Articles | Volume 15, issue 9
https://doi.org/10.5194/hess-15-2853-2011
© Author(s) 2011. 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-15-2853-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Sep 2011
Research article |
| 13 Sep 2011
Low-frequency variability of European runoff
L. Gudmundsson
Department of Geosciences, University of Oslo, Oslo, Norway
L. M. Tallaksen
Department of Geosciences, University of Oslo, Oslo, Norway
K. Stahl
Department of Geosciences, University of Oslo, Oslo, Norway
Institute of Hydrology, University of Freiburg, Freiburg, Germany
A. K. Fleig
Department of Geosciences, University of Oslo, Oslo, Norway
current address: Norwegian Water Resources and Energy Directorate, Oslo, Norway
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
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Parsimonious statistical learning models for low-flow estimation
Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling
Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences
Effects of climate anomalies on warm-season low flows in Switzerland
Histogram via entropy reduction (HER): an information-theoretic alternative for geostatistics
Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework
A methodology to estimate flow duration curves at partially ungauged basins
The role of flood wave superposition in the severity of large floods
Contribution of low-frequency climatic–oceanic oscillations to streamflow variability in small, coastal rivers of the Sierra Nevada de Santa Marta (Colombia)
Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling
An assessment of trends and potential future changes in groundwater-baseflow drought based on catchment response times
More frequent flooding? Changes in flood frequency in the Pearl River basin, China, since 1951 and over the past 1000 years
Topography significantly influencing low flows in snow-dominated watersheds
A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data
Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China
Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information
On the probability distribution of daily streamflow in the United States
The European 2015 drought from a hydrological perspective
Heterogeneity measures in hydrological frequency analysis: review and new developments
ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction
Ordinary kriging as a tool to estimate historical daily streamflow records
Trends in floods in West Africa: analysis based on 11 catchments in the region
Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment
Spatial controls on groundwater response dynamics in a snowmelt-dominated montane catchment
Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?
Data compression to define information content of hydrological time series
Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?
Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain
Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses
Land cover and water yield: inference problems when comparing catchments with mixed land cover
An elusive search for regional flood frequency estimates in the River Nile basin
Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment
Contrasting trends in floods for two sub-arctic catchments in northern Sweden – does glacier presence matter?
Long-range forecasting of intermittent streamflow
Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization
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.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
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Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Johannes Laimighofer, Michael Melcher, and Gregor Laaha
Hydrol. Earth Syst. Sci., 26, 129–148, https://doi.org/10.5194/hess-26-129-2022, https://doi.org/10.5194/hess-26-129-2022, 2022
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This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.
Kailong Li, Guohe Huang, and Brian Baetz
Hydrol. Earth Syst. Sci., 25, 4947–4966, https://doi.org/10.5194/hess-25-4947-2021, https://doi.org/10.5194/hess-25-4947-2021, 2021
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We proposed a test statistic feature importance method to quantify the importance of predictor variables for random-forest-like models. The proposed method does not rely on any performance measures to evaluate variable rankings, which can thus result in unbiased variable rankings. The resulting variable rankings based on the proposed method could help random forest achieve its optimum predictive accuracy.
Wei Hu and Bing Si
Hydrol. Earth Syst. Sci., 25, 321–331, https://doi.org/10.5194/hess-25-321-2021, https://doi.org/10.5194/hess-25-321-2021, 2021
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Partial wavelet coherency method is improved to explore the bivariate relationships at different scales and locations after excluding the effects of other variables. The method was tested with artificial datasets and applied to a measured dataset. Compared with others, this method has the advantages of capturing phase information, dealing with multiple excluding variables, and producing more accurate results. This method can be used in different areas with spatial or temporal datasets.
Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
Hydrol. Earth Syst. Sci., 24, 5423–5438, https://doi.org/10.5194/hess-24-5423-2020, https://doi.org/10.5194/hess-24-5423-2020, 2020
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Low river flows affect societies and ecosystems. Here we study how precipitation and potential evapotranspiration shape low flows across a network of 380 Swiss catchments. Low flows in these rivers typically result from below-average precipitation and above-average potential evapotranspiration. Extreme low flows result from long periods of the combined effects of both drivers.
Stephanie Thiesen, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret
Hydrol. Earth Syst. Sci., 24, 4523–4540, https://doi.org/10.5194/hess-24-4523-2020, https://doi.org/10.5194/hess-24-4523-2020, 2020
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A spatial interpolator has been proposed for exploring the information content of the data in the light of geostatistics and information theory. It showed comparable results to traditional interpolators, with the advantage of presenting generalization properties. We discussed three different ways of combining distributions and their implications for the probabilistic results. By its construction, the method provides a suitable and flexible framework for uncertainty analysis and decision-making.
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 24, 4109–4133, https://doi.org/10.5194/hess-24-4109-2020, https://doi.org/10.5194/hess-24-4109-2020, 2020
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Annual runoff is a measure of how much water flows through a river during a year and is an important quantity, e.g. when planning infrastructure. In this paper, we suggest a new statistical model for annual runoff estimation. The model exploits correlation between rivers and is able to detect whether the annual runoff in the target river follows repeated patterns over time relative to neighbouring rivers. In our work we show for what cases the latter represents a benefit over comparable methods.
Elena Ridolfi, Hemendra Kumar, and András Bárdossy
Hydrol. Earth Syst. Sci., 24, 2043–2060, https://doi.org/10.5194/hess-24-2043-2020, https://doi.org/10.5194/hess-24-2043-2020, 2020
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The paper presents a new, simple and model-free methodology to estimate the streamflow at partially gauged basins, given the precipitation gauged at another basin. We show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Because of the dependence on the climate, discharge data at the target site are here retrieved using the Antecedent Precipitation Index (API) of the donor site as it represents in a streamflow-like way the precipitation of the basin.
Björn Guse, Bruno Merz, Luzie Wietzke, Sophie Ullrich, Alberto Viglione, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 24, 1633–1648, https://doi.org/10.5194/hess-24-1633-2020, https://doi.org/10.5194/hess-24-1633-2020, 2020
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Floods are influenced by river network processes, among others. Flood characteristics of tributaries may affect flood severity downstream of confluences. The impact of flood wave superposition is investigated with regard to magnitude and temporal matching of flood peaks. Our study in Germany and Austria shows that flood wave superposition is not the major driver of flood severity. However, there is the potential for large floods at some confluences in cases of temporal matching of flood peaks.
Juan Camilo Restrepo, Aldemar Higgins, Jaime Escobar, Silvio Ospino, and Natalia Hoyos
Hydrol. Earth Syst. Sci., 23, 2379–2400, https://doi.org/10.5194/hess-23-2379-2019, https://doi.org/10.5194/hess-23-2379-2019, 2019
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This study evaluated the influence of low-frequency oscillations that are linked to large-scale oceanographic–atmospheric processes, on streamflow variability in small mountain rivers of the Sierra Nevada de Santa Marta, Colombia, aiming to explore streamflow variability, estimate the net contribution to the energy of low-frequency oscillations to streamflow anomalies, and analyze the linkages between streamflow anomalies and large-scale, low-frequency oceanographic–atmospheric processes.
Jens Grundmann, Sebastian Hörning, and András Bárdossy
Hydrol. Earth Syst. Sci., 23, 225–237, https://doi.org/10.5194/hess-23-225-2019, https://doi.org/10.5194/hess-23-225-2019, 2019
Jost Hellwig and Kerstin Stahl
Hydrol. Earth Syst. Sci., 22, 6209–6224, https://doi.org/10.5194/hess-22-6209-2018, https://doi.org/10.5194/hess-22-6209-2018, 2018
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Due to the lack of long-term observations, insights into changes of groundwater resources are obscured. In this paper we assess past and potential future changes in groundwater drought in headwater catchments using a baseflow approach. There are a few past trends which are highly dependent on the period of analysis. Catchments with short response times are found to have a higher sensitivity to projected seasonal precipitation shifts, urging for a local management based on response times.
Qiang Zhang, Xihui Gu, Vijay P. Singh, Peijun Shi, and Peng Sun
Hydrol. Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/hess-22-2637-2018, https://doi.org/10.5194/hess-22-2637-2018, 2018
Qiang Li, Xiaohua Wei, Xin Yang, Krysta Giles-Hansen, Mingfang Zhang, and Wenfei Liu
Hydrol. Earth Syst. Sci., 22, 1947–1956, https://doi.org/10.5194/hess-22-1947-2018, https://doi.org/10.5194/hess-22-1947-2018, 2018
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Topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Topography plays a more dominant role in low flows than high flows. Our analysis also identified five significant TIs: perimeter, slope length factor, surface area, openness, and terrain characterization index. These can be used to compare watersheds when low flow assessments are conducted, specifically in snow-dominated regions.
Yan-Fang Sang, Fubao Sun, Vijay P. Singh, Ping Xie, and Jian Sun
Hydrol. Earth Syst. Sci., 22, 757–766, https://doi.org/10.5194/hess-22-757-2018, https://doi.org/10.5194/hess-22-757-2018, 2018
Zhi Li and Jiming Jin
Hydrol. Earth Syst. Sci., 21, 5531–5546, https://doi.org/10.5194/hess-21-5531-2017, https://doi.org/10.5194/hess-21-5531-2017, 2017
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We developed an efficient multisite and multivariate GCM downscaling method and generated climate change scenarios for SWAT to evaluate the streamflow variability within a watershed in China. The application of the ensemble techniques enables us to better quantify the model uncertainties. The peak values of precipitation and streamflow have a tendency to shift from the summer to spring season over the next 30 years. The number of extreme flooding and drought events will increase.
Nils-Otto Kitterød
Hydrol. Earth Syst. Sci., 21, 4195–4211, https://doi.org/10.5194/hess-21-4195-2017, https://doi.org/10.5194/hess-21-4195-2017, 2017
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The GRANADA open-access database (NGU, 2016a) was used to derive point recordings of thickness of sediment above the bedrock D(u). For each D(u) the horizontal distance to nearest outcrop L(u) was derived from geological maps. The purpose was to utilize L(u) as a secondary function for estimation of D(u). Two estimation methods were employed: ordinary kriging (OK) and co-kriging (CK). A cross-validation analysis was performed to evaluate the additional information in the secondary function L(u).
Annalise G. Blum, Stacey A. Archfield, and Richard M. Vogel
Hydrol. Earth Syst. Sci., 21, 3093–3103, https://doi.org/10.5194/hess-21-3093-2017, https://doi.org/10.5194/hess-21-3093-2017, 2017
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Flow duration curves are ubiquitous in surface water hydrology for applications including water allocation and protection of ecosystem health. We identify three probability distributions that can provide a reasonable fit to daily streamflows across much of United States. These results help us understand of the behavior of daily streamflows and enhance our ability to predict streamflows at ungaged river locations.
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, https://doi.org/10.5194/hess-21-3001-2017, 2017
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In 2015 large parts of Europe were affected by a drought. In terms of low flow magnitude, a region around the Czech Republic was most affected, with return periods > 100 yr. In terms of deficit volumes, the drought was particularly severe around S. Germany where the event lasted notably long. Meteorological and hydrological events developed differently in space and time. For an assessment of drought impacts on water resources, hydrological data are required in addition to meteorological indices.
Ana I. Requena, Fateh Chebana, and Taha B. M. J. Ouarda
Hydrol. Earth Syst. Sci., 21, 1651–1668, https://doi.org/10.5194/hess-21-1651-2017, https://doi.org/10.5194/hess-21-1651-2017, 2017
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The notion of a measure to quantify the degree of heterogeneity of a region from which information is required to estimate the magnitude of events at ungauged sites is introduced. These heterogeneity measures are needed to compare regions, evaluate the impact of particular sites, and rank the performance of delineating methods. A framework to define and assess their desirable properties is proposed. Several heterogeneity measures are presented and/or developed to be assessed, giving guidelines.
Joost V. L. Beckers, Albrecht H. Weerts, Erik Tijdeman, and Edwin Welles
Hydrol. Earth Syst. Sci., 20, 3277–3287, https://doi.org/10.5194/hess-20-3277-2016, https://doi.org/10.5194/hess-20-3277-2016, 2016
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Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the streamflow regime in many rivers around the world. A new method is presented for ENSO conditioning of the ensemble streamflow prediction (ESP) method, which is often used for seasonal streamflow forecasting. The method was tested on three tributaries of the Columbia River, OR. Results show an improvement in forecast skill compared to the standard ESP.
William H. Farmer
Hydrol. Earth Syst. Sci., 20, 2721–2735, https://doi.org/10.5194/hess-20-2721-2016, https://doi.org/10.5194/hess-20-2721-2016, 2016
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The potential of geostatistical tools, leveraging the spatial structure and dependency of correlated time series, for the prediction of daily streamflow time series at unmonitored locations is explored. Simple geostatistical tools improve on traditional estimates of daily streamflow. The temporal evolution of spatial structure, including seasonal fluctuations, is also explored. The proposed method is contrasted with more advanced geostatistical methods and shown to be comparable.
B. N. Nka, L. Oudin, H. Karambiri, J. E. Paturel, and P. Ribstein
Hydrol. Earth Syst. Sci., 19, 4707–4719, https://doi.org/10.5194/hess-19-4707-2015, https://doi.org/10.5194/hess-19-4707-2015, 2015
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The region of West Africa is undergoing important climate and environmental changes affecting the magnitude and occurrence of floods. This study aims to analyze the evolution of flood hazard in the region and to find links between flood hazards pattern and rainfall or vegetation index patterns.
D. E. Keller, A. M. Fischer, C. Frei, M. A. Liniger, C. Appenzeller, and R. Knutti
Hydrol. Earth Syst. Sci., 19, 2163–2177, https://doi.org/10.5194/hess-19-2163-2015, https://doi.org/10.5194/hess-19-2163-2015, 2015
R. S. Smith, R. D. Moore, M. Weiler, and G. Jost
Hydrol. Earth Syst. Sci., 18, 1835–1856, https://doi.org/10.5194/hess-18-1835-2014, https://doi.org/10.5194/hess-18-1835-2014, 2014
C. Teutschbein and J. Seibert
Hydrol. Earth Syst. Sci., 17, 5061–5077, https://doi.org/10.5194/hess-17-5061-2013, https://doi.org/10.5194/hess-17-5061-2013, 2013
S. V. Weijs, N. van de Giesen, and M. B. Parlange
Hydrol. Earth Syst. Sci., 17, 3171–3187, https://doi.org/10.5194/hess-17-3171-2013, https://doi.org/10.5194/hess-17-3171-2013, 2013
S. A. Archfield, A. Pugliese, A. Castellarin, J. O. Skøien, and J. E. Kiang
Hydrol. Earth Syst. Sci., 17, 1575–1588, https://doi.org/10.5194/hess-17-1575-2013, https://doi.org/10.5194/hess-17-1575-2013, 2013
P. Cowpertwait, D. Ocio, G. Collazos, O. de Cos, and C. Stocker
Hydrol. Earth Syst. Sci., 17, 479–494, https://doi.org/10.5194/hess-17-479-2013, https://doi.org/10.5194/hess-17-479-2013, 2013
L. Cheng, M. Yaeger, A. Viglione, E. Coopersmith, S. Ye, and M. Sivapalan
Hydrol. Earth Syst. Sci., 16, 4435–4446, https://doi.org/10.5194/hess-16-4435-2012, https://doi.org/10.5194/hess-16-4435-2012, 2012
A. I. J. M. van Dijk, J. L. Peña-Arancibia, and L. A. (Sampurno) Bruijnzeel
Hydrol. Earth Syst. Sci., 16, 3461–3473, https://doi.org/10.5194/hess-16-3461-2012, https://doi.org/10.5194/hess-16-3461-2012, 2012
P. Nyeko-Ogiramoi, P. Willems, F. M. Mutua, and S. A. Moges
Hydrol. Earth Syst. Sci., 16, 3149–3163, https://doi.org/10.5194/hess-16-3149-2012, https://doi.org/10.5194/hess-16-3149-2012, 2012
J. Oh and A. Sankarasubramanian
Hydrol. Earth Syst. Sci., 16, 2285–2298, https://doi.org/10.5194/hess-16-2285-2012, https://doi.org/10.5194/hess-16-2285-2012, 2012
H. Lee, D.-J. Seo, Y. Liu, V. Koren, P. McKee, and R. Corby
Hydrol. Earth Syst. Sci., 16, 2233–2251, https://doi.org/10.5194/hess-16-2233-2012, https://doi.org/10.5194/hess-16-2233-2012, 2012
H. E. Dahlke, S. W. Lyon, J. R. Stedinger, G. Rosqvist, and P. Jansson
Hydrol. Earth Syst. Sci., 16, 2123–2141, https://doi.org/10.5194/hess-16-2123-2012, https://doi.org/10.5194/hess-16-2123-2012, 2012
F. F. van Ogtrop, R. W. Vervoort, G. Z. Heller, D. M. Stasinopoulos, and R. A. Rigby
Hydrol. Earth Syst. Sci., 15, 3343–3354, https://doi.org/10.5194/hess-15-3343-2011, https://doi.org/10.5194/hess-15-3343-2011, 2011
S. J. Noh, Y. Tachikawa, M. Shiiba, and S. Kim
Hydrol. Earth Syst. Sci., 15, 3237–3251, https://doi.org/10.5194/hess-15-3237-2011, https://doi.org/10.5194/hess-15-3237-2011, 2011
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
Barlow, M., Nigam, S., and Berbery, E. H.: ENSO, Pacific Decadal Variability, and U.S. Summertime Precipitation, Drought, and Stream Flow, J. Climate, 14, 2105–2128, https://doi.org/10.1175/1520-0442(2001)014<2105:EPDVAU>2.0.CO;2, 2001.
Barstad, I., Grabowski, W. W., and Smolarkiewicz, P. K.: Characteristics of large-scale orographic precipitation: Evaluation of linear model in idealized problems, J. Hydrol., 340, 78–90, https://doi.org/10.1016/j.jhydrol.2007.04.005, 2007.
Barstad, I., Sorteberg, A., Flatø, F., and D{é}qu{é}, M.: Precipitation, temperature and wind in Norway: dynamical downscaling of ERA40, Clim. Dynam., 33, 769–776, https://doi.org/10.1007/s00382-008-0476-5, 2009.
Bierkens, M. F. P. and van den Hurk, B. J. J. M.: Groundwater convergence as a possible mechanism for multi-year persistence in rainfall, Geophys. Res. Lett., 34, L02402, https://doi.org/10.1029/2006GL028396, 2007.
Bjørnstad, O. N.: ncf: spatial nonparametric covariance functions, http://CRAN.R-project.org/package=ncf, last access: 9 September 2011, r package version 1.1-3, 2009.
Bjørnstad, O. N. and Falck, W.: Nonparametric spatial covariance functions: Estimation and testing, Environ. Ecol. Stat., 8, 53–70, https://doi.org/10.1023/A:1009601932481, 2001.
Bordi, I., Fraedrich, K., and Sutera, A.: Observed drought and wetness trends in Europe: an update, Hydrol. Earth Syst. Sci., 13, 1519–1530, https://doi.org/10.5194/hess-13-1519-2009, 2009.
Borg, I. and Groenen, P. J. F.: Modern Multidimensional Scaling – Theory and Applications, Springer Series in Statistics, 2 Edn., Springer, 2005.
Botter, G., Zanardo, S., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Ecohydrological model of flow duration curves and annual minima, Water Resour. Res., 44, W08418, https://doi.org/10.1029/2008WR006814, 2008.
Botter, G., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Nonlinear storage-discharge relations and catchment streamflow regimes, Water Resour. Res., 45, W10427, https://doi.org/10.1029/2008WR007658, 2009.
Bouwer, L. M., Vermaat, J. E., and Aerts, J. C. J. H.: Regional sensitivities of mean and peak river discharge to climate variability in Europe, J. Geophys. Res., 113, D19103, https://doi.org/10.1029/2008JD010301, 2008.
Canty, A. and Ripley, B. D.: boot: Bootstrap R (S-Plus) Functions, http://CRAN.R-project.org/package=boot, last access: 9 September 2011, r package version 1.3-2, 2011.
Carlstein, E.: The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence, Ann. Stat., 14, 1171–1179, 1986.
Castellarin, A., Galeati, G., Brandimarte, L., Montanari, A., and Brath, A.: Regional flow-duration curves: reliability for ungauged basins, Adv. Water Resour., 27, 953–965, https://doi.org/10.1016/j.advwatres.2004.08.005, 2004.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., McMillan, H. K., Collins, D. B. G., Kavetski, D., and Woods, R. A.: Hydrological field data from a modeller's perspective: Part 2: process-based evaluation of model hypotheses, Hydrol. Process., 25, 523–543, https://doi.org/10.1002/hyp.7902, 2011.
Cleveland, R. B., Cleveland, W. S., Mcrae, J. E., and Terpenning, I.: STL: A Seasonal-Trend Decomposition Procedure Based on Loess, J. Official Stat., 6, 3–73, 1990.
Cleveland, W. S. and Devlin, S. J.: Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, J. Am. Stat. Assoc., 83, 596–610, 1988.
Clifford, P., Richardson, S., and Hemon, D.: Assessing the Significance of the Correlation between Two Spatial Processes, Biometrics, 45, 123–134, 1989.
Constantine, W. and Percival, D.: sapa: Insightful Spectral Analysis for Physical Applications, REvolution Computing and Applied Physics Laboratory and University of Washington, http://CRAN.R-project.org/package=sapa, last access: 9 September 2011, r package version 1.0-3, 2010.
Dale, M. and Fortin, M.-J.: Spatial autocorrelation and statistical tests: Some solutions, J. Agr. Biol. Envir. St., 14, 188–206, https://doi.org/10.1198/jabes.2009.0012, 2009.
Dijkstra, E. W.: A note on two problems in connexion with graphs, Numer. Math., 1, 269–271, https://doi.org/10.1007/BF01386390, 1959.
Dooge, J. C. I.: Sensitivity of Runoff to Climate Change: A Hortonian Approach, B. Am. Meteorol. Soc., 73, 2013–2024, https://doi.org/10.1175/1520-0477(1992)073<2013:SORTCC>2.0.CO;2, 1992.
Dutilleul, P., Clifford, P., Richardson, S., and Hemon, D.: Modifying the t Test for Assessing the Correlation Between Two Spatial Processes, Biometrics, 49, 305–314, 1993.
Efron, B. and Tibshirani, R. J.: An Introduction to the bootstrap, no. 57 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, Boca Raton, Fla., 1993.
Gámez, A. J., Zhou, C. S., Timmermann, A., and Kurths, J.: Nonlinear dimensionality reduction in climate data, Nonlin. Processes Geophys., 11, 393–398, https://doi.org/10.5194/npg-11-393-2004, 2004.
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E., Robertson, A. W., Saunders, A., Tian, Y., Varadi, F., and Yiou, P.: Advanced spectral methods for climatic time series, Rev. Geophys., 40, 1003, https://doi.org/10.1029/2000RG000092, 2002.
Gudmundsson, L., Tallaksen, L. M., and Stahl, K.: Spatial cross-correlation patterns of European low, mean and high flows, Hydrol. Process., 25, 1034–1045, https://doi.org/10.1002/hyp.7807, 2011.
Gustard, A., Bullock, A., and Dixon, J. M.: Low flow estimation in the United Kingdom, Tech. Rep. 108, Institute of Hydrology, http://www.ceh.ac.uk/products/publications/lowflowestimationintheunitedkingdom.html (last access: 15 May 2011), 1992.
Haines, A. T., Finlayson, B. L., and McMahon, T. A.: A global classification of river regimes, Appl. Geogr., 8, 255–272, https://doi.org/10.1016/0143-6228(88)90035-5, 1988.
Haining, R.: Bivariate Correlation with Spatial Data, Geogr. Anal., 23, 210–227, https://doi.org/10.1111/j.1538-4632.1991.tb00235.x, 1991.
Hanson, R. T., Newhouse, M. W., and Dettinger, M. D.: A methodology to asess relations between climatic variability and variations in hydrologic time series in the southwestern United States, J. Hydrol., 287, 252–269, 2004.
Harris, N. M., Gurnell, A. M., Hannah, D. M., and Petts, G. E.: Classification of river regimes: a context for hydroecology, Hydrol. Process., 14, 2831–2848, https://doi.org/10.1002/1099-1085(200011/12)14:16/17<2831::AID-HYP122>3.0.CO;2-O, 2000.
Hisdal, H. and Tallaksen, L. M.: Estimation of regional meteorological and hydrological drought characteristics: a case study for Denmark, J. Hydrol., 281, 230–247, https://doi.org/10.1016/S0022-1694(03)00233-6, 2003.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H.: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, Proceedings: Mathematical, Phys. Eng. Sci., 454, 903–995, 1998.
Hurrell, J. and van Loon, H.: Decadal variations in climate associated with the North Atlantic Oscillation, Climatic Change, 36, 301–326, https://doi.org/10.1023/A:1005314315270, 1997.
Hurrell, J. W.: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation, Science, 269, 676–679, https://doi.org/10.1126/science.269.5224.676, 1995.
J{ó}nsd{ó}ttir, J. F. and Uvo, C. B.: Long-term variability in precipitation and streamflow in Iceland and relations to atmospheric circulation, Int. J. Climatol., 29, 1369–1380, https://doi.org/10.1002/joc.1781, 2009.
Kalayci, S. and Kahya, E.: Assessment of streamflow variability modes in Turkey: 1964–1994, J. Hydrol., 324, 163–177, https://doi.org/10.1016/j.jhydrol.2005.10.002, 2006.
Kingston, D. G., Todd, M. C., Taylor, R. G., Thompson, J. R., and Arnell, N. W.: Uncertainty in the estimation of potential evapotranspiration under climate change, Geophys. Res. Lett., 36, L20403, https://doi.org/10.1029/2009GL040267, 2009.
Kleme{\v s}, V.: The Hurst Phenomenon: A puzzle?, Water Resour. Res., 10, 675–688, https://doi.org/10.1029/WR010i004p00675, 1974.
Koster, R. D. and Suarez, M. J.: A Simple Framework for Examining the Interannual Variability of Land Surface Moisture Fluxes, J. Climate, 12, 1911–1917, https://doi.org/10.1175/1520-0442(1999)012<1911:ASFFET>2.0.CO;2, 1999.
Koutsoyiannis, D.: The Hurst phenomenon and fractional Gaussian noise made easy, Hydrolog. Sci. J., 47, 573–595, 2002.
Koutsoyiannis, D.: Climate change, the Hurst phenomenon, and hydrological statistics, Hydrolog. Sci. J., 48, 3–24, 2003.
Koutsoyiannis, D.: HESS Opinions "A random walk on water", Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, 2010.
Krasovskaia, I., Gottschalk, L., Leblois, E., and Sauquet, E.: Dynamics of River Flow Regimes Viewed through Attractors, Nord. Hydrol., 34, 461–476, 2003.
Kumar, M. and Duffy, C. J.: Detecting Hydroclimatic Change Using Spatio-Temporal Analysis of Time Series in Colorado River Basin, J. Hydrol., 374, 1–15, https://doi.org/10.1016/j.jhydrol.2009.03.039, 2009.
Lange, H. and Bernhardt, K.: Long-term components and regional synchronization of river runoffs, in: Hydrology: Science and Practice for the 21st Century, edited by: Butler, A., British Hydrological Society, London, 165–170, 2004.
Leisch, F.: bootstrap: Functions for the Book "An Introduction to the Bootstrap", http://CRAN.R-project.org/package=bootstrap, last access: 9 September 2011, r package version 1.0-22, 2007.
Lins, H. F.: Regional streamflow regimes and hydroclimatology of the United States, Water Resour. Res., 33, 1655–1667, https://doi.org/10.1029/97WR00615, 1997.
López-Moreno, J. I. and Vicente-Serrano, S. M.: Positive and Negative Phases of the Wintertime North Atlantic Oscillation and Drought Occurrence over Europe: A Multitemporal-Scale Approach, J. Climate, 21, 1220–1243, https://doi.org/10.1175/2007JCLI1739.1, 2008.
Lorenzo-Lacruz, J., Vicente-Serrano, S. M., López-Moreno, J. I., González-Hidalgo, J. C., and Morán-Tejeda, E.: The response of Iberian rivers to the North Atlantic Oscillation, Hydrol. Earth Syst. Sci., 15, 2581–2597, https://doi.org/10.5194/hess-15-2581-2011, 2011.
Mahecha, M. D., Martinez, A., Lischeid, G., and Beck, E.: Nonlinear dimensionality reduction: Alternative ordination approaches for extracting and visualizing biodiversity patterns in tropical montane forest vegetation data, Ecol. Inform., 2, 138–149, https://doi.org/10.1016/j.ecoinf.2007.05.002, 2007.
Mahecha, M. D., Fürst, L. M., Gobron, N., and Lange, H.: Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity, Pattern Recogn. Lett., 31, 2309–2317, https://doi.org/10.1016/j.patrec.2010.06.021, 2010.
Mandelbrot, B. B. and Wallis, J. R.: Noah, Joseph, and Operational Hydrology, Water Resour. Res., 4, 909–918, https://doi.org/10.1029/WR004i005p00909, 1968.
Mandelbrot, B. B. and Wallis, J. R.: Some Long-Run Properties of Geophysical Records, Water Resour. Res., 5, 321–340, https://doi.org/10.1029/WR005i002p00321, 1969.
McMahon, T. A., Vogel, R. M., Peel, M. C., and Pegram, G. G.: Global streamflows – Part 1: Characteristics of annual streamflows, J. Hydrol., 347, 243–259, https://doi.org/10.1016/j.jhydrol.2007.09.002, 2007.
Milly, P. C. D. and Dunne, K. A.: Macroscale water fluxes 2. Water and energy supply control of their interannual variability, Water Resour. Res., 38, 1206, https://doi.org/10.1029/2001WR000760, 2002.
Milly, P. C. D. and Wetherald, R. T.: Macroscale water fluxes 3. Effects of land processes on variability of monthly river discharge, Water Resour. Res., 38, 1235, https://doi.org/10.1029/2001WR000761, 2002.
Monahan, A. H.: Nonlinear Principal Component Analysis: Tropical Indo-Pacific Sea Surface Temperature and Sea Level Pressure, J. Climate, 14, 219–233, https://doi.org/10.1175/1520-0442(2001)013<0219:NPCATI>2.0.CO;2, 2001.
Montanari, A., Rosso, R., and Taqqu, M. S.: Fractionally Differenced ARIMA Models Applied to Hydrologic Time Series: Identification, Estimation, and Simulation, Water Resour. Res., 33, 1035–1044, https://doi.org/10.1029/97WR00043, 1997.
Moran, P. A. P.: Notes on Continuous Stochastic Phenomena, Biometrika, 37, 17–23, 1950.
Mudelsee, M.: Long memory of rivers from spatial aggregation, Water Resour. Res., 43, W01202, https://doi.org/10.1029/2006WR005721, 2007.
Mudelsee, M.: Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, vol. 42 of Atmospheric and Oceanographic Sciences Library, Springer, Dordrecht, https://doi.org/10.1007/978-90-481-9482-7, 2010.
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., and Wagner, H.: vegan: Community Ecology Package, http://CRAN.R-project.org/package=vegan, last access: 9 September 2011, r package version 1.17-4, 2010.
Peltonen, M., Liebhold, A. M., Bjornstad, O. N., and Williams, D. W.: Spatial synchrony in forest insect outbreaks: roles of regional stochasticity and dispersal, Ecology, 83, 3120–3129, https://doi.org/10.1890/0012-9658(2002)083[3120:SSIFIO]2.0.CO;2, 2002.
Pepin, N. C., Daly, C., and Lundquist, J.: The influence of surface versus free-air decoupling on temperature trend patterns in the western United States, J. Geophys. Res., 116, D10109, https://doi.org/10.1029/2010JD014769, 2011.
Peres-Neto, P. and Jackson, D.: How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test, Oecologia, 129, 169–178, https://doi.org/10.1007/s004420100720, 2001.
Potter, K. W.: Evidence for nonstationarity as a physical explanation of the Hurst Phenomenon, Water Resour. Res., 12, 1047–1052, https://doi.org/10.1029/WR012i005p01047, 1976.
R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org, last access: 15 May 2011.
Renner, M. and Bernhofer, C.: Long term variability of the annual hydrological regime and sensitivity to temperature phase shifts in Saxony/Germany, Hydrol. Earth Syst. Sci., 15, 1819–1833, https://doi.org/10.5194/hess-15-1819-2011, 2011.
Sankarasubramanian, A. and Vogel, R. M.: Annual hydroclimatology of the United States, Water Resour. Res., 38, 1083, https://doi.org/10.1029/2001WR000619, 2002.
Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate Elasticity of Streamflow in the United States, Water Resour. Res., 37, 1771–1781, https://doi.org/10.1029/2000WR900330, 2000.
Shamsudduha, M., Chandler, R. E., Taylor, R. G., and Ahmed, K. M.: Recent trends in groundwater levels in a highly seasonal hydrological system: the Ganges-Brahmaputra-Meghna Delta, Hydrol. Earth Syst. Sci., 13, 2373–2385, https://doi.org/10.5194/hess-13-2373-2009, 2009.
Sherman, M., Speed, F. M., and Speed, F. M.: Analysis of tidal data via the blockwise bootstrap, J. Appl. Stat., 25, 333–340, 1998.
Shorthouse, C. and Arnell, N.: FRIEND'97 – Regional Hydrology : Concepts and Models for Sustainable Water Resource Management, vol. 246, chap. Spatial and temporal variability in European river flows and the North Atlantic oscillation, The International Association of Hydrological Sciences (IAHS), 77–85, 1997.
Shorthouse, C. and Arnell, N.: The effects of climatic variability on spatial characteristics of European river flows, Phys. Chem. Earth B, 24, 7–13, https://doi.org/10.1016/S1464-1909(98)00003-3, 1999.
Shun, T. and Duffy, C. J.: Low-frequency oscillations in precipitation, temperature, and runoff on a west facing mountain front: A hydrogeologic interpretation, Water Resour. Res., 35, 191–201, https://doi.org/10.1029/98WR02818, 1999.
Spearman, C.: The Proof and Measurement of Association between Two Things, Am. J. Psychol., 100, 441–471, 1987.
Stahl, K. and Demuth, S.: Linking streamflow drought to the occurrence of atmospheric circulation patterns, Hydrolog. Sci. J. – Journal Des Sciences Hydrologiques, 44, 467–482, 1999.
Stahl, K., Hisdal, H., Tallaksen, L., van Lanen, H., Hannaford, J., and Sauquet, E.: Trends in low flows and streamflow droughts across Europe, Tech. rep., UNESCO, Paris, 2008.
Stahl, K., Hisdal, H., Hannaford, J., Tallaksen, L. M., van Lanen, H. A. J., Sauquet, E., Demuth, S., Fendekova, M., and Jódar, J.: Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrol. Earth Syst. Sci., 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010, 2010.
Tallaksen, L. M., Madsen, H., and Clausen, B.: On the definition and modelling of streamflow drought duration and deficit volume, Hydrolog. Sci. J. – Journal Des Sciences Hydrologiques, 42, 15–33, 1997.
Tenenbaum, J. B., de Silva, V., and Langford, J. C.: A global geometric framework for nonlinear dimensionality reduction, Science, 290, 2319–2323, 2000.
Thomson, D.: Spectrum estimation and harmonic analysis, Proc. IEEE, 70, 1055–1096, 1982.
Tootle, G. A. and Piechota, T. C.: Relationships between Pacific and Atlantic ocean sea surface temperatures and U.S. streamflow variability, Water Resour. Res., 42, W07411, https://doi.org/10.1029/2005WR004184, 2006.
Torgerson, W. S.: Multidimensional scaling: I. Theory and method, Psychometrika, 17, 401–419, 1952.
Torrence, C. and Compo, G. P.: A Practical Guide to Wavelet Analysis, B. Am. Meteorol. Soc., 79, 61–78, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2, 1998.
Trapletti, A. and Hornik, K.: tseries: Time Series Analysis and Computational Finance, http://CRAN.R-project.org/package=tseries, last access: 9 September 2011, r package version 0.10-22., 2009.
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteorol. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
van Loon, H. and Rogers, J. C.: The Seesaw in Winter Temperatures between Greenland and Northern Europe, Part I: General Description, Mon. Weather Rev., 106, 296–310, https://doi.org/10.1175/1520-0493(1978)106<0296:TSIWTB>2.0.CO;2, 1978.
Vogel, R. M. and Fennessey, N. M.: Flow-duration, I: New interpretation and confidence intervals, J. Water Resour. Plan. Manage., 120, 458–504, 1994.
Vogel, R. M., Tsai, Y., and Limbrunner, J. F.: The Regional Persistence and Variability of Annual Streamflow in the United States, Water Resour. Res., 34, 3445–3459, https://doi.org/10.1029/98WR02523, 1998.
Vogt, J., Soille, P., de Jager, A., Rimaviciute, E., and Mehl, W.: A pan-European River and Catchment Database, Tech. Rep. EUR 22920 EN, EC-JRC, Luxembourg, http://ccm.jrc.ec.europa.eu/documents/CCM2-Report_EUR-22920-EN_2007_STD.pdf (last accesse: 7 August 2011), 2007.
von Storch, H. and Zwiers, F. W.: Statistical analysis in climate research, Cambridge University Press, Cambridge, UK, 1999.
Ward, J.: Hierarchical Clustering to Optimize an Objective Function, J. Am. Stat. Assoc., 58, 236–244, 1967.
Weedon, G., Gomes, S., Viterbo, P., Österle, H., Adam, J., Bellouin, N., Boucher, O., and Best, M.: The WATCH forcing data 1958–2001: a meteorological forcing dataset for land surface- and hydrological-models, WATCH Technical Report 22, http://www.eu-watch.org (last access: 15 May 2011), 2010.
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., Adam, J. C., Bellouin, N., Boucher, O., and Best, M.: Creation of the WATCH Forcing Data and its use to assess global and regional reference crop evaporation over land during the twentieth century, J. Hydrometeorol., in press, https://doi.org/10.1175/2011JHM1369.1, 2011.
Wibig, J.: Precipitation in Europe in relation to circulation patterns at the 500 hPa level, Int. J. Climatol., 19, 253–269, https://doi.org/10.1002/(SICI)1097-0088(19990315)19:3<253::AID-JOC366>3.0.CO;2-0, 1999.
Wilcoxon, F.: Individual Comparisons by Ranking Methods, Biometrics Bull., 1, 80–83, 1945.
