Articles | Volume 16, issue 7
https://doi.org/10.5194/hess-16-2285-2012
© Author(s) 2012. 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-16-2285-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Jul 2012
Research article |
| 24 Jul 2012
Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
J. Oh
Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-5908, USA
A. Sankarasubramanian
Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-5908, USA
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
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
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
On accuracy of upper quantiles estimation
A consistent set of trans-basin floods in Germany between 1952–2002
Factors influencing chloride deposition in a coastal hilly area and application to chloride deposition mapping
Effects of intersite dependence of nested catchment structures on probabilistic regional envelope curves
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
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
I. Markiewicz, W. G. Strupczewski, and K. Kochanek
Hydrol. Earth Syst. Sci., 14, 2167–2175, https://doi.org/10.5194/hess-14-2167-2010, https://doi.org/10.5194/hess-14-2167-2010, 2010
S. Uhlemann, A. H. Thieken, and B. Merz
Hydrol. Earth Syst. Sci., 14, 1277–1295, https://doi.org/10.5194/hess-14-1277-2010, https://doi.org/10.5194/hess-14-1277-2010, 2010
H. Guan, A. J. Love, C. T. Simmons, O. Makhnin, and A. S. Kayaalp
Hydrol. Earth Syst. Sci., 14, 801–813, https://doi.org/10.5194/hess-14-801-2010, https://doi.org/10.5194/hess-14-801-2010, 2010
B. Guse, A. Castellarin, A. H. Thieken, and B. Merz
Hydrol. Earth Syst. Sci., 13, 1699–1712, https://doi.org/10.5194/hess-13-1699-2009, https://doi.org/10.5194/hess-13-1699-2009, 2009
Cited articles
Alexander, R. B. and Smith, R. A.: Trends in the nutrient enrichment of US rivers during the late 20th century and their relation to changes in probable stream trophic conditions, Limnol. Oceanogr., 51, 639–654, 2006.
Alexander, R. B., Slack, J. R., Ludtke, A. S., Fitzgerald, K. K., and Schertz, T. L.: Data from selected US Geological Survey national stream water quality monitoring networks, Water Resour. Res., 34, 2401–2405, 1998.
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974.
Arhonditsis, G. B., Winder, M., Brett, M. T., and Schindler, D. E.: Patterns and mechanisms of phytoplankton variability in Lake Washington (USA), Water Res., 38, 4013–4027, 2004.
Borsuk, M. E., Stow, C. A., and Reckhow, K. H.: Predicting the frequency of water quality standard violations: A probabilistic approach for TMDL development, Environ. Sci. Technol., 36, 2109–2115, 2002.
Borsuk, M. E., Stow, C. A., and Reckhow, K. H.: Confounding effect of flow on estuarine response to nitrogen loading, J. Environ. Eng., 130, 605–614, 2004.
Chen, C. F., Ma, H. W., and Reckhow, K. H.: Assessment of water quality management with a systematic qualitative uncertainty analysis, Sci. Total Environ., 374, 13–25, 2007.
Childers, D. L., Boyer, J. N., Davis, S. E., Madden, C. J., Rudnick, D. T., and Sklar, F. H.: Relating precipitation and water management to nutrient concentrations in the oligotrophic "upside-down" estuaries of the Florida Everglades, Limnol. Oceanogr., 51, 602–616, 2006.
Cho, H. J. and Poirrier, M. A.: Response of submersed aquatic vegetation (SAV) in Lake Pontchartrain, Louisiana to the 1997–2001 El Niño Southern Oscillation shifts, Estuaries, 28, 215–225, 2005.
Cohn, T. A.: Estimating contaminant loads in rivers: An application of adjusted maximum likelihood to type 1 censored data, Water Resour. Res., 41, W07003, https://doi.org/10.1029/2004WR003833, 2005.
Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D., and Summers, R. M.: The Validity of a Simple Statistical-Model for Estimating Fluvial Constituent Loads – an Empirical-Study Involving Nutrient Loads Entering Chesapeake Bay, Water Resour. Res., 28, 2353–2363, 1992.
Dettinger, M. D. and Diaz, H. F.: Global characteristics of stream flow seasonality and variability, J. Hydrometeorol., 1, 289–310, 2000.
Devineni, N. and Sankarasubramanian, A.: Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs, Geophys. Res. Lett., 37, L24704, https://doi.org/10.1029/2010GL044989, 2010.
Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W09404, https://doi.org/10.1029/2006WR005855, 2008.
Douglas, E. M., Vogel, R. M., and Kroll, C. N.: Trends in Flood and Low Flows in the United States, J. Hydrol., 240, 90–105, 2000.
Hartigan, J. A. and Wong, M. A.: Algorithm AS 136: A k-means clustering algorithm, Appl. Statist., 28.1, 100–108, 1979.
Kaplan, A., Cane, M., Kushnir, Y., Clement, A., Blumenthal, M., and Rajagopalan, B.: Analyses of global sea surface temperature 1856–1991, J. Geophys. Res., 103, 18567–18589, 1998.
Goddard, L., Barnston, A. G., and Mason, S. J.: Evaluation of the IRI's "net assessment" seasonal climate forecasts 1997–2001, B. Am. Meteorol. Soc., 84, 1761–1781, 2003.
Leung, L. R., Hamlet, A. F., Lettenmaier, D. P., and Kumar, A.: Simulations of the ENSO Hydroclimate Signals in the Pacific Northwest Columbia River Basin, B. Am. Meteorol. Soc., 80, 2313–2329, 1999.
Li, S. and Goddard, L.: Retrospective Forecasts with the ECHAM4.5 AGCM, IRI Technical Report, 05-02, 2005.
Lin, J., Xie, L., Pietrafesa, L. J., Ramus, J. S., and Paerl, H. W.: Water quality gradients across Albemarle-Pamlico estuarine system: Seasonal variations and model applications, J. Coastal Res., 23, 213–229, 2007.
Meybeck, M.: Carbon, Nitrogen, and Phosphorus Transport by World Rivers, Am. J. Sci., 282, 401–450, 1982.
Mueller, D. K. and Spahr, N. E.: Nutrients in Streams and Rivers across the nation-1992–2001, USGS Scientific Investigations Report, 2006.
Mueller, D. K., Ruddy, B. C., and Battaglin, W. A.: Logistic model of nitrate in streams of the upper-midwestern United States, J. Environ. Qual., 26, 1223–1230, 1997.
National Research Council: Assessing the TMDL approach to water quality management, 2001.
National Research Council: Florida Bay Research Programs and Their Relation to the Comprehensive Everglades Restoration Plan, 2002.
Paerl, H. W., Valdes, L. M., Peierls, B. L., Adolf, J. E., and Harding, L. W.: Anthropogenic and climatic influences on the eutrophication of large estuarine ecosystems, Limnol. Oceanogr., 51, 448–462, 2006.
Ropelewski, C. F. and Halpert, M. S.: Global and Regional Scale Precipitation Patterns Associated with the El-Niño Southern Oscillation, Mon. Weather Rev., 115, 1606–1626, 1987.
Runkel, R. L., Crawford, C. G., and Cohn, T. A.: Load Estimator (LOADEST): A FORTRAN Program for Estimating Constituent Loads in Streams and Rivers, US Geological Survey Report, 2004.
Sankarasubramanian, A., Sharma, A., Lall, U., and Espinueva S.: Role of retrospective forecasts of GCMs forced with persisted SST anomalies in operational streamflow forecasts development, J. Hydrometeor., 9, 212–227, 2008.
Sankarasubramanian, A., Lall, U., Souza Filho, F. D., and Sharma, A.: Improved Water Allocation utilizing Probabilistic Climate Forecasts: Short Term Water Contracts in a Risk Management Framework, Water Resour. Res., 45, W11409, https://doi.org/10.1029/2009WR007821, 2009.
Slack, J. R., Lumb, A., and Landwehr, J. M.: Hydro-Climatic Data Network (HCDN) Streamflow Data Set, 1874–1988, US Geological Survey Report, 1993.
Smith, R. A., Schwarz, G. E., and Alexander, R. B.: Regional interpretation of water-quality monitoring data, Water Resour. Res., 33, 2781–2798, 1998.
Smith, R. A., Alexander, R. B., and Schwarz, G. E.: Natural background concentrations of nutrients in streams and rivers of the conterminous United States, Environ. Sci. Technol., 37, 3039–3047, 2003.
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble square-root filters, Mon. Weather Rev., 131, 1485–90, 2003.
Towler, E., Rajagopalan, B., and Summers, R. S.: Using Parametric and Nonparametric Methods to Model Total Organic Carbon, Alkalinity, and pH after Conventional Surface Water Treatment, Environ. Eng. Sci., 26, 1299–1307, 2009.
Towler, E., Rajagopalan, B., Summers, R. S., and Yates, D.: An approach for probabilistic forecasting of seasonal turbidity threshold exceedance, Water Resour. Res., 46, W06511, https://doi.org/10.1029/2009WR007834, 2010.
Vecchia, A. V.: Relation Between Climate Variability and Stream Water Quality in the Continental United States, Hydrological Science and Technology, 19, 77–98, 2003.
Vitousek, P. M., Aber, J.D., Howarth, R. W., Likens, G. E., Matson, P. A., Schindler, D. W., Schlesinger, W. H., and Tilman, G. D.: Human alteration of the global nitrogen cycle: Sources and consequences, Ecol. Appl., 7, 737–750, 1997.
Wilks, D. S.: Statistical Methods in the Atmospheric Science, Academic Press, 1995.
