Articles | Volume 26, issue 17
https://doi.org/10.5194/hess-26-4553-2022
© 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-26-4553-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Sep 2022
Research article |
| 13 Sep 2022
| 13 Sep 2022
Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria
Johannes Laimighofer
CORRESPONDING AUTHOR
Department of Landscape, Spatial and Infrastructure Sciences, Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Strasse 82/I, 1190 Vienna, Austria
Michael Melcher
Institute of Information Management, FH JOANNEUM – University of Applied Sciences, Graz, Austria
Gregor Laaha
Department of Landscape, Spatial and Infrastructure Sciences, Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Strasse 82/I, 1190 Vienna, Austria
Related authors
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
Short summary
Short summary
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.
Klaus Haslinger, Wolfgang Schöner, Jakob Abermann, Gregor Laaha, Konrad Andre, Marc Olefs, and Roland Koch
Nat. Hazards Earth Syst. Sci., 23, 2749–2768, https://doi.org/10.5194/nhess-23-2749-2023, https://doi.org/10.5194/nhess-23-2749-2023, 2023
Short summary
Short summary
Future changes of surface water availability in Austria are investigated. Alterations of the climatic water balance and its components are analysed along different levels of elevation. Results indicate in general wetter conditions with particular shifts in timing of the snow melt season. On the contrary, an increasing risk for summer droughts is apparent due to increasing year-to-year variability and decreasing snow melt under future climate conditions.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Kerstin Stahl, Jean-Philippe Vidal, Jamie Hannaford, Erik Tijdeman, Gregor Laaha, Tobias Gauster, and Lena M. Tallaksen
Proc. IAHS, 383, 291–295, https://doi.org/10.5194/piahs-383-291-2020, https://doi.org/10.5194/piahs-383-291-2020, 2020
Short summary
Short summary
Numerous indices exist for the description of hydrological drought, some are based on absolute thresholds of overall streamflows or water levels and some are based on relative anomalies with respect to the season. This article discusses paradigms and experiences with such index uses in drought monitoring and drought analysis to raise awareness of the different interpretations of drought severity.
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
Short summary
Short summary
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.
Matthias Schlögl and Gregor Laaha
Nat. Hazards Earth Syst. Sci., 17, 515–531, https://doi.org/10.5194/nhess-17-515-2017, https://doi.org/10.5194/nhess-17-515-2017, 2017
Short summary
Short summary
Different extreme value analysis approaches and fitting methods are compared with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. The merits of simultaneous analysis and combined plotting of various approaches are shown. The use of conditional performance metrics is proposed as an additional measure for assessing model goodness of fit. Findings of the study can be transferred to a range of environmental variables.
Monica Ionita, Lena M. Tallaksen, Daniel G. Kingston, James H. Stagge, Gregor Laaha, Henny A. J. Van Lanen, Patrick Scholz, Silvia M. Chelcea, and Klaus Haslinger
Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017, https://doi.org/10.5194/hess-21-1397-2017, 2017
Short summary
Short summary
This paper analyses the European summer drought of 2015 from a climatological perspective, including its origin and spatial and temporal development, and how it compares with the 2003 event. It discusses the main contributing factors controlling the occurrence and persistence of the event: temperature and precipitation anomalies, blocking episodes and sea surface temperatures. The results represent the outcome of a collaborative initiative of members of UNESCO’s FRIEND-Water program.
Gregor Laaha, Juraj Parajka, Alberto Viglione, Daniel Koffler, Klaus Haslinger, Wolfgang Schöner, Judith Zehetgruber, and Günter Blöschl
Hydrol. Earth Syst. Sci., 20, 3967–3985, https://doi.org/10.5194/hess-20-3967-2016, https://doi.org/10.5194/hess-20-3967-2016, 2016
Short summary
Short summary
We present a framework for assessing climate impacts on future low flows that combines different sources of information termed pillars. To illustrate the framework, three pillars are chosen: low-flow observation, climate observations and climate projections. By combining different sources of information we aim at more robust projections than obtained from each pillar alone. The viability of the framework is illustrated for four example catchments from Austria.
Juraj Parajka, Alfred Paul Blaschke, Günter Blöschl, Klaus Haslinger, Gerold Hepp, Gregor Laaha, Wolfgang Schöner, Helene Trautvetter, Alberto Viglione, and Matthias Zessner
Hydrol. Earth Syst. Sci., 20, 2085–2101, https://doi.org/10.5194/hess-20-2085-2016, https://doi.org/10.5194/hess-20-2085-2016, 2016
Short summary
Short summary
Streamflow estimation during low-flow conditions is important for estimation of environmental flows, effluent water quality, hydropower operations, etc. However, it is not clear how the uncertainties in assumptions used in the projections translate into uncertainty of estimated future low flows. The objective of the study is to explore the relative role of hydrologic model calibration and climate scenarios in the uncertainty of low-flow projections in Austria.
A. F. Van Loon, S. W. Ploum, J. Parajka, A. K. Fleig, E. Garnier, G. Laaha, and H. A. J. Van Lanen
Hydrol. Earth Syst. Sci., 19, 1993–2016, https://doi.org/10.5194/hess-19-1993-2015, https://doi.org/10.5194/hess-19-1993-2015, 2015
Short summary
Short summary
Hydrological drought types in cold climates have complex causing factors and impacts. In Austria and Norway, a lack of snowmelt is mainly related to below-normal winter precipitation, and a lack of glaciermelt is mainly related to below-normal summer temperature. These and other hydrological drought types impacted hydropower production, water supply, and agriculture in Europe and the US in the recent and far past. For selected drought events in Norway impacts could be coupled to causing factors.
J. L. Salinas, G. Laaha, M. Rogger, J. Parajka, A. Viglione, M. Sivapalan, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 2637–2652, https://doi.org/10.5194/hess-17-2637-2013, https://doi.org/10.5194/hess-17-2637-2013, 2013
A. Viglione, J. Parajka, M. Rogger, J. L. Salinas, G. Laaha, M. Sivapalan, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 2263–2279, https://doi.org/10.5194/hess-17-2263-2013, https://doi.org/10.5194/hess-17-2263-2013, 2013
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
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geog., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012. a
Aigner, D. J., Amemiya, T., and Poirier, D. J.: On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function, Int. Econ. Rev., 17, 377–396,
https://doi.org/10.2307/2525708, 1976. a, b
Arnold, J. B.: ggthemes: Extra Themes, Scales and Geoms for “ggplot2”, r package version 4.2.4, https://CRAN.R-project.org/package=ggthemes (last access: 8 April 2022), 2021. a
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardized
precipitation evapotranspiration index (SPEI) revisited: parameter fitting,
evapotranspiration models, tools, datasets and drought monitoring,
Int. J. Climatol., 34, 3001–3023, https://doi.org/10.1002/joc.3887, 2014. a
Blöschl, G., Sivapalan, M., Wagener, T., Savenije, H., and Viglione, A.: Runoff prediction in ungauged basins: synthesis across processes, places and
scales, Cambridge University Press, https://doi.org/10.1017/CBO9781139235761, 2013. a, b
Castiglioni, S., Castellarin, A., and Montanari, A.: Prediction of low-flow indices in ungauged basins through physiographical space-based interpolation,
J. Hydrol., 378, 272–280, https://doi.org/10.1016/j.jhydrol.2009.09.032, 2009. a
Castiglioni, S., Castellarin, A., Montanari, A., Skøien, J. O., Laaha, G., and Blöschl, G.: Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging, Hydrol. Earth Syst. Sci., 15, 715–727, https://doi.org/10.5194/hess-15-715-2011, 2011. a
Chang, W. and Chen, X.: Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches, Water, 10, 1116, https://doi.org/10.3390/w10091116, 2018. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, KDD '16, Association for Computing Machinery, New York, NY, USA, 785–794,
https://doi.org/10.1145/2939672.2939785, 2016. a, b
Chen, T. and He, T.: Higgs Boson Discovery with Boosted Trees, in: Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, 8–13 December 2014, Montreal, Canada, edited by: Cowan, G., Germain, C., Guyon, I., Kégl, B., and Rousseau, D., vol. 42 of Proceedings of Machine Learning Research, PMLR, Montreal, Canada, 69–80, https://proceedings.mlr.press/v42/chen14.html (last access: 8 April 2022), 2015. a, b
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., and Li, Y.: xgboost: Extreme Gradient Boosting, r package version 1.4.1.1, https://CRAN.R-project.org/package=xgboost (last access: 8 April 2022), 2021. a
Cutore, P., Cristaudo, G., Campisano, A., Modica, C., Cancelliere, A., and
Rossi, G.: Regional models for the estimation of streamflow series in
ungauged basins, Water Resour. Manag., 21, 789–800,
https://doi.org/10.1007/s11269-006-9110-7, 2007. a, b, c, d
Dawson, C. and Wilby, R.: Hydrological modelling using artificial neural
networks, Prog. Phys. Geog., 25, 80–108,
https://doi.org/10.1177/030913330102500104, 2001. a
Euser, T., Winsemius, H. C., Hrachowitz, M., Fenicia, F., Uhlenbrook, S., and Savenije, H. H. G.: A framework to assess the realism of model structures using hydrological signatures, Hydrol. Earth Syst. Sci., 17, 1893–1912, https://doi.org/10.5194/hess-17-1893-2013, 2013. a
Ferreira, R. G., da Silva, D. D., Elesbon, A. A. A., Fernandes-Filho, E. I., Veloso, G. V., de Souza Fraga, M., and Ferreira, L. B.: Machine learning models for streamflow regionalization in a tropical watershed, J. Environ. Manage., 280, 111713, https://doi.org/10.1016/j.jenvman.2020.111713, 2021. a
Friedman, J., Hastie, T., and Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), Ann. Stat., 28, 337–407, https://doi.org/10.1214/aos/1016218223, 2000. a
Friedman, J., Hastie, T., and Tibshirani, R.: Regularization Paths for Generalized Linear Models via Coordinate Descent, J. Stat. Softw., 33, 1–22, https://doi.org/10.18637/jss.v033.i01, 2010. a
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine,
Ann. Stat., 29, 1189–1232, http://www.jstor.org/stable/2699986 (last access: 8 April 2022), 2001. a
Granitto, P. M., Furlanello, C., Biasioli, F., and Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial
products, Chemometr. Intell. Lab., 83, 83–90,
https://doi.org/10.1016/j.chemolab.2006.01.007, 2006. a
Grolemund, G. and Wickham, H.: Dates and Times Made Easy with lubridate, J. Stat. Softw., 40, 1–25, https://doi.org/10.18637/jss.v040.i03, 2011. a
Henry, L. and Wickham, H.: purrr: Functional Programming Tools, r package version 0.3.4, https://CRAN.R-project.org/package=purrr (last access: 8 April 2022), 2020. a
Henry, L. and Wickham, H.: tidyselect: Select from a Set of Strings, r package version 1.1.1, https://CRAN.R-project.org/package=tidyselect (last access: 8 April 2022), 2021. a
Huang, S., Kumar, R., Flörke, M., Yang, T., Hundecha, Y., Kraft, P., Gao, C., Gelfan, A., Liersch, S., Lobanova, A., Strauch, M., van Ogtrop, F., Reinhardt, J., Haberlandt, U., and Krysanova, V.: Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide, Climatic Change, 141, 381–397, https://doi.org/10.1007/s10584-016-1841-8, 2017. a
Kneib, T.: Beyond mean regression, Stat. Model., 13, 275–303,
https://doi.org/10.1177/1471082X13494159, 2013. a, b
Kneib, T., Silbersdorff, A., and Säfken, B.: Rage against the mean – a review of distributional regression approaches, Econometrics and Statistics,
https://doi.org/10.1016/j.ecosta.2021.07.006, online first, 2021. a
Koenker, R. and Bassett, G.: Regression Quantiles, Econometrica, 46, 33–50, https://doi.org/10.2307/1913643, 1978. a
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and
Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting
the power of machine learning, Water Resour. Res., 55,
11344–11354, https://doi.org/10.1029/2019WR026065, 2019a. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019b. a
Kuhn, M.: caret: Classification and Regression Training, r package version 6.0-88, https://CRAN.R-project.org/package=caret (last access: 8 April 2022), 2021. a
Kuhn, M. and Wickham, H.: recipes: Preprocessing Tools to Create Design Matrices, r package version 0.1.16, https://CRAN.R-project.org/package=recipes (last access: 8 April 2022), 2021. a
Laaha, G. and Blöschl, G.: A comparison of low flow regionalisation
methods–catchment grouping, J. Hydrol., 323, 193–214,
https://doi.org/10.1016/j.jhydrol.2005.09.001, 2006. a, b, c, d
Laaha, G. and Blöschl, G.: A national low flow estimation procedure for
Austria, Hydrolog. Sci. J., 52, 625–644, https://doi.org/10.1623/hysj.52.4.625, 2007. a, b
Laaha, G., Demuth, S., Hisdal, H., Kroll, C. N., van Lanen, H. A. J., Nester,
T., Rogger, M., Sauquet, E., Tallaksen, L. M., Woods, R. A., and Young, A.:
Prediction of low flows in ungauged basins, Cambridge
University Press, 163–188, https://doi.org/10.1017/CBO9781139235761.011, 2013. a
Laaha, G., Skøien, J., and Blöschl, G.: Spatial prediction on river
networks: comparison of top-kriging with regional regression, Hydrol.
Process., 28, 315–324, https://doi.org/10.1002/hyp.9578, 2014. a, b
Laaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.: The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017. a
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021. a, b
Lima, C. H. and Lall, U.: Spatial scaling in a changing climate: A hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow, J. Hydrol., 383, 307–318, https://doi.org/10.1016/j.jhydrol.2009.12.045, 2010. a, b, c
Lindström, J., Szpiro, A. A., Sampson, P. D., Oron, A. P., Richards, M., Larson, T. V., and Sheppard, L.: A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates, Environ. Ecol. Stat., 21, 411–433, 2014. a
Lu, H. and Ma, X.: Hybrid decision tree-based machine learning models for
short-term water quality prediction, Chemosphere, 249, 126169,
https://doi.org/10.1016/j.chemosphere.2020.126169, 2020. a
Müller, K. and Wickham, H.: tibble: Simple Data Frames, r package version 3.1.2, https://CRAN.R-project.org/package=tibble (last access: 8 April 2022), 2021. a
Newey, W. K. and Powell, J. L.: Asymmetric Least Squares Estimation and Testing, Econometrica, 55, 819–847, https://doi.org/10.2307/1911031, 1987. a, b
Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., and Liu, J.: Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model, J. Hydrol., 586, 124901,
https://doi.org/10.1016/j.jhydrol.2020.124901, 2020. a
Onyutha, C.: Influence of hydrological model selection on simulation of
moderate and extreme flow events: a case study of the Blue Nile basin,
Adv. Meteorol., 2016, 7148326, https://doi.org/10.1155/2016/7148326, 2016. a
Ossandón, Á., Brunner, M. I., Rajagopalan, B., and Kleiber, W.: A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow, Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, 2022. a, b, c
Parajka, J., Viglione, A., Rogger, M., Salinas, J. L., Sivapalan, M., and Blöschl, G.: Comparative assessment of predictions in ungauged basins – Part 1: Runoff-hydrograph studies, Hydrol. Earth Syst. Sci., 17, 1783–1795, https://doi.org/10.5194/hess-17-1783-2013, 2013. a, b, c, d
Parajka, J., Blaschke, A. P., Blöschl, G., Haslinger, K., Hepp, G., Laaha, G., Schöner, W., Trautvetter, H., Viglione, A., and Zessner, M.: Uncertainty contributions to low-flow projections in Austria, Hydrol. Earth Syst. Sci., 20, 2085–2101, https://doi.org/10.5194/hess-20-2085-2016, 2016. a, b
Parisouj, P., Mohebzadeh, H., and Lee, T.: Employing machine learning algorithms for streamflow prediction: a case study of four river basins with
different climatic zones in the United States, Water Resour. Manage.,
34, 4113–4131, https://doi.org/10.1007/s11269-020-02659-5, 2020. a
Pumo, D., Viola, F., and Noto, L. V.: Generation of natural runoff monthly
series at ungauged sites using a regional regressive model, Water, 8, 209,
https://doi.org/10.3390/w8050209, 2016. a, b, c, d
Ram, K. and Wickham, H.: wesanderson: A Wes Anderson Palette Generator, r package version 0.3.6, https://CRAN.R-project.org/package=wesanderson (last access: 8 April 2022), 2018. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 8 April 2022), 2021. a
Roksvåg, T., Steinsland, I., and Engeland, K.: Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework, Hydrol. Earth Syst. Sci., 24, 4109–4133, https://doi.org/10.5194/hess-24-4109-2020, 2020. a, b, c
Sahour, H., Gholami, V., and Vazifedan, M.: A comparative analysis of
statistical and machine learning techniques for mapping the spatial
distribution of groundwater salinity in a coastal aquifer, J.
Hydrol., 591, 125321, https://doi.org/10.1016/j.jhydrol.2020.125321, 2020. a
Salinas, J. L., Laaha, G., Rogger, M., Parajka, J., Viglione, A., Sivapalan, M., and Blöschl, G.: Comparative assessment of predictions in ungauged basins – Part 2: Flood and low flow studies, Hydrol. Earth Syst. Sci., 17, 2637–2652, https://doi.org/10.5194/hess-17-2637-2013, 2013. a
Shortridge, J. E., Guikema, S. D., and Zaitchik, B. F.: Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds, Hydrol. Earth Syst. Sci., 20, 2611–2628, https://doi.org/10.5194/hess-20-2611-2016, 2016. a
Shrestha, R. R., Peters, D. L., and Schnorbus, M. A.: Evaluating the ability of a hydrologic model to replicate hydro-ecologically relevant indicators,
Hydrol. Process., 28, 4294–4310, 2014. a
Smakhtin, V.: Low flow hydrology: a review, J. Hydrol., 240,
147–186, https://doi.org/10.1016/S0022-1694(00)00340-1, 2001. a
Sobotka, F. and Kneib, T.: Geoadditive expectile regression, Comput. Stat. Data An., 56, 755–767, https://doi.org/10.1016/j.csda.2010.11.015, 2012. a
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past experiences and new approaches, J. Hydroinform., 10, 3–22,
https://doi.org/10.2166/hydro.2008.015, 2008. a
Staudinger, M. and Seibert, J.: Predictability of low flow – An assessment
with simulation experiments, J. Hydrol., 519, 1383–1393,
https://doi.org/10.1016/j.jhydrol.2014.08.061, 2014. a
Staudinger, M., Stahl, K., Seibert, J., Clark, M. P., and Tallaksen, L. M.: Comparison of hydrological model structures based on recession and low flow simulations, Hydrol. Earth Syst. Sci., 15, 3447–3459, https://doi.org/10.5194/hess-15-3447-2011, 2011. a, b
Steinschneider, S., Yang, Y.-C. E., and Brown, C.: Combining regression and
spatial proximity for catchment model regionalization: a comparative study,
Hydrolog. Sci. J., 60, 1026–1043, https://doi.org/10.1080/02626667.2014.899701, 2015. a, b, c
Toth, E.: Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions, Hydrol. Earth Syst. Sci., 20, 2383–2394, https://doi.org/10.5194/hess-20-2383-2016, 2016. a
Tyralis, H., Papacharalampous, G., and Langousis, A.: Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms, Neural Comput. Appl.,
33, 3053–3068, https://doi.org/10.1007/s00521-020-05172-3, 2021a. a
Tyralis, H., Papacharalampous, G., Langousis, A., and Papalexiou, S. M.:
Explanation and probabilistic prediction of hydrological signatures with
statistical boosting algorithms, Remote Sens., 13, 333,
https://doi.org/10.3390/rs13030333, 2021b. a
Tyralis, H., Papacharalampous, G., and Khatami, S.: Expectile-based
hydrological modelling for uncertainty estimation: Life after mean, arXiv [preprint], https://doi.org/10.48550/arXiv.2201.05712, 14 January 2022. a
Vandewiele, G. and Elias, A.: Monthly water balance of ungauged catchments
obtained by geographical regionalization, J. Hydrol., 170,
277–291, https://doi.org/10.1016/0022-1694(95)02681-E, 1995. a, b
Varmuza, K. and Filzmoser, P.: Introduction to multivariate statistical
analysis in chemometrics, CRC press, https://doi.org/10.1201/9781420059496, 2016. a, b
Vicente-Guillén, J., Ayuga-Telléz, E., Otero, D., Chávez, J.,
Ayuga, F., and García, A.: Performance of a monthly Streamflow
prediction model for Ungauged watersheds in Spain, Water Resour.
Manage., 26, 3767–3784, https://doi.org/10.1007/s11269-012-0102-5, 2012. a, b, c
Waltrup, L. S., Sobotka, F., Kneib, T., and Kauermann, G.: Expectile and
quantile regression—David and Goliath?, Stat. Model., 15,
433–456, https://doi.org/10.1177/1471082X14561155, 2015. a
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New
York, https://ggplot2.tidyverse.org (last access: 8 April 2022), 2016. a
Wickham, H.: tidyr: Tidy Messy Data, r package version 1.1.3, https://CRAN.R-project.org/package=tidyr (last access: 8 April 2022), 2021. a
Wickham, H., François, R., Henry, L., and Müller, K.: dplyr: A Grammar of Data Manipulation, r package version 1.0.7, https://CRAN.R-project.org/package=dplyr (last access: 8 April 2022), 2021.
a
Worland, S. C., Farmer, W. H., and Kiang, J. E.: Improving predictions of
hydrological low-flow indices in ungaged basins using machine learning,
Environ. Model. Softw., 101, 169–182,
https://doi.org/10.1016/j.envsoft.2017.12.021, 2018. a
Yang, X., Magnusson, J., Rizzi, J., and Xu, C.-Y.: Runoff prediction in
ungauged catchments in Norway: comparison of regionalization approaches,
Hydrol. Res., 49, 487–505, https://doi.org/10.2166/nh.2017.071, 2017. a, b
Yu, X., Wang, Y., Wu, L., Chen, G., Wang, L., and Qin, H.: Comparison of
support vector regression and extreme gradient boosting for
decomposition-based data-driven 10-day streamflow forecasting, J.
Hydrol., 582, 124293, https://doi.org/10.1016/j.jhydrol.2019.124293, 2020. a
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., and Hinkelmann, R.: Ensemble machine learning paradigms in hydrology: A review, J. Hydrol., 598,
126266, https://doi.org/10.1016/j.jhydrol.2021.126266, 2021. a
Short summary
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.
Our study uses a statistical boosting model for estimating low flows on a monthly basis, which...
