Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
© 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-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jan 2022
Research article |
| 12 Jan 2022
| 12 Jan 2022
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Álvaro Ossandón
CORRESPONDING AUTHOR
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA
Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Valparaíso, Chile
Manuela I. Brunner
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany
Balaji Rajagopalan
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
William Kleiber
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, USA
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Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2905, https://doi.org/10.5194/egusphere-2024-2905, 2024
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Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature from four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.
Alessia Matanó, Raed Hamed, Manuela I. Brunner, Marlies H. Barendrecht, and Anne F. Van Loon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2715, https://doi.org/10.5194/egusphere-2024-2715, 2024
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Persistent droughts change how rivers respond to rainfall. Our study of over 5,000 catchments worldwide found that hydrological and soil moisture droughts decrease river flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short, intense droughts reducing river response to rainfall and prolonged droughts increasing it.
Sebastian I. Cantarero, Edgart Flores, Harry Allbrook, Paulina Aguayo, Cristian A. Vargas, John E. Tamanaha, J. Bentley C. Scholz, Lennart T. Bach, Carolin R. Löscher, Ulf Riebesell, Balaji Rajagopalan, Nadia Dildar, and Julio Sepúlveda
Biogeosciences, 21, 3927–3958, https://doi.org/10.5194/bg-21-3927-2024, https://doi.org/10.5194/bg-21-3927-2024, 2024
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Our study explores lipid remodeling in response to environmental stress, specifically how cell membrane chemistry changes. We focus on intact polar lipids in a phytoplankton community exposed to diverse stressors in a mesocosm experiment. The observed remodeling indicates acyl chain recycling for energy storage in intact polar lipids during stress, reallocating resources based on varying growth conditions. This understanding is essential to grasp the system's impact on cellular pools.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
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Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Julia Miller, Andrea Böhnisch, Ralf Ludwig, and Manuela I. Brunner
Nat. Hazards Earth Syst. Sci., 24, 411–428, https://doi.org/10.5194/nhess-24-411-2024, https://doi.org/10.5194/nhess-24-411-2024, 2024
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We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of central Europe, using the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by 2099. High fire danger (FWI > 21.3) becomes the mean condition by 2099 under an RCP8.5 scenario. This study highlights the potential for severe fire events in central Europe from a meteorological perspective.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
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CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
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I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
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Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
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Reservoir regulation affects various streamflow characteristics. Still, information on when water is stored in and released from reservoirs is hardly available. We develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series. By applying this approach to 74 catchments in the Alps, we find that reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
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Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
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In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
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Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Josh Jacobson, William Kleiber, Michael Scheuerer, and Joseph Bellier
Nonlin. Processes Geophys., 27, 411–427, https://doi.org/10.5194/npg-27-411-2020, https://doi.org/10.5194/npg-27-411-2020, 2020
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Most verification metrics for ensemble forecasts assess the representation of uncertainty at a particular location and time. We study a new diagnostic tool based on fractions of threshold exceedance (FTE) which evaluates an additional important attribute: the ability of ensemble forecast fields to reproduce the spatial structure of observed fields. The utility of this diagnostic tool is demonstrated through simulations and an application to ensemble precipitation forecasts.
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, https://doi.org/10.5194/hess-24-3967-2020, 2020
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Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Manuela I. Brunner, Lieke A. Melsen, Andrew J. Newman, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 24, 3951–3966, https://doi.org/10.5194/hess-24-3951-2020, https://doi.org/10.5194/hess-24-3951-2020, 2020
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Streamflow seasonality is changing and expected to further change under the influence of climate change. We here assess how annual streamflow hydrographs will change in future by using a newly developed classification scheme. Our comparison of future with current annual hydrograph classes shows that robust changes are expected only for currently melt-influenced regions in the Rocky Mountains. These upstream changes may require the adaptation of management strategies in downstream regions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
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Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Manuela I. Brunner, Daniel Farinotti, Harry Zekollari, Matthias Huss, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 23, 4471–4489, https://doi.org/10.5194/hess-23-4471-2019, https://doi.org/10.5194/hess-23-4471-2019, 2019
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River flow regimes are expected to change and so are extreme flow regimes. We propose two methods for estimating extreme flow regimes and show on a data set from Switzerland how these extreme regimes are expected to change. Our results show that changes in low- and high-flow regimes are distinct for rainfall- and melt-dominated regions. Our findings provide guidance in water resource planning and management.
Manuela I. Brunner, Katharina Liechti, and Massimiliano Zappa
Nat. Hazards Earth Syst. Sci., 19, 2311–2323, https://doi.org/10.5194/nhess-19-2311-2019, https://doi.org/10.5194/nhess-19-2311-2019, 2019
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The 2018 drought event had severe ecological, economic, and social impacts. How extreme was it in Switzerland? We addressed this question by looking at different types of drought, including meteorological, hydrological, agricultural, and groundwater drought, and at the two characteristics deficit and deficit duration. The return period estimates depended on the region, variable, and return period considered.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
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This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Manuela I. Brunner, Reinhard Furrer, and Anne-Catherine Favre
Hydrol. Earth Syst. Sci., 23, 107–124, https://doi.org/10.5194/hess-23-107-2019, https://doi.org/10.5194/hess-23-107-2019, 2019
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Floods often affect a whole region and not only a single location. When estimating the rarity of regional events, the dependence of floods at different locations should be taken into account. We propose a simple model that considers the dependence of flood events at different locations and the network structure of the river system. We test this model on a medium-sized catchment in Switzerland. The model allows for the simulations of flood event sets at multiple gauged and ungauged locations.
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
Flood frequency analysis using mean daily flows vs. instantaneous peak flows
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
A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality
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
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
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.
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.
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
Akaike, H.: Akaike's Information Criterion, Springer Berlin
Heidelberg, Berlin, Heidelberg, p. 25, ISBN 978-3-642-04898-2, https://doi.org/10.1007/978-3-642-04898-2_110, 2011. a
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and
Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir
operations for water supply in snow-dominated river catchments, Water
Res. Res., 52, 4209–4225, https://doi.org/10.1002/2015WR017864, 2016. a
Apputhurai, P. and Stephenson, A. G.: Spatiotemporal hierarchical modelling of
extreme precipitation in Western Australia using anisotropic Gaussian random
fields, Environ. Ecol. Stat., 20, 667–677,
https://doi.org/10.1007/s10651-013-0240-9, 2013. a
Atyeo, J. and Walshaw, D.: A region-based hierarchical model for extreme
rainfall over the UK, incorporating spatial dependence and temporal trend,
Environmetrics, 23, 509–521, https://doi.org/10.1002/env.2155, 2012. a
Bracken, C., Rajagopalan, B., and Prairie, J.: A multisite seasonal ensemble
streamflow forecasting technique, Water Resour. Res., 46, W03532,
https://doi.org/10.1029/2009WR007965, 2010. a
Bracken, C., Rajagopalan, B., and Woodhouse, C.: A Bayesian hierarchical
nonhomogeneous hidden Markov model for multisite streamflow reconstructions,
Water Resour. Res., 52, 7837–7850, https://doi.org/10.1002/2016WR018887, 2016. a
Bracken, C., Holman, K. D., Rajagopalan, B., and Moradkhani, H.: A Bayesian
Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency
Analysis, Water Resour. Res., 54, 243–255,
https://doi.org/10.1002/2017WR020403, 2018. a, b
Brunner, M. I., Furrer, R., and Favre, A.-C.: Modeling the spatial dependence of floods using the Fisher copula, Hydrol. Earth Syst. Sci., 23, 107–124, https://doi.org/10.5194/hess-23-107-2019, 2019. a
Brunner, M. I., Papalexiou, S., Clark, M. P., and Gilleland, E.: How Probable
Is Widespread Flooding in the United States?, Water Resour. Res., 56,
e2020WR028096, https://doi.org/10.1029/2020WR028096, 2020. a
Chen, L., Zhang, Y., Zhou, J., Singh, V. P., Guo, S., and Zhang, J.: Real-time
error correction method combined with combination flood forecasting technique
for improving the accuracy of flood forecasting, J. Hydrol., 521,
157–169, https://doi.org/10.1016/j.jhydrol.2014.11.053, 2015. a
Clark, M. P. and Hay, L. E.: Use of medium-range numerical weather prediction
model output to produce forecasts of streamflow, J.
Hydrometeorol., 5, 15–32,
https://doi.org/10.1175/1525-7541(2004)005<0015:UOMNWP>2.0.CO;2, 2004. a
Coles, S.: An introduction to statistical modeling of extreme values,
Springer, London, ISBN 978-1-84996-874-4, 208 pp.,
https://doi.org/10.1007/978-1-4471-3675-0,
2001. a
Colorado's Decision Support Systems: Upper Colorado River Basin
Information, Tech. rep., Colorado Water Conservation Board, 125 pp., 2007. a
Cooley, D. and Sain, S. R.: Spatial hierarchical modeling of precipitation
extremes from a regional climate model, J. Agr. Biol.
Envir. St., 15, 381–402, https://doi.org/10.1007/s13253-010-0023-9,
2010. a
Cooley, D., Nychka, D., and Naveau, P.: Bayesian spatial modeling of extreme
precipitation return levels, J. Am. Stat.
Assoc., 102, 824–840, https://doi.org/10.1198/016214506000000780, 2007. a
D'Agostino, R. B. and Stephens, M. A.: Goodness-of-Fit Techniques, Statistics, textbooks and monographs, Marcel
Dekker, 68, 560 pp.,
ISBN 0-8247-8705-6, 1986. a
De Cicco, L. A., Hirsch, R. M., Lorenz, D., and Watkins, W.: DataRetrieval:
R packages for discovering and retrieving water data available from U.S.
federal hydrologic web services, Version: 2.7.9, U.S. Geological Survey,
https://doi.org/10.5066/P9X4L3GE, 2018. a
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic
Multidecadal Oscillation and its relation to rainfall and river flows in the
continental U.S., Geophys. Res. Lett., 28, 2077–2080,
https://doi.org/10.1029/2000GL012745, 2001. a
Gelman, A. and Hill, J.: Data Analysis Using Regression and
Multilevel/Hierarchical Models, Analytical Methods for Social Research,
Cambridge University Press, Cambridge, 625 pp., ISBN 9780511790942,
https://doi.org/10.1017/CBO9780511790942, 2006. a, b
Gelman, A. and Rubin, D. B.: Inference from iterative simulation using
multiple sequences, Stat. Sci., 7, 457–472,
https://doi.org/10.1214/ss/1177011136, 1992. a
Genest, C. and Favre, A.-C.: Everything You Always Wanted to Know about Copula
Modeling but Were Afraid to Ask, J. Hydrol. Eng., 12,
347–368, https://doi.org/10.1061/(asce)1084-0699(2007)12:4(347), 2007. a, b
Ghile, Y. B. and Schulze, R. E.: Evaluation of three numerical weather
prediction models for short and medium range agrohydrological applications,
Water Resour. Manag., 24, 1005–1028, https://doi.org/10.1007/s11269-009-9483-5,
2010. a
Gibbons, J. D. and Chakraborti, S.: Nonparametric Statistical Inference,
Marcel Dekker, Inc., New York, NY, 4 edn., 645 pp., ISBN 0-8247-4052-1,
1992. a
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction,
and estimation, J. Am. Stat. Assoc., 102,
359–378, https://doi.org/10.1198/016214506000001437, 2007. a, b, c
Gneiting, T., Stanberry, L. I., Grimit, E. P., Held, L., and Johnson, N. A.:
Assessing probabilistic forecasts of multivariate quantities, with an
application to ensemble predictions of surface winds, Test, 17, 211–235,
https://doi.org/10.1007/s11749-008-0114-x, 2008. a, b
Grantz, K., Rajagopalan, B., Clark, M., and Zagona, E.: A technique for
incorporating large-scale climate information in basin-scale ensemble
streamflow forecasts, Water Resour. Res., 41, 10410,
https://doi.org/10.1029/2004WR003467, 2005. a
He, J., Anderson, A., and Valeo, C.: Bias compensation in flood frequency
analysis, Hydrolog. Sci. J., 60, 381–401,
https://doi.org/10.1080/02626667.2014.885651, 2015. a
Henze, N. and Zirkler, B.: A class of invariant consistent tests for
multivariate normality, Commun. Stat. Theory,
19, 3595–3617, https://doi.org/10.1080/03610929008830400, 1990. a
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hidalgo, H. G.: Climate precursors of multidecadal drought variability in the
western United States, Water Resour. Res., 40, 1–10,
https://doi.org/10.1029/2004WR003350, 2004. a, b
Hochrainer-Stigler, S.: Extreme and Systemic Risk Analysis, Integrated
Disaster Risk Management, Springer Singapore, Singapore, 156 pp.,
https://doi.org/10.1007/978-981-15-2689-3, 2020. a, b, c
Kahya, E. and Dracup, J. A.: The influences of Type 1 El Nino and La Nina
events on streamflows in the Pacific southwest of the United States, J.
Climate, 7, 965–976,
https://doi.org/10.1175/1520-0442(1994)007<0965:TIOTEN>2.0.CO;2, 1994. a
Katz, R. W.: Statistical Methods for Nonstationary Extremes, in: Extremes in
a Changing Climate, edited by: AghaKouchak, A., Easterling, D., Hsu, K.,
Schubert, S., and Sorooshian, S., Springer, Dordrecht, Dordrecht,
65 edn., 15–37, https://doi.org/10.1007/978-94-007-4479-0_2, 2013. a
Koster, R. D., Mahanama, S. P., Livneh, B., Lettenmaier, D. P., and Reichle,
R. H.: Skill in streamflow forecasts derived from large-scale estimates of
soil moisture and snow, Nat. Geosci., 3, 613–616,
https://doi.org/10.1038/ngeo944, 2010. a, b, c
Kurian, C., Sudheer, K. P., Vema, V. K., and Sahoo, D.: Effective flood
forecasting at higher lead times through hybrid modelling framework, J.
Hydrol., 587, 124945, https://doi.org/10.1016/j.jhydrol.2020.124945, 2020. a
Kwon, H. H., Brown, C., Xu, K., and Lall, U.: Seasonal and annual maximum
streamflow forecasting using climate information: Application to the Three
Gorges Dam in the Yangtze River basin, China, Hydrolog. Sci. J.,
54, 582–595, https://doi.org/10.1623/hysj.54.3.582, 2009. a, b, c
Lima, C. H. and Lall, U.: Climate informed monthly streamflow forecasts for
the Brazilian hydropower network using a periodic ridge regression model,
J. Hydrol., 380, 438–449, https://doi.org/10.1016/j.jhydrol.2009.11.016,
2010. a, b
Livneh, B. and Badger, A. M.: Drought less predictable under declining future
snowpack, Nat. Clim. Change, 10, 452–458,
https://doi.org/10.1038/s41558-020-0754-8, 2020. a, b, c, d
Mardia, K. V.: Measures of Multivariate Skewness and Kurtosis with
Applications, Biometrika, 57, 519, https://doi.org/10.2307/2334770, 1970. a
McCabe, G. J. and Dettinger, M. D.: Decadal variations in the strength of ENSO
teleconnections with precipitation in the western United States,
Int. J. Climatol., 19, 1399–1410,
https://doi.org/10.1002/(SICI)1097-0088(19991115)19:13<1399::AID-JOC457>3.0.CO;2-A,
1999. a
McCabe, G. J., Clark, M. P., and Hay, L. E.: Rain-on-snow events in the
western United States, B. Am. Meteorol. Soc., 88,
319–328, https://doi.org/10.1175/BAMS-88-3-319, 2007. a, b
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database,
J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012. a
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C.,
Barlage, M., and Rasmussen, R.: Projected increases and shifts in
rain-on-snow flood risk over western North America, Nat. Clim. Change,
8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018. a, b
Nowak, K., Hoerling, M., Rajagopalan, B., and Zagona, E.: Colorado river basin
hydroclimatic variability, J. Climate, 25, 4389–4403,
https://doi.org/10.1175/JCLI-D-11-00406.1, 2012. a
Ossandon, A.: Projection of Seasonal Streamflow Extremes for UCRB Dataset, Hydroshare [data set],
https://doi.org/10.4211/hs.d8c1b413951843cf9be968e9d2a4aa79, 2021. a
Pagano, T.: Soils, snow and streamflow, Nat. Geosci., 3, 591–592, https://doi.org/10.1038/ngeo948, 2010. a
Pagano, T. C., Garen, D. C., Perkins, T. R., and Pasteris, P. A.: Daily
Updating of Operational Statistical Seasonal Water Supply Forecasts for the
western U.S., J. Am. Water Resour. As., 45,
767–778, https://doi.org/10.1111/j.1752-1688.2009.00321.x, 2009. a, b
Penn, C. A., Clow, D. W., Sexstone, G. A., and Murphy, S. F.: Changes in
Climate and Land Cover Affect Seasonal Streamflow Forecasts in the Rio Grande
Headwaters, J. Am. Water Resour. As., 56,
882–902, https://doi.org/10.1111/1752-1688.12863, 2020. a, b
Plummer, M.: JAGS: A program for analysis of Bayesian graphical models using
Gibbs sampling, Proceedings of the 3rd international workshop on distributed statistical computing (DSC 2003), Vienna, Austria, 20–22 March 2003, 1609-395X,
2003. a
Plummer, M.: rjags: Bayesian graphical models using MCMC, R package version 4-10, [code], available at:
https://CRAN.R-project.org/package=rjags (last access: 16 June 2021),
2019. a
Rajagopalan, B., Cook, E., Lall, U., and Ray, B. K.: Spatiotemporal
variability of ENSO and SST teleconnections to summer drought over the United
States during the twentieth century, J. Climate, 13, 4244–4255,
https://doi.org/10.1175/1520-0442(2000)013<4244:SVOEAS>2.0.CO;2, 2000. a
R Core, T.: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing [code], available at: https://www.R-project.org/ (last access: 16 June 2021),
2017. a
Redmond, K. T. and Koch, R. W.: Surface Climate and Streamflow Variability in
the Western United States and Their Relationship to Large-Scale Circulation
Indices, Water Resour. Res., 27, 2381–2399, https://doi.org/10.1029/91WR00690,
1991. a
Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E.: A multimodel
ensemble forecast framework: Application to spring seasonal flows in the
Gunnison River Basin, Water Resour. Res., 42, 9404,
https://doi.org/10.1029/2005WR004653, 2006. a
Renard, B.: A Bayesian hierarchical approach to regional frequency analysis,
Water Resour. Res., 47, W11513, https://doi.org/10.1029/2010WR010089, 2011. a
Robert, C. and Casella, G.: A short history of Markov Chain Monte Carlo:
Subjective recollections from incomplete data, Stat. Sci., 26,
102–115, https://doi.org/10.1214/10-STS351, 2011. a
Robertson, D. E. and Wang, Q. J.: A Bayesian approach to predictor selection
for seasonal streamflow forecasting, J. Hydrometeorol., 13,
155–171, https://doi.org/10.1175/JHM-D-10-05009.1, 2012. a, b
Royston, J. P.: An Extension of Shapiro and Wilk's W Test for Normality to
Large Samples, Appl. Stat., 31, 115, https://doi.org/10.2307/2347973, 1982. a
Ruiz, J. E., Cordery, I., and Sharma, A.: Forecasting streamflows in Australia
using the tropical Indo-Pacific thermocline as predictor, J.
Hydrol., 341, 156–164, https://doi.org/10.1016/j.jhydrol.2007.04.021, 2007. a, b
Safeeq, M., Shukla, S., Arismendi, I., Grant, G. E., Lewis, S. L., and Nolin,
A.: Influence of winter season climate variability on snow–precipitation
ratio in the western United States, Int. J. Climatol.,
36, 3175–3190, https://doi.org/10.1002/JOC.4545, 2016. a
Sankarasubramanian, A. and Lall, U.: Flood quantiles in a changing climate:
Seasonal forecasts and causal relations, Water Resour. Res., 39, 1134,
https://doi.org/10.1029/2002WR001593, 2003. a
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A.:
Bayesian measures of model complexity and fit, J. Roy.
Stat. Soc. B Met., 64, 583–639,
https://doi.org/10.1111/1467-9868.00353, 2002. a
Thieken, A. H., Apel, H., and Merz, B.: Assessing the probability of
large-scale flood loss events: a case study for the river Rhine, Germany,
J. Flood Risk Manag., 8, 247–262, https://doi.org/10.1111/JFR3.12091,
2015.
a
Thomson, A. M., Brown, R. A., Rosenberg, N. J., Izaurralde, R. C., Legler,
D. M., and Srinivasan, R.: Simulated impacts of El Niño/Southern
Oscillation on United States water resources, J. Am. Water
Resour. As., 39, 137–148,
https://doi.org/10.1111/j.1752-1688.2003.tb01567.x, 2003. a
Timilsena, J., Piechota, T., Tootle, G., and Singh, A.: Associations of
interdecadal/interannual climate variability and long-term colorado river
basin streamflow, J. Hydrol., 365, 289–301,
https://doi.org/10.1016/j.jhydrol.2008.11.035, 2009. a, b, c
Tootle, G. A., Piechota, T. C., and Singh, A.: Coupled oceanic-atmospheric
variability and U.S. streamflow, Water Resour. Res., 41, 1–11,
https://doi.org/10.1029/2005WR004381, 2005. a, b
Werner, K. and Yeager, K.: Challenges in forecasting the 2011 runoff season in
the colorado basin, J. Hydrometeorol., 14, 1364–1371,
https://doi.org/10.1175/JHM-D-12-055.1, 2013. a, b, c, d
Werner, K., Brandon, D., Clark, M., and Gangopadhyay, S.: Climate Index
Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts, J.
Hydrometeorol., 5, 1076–1090, https://doi.org/10.1175/JHM-381.1, 2004. a
Wijayarathne, D. B. and Coulibaly, P.: Identification of hydrological models
for operational flood forecasting in St. John's, Newfoundland, Canada,
J. Hydrol.-Regional Studies, 27, 100646, https://doi.org/10.1016/j.ejrh.2019.100646,
2020. a
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.:
Quantifying streamflow forecast skill elasticity to initial condition and
climate prediction skill, J. Hydrometeorol., 17, 651–668,
https://doi.org/10.1175/JHM-D-14-0213.1, 2016. a, b
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Short summary
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.
Timely projections of seasonal streamflow extremes on a river network can be useful for flood...
