Articles | Volume 24, issue 8
https://doi.org/10.5194/hess-24-4109-2020
© Author(s) 2020. 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-24-4109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Aug 2020
Research article |
| 21 Aug 2020
| 21 Aug 2020
Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework
Norwegian University of Science and Technology, NTNU, Department of Mathematical Sciences, Trondheim, Norway
Ingelin Steinsland
Norwegian University of Science and Technology, NTNU, Department of Mathematical Sciences, Trondheim, Norway
Kolbjørn Engeland
The Norwegian Water Resources and Energy Directorate, NVE, Oslo, Norway
Related authors
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.
Danielle M. Barna, Kolbjørn Engeland, Thomas Kneib, Thordis L. Thorarinsdottir, and Chong-Yu Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2335, https://doi.org/10.5194/egusphere-2023-2335, 2023
Preprint archived
Short summary
Short summary
Estimating flood quantiles at data-scarce sites often involves single-duration regression models. However, floodplain management and reservoir design, for example, need estimates at several durations, posing challenges. Our flexible generalized additive model (GAM) enhances accuracy and explanation, revealing that single-duration models may underperform elsewhere, emphasizing the need for adaptable approaches.
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.
Trine J. Hegdahl, Kolbjørn Engeland, Ingelin Steinsland, and Andrew Singleton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-13, https://doi.org/10.5194/hess-2021-13, 2021
Manuscript not accepted for further review
Short summary
Short summary
To provide improved and reliable flood forecasts we have evaluated ensemble forecasts for 119 catchments. We applied correction (processing) schemes to temperature, precipitation, and streamflow. The performance of the corrections schemes depends on catchments, season, and lead time. Postprocessing streamflow was less important for floods. Spring floods had a longer predictability than autumn floods, and the study revealed regions and seasons with potential for improvements.
Kolbjørn Engeland, Anna Aano, Ida Steffensen, Eivind Støren, and Øyvind Paasche
Hydrol. Earth Syst. Sci., 24, 5595–5619, https://doi.org/10.5194/hess-24-5595-2020, https://doi.org/10.5194/hess-24-5595-2020, 2020
Short summary
Short summary
We combine systematic, historical, and paleo information to obtain flood information from the last 10 300 years for the Glomma River in Norway. We identify periods with increased flood activity (4000–2000 years ago and the recent 1000 years) that correspond broadly to periods with low summer temperatures and glacier growth. The design floods in Glomma were more than 20 % higher during the 18th century than today. We suggest that trends in flood variability are linked to snow in late spring.
Trine J. Hegdahl, Kolbjørn Engeland, Ingelin Steinsland, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 23, 723–739, https://doi.org/10.5194/hess-23-723-2019, https://doi.org/10.5194/hess-23-723-2019, 2019
Short summary
Short summary
Flood forecasting relies on high-quality meteorological data. This study shows how improved temperature forecasts improve streamflow forecasts in most cases, with the degree of improvement depending on season and region. To improve temperature forecasts further, catchment-specific methods should be developed to account for these seasonal and regional differences. In short, for climates with a seasonal snow cover, higher-quality temperature forecasts clearly improve flood forecasts.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
Monthly new water fractions and their relationships with climate and catchment properties across Alpine rivers
Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments
Flood frequency analysis using mean daily flows vs. instantaneous peak flows
On the regional-scale variability in flow duration curves in Peninsular India
Towards a conceptualization of the hydrological processes behind changes of young water fraction with elevation: a focus on mountainous alpine catchments
A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework
A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality
Significant regime shifts in historical water yield in the Upper Brahmaputra River basin
A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records
Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Parsimonious statistical learning models for low-flow estimation
Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling
Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences
Effects of climate anomalies on warm-season low flows in Switzerland
Histogram via entropy reduction (HER): an information-theoretic alternative for geostatistics
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.
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
Short summary
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.
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.
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
Adamowski, K. and Bocci, C.: Geostatistical regional trend detection in river
flow data, Hydrol. Process., 15, 3331–3341, https://doi.org/10.1002/hyp.1045,
2001. a
Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J.,
Krainski, E., Simpson, D., and Lindgren, F.: Spatial modeling with R-INLA: A
review, WIREs Computational Statistics, 10, e1443, https://doi.org/10.1002/wics.1443, 2018. a
Banerjee, S., Gelfand, A., and Carlin, B.: Hierarchical Modeling and Analysis
for Spatial Data, vol. 101 of Monographs on Statistics and Applied
Probability, Chapman & Hall, Boca Raton, Florida, 2004. a
Brenner, S. and Scott, L.: The Mathematical Theory of Finite Element
Methods, 3rd Edition. Vol. 15 of Texts in Applied Mathematics,
Springer, New York, 2008. a
Casella, G. and Berger, R.: Statistical Inference, Duxbury Press, Belmont, 1990. a
Eidsvik, J., Finley, A. O., Banerjee, S., and Rue, H.: Approximate Bayesian
inference for large spatial datasets using predictive process models,
Comput. Stat. Data An., 56, 1362–1380,
https://doi.org/10.1016/j.csda.2011.10.022, 2012. a
Eidsvik, J., Mukerji, T., and Bhattacharjya, D.: Value of Information in the
Earth Sciences: Integrating Spatial Modeling and Decision Analysis, Cambridge
University Press, Cambridge, https://doi.org/10.1017/CBO9781139628785, 2015. a
Engeland, K. and Hisdal, H.: A Comparison of Low Flow Estimates in Ungauged
Catchments Using Regional Regression and the HBV-Model, Water Resour.
Manag., 23, 2567–2586, https://doi.org/10.1007/s11269-008-9397-7, 2009. a
Farmer, W. H.: Ordinary kriging as a tool to estimate historical daily streamflow records, Hydrol. Earth Syst. Sci., 20, 2721–2735, https://doi.org/10.5194/hess-20-2721-2016, 2016. a
Ferkingstad, E. and Rue, H.: Improving the INLA approach for approximate
Bayesian inference for latent Gaussian models, Electron. J.
Stat., 9, 2706–2731, https://doi.org/10.1214/15-EJS1092, 2015. a
Fong, Y., Rue, H., and Wakefield, J.: Bayesian inference for generalized linear
mixed models, Biostatistics, 11, 397–412,
https://doi.org/10.1093/biostatistics/kxp053, 2009. a
Fuglstad, G.-A., Simpson, D., Lindgren, F., and Rue, H.: Constructing Priors
that Penalize the Complexity of Gaussian Random Fields, J.
Am. Stat. Assoc., 114, 445–452,
https://doi.org/10.1080/01621459.2017.1415907, 2019. a
Førland, E. J.: Nedbørens høydeavhengighet, Klima, 2, 3–24, 1979. a
Gamerman, D. and Lopes, H. F.: Markov chain Monte Carlo: stochastic simulation
for Bayesian inference, 2 Edn., Chapman and Hall/CRC, Boca Raton, 2006. a
Gneiting, T. and Raftery, A. E.: Strictly Proper Scoring Rules,
Prediction, and Estimation, J. Am. Stat.
Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a
Gottschalk, L.: Correlation and covariance of runoff, Stoch. Hydrol.
Hydraul., 7, 85–101, https://doi.org/10.1007/BF01581418, 1993. a, b
Gottschalk, L., Jensen, J. L., Lundquist, D., Solantie, R., and Tollan, A.:
Hydrologic Regions in the Nordic Countries, Hydrol. Res., 10, 273–286,
1979. a
Guillot, G., Vitalis, R., le Rouzic, A., and Gautier, M.: Detecting correlation
between allele frequencies and environmental variables as a signature of
selection. A fast computational approach for genome-wide studies, Spat.
Stat., 8, 145–155, https://doi.org/10.1016/j.spasta.2013.08.001, 2014. a
Guttorp, P. and Gneiting, T.: Studies in the history of probability and
statistics XLIX On the Matérn correlation family, Biometrika, 93,
989–995, https://doi.org/10.1093/biomet/93.4.989, 2006. a
Huang, J., Malone, B., Minasny, B., Mcbratney, A., and Triantafilis, J.:
Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental
mapping, Sci. Total Environ., 609, 621–632,
https://doi.org/10.1016/j.scitotenv.2017.07.201, 2017. a, b
Ingebrigtsen, R., Lindgren, F., and Steinsland, I.: Spatial models with
explanatory variables in the dependence structure, Spat. Stat., 8, 20–38, https://doi.org/10.1016/j.spasta.2013.06.002, 2014. a
Ingebrigtsen, R., Lindgren, F., Steinsland, I., and Martino, S.: Estimation of
a non-stationary model for annual precipitation in southern Norway using
replicates of the spatial field, Spat. Stat., 14, 338–364,
https://doi.org/10.1016/j.spasta.2015.07.003, 2015. a
Khan, D. and Warner, M.: A Bayesian spatial and temporal modeling approach to
mapping geographic variation in mortality rates for subnational areas with
r-inla, Journal of Data Science, 18, 147–182, 2018. a
Laaha, G. and Blöschl, G.: Low flow estimates from short stream flow records
– a comparison of methods, J. Hydrol., 306, 264–286,
https://doi.org/10.1016/j.jhydrol.2004.09.012, 2005. a, b, c, d
Lindgren, F., Rue, H., and Lindström, J.: An explicit link between Gaussian
fields and Gaussian Markov random fields: the stochastic partial
differential equation approach, J. Roy. Stat. Soc.-B, 73, 423–498,
https://doi.org/10.1111/j.1467-9868.2011.00777.x, 2011. a, b, c
Martino, S., Akerkar, R., and Rue, H.: Approximate Bayesian Inference for
Survival Models, Scand. J. Stat., 38, 514–528,
https://doi.org/10.1111/j.1467-9469.2010.00715.x, 2011. a
Matalas, N. C. and Jacobs, B.: A correlation procedure for augmenting
hydrologic data, U.S. Geol. Surv. Prof. Pap., 434-E, https://doi.org/10.3133/pp434E, 1964. a, b
Merz, R. and Blöschl, G.: Flood frequency regionalisation – spatial proximity
vs. catchment attributes, J. Hydrol., 302, 283–306,
https://doi.org/10.1016/j.jhydrol.2004.07.018, 2005. a, b, c
Moraga, P., Cramb, S. M., Mengersen, K. L., and Pagano, M.: A geostatistical
model for combined analysis of point-level and area-level data using INLA
and SPDE, Spat. Stat., 21, 27–41,
https://doi.org/10.1016/j.spasta.2017.04.006, 2017. a
Opitz, T., Huser, R., Bakka, H., and Rue, H.: INLA goes extreme: Bayesian tail
regression for the estimation of high spatio-temporal quantiles, Extremes,
21, 331–462, https://doi.org/10.1007/s10687-018-0324-x, 2018. a
Patil, S. and Stieglitz, M.: Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment, Hydrol. Earth Syst. Sci., 16, 551–562, https://doi.org/10.5194/hess-16-551-2012, 2012. a
Petersen-Øverleir, A.: Accounting for heteroscedasticity in rating curve
estimates, J. Hydrol., 292, 173–181,
https://doi.org/10.1016/j.jhydrol.2003.12.024, 2004. a
Roksvåg, T.:
Code for implementing the centroid model, https://doi.org/10.5281/zenodo.3630348, last access: 29 January 2020.
Rue, H., Martino, S., and Chopin, N.: Approximate Bayesian inference for
latent Gaussian models using integrated nested Laplace approximations,
J. Roy. Stat. Soc.-B,
71, 319–392, https://doi.org/10.1111/j.1467-9868.2008.00700.x, 2009. a, b
Sauquet, E., Gottschalk, L., and Lebois, E.: Mapping average annual runoff: A
hierarchical approach applying a stochastic interpolation scheme,
Hydrolog. Sci. J., 45, 799–815, https://doi.org/10.1080/02626660009492385,
2000. a, b, c
Siegel, S.: Non-parametric statistics for the behavioral sciences,
McGraw-Hill, New York, 1956. a
Simpson, D., Rue, H., Riebler, A., Martins, T. G., and Sørbye, S. H.:
Penalising Model Component Complexity: A Principled, Practical
Approach to Constructing Priors, Stat. Sci., 32, 1–28,
https://doi.org/10.1214/16-STS576, 2017. a
Skøien, J., Blöschl, G., Laaha, G., Pebesma, E., Parajka, J., and Viglione,
A.: rtop: An R package for interpolation of data with a variable spatial
support, with an example from river networks, Comput. Geosci., 67,
180–190, https://doi.org/10.1016/j.cageo.2014.02.009, 2014. a
Skøien, J. O. and Blöschl, G.: Spatiotemporal topological kriging of runoff
time series, Water Resour. Res., 43, W09419, https://doi.org/10.1029/2006WR005760, 2007. a
Skøien, J. O., Blöschl, G., and Western, A. W.: Characteristic space scales
and timescales in hydrology, Water Resour. Res., 39, 1304,
https://doi.org/10.1029/2002WR001736, 2003. a, b
Stohl, A., Forster, C., and Sodemann, H.: Remote sources of water vapor forming
precipitation on the Norwegian west coast at 60∘ N – a tale of
hurricanes and an atmospheric river, J. Geophys. Res.-Atmos., 113, D05102, https://doi.org/10.1029/2007JD009006, 2008. a
Sælthun, N., Tveito, O., Bøsnes, T., and Roald, L.: Regional
flomfrekvensanalyse for norske vassdrag, Tech. Rep., Oslo, NVE, 1997. a
Viglione, A., Parajka, J., Rogger, M., Salinas, J. L., Laaha, G., Sivapalan, M., and Blöschl, G.: Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria, Hydrol. Earth Syst. Sci., 17, 2263–2279, https://doi.org/10.5194/hess-17-2263-2013, 2013. a, b
Vogel, R. M. and Stedinger, J. R.: Minimum variance streamflow record
augmentation procedures, Water Resour. Res., 21, 715–723,
https://doi.org/10.1029/WR021i005p00715, 1985. a
Yuan, Y., Bachl, F., Lindgren, F., Borchers, D., Illian, J., Buckland, S., Rue,
H., and Gerrodette, T.: Point process models for spatio-temporal distance
sampling data from a large-scale survey of blue whales, Ann. Appl. Stat., 11, 2270–2297, https://doi.org/10.1214/17-AOAS1078, 2017. a
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.
Annual runoff is a measure of how much water flows through a river during a year and is an...
