
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The goal of this work was to make a map of the mean annual runoff for Norway for a 30-year period. We first simulated runoff by using a process-based model that models the relationship between runoff, precipitation, temperature, and land use. Next, we corrected the map based on runoff observations from streams by using a statistical method. We were also able to use data from rivers that only had a few annual observations. We find that the statistical correction improves the runoff estimates.
Johannes Laimighofer, Michael Melcher, and Gregor Laaha
Hydrol. Earth Syst. Sci., 26, 4553–4574, https://doi.org/10.5194/hess-26-4553-2022, https://doi.org/10.5194/hess-26-4553-2022, 2022
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Our study uses a statistical boosting model for estimating low flows on a monthly basis, which can be applied to estimate low flows at sites without measurements. We use an extensive dataset of 260 stream gauges in Austria for model development. As we are specifically interested in low-flow events, our method gives specific weight to such events. We found that our method can considerably improve the predictions of low-flow events and yields accurate estimates of the seasonal low-flow variation.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
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Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Johannes Laimighofer, Michael Melcher, and Gregor Laaha
Hydrol. Earth Syst. Sci., 26, 129–148, https://doi.org/10.5194/hess-26-129-2022, https://doi.org/10.5194/hess-26-129-2022, 2022
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This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.
Kailong Li, Guohe Huang, and Brian Baetz
Hydrol. Earth Syst. Sci., 25, 4947–4966, https://doi.org/10.5194/hess-25-4947-2021, https://doi.org/10.5194/hess-25-4947-2021, 2021
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We proposed a test statistic feature importance method to quantify the importance of predictor variables for random-forest-like models. The proposed method does not rely on any performance measures to evaluate variable rankings, which can thus result in unbiased variable rankings. The resulting variable rankings based on the proposed method could help random forest achieve its optimum predictive accuracy.
Wei Hu and Bing Si
Hydrol. Earth Syst. Sci., 25, 321–331, https://doi.org/10.5194/hess-25-321-2021, https://doi.org/10.5194/hess-25-321-2021, 2021
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Partial wavelet coherency method is improved to explore the bivariate relationships at different scales and locations after excluding the effects of other variables. The method was tested with artificial datasets and applied to a measured dataset. Compared with others, this method has the advantages of capturing phase information, dealing with multiple excluding variables, and producing more accurate results. This method can be used in different areas with spatial or temporal datasets.
Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
Hydrol. Earth Syst. Sci., 24, 5423–5438, https://doi.org/10.5194/hess-24-5423-2020, https://doi.org/10.5194/hess-24-5423-2020, 2020
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Low river flows affect societies and ecosystems. Here we study how precipitation and potential evapotranspiration shape low flows across a network of 380 Swiss catchments. Low flows in these rivers typically result from below-average precipitation and above-average potential evapotranspiration. Extreme low flows result from long periods of the combined effects of both drivers.
Stephanie Thiesen, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret
Hydrol. Earth Syst. Sci., 24, 4523–4540, https://doi.org/10.5194/hess-24-4523-2020, https://doi.org/10.5194/hess-24-4523-2020, 2020
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A spatial interpolator has been proposed for exploring the information content of the data in the light of geostatistics and information theory. It showed comparable results to traditional interpolators, with the advantage of presenting generalization properties. We discussed three different ways of combining distributions and their implications for the probabilistic results. By its construction, the method provides a suitable and flexible framework for uncertainty analysis and decision-making.
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 24, 4109–4133, https://doi.org/10.5194/hess-24-4109-2020, https://doi.org/10.5194/hess-24-4109-2020, 2020
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Annual runoff is a measure of how much water flows through a river during a year and is an important quantity, e.g. when planning infrastructure. In this paper, we suggest a new statistical model for annual runoff estimation. The model exploits correlation between rivers and is able to detect whether the annual runoff in the target river follows repeated patterns over time relative to neighbouring rivers. In our work we show for what cases the latter represents a benefit over comparable methods.
Elena Ridolfi, Hemendra Kumar, and András Bárdossy
Hydrol. Earth Syst. Sci., 24, 2043–2060, https://doi.org/10.5194/hess-24-2043-2020, https://doi.org/10.5194/hess-24-2043-2020, 2020
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The paper presents a new, simple and model-free methodology to estimate the streamflow at partially gauged basins, given the precipitation gauged at another basin. We show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Because of the dependence on the climate, discharge data at the target site are here retrieved using the Antecedent Precipitation Index (API) of the donor site as it represents in a streamflow-like way the precipitation of the basin.
Björn Guse, Bruno Merz, Luzie Wietzke, Sophie Ullrich, Alberto Viglione, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 24, 1633–1648, https://doi.org/10.5194/hess-24-1633-2020, https://doi.org/10.5194/hess-24-1633-2020, 2020
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Floods are influenced by river network processes, among others. Flood characteristics of tributaries may affect flood severity downstream of confluences. The impact of flood wave superposition is investigated with regard to magnitude and temporal matching of flood peaks. Our study in Germany and Austria shows that flood wave superposition is not the major driver of flood severity. However, there is the potential for large floods at some confluences in cases of temporal matching of flood peaks.
Juan Camilo Restrepo, Aldemar Higgins, Jaime Escobar, Silvio Ospino, and Natalia Hoyos
Hydrol. Earth Syst. Sci., 23, 2379–2400, https://doi.org/10.5194/hess-23-2379-2019, https://doi.org/10.5194/hess-23-2379-2019, 2019
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This study evaluated the influence of low-frequency oscillations that are linked to large-scale oceanographic–atmospheric processes, on streamflow variability in small mountain rivers of the Sierra Nevada de Santa Marta, Colombia, aiming to explore streamflow variability, estimate the net contribution to the energy of low-frequency oscillations to streamflow anomalies, and analyze the linkages between streamflow anomalies and large-scale, low-frequency oceanographic–atmospheric processes.
Jens Grundmann, Sebastian Hörning, and András Bárdossy
Hydrol. Earth Syst. Sci., 23, 225–237, https://doi.org/10.5194/hess-23-225-2019, https://doi.org/10.5194/hess-23-225-2019, 2019
Jost Hellwig and Kerstin Stahl
Hydrol. Earth Syst. Sci., 22, 6209–6224, https://doi.org/10.5194/hess-22-6209-2018, https://doi.org/10.5194/hess-22-6209-2018, 2018
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Due to the lack of long-term observations, insights into changes of groundwater resources are obscured. In this paper we assess past and potential future changes in groundwater drought in headwater catchments using a baseflow approach. There are a few past trends which are highly dependent on the period of analysis. Catchments with short response times are found to have a higher sensitivity to projected seasonal precipitation shifts, urging for a local management based on response times.
Qiang Zhang, Xihui Gu, Vijay P. Singh, Peijun Shi, and Peng Sun
Hydrol. Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/hess-22-2637-2018, https://doi.org/10.5194/hess-22-2637-2018, 2018
Qiang Li, Xiaohua Wei, Xin Yang, Krysta Giles-Hansen, Mingfang Zhang, and Wenfei Liu
Hydrol. Earth Syst. Sci., 22, 1947–1956, https://doi.org/10.5194/hess-22-1947-2018, https://doi.org/10.5194/hess-22-1947-2018, 2018
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Topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Topography plays a more dominant role in low flows than high flows. Our analysis also identified five significant TIs: perimeter, slope length factor, surface area, openness, and terrain characterization index. These can be used to compare watersheds when low flow assessments are conducted, specifically in snow-dominated regions.
Yan-Fang Sang, Fubao Sun, Vijay P. Singh, Ping Xie, and Jian Sun
Hydrol. Earth Syst. Sci., 22, 757–766, https://doi.org/10.5194/hess-22-757-2018, https://doi.org/10.5194/hess-22-757-2018, 2018
Zhi Li and Jiming Jin
Hydrol. Earth Syst. Sci., 21, 5531–5546, https://doi.org/10.5194/hess-21-5531-2017, https://doi.org/10.5194/hess-21-5531-2017, 2017
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We developed an efficient multisite and multivariate GCM downscaling method and generated climate change scenarios for SWAT to evaluate the streamflow variability within a watershed in China. The application of the ensemble techniques enables us to better quantify the model uncertainties. The peak values of precipitation and streamflow have a tendency to shift from the summer to spring season over the next 30 years. The number of extreme flooding and drought events will increase.
Nils-Otto Kitterød
Hydrol. Earth Syst. Sci., 21, 4195–4211, https://doi.org/10.5194/hess-21-4195-2017, https://doi.org/10.5194/hess-21-4195-2017, 2017
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The GRANADA open-access database (NGU, 2016a) was used to derive point recordings of thickness of sediment above the bedrock D(u). For each D(u) the horizontal distance to nearest outcrop L(u) was derived from geological maps. The purpose was to utilize L(u) as a secondary function for estimation of D(u). Two estimation methods were employed: ordinary kriging (OK) and co-kriging (CK). A cross-validation analysis was performed to evaluate the additional information in the secondary function L(u).
Annalise G. Blum, Stacey A. Archfield, and Richard M. Vogel
Hydrol. Earth Syst. Sci., 21, 3093–3103, https://doi.org/10.5194/hess-21-3093-2017, https://doi.org/10.5194/hess-21-3093-2017, 2017
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Flow duration curves are ubiquitous in surface water hydrology for applications including water allocation and protection of ecosystem health. We identify three probability distributions that can provide a reasonable fit to daily streamflows across much of United States. These results help us understand of the behavior of daily streamflows and enhance our ability to predict streamflows at ungaged river locations.
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, https://doi.org/10.5194/hess-21-3001-2017, 2017
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In 2015 large parts of Europe were affected by a drought. In terms of low flow magnitude, a region around the Czech Republic was most affected, with return periods > 100 yr. In terms of deficit volumes, the drought was particularly severe around S. Germany where the event lasted notably long. Meteorological and hydrological events developed differently in space and time. For an assessment of drought impacts on water resources, hydrological data are required in addition to meteorological indices.
Ana I. Requena, Fateh Chebana, and Taha B. M. J. Ouarda
Hydrol. Earth Syst. Sci., 21, 1651–1668, https://doi.org/10.5194/hess-21-1651-2017, https://doi.org/10.5194/hess-21-1651-2017, 2017
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The notion of a measure to quantify the degree of heterogeneity of a region from which information is required to estimate the magnitude of events at ungauged sites is introduced. These heterogeneity measures are needed to compare regions, evaluate the impact of particular sites, and rank the performance of delineating methods. A framework to define and assess their desirable properties is proposed. Several heterogeneity measures are presented and/or developed to be assessed, giving guidelines.
Joost V. L. Beckers, Albrecht H. Weerts, Erik Tijdeman, and Edwin Welles
Hydrol. Earth Syst. Sci., 20, 3277–3287, https://doi.org/10.5194/hess-20-3277-2016, https://doi.org/10.5194/hess-20-3277-2016, 2016
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Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the streamflow regime in many rivers around the world. A new method is presented for ENSO conditioning of the ensemble streamflow prediction (ESP) method, which is often used for seasonal streamflow forecasting. The method was tested on three tributaries of the Columbia River, OR. Results show an improvement in forecast skill compared to the standard ESP.
William H. Farmer
Hydrol. Earth Syst. Sci., 20, 2721–2735, https://doi.org/10.5194/hess-20-2721-2016, https://doi.org/10.5194/hess-20-2721-2016, 2016
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The potential of geostatistical tools, leveraging the spatial structure and dependency of correlated time series, for the prediction of daily streamflow time series at unmonitored locations is explored. Simple geostatistical tools improve on traditional estimates of daily streamflow. The temporal evolution of spatial structure, including seasonal fluctuations, is also explored. The proposed method is contrasted with more advanced geostatistical methods and shown to be comparable.
B. N. Nka, L. Oudin, H. Karambiri, J. E. Paturel, and P. Ribstein
Hydrol. Earth Syst. Sci., 19, 4707–4719, https://doi.org/10.5194/hess-19-4707-2015, https://doi.org/10.5194/hess-19-4707-2015, 2015
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The region of West Africa is undergoing important climate and environmental changes affecting the magnitude and occurrence of floods. This study aims to analyze the evolution of flood hazard in the region and to find links between flood hazards pattern and rainfall or vegetation index patterns.
D. E. Keller, A. M. Fischer, C. Frei, M. A. Liniger, C. Appenzeller, and R. Knutti
Hydrol. Earth Syst. Sci., 19, 2163–2177, https://doi.org/10.5194/hess-19-2163-2015, https://doi.org/10.5194/hess-19-2163-2015, 2015
R. S. Smith, R. D. Moore, M. Weiler, and G. Jost
Hydrol. Earth Syst. Sci., 18, 1835–1856, https://doi.org/10.5194/hess-18-1835-2014, https://doi.org/10.5194/hess-18-1835-2014, 2014
C. Teutschbein and J. Seibert
Hydrol. Earth Syst. Sci., 17, 5061–5077, https://doi.org/10.5194/hess-17-5061-2013, https://doi.org/10.5194/hess-17-5061-2013, 2013
S. V. Weijs, N. van de Giesen, and M. B. Parlange
Hydrol. Earth Syst. Sci., 17, 3171–3187, https://doi.org/10.5194/hess-17-3171-2013, https://doi.org/10.5194/hess-17-3171-2013, 2013
S. A. Archfield, A. Pugliese, A. Castellarin, J. O. Skøien, and J. E. Kiang
Hydrol. Earth Syst. Sci., 17, 1575–1588, https://doi.org/10.5194/hess-17-1575-2013, https://doi.org/10.5194/hess-17-1575-2013, 2013
P. Cowpertwait, D. Ocio, G. Collazos, O. de Cos, and C. Stocker
Hydrol. Earth Syst. Sci., 17, 479–494, https://doi.org/10.5194/hess-17-479-2013, https://doi.org/10.5194/hess-17-479-2013, 2013
L. Cheng, M. Yaeger, A. Viglione, E. Coopersmith, S. Ye, and M. Sivapalan
Hydrol. Earth Syst. Sci., 16, 4435–4446, https://doi.org/10.5194/hess-16-4435-2012, https://doi.org/10.5194/hess-16-4435-2012, 2012
A. I. J. M. van Dijk, J. L. Peña-Arancibia, and L. A. (Sampurno) Bruijnzeel
Hydrol. Earth Syst. Sci., 16, 3461–3473, https://doi.org/10.5194/hess-16-3461-2012, https://doi.org/10.5194/hess-16-3461-2012, 2012
P. Nyeko-Ogiramoi, P. Willems, F. M. Mutua, and S. A. Moges
Hydrol. Earth Syst. Sci., 16, 3149–3163, https://doi.org/10.5194/hess-16-3149-2012, https://doi.org/10.5194/hess-16-3149-2012, 2012
J. Oh and A. Sankarasubramanian
Hydrol. Earth Syst. Sci., 16, 2285–2298, https://doi.org/10.5194/hess-16-2285-2012, https://doi.org/10.5194/hess-16-2285-2012, 2012
H. Lee, D.-J. Seo, Y. Liu, V. Koren, P. McKee, and R. Corby
Hydrol. Earth Syst. Sci., 16, 2233–2251, https://doi.org/10.5194/hess-16-2233-2012, https://doi.org/10.5194/hess-16-2233-2012, 2012
H. E. Dahlke, S. W. Lyon, J. R. Stedinger, G. Rosqvist, and P. Jansson
Hydrol. Earth Syst. Sci., 16, 2123–2141, https://doi.org/10.5194/hess-16-2123-2012, https://doi.org/10.5194/hess-16-2123-2012, 2012
F. F. van Ogtrop, R. W. Vervoort, G. Z. Heller, D. M. Stasinopoulos, and R. A. Rigby
Hydrol. Earth Syst. Sci., 15, 3343–3354, https://doi.org/10.5194/hess-15-3343-2011, https://doi.org/10.5194/hess-15-3343-2011, 2011
S. J. Noh, Y. Tachikawa, M. Shiiba, and S. Kim
Hydrol. Earth Syst. Sci., 15, 3237–3251, https://doi.org/10.5194/hess-15-3237-2011, https://doi.org/10.5194/hess-15-3237-2011, 2011
L. Gudmundsson, L. M. Tallaksen, K. Stahl, and A. K. Fleig
Hydrol. Earth Syst. Sci., 15, 2853–2869, https://doi.org/10.5194/hess-15-2853-2011, https://doi.org/10.5194/hess-15-2853-2011, 2011
E. Sauquet and C. Catalogne
Hydrol. Earth Syst. Sci., 15, 2421–2435, https://doi.org/10.5194/hess-15-2421-2011, https://doi.org/10.5194/hess-15-2421-2011, 2011
F. Viola, L. V. Noto, M. Cannarozzo, and G. La Loggia
Hydrol. Earth Syst. Sci., 15, 323–331, https://doi.org/10.5194/hess-15-323-2011, https://doi.org/10.5194/hess-15-323-2011, 2011
Cited articles
Addor, N. and Fischer, E. M.: The influence of natural variability and
interpolation errors on bias characterization in RCM simulations, J. Geophys. Res.-Atmos., 120, 10–180, https://doi.org/10.1002/2014JD022824, 2015. a
Addor, N. and Seibert, J.: Bias correction for hydrological impact studies –
beyond the daily perspective, Hydrol. Process., 28, 4823–4828,
https://doi.org/10.1002/hyp.10238, 2014. a
Argüeso, D., Evans, J. P., and Fita, L.: Precipitation bias correction of very high resolution regional climate models, Hydrol. Earth Syst. Sci., 17, 4379–4388, https://doi.org/10.5194/hess-17-4379-2013, 2013. a
Bellprat, O., Kotlarski, S., Lüthi, D., and Schär, C.: Physical
constraints for temperature biases in climate models, Geophys. Res.
Lett., 40, 4042–4047, https://doi.org/10.1002/grl.50737, 2013. a
Berg, P., Feldmann, H., and Panitz, H.-J.: Bias correction of high resolution
regional climate model data, J. Hydrol., 448, 80–92,
https://doi.org/10.1016/j.jhydrol.2012.04.026, 2012. a
Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., and Schär, C.:
Bayesian multi-model projection of climate: bias assumptions and interannual
variability, Clim. Dynam., 33, 849–868, https://doi.org/10.1007/s00382-009-0588-6,
2009. a
Cabus, P.: River flow prediction through rainfall–runoff modelling
with a probability-distributed model (PDM) in Flanders, Belgium,
Agr. Water Manage., 95, 859–868,
https://doi.org/10.1016/j.agwat.2008.02.013, 2008. a
Cannon, A. J.: Multivariate bias correction of climate model output: Matching
marginal distributions and intervariable dependence structure, J.
Climate, 29, 7045–7064, https://doi.org/10.1175/JCLI-D-15-0679.1, 2016. a, b, c
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM
precipitation by quantile mapping: How well do methods preserve changes in
quantiles and extremes?, J. Climate, 28, 6938–6959,
https://doi.org/10.1175/JCLI-D-14-00754.1, 2015. a, b
Chen, J., Brissette, F. P., and Lucas-Picher, P.: Assessing the limits of
bias-correcting climate model outputs for climate change impact studies,
J. Geophys. Res.-Atmos., 120, 1123–1136,
https://doi.org/10.1002/2014JD022635, 2015. a, b, c
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On
the need for bias correction of regional climate change projections of
temperature and precipitation, Geophys. Res. Lett., 35, L20709,
https://doi.org/10.1029/2008GL035694, 2008. a, b
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.: The
Schaake shuffle: A method for reconstructing space–time variability in
forecasted precipitation and temperature fields, J. Hydrometeorol.,
5, 243–262, https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2, 2004. a, b
De Jongh, I. L. M., Verhoest, N. E. C., and De Troch, F. P.: Analysis of a
105-year time series of precipitation observed at Uccle, Belgium,
Int. J. Climatol., 26, 2023–2039, https://doi.org/10.1002/joc.1352,
2006. a
Dekens, L., Parey, S., Grandjacques, M., and Dacunha-Castelle, D.: Multivariate
distribution correction of climate model outputs: A generalization of
quantile mapping approaches, Environmetrics, 28, e2454,
https://doi.org/10.1002/env.2454, 2017. a, b
Demarée, G. R.: The centennial recording raingauge of the Uccle Plateau:
Its history, its data and its applications, Houille Blanche, 4, 95–102,
2003. a
Derbyshire, J.: The siren call of probability: Dangers associated with using
probability for consideration of the future, Futures, 88, 43–54,
https://doi.org/10.1016/j.futures.2017.03.011, 2017. a
Di Luca, A., de Elía, R., and Laprise, R.: Challenges in the quest for
added value of regional climate dynamical downscaling, Current Climate Change
Reports, 1, 10–21, https://doi.org/10.1007/s40641-015-0003-9, 2015. a
Eberhart, R. and Kennedy, J.: A new optimizer using Particle Swarm Theory, in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, 39–43, https://doi.org/10.1109/MHS.1995.494215, 1995. a
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions ”Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fosser, G., Kendon, E. J., Stephenson, D., and Tucker, S.:
Convection-permitting models offer promise of more certain extreme rainfall
projections, Geophys. Res. Lett., 47, e2020GL088151, https://doi.org/10.1029/2020GL088151, 2020. a
François, B., Thao, S., and Vrac, M.: Adjusting spatial dependence of
climate model outputs with cycle-consistent adversarial networks, Clim. Dynam., 57, 3323–3353, https://doi.org/10.1007/s00382-021-05869-8,
2021. a
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a, b
Guo, Q., Chen, J., Zhang, X. J., Xu, C.-Y., and Chen, H.: Impacts of using
state-of-the-art multivariate bias correction methods on hydrological
modeling over North America, Water Resour. Res., 56,
e2019WR026659, https://doi.org/10.1029/2019WR026659, 2020. a, b, c, d
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E.,
Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S.,
Martín, D. S., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R.,
Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J.,
Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J., Ramos,
P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P.,
Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M.,
Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a
large ensemble of statistical downscaling methods over Europe: results from
the VALUE perfect predictor cross-validation experiment, Int.
J. Climatol., 39, 3750–3785, https://doi.org/10.1002/joc.5462, 2019. a
Gutjahr, O. and Heinemann, G.: Comparing precipitation bias correction methods
for high-resolution regional climate simulations using COSMO-CLM,
Theor. Appl. Climatol., 114, 511–529,
https://doi.org/10.1007/s00704-013-0834-z, 2013. a
Gutowski, William J., J., Decker, S. G., Donavon, R. A., Pan, Z., Arritt,
R. W., and Takle, E. S.: Temporal–spatial scales of observed and simulated
precipitation in central US climate, J. Climate, 16, 3841–3847,
https://doi.org/10.1175/1520-0442(2003)016<3841:TSOOAS>2.0.CO;2, 2003. a
Haerter, J. O., Hagemann, S., Moseley, C., and Piani, C.: Climate model bias correction and the role of timescales, Hydrol. Earth Syst. Sci., 15, 1065–1079, https://doi.org/10.5194/hess-15-1065-2011, 2011. a
Hagemann, S., Chen, C., Haerter, J. O., Heinke, J., Gerten, D., and Piani, C.:
Impact of a statistical bias correction on the projected hydrological changes
obtained from three GCMs and two hydrology models, J.
Hydrometeorol., 12, 556–578, https://doi.org/10.1175/2011JHM1336.1, 2011. a
Hakala, K., Addor, N., and Seibert, J.: Hydrological modeling to evaluate
climate model simulations and their bias correction, J.
Hydrometeorol., 19, 1321–1337, https://doi.org/10.1175/JHM-D-17-0189.1, 2018. a
Hay, L. E. and Clark, M. P.: Use of statistically and dynamically downscaled
atmospheric model output for hydrologic simulations in three mountainous
basins in the western United States, J. Hydrol., 282, 56–75,
https://doi.org/10.1016/S0022-1694(03)00252-X, 2003. a
Helsen, S., van Lipzig, N. P. M., Demuzere, M., Vanden Broucke, S.,
Caluwaerts, S., De Cruz, L., De Troch, R., Hamdi, R., Termonia, P.,
Van Schaeybroeck, B., and Wouters, H.: Consistent scale-dependency of future
increases in hourly extreme precipitation in two convection-permitting
climate models, Clim. Dynam., 54, 1–14,
https://doi.org/10.1007/s00382-019-05056-w, 2019. a
Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F., and Jack, C.:
Interrogating empirical-statistical downscaling, Climatic change, 122,
539–554, https://doi.org/10.1007/s10584-013-1021-z, 2014. a
Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T., and Brown, S. J.:
Calibration strategies: a source of additional uncertainty in climate change
projections, B. Am. Meteorol. Soc., 93, 21–26,
https://doi.org/10.1175/2011BAMS3110.1, 2012. a
Hui, Y., Chen, J., Xu, C.-Y., Xiong, L., and Chen, H.: Bias nonstationarity of
global climate model outputs: The role of internal climate variability and
climate model sensitivity, Int. J. Climatol., 39,
2278–2294, https://doi.org/10.1002/joc.5950, 2019. a
Hui, Y., Xu, Y., Chen, J., Xu, C.-Y., and Chen, H.: Impacts of bias
nonstationarity of climate model outputs on hydrological simulations,
Hydrol. Res., 51, 925–941, https://doi.org/10.2166/nh.2020.254, 2020. a
Ines, A. V. M. and Hansen, J. W.: Bias correction of daily GCM rainfall for
crop simulation studies, Agr. Forest Meteorol., 138, 44–53,
https://doi.org/10.1016/j.agrformet.2006.03.009, 2006. a, b
IPCC: Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation, in: A Special Report of Working Groups I and II of the
Intergovernmental Panel on Climate Change, edited by: Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M., Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp., 2012. a
IPCC: Climate Change 2013: The Physical Science Basis, in: Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 1535 pp., 2013. a
Ivanov, M. A., Luterbacher, J., and Kotlarski, S.: Climate model biases and
modification of the climate change signal by intensity-dependent bias
correction, J. Climate, 31, 6591–6610,
https://doi.org/10.1175/JCLI-D-17-0765.1, 2018. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski,
S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S.,
Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M.,
Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R.,
Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution
climate change projections for European impact research, Reg.
Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a, b
Johnson, F. and Sharma, A.: Accounting for interannual variability: A
comparison of options for water resources climate change impact assessments,
Water Resour. Res., 47, W04508, https://doi.org/10.1029/2010WR009272, 2011. a, b
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability
at multiple time scales in general circulation model precipitation
simulations, Water Resour. Res., 48, W01504,
https://doi.org/10.1029/2011WR010464, 2012. a
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan,
S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.: Do
convection-permitting regional climate models improve projections of future
precipitation change?, B. Am. Meteorol. Soc., 98,
79–93, https://doi.org/10.1175/BAMS-D-15-0004.1, 2017. a
Kerkhoff, C., Künsch, H. R., and Schär, C.: Assessment of bias
assumptions for climate models, J. Climate, 27, 6799–6818,
https://doi.org/10.1175/JCLI-D-13-00716.1, 2014. a, b
Klemeš, V.: Operational testing of hydrological simulation models,
Hydrolog. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024,
1986. a
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014. a
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007. a
Li, C., Sinha, E., Horton, D. E., Diffenbaugh, N. S., and Michalak, A. M.:
Joint bias correction of temperature and precipitation in climate model
simulations, J. Geophys. Res.-Atmos., 119, 13–153,
https://doi.org/10.1002/2014JD022514, 2014. a, b
Li, H., Sheffield, J., and Wood, E. F.: Bias correction of monthly
precipitation and temperature fields from Intergovernmental Panel on
Climate Change AR4 models using equidistant quantile matching, J. Geophys. Res.-Atmos., 115, D10101,
https://doi.org/10.1029/2009JD012882, 2010. a, b, c
Lorenz, E. N.: Atmospheric predictability as revealed by naturally occurring
analogues, J. Atmos. Sci., 26, 636–646,
https://doi.org/10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2, 1969. a
Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res. Lett., 39, L06706, https://doi.org/10.1029/2012GL051210, 2012. a, b, c
Maraun, D.: Bias correcting climate change simulations-a critical review,
Current Climate Change Reports, 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x,
2016. a, b, c, d
Maraun, D. and Widmann, M.: Cross-validation of bias-corrected climate simulations is misleading, Hydrol. Earth Syst. Sci., 22, 4867–4873, https://doi.org/10.5194/hess-22-4867-2018, 2018a. a, b
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for
Climate Research, Cambridge University Press, https://doi.org/10.1017/9781107588783,
2018b. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E.,
Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A. I.: VALUE: A framework
to validate downscaling approaches for climate change studies, Earth's
Future, 3, 1–14, https://doi.org/10.1002/2014EF000259, 2015. a
Maurer, E. P., Das, T., and Cayan, D. R.: Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction, Hydrol. Earth Syst. Sci., 17, 2147–2159, https://doi.org/10.5194/hess-17-2147-2013, 2013. a, b, c, d
Mehrotra, R. and Sharma, A.: An improved standardization procedure to remove
systematic low frequency variability biases in GCM simulations, Water Resour. Res., 48, W12601, https://doi.org/10.1029/2012WR012446, 2012. a
Mehrotra, R. and Sharma, A.: Correcting for systematic biases in multiple raw
GCM variables across a range of timescales, J. Hydrol., 520,
214–223, https://doi.org/10.1016/j.jhydrol.2014.11.037, 2015. a
Meyer, J., Kohn, I., Stahl, K., Hakala, K., Seibert, J., and Cannon, A. J.: Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments, Hydrol. Earth Syst. Sci., 23, 1339–1354, https://doi.org/10.5194/hess-23-1339-2019, 2019. a, b, c
Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling
approaches: Application to wind cumulative distribution functions,
Geophys. Res. Lett., 36, L11708, https://doi.org/10.1029/2009GL038401,
2009. a
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead:
Whither water management?, Science, 319, 573–574,
https://doi.org/10.1126/science.1151915, 2008. a
Moore, R. J.: The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11, 483–499, https://doi.org/10.5194/hess-11-483-2007, 2007. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Nahar, J., Johnson, F., and Sharma, A.: Assessing the extent of non-stationary
biases in GCMs, J. Hydrol., 549, 148–162,
https://doi.org/10.1016/j.jhydrol.2017.03.045, 2017. a
Nelsen, R. B.: An Introduction to Copulas, 2nd Edn., New York, Springer Science Business Media, ISBN 978-0387-28659-4, 2006. a
Nguyen, H., Mehrotra, R., and Sharma, A.: Correcting for systematic biases in
GCM simulations in the frequency domain, J. Hydrol., 538,
117–126, https://doi.org/10.1016/j.jhydrol.2016.04.018, 2016. a
Nguyen, H., Mehrotra, R., and Sharma, A.: Correcting systematic biases across
multiple atmospheric variables in the frequency domain, Clim. Dynam., 52,
1283–1298, https://doi.org/10.1007/s00382-018-4191-6, 2018. a, b
Olsson, J., Berggren, K., Olofsson, M., and Viklander, M.: Applying climate
model precipitation scenarios for urban hydrological assessment: A case
study in Kalmar City, Sweden, Atmos. Res., 92, 364–375,
https://doi.org/10.1016/j.atmosres.2009.01.015, 2009. a
Panofsky, H. A., Brier, G. W., and Best, W. H.: Some Application of Statistics to Meteorology, Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences, Pennsylvania State University, 1958. a
Papalexiou, S. M. and Montanari, A.: Global and regional increase of
precipitation extremes under global warming, Water Resour. Res., 55,
4901–4914, https://doi.org/10.1029/2018WR024067, 2019. a
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P.
R. Soc. Lond. A, 193, 120–145, 1948. a
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of
the AR4 climate models’ simulated daily maximum temperature, minimum
temperature, and precipitation over Australia using probability density
functions, J. Climate, 20, 4356–4376,
https://doi.org/10.1175/JCLI4253.1, 2007. a
Peyré, G. and Cuturi, M.: Computational Optimal Transport, vol. 11, Now Publishers, https://doi.org/10.1561/2200000073, 2019. a
Pham, M. T., Vernieuwe, H., De Baets, B., and Verhoest, N. E. C.: A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis, Hydrol. Earth Syst. Sci., 22, 1263–1283, https://doi.org/10.5194/hess-22-1263-2018, 2018. a, b, c
Piani, C. and Haerter, J. O.: Two dimensional bias correction of temperature
and precipitation copulas in climate models, Geophys. Res. Lett., 39, L20401, https://doi.org/10.1029/2012gl053839, 2012. a, b
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for
daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a
Popke, D., Stevens, B., and Voigt, A.: Climate and climate change in a
radiative-convective equilibrium version of ECHAM6, J. Adv.
Model. Earth Sy., 5, 1–14, https://doi.org/10.1029/2012MS000191, 2013. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., Lipzig, N. P. M., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475,
2015. a
Räty, O., Räisänen, J., Bosshard, T., and Donnelly, C.:
Intercomparison of univariate and joint bias correction methods in changing
climate from a hydrological perspective, Climate, 6, 33,
https://doi.org/10.3390/cli6020033, 2018. a, b, c
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, https://www.R-project.org/ (last access: 29 April 2022), 2021. a
Reiter, P., Gutjahr, O., Schefczyk, L., Heinemann, G., and Casper, M.: Does
applying quantile mapping to subsamples improve the bias correction of daily
precipitation?, Int. J. Climatol., 38, 1623–1633,
https://doi.org/10.1002/joc.5283, 2018. a
Rizzo, M. L. and Székely, G. J.: Energy distance, Wiley Interdisciplinary
Reviews: Computational Statistics, 8, 27–38, https://doi.org/10.1002/wics.1375, 2016. a
Rojas, R., Feyen, L., Dosio, A., and Bavera, D.: Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations, Hydrol. Earth Syst. Sci., 15, 2599–2620, https://doi.org/10.5194/hess-15-2599-2011, 2011. a
Schmidli, J., Frei, C., and Vidale, P. L.: Downscaling from GCM
precipitation: a benchmark for dynamical and statistical downscaling methods,
Int. J. Climatol., 26, 679–689, https://doi.org/10.1002/joc.1287,
2006. a
Schölzel, C. and Friederichs, P.: Multivariate non-normally distributed random variables in climate research – introduction to the copula approach, Nonlin. Processes Geophys., 15, 761–772, https://doi.org/10.5194/npg-15-761-2008, 2008. a
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If precipitation extremes are
increasing, why aren't floods?, Water Resour. Res., 54, 8545–8551,
https://doi.org/10.1029/2018WR023749, 2018. a
Strandberg, G., Bärring, L., Hansson, U., Jansson, C., Jones, C.,
Kjellström, E., Kupiainen, M., Nikulin, G., Samuelsson, P., and
Ullerstig, A.: CORDEX scenarios for Europe from the Rossby Centre
regional climate model RCA4, Tech. rep., SMHI, https://www.diva-portal.org/smash/record.jsf?pid=diva2:948136&dswid=-7052 (last access: 29 April 2022), 2015. a
Sunyer, M. A., Madsen, H., Rosbjerg, D., and Arnbjerg-Nielsen, K.: A Bayesian
approach for uncertainty quantification of extreme precipitation projections
including climate model interdependency and nonstationary bias, J.
Climate, 27, 7113–7132, https://doi.org/10.1175/JCLI-D-13-00589.1, 2014. a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a, b, c
Teutschbein, C. and Seibert, J.: Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?, Hydrol. Earth Syst. Sci., 17, 5061–5077, https://doi.org/10.5194/hess-17-5061-2013, 2013. a
Themeßl, M. J., Gobiet, A., and Heinrich, G.: Empirical-statistical
downscaling and error correction of regional climate models and its impact on
the climate change signal, Climatic Change, 112, 449–468,
https://doi.org/10.1007/s10584-011-0224-4, 2012. a, b
Vandenberghe, S., Verhoest, N. E. C., Onof, C., and De Baets, B.: A
comparative copula-based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett-Lewis modeled rainfall, Water Resour. Res., 47, W07529, https://doi.org/10.1029/2009WR008388, 2011. a
Van de Velde, J.: h-cel/ImpactofBiasNonstationarity: Impact of bias nonstationarity: calculations (2.0), Zenodo [code], https://doi.org/10.5281/zenodo.6501634, 2022. a
Van Schaeybroeck, B. and Vannitsem, S.: Assessment of calibration assumptions
under strong climate changes, Geophys. Res. Lett., 43, 1314–1322,
https://doi.org/10.1002/2016GL067721, 2016. a
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Climatic Change, 109, 5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011. a
Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K.,
Coppola, E., Corre, L., Meijgaard, E., Nogherotto, R., Sandstad, M.,
Schwingshackl, C., Somot, S., Aalbers, E., Christensen, O. B., Ciarlo, J. M.,
Demory, M.-E., Giorgi, F., Jacob, D., Jones, R. G., Keuler, K., Kjellström,
E., Lenderink, G., Levavasseur, G., Nikulin, G., Sillmann, J., Solidoro, C.,
Sørland, S. L., Steger, C., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Evaluation of the large EURO-CORDEX regional climate model
ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032344, https://doi.org/10.1029/2019JD032344, 2021. a, b, c
Velázquez, J. A., Troin, M., Caya, D., and Brissette, F.: Evaluating the
time-invariance hypothesis of climate model bias correction: implications for
hydrological impact studies, J. Hydrometeorol., 16, 2013–2026,
https://doi.org/10.1175/JHM-D-14-0159.1, 2015. a
Verhoest, N. E. C., Troch, P. A., and De Troch, F. P.: On the applicability of
Bartlett–Lewis rectangular pulses models in the modeling of design
storms at a point, J. Hydrol., 202, 108–120,
https://doi.org/10.1016/S0022-1694(97)00060-7, 1997. a
Verstraeten, G., Poesen, J., Demarée, G. R., and Salles, C.: Long-term
(105 years) variability in rain erosivity as derived from 10-min rainfall
depth data for Ukkel (Brussels, Belgium): Implications for assessing
soil erosion rates, J. Geophys. Res., 111, D22109,
https://doi.org/10.1029/2006jd007169, 2006. a
Vrac, M. and Thao, S.: R package R2D2, Zenodo [code], https://doi.org/10.5281/zenodo.4021981, 2020a. a
Wang, L. and Chen, W.: Equiratio cumulative distribution function matching as
an improvement to the equidistant approach in bias correction of
precipitation, Atmos. Sci. Lett., 15, 1–6, https://doi.org/10.1002/asl2.454,
2014. a, b, c
Wang, Y., Sivandran, G., and Bielicki, J. M.: The stationarity of two
statistical downscaling methods for precipitation under different choices of
cross-validation periods, Int. J. Climatol., 38,
e330–e348, https://doi.org/10.1002/joc.5375, 2018. a
Wilcke, R. A. I., Mendlik, T., and Gobiet, A.: Multi-variable error correction
of regional climate models, Climatic Change, 120, 871–887,
https://doi.org/10.1007/s10584-013-0845-x, 2013. a
Willems, P.: Revision of urban drainage design rules after assessment of
climate change impacts on precipitation extremes at Uccle, Belgium,
J. Hydrol., 496, 166–177, https://doi.org/10.1016/j.jhydrol.2013.05.037,
2013. a
Willems, P. and Vrac, M.: Statistical precipitation downscaling for small-scale
hydrological impact investigations of climate change, J. Hydrol.,
402, 193–205, https://doi.org/10.1016/j.jhydrol.2011.02.030, 2011.
a
Yang, W., Gardelin, M., Olsson, J., and Bosshard, T.: Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden, Nat. Hazards Earth Syst. Sci., 15, 2037–2057, https://doi.org/10.5194/nhess-15-2037-2015, 2015. a
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson,
T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in
extremes based on daily temperature and precipitation data, Wiley
Interdisciplinary Reviews: Climate Change, 2, 851–870,
https://doi.org/10.1002/wcc.147, 2011. a, b
Zorita, E. and Von Storch, H.: The analog method as a simple statistical
downscaling technique: Comparison with more complicated methods, J.
Climate, 12, 2474–2489,
https://doi.org/10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2, 1999. a
Zscheischler, J., Westra, S., Hurk, B. J. J. M., Seneviratne, S. I., Ward,
P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and
Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8,
469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton,
R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha,
M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.:
A typology of compound weather and climate events, Nature Reviews Earth &
Environment, https://doi.org/10.1038/s43017-020-0060-z, 2020. a
Short summary
An important step in projecting future climate is the bias adjustment of the climatological and hydrological variables. In this paper, we illustrate how bias adjustment can be impaired by bias nonstationarity. Two univariate and four multivariate methods are compared, and for both types bias nonstationarity can be linked with less robust adjustment.
An important step in projecting future climate is the bias adjustment of the climatological and...
