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Breinl, Korbinian
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Publications (10 of 10) Show all publications
Breinl, K. & Di Baldassarre, G. (2019). Space-time disaggregation of precipitation and temperature across different climates and spatial scales. JOURNAL OF HYDROLOGY-REGIONAL STUDIES, 21, 126-146
Open this publication in new window or tab >>Space-time disaggregation of precipitation and temperature across different climates and spatial scales
2019 (English)In: JOURNAL OF HYDROLOGY-REGIONAL STUDIES, ISSN 2214-5818, Vol. 21, p. 126-146Article in journal (Refereed) Published
Abstract [en]

Study region: This study focuses on two study areas: the Province of Trento (Italy; 6200 km(2)), and entire Sweden (447000km(2)). The Province of Trento is a complex mountainous area including subarctic, humid continental and Tundra climates. Sweden, instead, is mainly dominated by a subarctic climate in the North and an oceanic climate in the South. Study focus: Hydrological predictions often require long weather time series of high temporal resolution. Daily observations typically exceed the length of sub-daily observations, and daily gauges are more widely available than sub-daily gauges. The issue can be overcome by disaggregating daily into sub-daily values. We present an open-source tool for the non-parametric space-time disaggregation of daily precipitation and temperature into hourly values called spatial method of fragments (S-MOF). A large number of comparative experiments was conducted for both S-MOF and MOF in the two study regions. New hydrological insights for the region: Our experiments demonstrate the applicability of the univariate and spatial method of fragments in the two temperate/subarctic study regions where snow processes are important. S-MOF is able to produce consistent precipitation and temperature fields at sub-daily resolution with acceptable method related bias. For precipitation, although climatologically more complex, S-MOF generally leads to better results in the Province of Trento than in Sweden, mainly due to the smaller spatial extent of the former region.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Keywords
Precipitation, Temperature, Disaggregation, Space-time scaling, Non-parametric, Method of fragments
National Category
Ecology
Identifiers
urn:nbn:se:uu:diva-377221 (URN)10.1016/j.ejrh.2018.12.002 (DOI)000457248200010 ()
Funder
Swedish Research Council FormasEU, Horizon 2020, 793558
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-02-15Bibliographically approved
Di Baldassarre, G., Nohrstedt, D., Mård, J., Burchardt, S., Albin, C., Bondesson, S., . . . Parker, C. F. (2018). An Integrative Research Framework to Unravel the Interplay of Natural Hazards and Vulnerabilities. Earth's Future, 6(3), 305-310
Open this publication in new window or tab >>An Integrative Research Framework to Unravel the Interplay of Natural Hazards and Vulnerabilities
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2018 (English)In: Earth's Future, ISSN 1384-5160, E-ISSN 2328-4277, Vol. 6, no 3, p. 305-310Article in journal (Refereed) Published
Abstract [en]

Climate change, globalization, urbanization, social isolation, and increased interconnectedness between physical, human, and technological systems pose major challenges to disaster risk reduction (DRR). Subsequently, economic losses caused by natural hazards are increasing in many regions of the world, despite scientific progress, persistent policy action, and international cooperation. We argue that these dramatic figures call for novel scientific approaches and new types of data collection to integrate the two main approaches that still dominate the science underpinning DRR: the hazard paradigm and the vulnerability paradigm. Building from these two approaches, here we propose a research framework that specifies the scope of enquiry, concepts, and general relations among phenomena. We then discuss the essential steps to advance systematic empirical research and evidence-based DRR policy action. Plain Language Summary The recent deadly earthquake in Iran-Iraq has been yet another reminder of the topicality of natural hazards, and it has come just after an unprecedented series of catastrophic events, including the extensive flooding in South Asia and the string of devastating hurricanes in the Americas. He we identify three main puzzles in the nexus of natural hazards and vulnerabilities, and demonstrate how novel approaches are needed to solve them with reference to a flood risk example. Specifically, we show how a new research framework can guide systematic data collections to advance the fundamental understanding of socionatural interactions, which is an essential step to improve the development of policies for disaster risk reduction.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
Disaster risk reduction, Natural hazards, Vulnerability, Flood risk, Socio-hydrology
National Category
Environmental Sciences
Identifiers
urn:nbn:se:uu:diva-350188 (URN)10.1002/2017EF000764 (DOI)000430171600002 ()
Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-06-18Bibliographically approved
Okoli, K., Breinl, K., Brandimarte, L., Botto, A., Volpi, E. & Di Baldassarre, G. (2018). Model averaging versus model selection: estimating design floods with uncertain river flow data. Hydrological Sciences Journal, 63(13-14), 1913-1926
Open this publication in new window or tab >>Model averaging versus model selection: estimating design floods with uncertain river flow data
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2018 (English)In: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 63, no 13-14, p. 1913-1926Article in journal (Refereed) Published
Abstract [en]

This study compares model averaging and model selection methods to estimate design floods, while accounting for the observation error that is typically associated with annual maximum flow data. Model selection refers to methods where a single distribution function is chosen based on prior knowledge or by means of selection criteria. Model averaging refers to methods where the results of multiple distribution functions are combined. Numerical experiments were carried out by generating synthetic data using the Wakeby distribution function as the parent distribution. For this study, comparisons were made in terms of relative error and root mean square error (RMSE) referring to the 1-in-100 year flood. The experiments show that model averaging and model selection methods lead to similar results, especially when short samples are drawn from a highly asymmetric parent. Also, taking an arithmetic average of all design flood estimates gives estimated variances similar to those obtained with more complex weighted model averaging.

Keywords
model averaging, model selection, design flood, Akaike information criterion
National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:uu:diva-372899 (URN)10.1080/02626667.2018.1546389 (DOI)000453717400004 ()
Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-08-09Bibliographically approved
Di Baldassarre, G., Wanders, N., AghaKouchak, A., Kuil, L., Rangecroft, S., Veldkamp, T., . . . Van Loon, A. F. (2018). Water shortages worsened by reservoir effects. Nature Sustainability, 1, 617-622
Open this publication in new window or tab >>Water shortages worsened by reservoir effects
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2018 (English)In: Nature Sustainability, ISSN 2398-9629, Vol. 1, p. 617-622Article in journal (Refereed) Published
Abstract [en]

The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.

Place, publisher, year, edition, pages
London: Nature Publishing Group, 2018
National Category
Environmental Sciences
Research subject
Earth Science with specialization in Environmental Analysis
Identifiers
urn:nbn:se:uu:diva-366446 (URN)10.1038/s41893-018-0159-0 (DOI)000450118100006 ()
Available from: 2018-11-20 Created: 2018-11-20 Last updated: 2019-01-22Bibliographically approved
Breinl, K., Di Baldassarre, G., Girons Lopez, M., Hagenlocher, M., Vico, G. & Rutgersson, A. (2017). Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?. Scientific Reports, 7, Article ID 5449.
Open this publication in new window or tab >>Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 5449Article in journal (Refereed) Published
Abstract [en]

Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

National Category
Meteorology and Atmospheric Sciences Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:uu:diva-326713 (URN)10.1038/s41598-017-05822-y (DOI)000405464200086 ()28710411 (PubMedID)
Available from: 2017-07-26 Created: 2017-07-26 Last updated: 2018-01-13Bibliographically approved
Breinl, K. (2016). Driving a lumped hydrological model with precipitation output from weather generators of different complexity. Hydrological Sciences Journal, 61(8), 1395-1414
Open this publication in new window or tab >>Driving a lumped hydrological model with precipitation output from weather generators of different complexity
2016 (English)In: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 61, no 8, p. 1395-1414Article in journal (Refereed) Published
Abstract [en]

This paper deals with the question of whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Three different weather generators were tested: a univariate “Richardson type” model, an adapted univariate Richardson type model with an improved reproduction of the autocorrelation of precipitation amounts and a semi-parametric multi-site weather generator. The three modelling systems were evaluated in two Alpine study areas by comparing the hydrological output with respect to monthly and daily statistics as well as extreme design flows. The application of a univariate Richardson type weather generator to lumped precipitation time series requires additional attention. Established parametric distribution functions for single-site precipitation turned out to be unsuitable for lumped precipitation time series and led to a large bias in the hydrological simulations. Combining a multi-site weather generator with a hydrological model produced the least bias.

National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:uu:diva-297149 (URN)10.1080/02626667.2015.1036755 (DOI)
Funder
EU, FP7, Seventh Framework Programme, 263953
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2018-01-10
Breinl, K., Strasser, U., Bates, P. D. & Kienberger, S. (2015). A joint modelling framework for daily extremes of river discharge and precipitation in urban areas. Journal of Flood Risk Management
Open this publication in new window or tab >>A joint modelling framework for daily extremes of river discharge and precipitation in urban areas
2015 (English)In: Journal of Flood Risk Management, ISSN 1753-318X, E-ISSN 1753-318XArticle in journal (Refereed) Published
Abstract [en]

Human settlements are often at risk from multiple hydro-meteorological hazards, which include fluvial floods, short-time extreme precipitation (leading to ‘pluvial’ floods) or coastal floods. In the past, considerable scientific effort has been devoted to assessing fluvial floods. Only recently have methods been developed to assess the hazard and risk originating from pluvial phenomena, whereas little effort has been dedicated to joint approaches. The aim of this study was to develop a joint modelling framework for simulating daily extremes of river discharge and precipitation in urban areas. The basic framework is based on daily observations coupled with a novel precipitation disaggregation algorithm using nearest neighbour resampling combined with the method of fragments to overcome data limitations and facilitate its transferability. The framework generates dependent time series of river discharge and urban precipitation that allow for the identification of fluvial flood days (daily peak discharge), days of extreme precipitation potentially leading to pluvial phenomena (maximum hourly precipitation) and combined fluvial–pluvial flood days (combined time series). Critical thresholds for hourly extreme precipitation were derived from insurance and fire service data.

National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:uu:diva-297157 (URN)10.1111/jfr3.12150 (DOI)
Funder
EU, FP7, Seventh Framework Programme, 263953
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2018-01-10
Breinl, K., Turkington, T. & Stowasser, M. (2015). Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards. Meteorological Applications, 22(3), 334-347
Open this publication in new window or tab >>Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards
2015 (English)In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 22, no 3, p. 334-347Article in journal (Refereed) Published
Abstract [en]

Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (2013), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge networks. Precipitation amounts were simulated in a two-step process by first resampling observations and then sampling and reshuffling of parametric precipitation amounts. In the present study, the precipitation model by Breinl et al. (2013, J. Hydrol. 498: 23–35) is implemented in a weather generation framework for daily precipitation and temperature. It is extended to a considerably larger gauge station network of 19 stations and further improved to reduce the duplication of historical records in the simulation. Autoregressive-moving-average models (ARMA) are used to simulate mean daily temperature at three sites. Power transformations reduce the bias of simulated temperature extremes. Precipitation amounts are simulated by means of hybrid distributions consisting of a Weibull distribution for low precipitation amounts and a generalized Pareto distribution (GPD) for moderate and extreme precipitation amounts. The proposed weather generator is particularly suitable for assessing hydrometeorological hazards such as flooding as it reproduces the spatial variability of precipitation very well and can generate unobserved extremes.

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:uu:diva-297156 (URN)10.1002/met.1459 (DOI)
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2017-11-28
Turkington, T., Ettema, J., van Westen, C. & Breinl, K. (2014). Empirical atmospheric thresholds for debris flows and flash floodsin the southern French Alps. Natural hazards and earth system sciences, 14, 1517-1530
Open this publication in new window or tab >>Empirical atmospheric thresholds for debris flows and flash floodsin the southern French Alps
2014 (English)In: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 14, p. 1517-1530Article in journal (Refereed) Published
Abstract [en]

Debris flows and flash floods are often preceded by intense, convective rainfall. The establishment of reliable rainfall thresholds is an important component for quantitative hazard and risk assessment, and for the development of an early warning system. Traditional empirical thresholds based on peak intensity, duration and antecedent rainfall can be difficult to verify due to the localized character of the rainfall and the absence of weather radar or sufficiently dense rain gauge networks in mountainous regions. However, convective rainfall can be strongly linked to regional atmospheric patterns and profiles. There is potential to employ this in empirical threshold analysis. This work develops a methodology to determine robust thresholds for flash floods and debris flows utilizing regional atmospheric conditions derived from ECMWF ERA-Interim reanalysis data, comparing the results with rain-gauge-derived thresholds. The method includes selecting the appropriate atmospheric indicators, categorizing the potential thresholds, determining and testing the thresholds. The method is tested in the Ubaye Valley in the southern French Alps (548 km2), which is known to have localized convection triggered debris flows and flash floods. This paper shows that instability of the atmosphere and specific humidity at 700 hPa are the most important atmospheric indicators for debris flows and flash floods in the study area. Furthermore, this paper demonstrates that atmospheric reanalysis data are an important asset, and could replace rainfall measurements in empirical exceedance thresholds for debris flows and flash floods.

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:uu:diva-297160 (URN)10.5194/nhess-14-1517-2014 (DOI)000338650500013 ()
Funder
EU, FP7, Seventh Framework Programme, 263953
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2017-11-28Bibliographically approved
Breinl, K., Turkington, T. & Stowasser, M. (2013). Stochastic generation of multi-site daily precipitation for applications in risk management. Journal of Hydrology, 498, 23-35
Open this publication in new window or tab >>Stochastic generation of multi-site daily precipitation for applications in risk management
2013 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 498, p. 23-35Article in journal (Refereed) Published
Abstract [en]

Unlike single-site precipitation generators, multi-site precipitation generators make it possible to reproduce the space–time variation of precipitation at several sites. The extension of single-site approaches to multiple sites is a challenging task, and has led to a large variety of different model philosophies for multi-site models. This paper presents an alternative semi-parametric multi-site model for daily precipitation that is straightforward and easy to implement. Multi-site precipitation occurrences are simulated with a univariate Markov process, removing the need for individual Markov models at each site. Precipitation amounts are generated by first resampling observed values, followed by sampling synthetic precipitation amounts from parametric distribution functions. These synthetic precipitation amounts are subsequently reshuffled according to the ranks of the resampled observations in order to maintain important statistical properties of the observation network. The proposed method successfully combines the advantages of non-parametric bootstrapping and parametric modeling techniques. It is applied to two small rain gauge networks in France (Ubaye catchment) and Austria/Germany (Salzach catchment) and is shown to well reproduce the observations. Limitations of the model relate to the bias of the reproduced seasonal standard deviation of precipitation and the underestimation of maximum dry spells. While the lag-1 autocorrelation is well reproduced for precipitation occurrences, it tends to be underestimated for precipitation amounts. The model can generate daily precipitation amounts exceeding the ones in the observations, which can be crucial for risk management related applications. Moreover, the model deals particularly well with the spatial variability of precipitation. Despite its straightforwardness, the new concept makes a good alternative for risk management related studies concerned with producing daily synthetic multi-site precipitation time series.

National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:uu:diva-297152 (URN)10.1016/j.jhydrol.2013.06.015 (DOI)
Funder
EU, FP7, Seventh Framework Programme, 263953
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2018-01-10
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