Articles | Volume 18, issue 12
https://doi.org/10.5194/hess-18-5331-2014
© Author(s) 2014. 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-18-5331-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Dec 2014
Research article |
| 19 Dec 2014
Imperfect scaling in distributions of radar-derived rainfall fields
M. J. van den Berg
CORRESPONDING AUTHOR
Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Ghent, Belgium
L. Delobbe
Royal Meteorological Institute, Avenue Circulaire 3, 1180 Uccle, Belgium
N. E. C. Verhoest
Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Ghent, Belgium
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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.
Michel Journée, Edouard Goudenhoofdt, Stéphane Vannitsem, and Laurent Delobbe
Hydrol. Earth Syst. Sci., 27, 3169–3189, https://doi.org/10.5194/hess-27-3169-2023, https://doi.org/10.5194/hess-27-3169-2023, 2023
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The exceptional flood of July 2021 in central Europe impacted Belgium severely. This study aims to characterize rainfall amounts in Belgium from 13 to 16 July 2021 based on observational data (i.e., rain gauge data and a radar-based rainfall product). The spatial and temporal distributions of rainfall during the event aredescribed. In order to document such a record-breaking event as much as possible, the rainfall data are shared with the scientific community on Zenodo for further studies.
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022, https://doi.org/10.5194/hess-26-2319-2022, 2022
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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.
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.
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.
Laurent Delobbe, Arnaud Watlet, Svenja Wilfert, and Michel Van Camp
Hydrol. Earth Syst. Sci., 23, 93–105, https://doi.org/10.5194/hess-23-93-2019, https://doi.org/10.5194/hess-23-93-2019, 2019
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In this study, we explore the use of an underground superconducting gravimeter as a new source of in situ observations for the evaluation of radar-based precipitation estimates. The comparison of radar and gravity time series over 15 years shows that short-duration intense rainfall events cause a rapid decrease in the measured gravity. Rainfall amounts can be derived from this decrease. The gravimeter allows capture of rainfall at a much larger spatial scale than a traditional rain gauge.
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.
Dieter R. Poelman, Wolfgang Schulz, Rudolf Kaltenboeck, and Laurent Delobbe
Atmos. Meas. Tech., 10, 4561–4572, https://doi.org/10.5194/amt-10-4561-2017, https://doi.org/10.5194/amt-10-4561-2017, 2017
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Lightning data as observed by the European Cooperation for Lightning Detection network EUCLID are used in combination with radar data to retrieve the temporal and spatial behavior of lightning outliers, i.e. discharges located in a wrong place, over a 5-year period from 2011 to 2016 in Belgium and Austria.
Edouard Goudenhoofdt, Laurent Delobbe, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 5385–5399, https://doi.org/10.5194/hess-21-5385-2017, https://doi.org/10.5194/hess-21-5385-2017, 2017
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Knowing the characteristics of extreme precipitation is useful for flood management applications like sewer system design. The potential of a 12-year high-quality weather radar precipitation dataset is investigated by comparison with rain gauges. Despite known limitations, a good agreement is found between the radar and the rain gauges. Using the radar data allow us to reduce the uncertainty of the extreme value analysis, especially for short duration extremes related to thunderstorms.
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
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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.
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.
L. Foresti, M. Reyniers, A. Seed, and L. Delobbe
Hydrol. Earth Syst. Sci., 20, 505–527, https://doi.org/10.5194/hess-20-505-2016, https://doi.org/10.5194/hess-20-505-2016, 2016
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The Short-Term Ensemble Prediction System (STEPS) is implemented in real time at the Royal Meteorological Institute of Belgium (STEPS-BE). The idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic extrapolation of radar images. In this paper we present the deterministic, probabilistic and ensemble verification of STEPS-BE forecasts using four precipitation cases that caused sewer system overflow in the cities of Leuven and Ghent.
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. 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
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: Hydrometeorology | Techniques and Approaches: Stochastic approaches
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Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective
Stochastic daily rainfall generation on tropical islands with complex topography
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Compound flood potential from storm surge and heavy precipitation in coastal China: dependence, drivers, and impacts
Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin
Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process
Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought
Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks
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Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues
A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields
Modelling rainfall with a Bartlett–Lewis process: new developments
Nonstationary stochastic rain type generation: accounting for climate drivers
Conditional simulation of surface rainfall fields using modified phase annealing
Climate influences on flood probabilities across Europe
Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model
A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales
Mapping rainfall hazard based on rain gauge data: an objective cross-validation framework for model selection
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Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework
Dealing with non-stationarity in sub-daily stochastic rainfall models
Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?
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Modeling the changes in water balance components of the highly irrigated western part of Bangladesh
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David A. Jimenez, Andrea Menapace, Ariele Zanfei, Eber José de Andrade Pinto, and Bruno Brentan
Hydrol. Earth Syst. Sci., 28, 1981–1997, https://doi.org/10.5194/hess-28-1981-2024, https://doi.org/10.5194/hess-28-1981-2024, 2024
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Most studies that aim to identify the impacts of climate change employ general circulation models. However, due to their low spatial resolution, it is necessary to apply downscaling techniques. This work assesses the performance of three methodologies in developing frequency analyses and estimating the number of rainy days and total precipitation per year. Quantile mapping and regression trees excelled in frequency analysis, and the delta method best estimated multiyear total precipitation.
Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg
Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, https://doi.org/10.5194/hess-28-391-2024, 2024
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High-resolution precipitation data are often a “must” as input for hydrological and hydraulic models (i.e. urban drainage modelling). However, station or climate projection data usually do not provide the required (e.g. sub-hourly) resolution. In the work, we present two new statistical models of different types to disaggregate precipitation from a daily to a 10 min scale. Both models were validated using radar data and then applied to climate models for 10 stations in Germany and South Korea.
Marc Sanuy, Juan C. Peña, Sotiris Assimenidis, and José A. Jiménez
Hydrol. Earth Syst. Sci., 28, 283–302, https://doi.org/10.5194/hess-28-283-2024, https://doi.org/10.5194/hess-28-283-2024, 2024
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The work presents the first classification of weather types associated to compound events of extreme rainfall and coastal storms. These are found to be characterized by upper-level lows and troughs in conjunction with Mediterranean cyclones, resulting in severe to extreme coastal storms combined with convective systems. We used objective classification methods coupled with a Bayesian Network, testing different variables, domains and number of weather types.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
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Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
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The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
Yuan Li, Kangning Xü, Zhiyong Wu, Zhiwei Zhu, and Quan J. Wang
Hydrol. Earth Syst. Sci., 27, 4187–4203, https://doi.org/10.5194/hess-27-4187-2023, https://doi.org/10.5194/hess-27-4187-2023, 2023
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A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.
Hetal P. Dabhi, Mathias W. Rotach, and Michael Oberguggenberger
Hydrol. Earth Syst. Sci., 27, 2123–2147, https://doi.org/10.5194/hess-27-2123-2023, https://doi.org/10.5194/hess-27-2123-2023, 2023
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Spatiotemporally consistent high-resolution precipitation data on climate are needed for climate change impact assessments, but obtaining these data is challenging for areas with complex topography. We present a model that generates synthetic gridded daily precipitation data at a 1 km spatial resolution using observed meteorological station data as input, thereby providing data where historical observations are unavailable. We evaluate this model for a mountainous region in the European Alps.
Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023, https://doi.org/10.5194/hess-27-1077-2023, 2023
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Flash drought is referred to the rapid development of drought events with a fast decline of soil moisture, which has serious impacts on agriculture, the ecosystem, human health, and society. While flash droughts have received much research attention, there is no consensus on its definition. Here we used a stochastic water balance framework to quantify the timing of soil moisture crossing different thresholds, providing an efficient tool for diagnosing and monitoring flash droughts.
Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Remi Perrin, Lionel Sindt, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 26, 6477–6491, https://doi.org/10.5194/hess-26-6477-2022, https://doi.org/10.5194/hess-26-6477-2022, 2022
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Reference rainfall scenarios are indispensable for hydrological applications such as designing storm-water management infrastructure, including green roofs. Therefore, a new method is suggested for simulating rainfall scenarios of specified intensity, duration, and frequency, with realistic intermittency. Furthermore, novel comparison metrics are proposed to quantify the effectiveness of the presented simulation procedure.
Judith Meyer, Malte Neuper, Luca Mathias, Erwin Zehe, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 6163–6183, https://doi.org/10.5194/hess-26-6163-2022, https://doi.org/10.5194/hess-26-6163-2022, 2022
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We identified and analysed the major atmospheric components of rain-intense thunderstorms that can eventually lead to flash floods: high atmospheric moisture, sufficient latent instability, and weak thunderstorm cell motion. Between 1981 and 2020, atmospheric conditions became likelier to support strong thunderstorms. However, the occurrence of extreme rainfall events as well as their rainfall intensity remained mostly unchanged.
Yuan Liu and Daniel B. Wright
Hydrol. Earth Syst. Sci., 26, 5241–5267, https://doi.org/10.5194/hess-26-5241-2022, https://doi.org/10.5194/hess-26-5241-2022, 2022
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We present a new approach to estimate extreme rainfall probability and severity using the atmospheric water balance, where precipitation is the sum of water vapor components moving in and out of a storm. We apply our method to the Mississippi Basin and its five major subbasins. Our approach achieves a good fit to reference precipitation, indicating that the rainfall probability estimation can benefit from additional information from physical processes that control rainfall.
Yuan Li, Zhiyong Wu, Hai He, and Hao Yin
Hydrol. Earth Syst. Sci., 26, 4975–4994, https://doi.org/10.5194/hess-26-4975-2022, https://doi.org/10.5194/hess-26-4975-2022, 2022
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The relationship between atmospheric intraseasonal signals and precipitation is highly uncertain and depends on the region and lead time. In this study, we develop a spatiotemporal projection, based on a Bayesian hierarchical model (STP-BHM), to address the above challenge. The results suggest that the STP-BHM model is skillful and reliable for probabilistic subseasonal precipitation forecasts over China during the boreal summer monsoon season.
Lionel Benoit, Lydie Sichoix, Alison D. Nugent, Matthew P. Lucas, and Thomas W. Giambelluca
Hydrol. Earth Syst. Sci., 26, 2113–2129, https://doi.org/10.5194/hess-26-2113-2022, https://doi.org/10.5194/hess-26-2113-2022, 2022
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This study presents a probabilistic model able to reproduce the spatial patterns of rainfall on tropical islands with complex topography. It sheds new light on rainfall variability at the island scale, and explores the links between rainfall patterns and atmospheric circulation. The proposed model has been tested on two islands of the tropical Pacific, and demonstrates good skills in simulating both site-specific and island-scale rain behavior.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Jiayi Fang, Thomas Wahl, Jian Fang, Xun Sun, Feng Kong, and Min Liu
Hydrol. Earth Syst. Sci., 25, 4403–4416, https://doi.org/10.5194/hess-25-4403-2021, https://doi.org/10.5194/hess-25-4403-2021, 2021
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A comprehensive assessment of compound flooding potential is missing for China. We investigate dependence, drivers, and impacts of storm surge and precipitation for coastal China. Strong dependence exists between driver combinations, with variations of seasons and thresholds. Sea level rise escalates compound flood potential. Meteorology patterns are pronounced for low and high compound flood potential. Joint impacts from surge and precipitation were much higher than from each individually.
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
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We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Jieru Yan, Fei Li, András Bárdossy, and Tao Tao
Hydrol. Earth Syst. Sci., 25, 3819–3835, https://doi.org/10.5194/hess-25-3819-2021, https://doi.org/10.5194/hess-25-3819-2021, 2021
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Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.
Hossein Tabari, Santiago Mendoza Paz, Daan Buekenhout, and Patrick Willems
Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, https://doi.org/10.5194/hess-25-3493-2021, 2021
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021, https://doi.org/10.5194/hess-25-3207-2021, 2021
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In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 1587–1601, https://doi.org/10.5194/hess-25-1587-2021, https://doi.org/10.5194/hess-25-1587-2021, 2021
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We introduce a daily-scale index, termed the standardized compound drought and heat index (SCDHI), to measure the key features of compound dry-hot conditions. SCDHI can not only monitor the long-term compound dry-hot events, but can also capture such events at sub-monthly scale and reflect the related vegetation activity impacts. The index can provide a new tool to quantify sub-monthly characteristics of compound dry-hot events, which are vital for releasing early and timely warning.
Damien Raynaud, Benoit Hingray, Guillaume Evin, Anne-Catherine Favre, and Jérémy Chardon
Hydrol. Earth Syst. Sci., 24, 4339–4352, https://doi.org/10.5194/hess-24-4339-2020, https://doi.org/10.5194/hess-24-4339-2020, 2020
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This research paper proposes a weather generator combining two sampling approaches. A first generator recombines large-scale atmospheric situations. A second generator is applied to these atmospheric trajectories in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series in Switzerland. It reproduces adequately the observed climatology and improves the reproduction of extreme precipitation values.
Marc Schleiss
Hydrol. Earth Syst. Sci., 24, 3699–3723, https://doi.org/10.5194/hess-24-3699-2020, https://doi.org/10.5194/hess-24-3699-2020, 2020
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A new way to downscale rainfall fields based on the notion of equal-volume areas (EVAs) is proposed. Experiments conducted on 100 rainfall events in the Netherlands show that the EVA method outperforms classical methods based on fixed grid cell sizes, producing fields with more realistic spatial structures. The main novelty of the method lies in its adaptive sampling strategy, which avoids many of the mathematical challenges associated with the presence of zero rainfall values.
Christian Onof and Li-Pen Wang
Hydrol. Earth Syst. Sci., 24, 2791–2815, https://doi.org/10.5194/hess-24-2791-2020, https://doi.org/10.5194/hess-24-2791-2020, 2020
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The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
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At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Jieru Yan, András Bárdossy, Sebastian Hörning, and Tao Tao
Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, https://doi.org/10.5194/hess-24-2287-2020, 2020
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For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.
Eva Steirou, Lars Gerlitz, Heiko Apel, Xun Sun, and Bruno Merz
Hydrol. Earth Syst. Sci., 23, 1305–1322, https://doi.org/10.5194/hess-23-1305-2019, https://doi.org/10.5194/hess-23-1305-2019, 2019
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We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
Florian Ehmele and Michael Kunz
Hydrol. Earth Syst. Sci., 23, 1083–1102, https://doi.org/10.5194/hess-23-1083-2019, https://doi.org/10.5194/hess-23-1083-2019, 2019
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The risk estimation of precipitation events with high recurrence periods is difficult due to the limited timescale with meteorological observations and an inhomogeneous distribution of rain gauges, especially in mountainous terrains. In this study a spatially high resolved analytical model, designed for stochastic simulations of flood-related precipitation, is developed and applied to an investigation area in Germany but is transferable to other areas. High conformity with observations is found.
Jeongha Park, Christian Onof, and Dongkyun Kim
Hydrol. Earth Syst. Sci., 23, 989–1014, https://doi.org/10.5194/hess-23-989-2019, https://doi.org/10.5194/hess-23-989-2019, 2019
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Rainfall data are often unavailable for the analysis of water-related problems such as floods and droughts. In such cases, researchers use rainfall generators to produce synthetic rainfall data. However, data from most rainfall generators can serve only one specific purpose; i.e. one rainfall generator cannot be applied to analyse both floods and droughts. To overcome this issue, we invented a multipurpose rainfall generator that can be applied to analyse most water-related problems.
Juliette Blanchet, Emmanuel Paquet, Pradeebane Vaittinada Ayar, and David Penot
Hydrol. Earth Syst. Sci., 23, 829–849, https://doi.org/10.5194/hess-23-829-2019, https://doi.org/10.5194/hess-23-829-2019, 2019
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We propose an objective framework for estimating rainfall cumulative distribution functions in a region when data are only available at rain gauges. Our methodology allows us to assess goodness-of-fit of the full distribution, but with a particular focus on its tail. It is applied to daily rainfall in the Ardèche catchment in the south of France. Results show a preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with a thin plate spline.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 6591–6609, https://doi.org/10.5194/hess-22-6591-2018, https://doi.org/10.5194/hess-22-6591-2018, 2018
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The present study evaluates the skill of a seasonal forecasting system for hydrological relevant variables in Denmark. Linear scaling and quantile mapping were used to correct the forecasts. Uncorrected forecasts tend to be more skillful than climatology, in general, for the first month lead time only. Corrected forecasts show a reduced bias in the mean; are more consistent; and show a level of accuracy that is closer to, although no higher than, that of ensemble climatology, in general.
Kuganesan Sivasubramaniam, Ashish Sharma, and Knut Alfredsen
Hydrol. Earth Syst. Sci., 22, 6533–6546, https://doi.org/10.5194/hess-22-6533-2018, https://doi.org/10.5194/hess-22-6533-2018, 2018
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This study investigates the use of gauge precipitation and air temperature observations to ascertain radar precipitation in cold climates. The use of air temperature as an additional variable in a non-parametric model improved the estimation of radar precipitation significantly. Further, it was found that the temperature effects became insignificant when air temperature was above 10 °C. The findings from this study could be important for using radar precipitation for hydrological applications.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
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We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Hannes Müller-Thomy, Markus Wallner, and Kristian Förster
Hydrol. Earth Syst. Sci., 22, 5259–5280, https://doi.org/10.5194/hess-22-5259-2018, https://doi.org/10.5194/hess-22-5259-2018, 2018
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Rainfall time series are disaggregated from daily to hourly values to be used for rainfall–runoff modeling of mesoscale catchments. Spatial rainfall consistency is implemented afterwards using simulated annealing. With the calibration process applied, observed runoff statistics (e.g., summer and winter peak flows) are represented well. However, rainfall datasets with under- or over-estimation of spatial consistency lead to similar results, so the need for a good representation can be questioned.
Xinjun Tu, Yiliang Du, Vijay P. Singh, Xiaohong Chen, Kairong Lin, and Haiou Wu
Hydrol. Earth Syst. Sci., 22, 5175–5189, https://doi.org/10.5194/hess-22-5175-2018, https://doi.org/10.5194/hess-22-5175-2018, 2018
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For given frequencies of precipitation of a large region, design water demands of irrigation of the entire region among three methods, i.e., equalized frequency, typical year and most-likely weight function, slightly differed, but their alterations in sub-regions were complicated. A design procedure using the most-likely weight function in association with a high-dimensional copula, which built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended.
A. T. M. Sakiur Rahman, M. Shakil Ahmed, Hasnat Mohammad Adnan, Mohammad Kamruzzaman, M. Abdul Khalek, Quamrul Hasan Mazumder, and Chowdhury Sarwar Jahan
Hydrol. Earth Syst. Sci., 22, 4213–4228, https://doi.org/10.5194/hess-22-4213-2018, https://doi.org/10.5194/hess-22-4213-2018, 2018
Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich
Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, https://doi.org/10.5194/hess-22-4183-2018, 2018
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Kilometre-scale climate-model data are of great benefit to both hydrologists and end users studying extreme precipitation, though often unavailable due to the computational expense associated with such high-resolution simulations. We develop a method which identifies days with enhanced risk of extreme rainfall over a catchment, so that high-resolution simulations can be performed only when such a risk exists, reducing computational expense by over 90 % while still well capturing the extremes.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 3601–3617, https://doi.org/10.5194/hess-22-3601-2018, https://doi.org/10.5194/hess-22-3601-2018, 2018
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The skill of an experimental streamflow forecast system in the Ahlergaarde catchment, Denmark, is analyzed. Inputs to generate the forecasts are taken from the ECMWF System 4 seasonal forecasting system and an ensemble of observations (ESP). Reduction of biases is achieved by processing the meteorological and/or streamflow forecasts. In general, this is not sufficient to ensure a higher level of accuracy than the ESP, indicating a modest added value of a seasonal meteorological system.
Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 22, 1957–1969, https://doi.org/10.5194/hess-22-1957-2018, https://doi.org/10.5194/hess-22-1957-2018, 2018
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The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Pere Quintana-Seguí, and Anaïs Barella-Ortiz
Hydrol. Earth Syst. Sci., 22, 1371–1389, https://doi.org/10.5194/hess-22-1371-2018, https://doi.org/10.5194/hess-22-1371-2018, 2018
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This study investigates the use of a nonparametric model for combining multiple global precipitation datasets and characterizing estimation uncertainty. Inputs to the model included three satellite precipitation products, an atmospheric reanalysis precipitation dataset, satellite-derived near-surface daily soil moisture data, and terrain elevation. We evaluated the technique based on high-resolution reference precipitation data and further used generated ensembles to force a hydrological model.
David Cross, Christian Onof, Hugo Winter, and Pietro Bernardara
Hydrol. Earth Syst. Sci., 22, 727–756, https://doi.org/10.5194/hess-22-727-2018, https://doi.org/10.5194/hess-22-727-2018, 2018
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Extreme rainfall is one of the most significant natural hazards. However, estimating very large events is highly uncertain. We present a new approach to construct intense rainfall using the structure of rainfall generation in clouds. The method is particularly effective at estimating short-duration extremes, which can be the most damaging. This is expected to have immediate impact for the estimation of very rare downpours, with the potential to improve climate resilience and hazard preparedness.
Jérémy Chardon, Benoit Hingray, and Anne-Catherine Favre
Hydrol. Earth Syst. Sci., 22, 265–286, https://doi.org/10.5194/hess-22-265-2018, https://doi.org/10.5194/hess-22-265-2018, 2018
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We present a two-stage statistical downscaling model for the probabilistic prediction of local precipitation, where the downscaling statistical link is estimated from atmospheric circulation analogs of the current prediction day.
The model allows for a day-to-day adaptive and tailored downscaling. It can reveal specific predictors for peculiar and non-frequent weather configurations. This approach noticeably improves the skill of the prediction for both precipitation occurrence and quantity.
Christoph Ritschel, Uwe Ulbrich, Peter Névir, and Henning W. Rust
Hydrol. Earth Syst. Sci., 21, 6501–6517, https://doi.org/10.5194/hess-21-6501-2017, https://doi.org/10.5194/hess-21-6501-2017, 2017
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A stochastic model for precipitation is used to simulate an observed precipitation series; it is compared to the original series in terms of intensity–duration frequency curves. Basis for the latter curves is a parametric model for the duration dependence of the underlying extreme value model allowing a consistent estimation of one single duration-dependent distribution using all duration series simultaneously. The stochastic model reproduces the curves except for very rare extreme events.
Poulomi Ganguli and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 21, 6461–6483, https://doi.org/10.5194/hess-21-6461-2017, https://doi.org/10.5194/hess-21-6461-2017, 2017
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Using statistical models, we test whether nonstationary versus stationary models show any significant differences in terms of design storm intensity at different durations across Southern Ontario. We find that detectable nonstationarity in rainfall extremes does not necessarily lead to significant differences in design storm intensity, especially for shorter return periods. An update of 2–44 % is required in current design standards to mitigate the risk of storm-induced urban flooding.
Daniele Nerini, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti
Hydrol. Earth Syst. Sci., 21, 2777–2797, https://doi.org/10.5194/hess-21-2777-2017, https://doi.org/10.5194/hess-21-2777-2017, 2017
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Stochastic generators are effective tools for the quantification of uncertainty in a number of applications with weather radar data, including quantitative precipitation estimation and very short-term forecasting. However, most of the current stochastic rainfall field generators cannot handle spatial non-stationarity. We propose an approach based on the short-space Fourier transform, which aims to reproduce the local spatial structure of the observed rainfall fields.
Tobias Mosthaf and András Bárdossy
Hydrol. Earth Syst. Sci., 21, 2463–2481, https://doi.org/10.5194/hess-21-2463-2017, https://doi.org/10.5194/hess-21-2463-2017, 2017
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Parametric distribution functions are commonly used to model precipitation amounts at gauged and ungauged locations. Nonparametric distributions offer a more flexible way to model precipitation amounts. However, the nonparametric models do not exhibit parameters that can be easily regionalized for application at ungauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate the usage of daily gauges for sub-daily resolutions.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
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For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Aline Murawski, Gerd Bürger, Sergiy Vorogushyn, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4283–4306, https://doi.org/10.5194/hess-20-4283-2016, https://doi.org/10.5194/hess-20-4283-2016, 2016
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To understand past flood changes in the Rhine catchment and the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. Here the link between patterns and local climate is tested, and the skill of GCMs in reproducing these patterns is evaluated.
Kue Bum Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci., 20, 2019–2034, https://doi.org/10.5194/hess-20-2019-2016, https://doi.org/10.5194/hess-20-2019-2016, 2016
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A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.
E. P. Maurer, D. L. Ficklin, and W. Wang
Hydrol. Earth Syst. Sci., 20, 685–696, https://doi.org/10.5194/hess-20-685-2016, https://doi.org/10.5194/hess-20-685-2016, 2016
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To translate climate model output from its native coarse scale to a finer scale more representative of that at which societal impacts are experienced, a common method applied is statistical downscaling. A component of many statistical downscaling techniques is quantile mapping (QM). QM can be applied at different spatial scales, and here we study how skill varies with spatial scale. We find the highest skill is generally obtained when applying QM at approximately a 50 km spatial scale.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
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Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
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