Table 1 - uploaded by Korbinian Breinl
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Names, coordinates, altitudes and annual precipitation statistics of the gauge stations in the Ubaye study area.
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This paper deals with the question whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results, compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently...
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Context 1
... time series were extracted from the grid cell closest to the catchment centre (Fig. 3). Tables 1 and 2 give details on the gauge stations. 3 Model evaluation procedure ...
Context 2
... bootstrapping (Reiss and Thomas 2007). Return periods and confi- dence intervals of the simulations were estimated using a "balanced resampling" approach by Burn (2003). The evaluation was conducted for the two study areas, the three weather generator models, the bootstrap time series as well as the four parametric distribution functions. .0444 1250 1278 105 1124 1180 1350 1443 2 Paß Thurn 47.2997 12.4222 1200 1206 131 1004 1096 1312 1402 3 Hochfilzen 47.4703 12.6217 960 1781 185 1479 1643 1925 2080 4 Felbertauerntunnel 47.1181 12.5056 1650 1403 133 1184 1287 1488 1624 5 Böckstein 47.0872 13.1158 1140 1342 132 1158 1268 1363 1622 6 Flachau 47.3472 13.3958 910 1154 93 1024 1087 1206 1304 7 Golling 4 Model ...
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... Lumped models are designed to simulate the total runoff at the catchment outlet point, considering the entire catchment as homogeneous ignoring spatial variability of model parameters. The lump modelling process is efficient in terms of computational time, however, may not accurately represent large catchments since such models are developed based on many assumptions and averaged conditions [14,15]. However, in contrast, semi-distributed and fully distributed models are significantly different compared to lumped models with features of distributed models. ...
... A stochastic rainfall generator is a statistical model that produces synthetic rainfall time series with desired statistical properties. Synthetic rainfall time series can be used for various purposes, such as rainfall-runoff modeling [1,2], design flood estimation [3][4][5], rainfall projection under climate change scenarios [6][7][8], and prediction in ungauged basins [9,10]. The Richardson-type rainfall generator [11,12], a popular stochastic rainfall generator, can reproduce long-term continuous daily precipitation time series. ...
In this study, a stochastic rainfall generator was developed to create continuous rainfall time series with a high temporal resolution of 10 min. The rainfall-generation process involved Monte Carlo simulation for stochastically generating rainfall parameters such as rainfall quantity, duration, inter-event time, and type. A bivariate copula was used to preserve the correlation between rainfall quantity and rainfall duration in the generated rainfall series. A modified Huff curve method was used to overcome the drawbacks of rainfall type classification by using the conventional Huff curve method. The number of discarded rainfall events was lower in the modified Huff curve method than in the conventional Huff curve method. Moreover, the modified method includes a new rainfall type that better represents rainfall events with a relatively uniform temporal pattern. The developed rainfall generator was used to reproduce rainfall series for the Yilan River Basin in Taiwan. The statistical indices of the generated rainfall series were close to those of the observed rainfall series. The results obtained for rainfall type classification indicated the necessity and suitability of the proposed new rainfall type. Overall, the developed stochastic rainfall generator can suitably reproduce continuous rainfall time series with a resolution of 10 min.
... These synthetic precipitation series are then utilized as the input of rainfall-runoff models to generate various runoff processes, which can support water resources regime evolution, system planning and design, and risk analysis. This combination has been widely used in hydrological impact assessment and gained many advantages (Breinl 2016;Mukundan et al. 2019;Qin and Lu 2014). ...
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... The models are forced by state-of-the-art climate reanalysis data representing the present climate and weather attributes (e.g., temperature, precipitation, windspeed, evapotranspiration, and rainfall/snowfall characteristics), while the future climate is represented by two emission scenarios from the ensemble of five GCMs. To address goal 2), we use stochastic weather generators as it is one of the recommended methods for estimating uncertainties in climate change projections (Katz, 2002) and in hydrological forecasting (e.g., Caron et al., 2008;Breinl, 2016). By combining the projections of water supply and demand, we aim to provide a spatial pattern of seasonal municipal water shortage risks, together with estimated credibility in these risks across the whole region. ...
While anthropogenic climate change poses a risk to freshwater resources across the globe through increases in evapotranspiration and temperature, it is essential to quantify the risks at local scales in response to projected trends in both freshwater supply and demand. In this study, we use empirical modeling to estimate the risks of municipal water shortages across North America by assessing the effects of climate change on streamflow and urban water demand. In addition, we aim to quantify uncertainties in both supply and demand predictions. Using streamflow data from both the US and Canada, we first cluster 4,290 streamflow gauges based on hydrograph similarity and geographical location. We develop a set of multiple linear regression (MLR) models, as a simplified analog to a distributed hydrological model, with minimum input data requirements. These MLR models are calibrated to simulate streamflow for the 1993–2012 period using the ERA5 climate reanalysis data. The models are then used to predict streamflow for the 2080–2099 period in response to two climate scenarios (RCP4.5 and RCP8.5) from five global climate models. Another set of MLR models are constructed to project seasonal changes in municipal water consumption for the clustered domains. The models are calibrated against collected data on urban water use from 47 cities across the study region. For both streamflow and water use, we quantified uncertainties in our predictions using stochastic weather generators and Monte Carlo methods. Our study shows the strong predictive power of the MLR models for simulating both streamflow regimes (Kling-Gupta efficiency >0.5) and urban water use (correlation coefficient ≈0.7) in most regions. Under the RCP4.5 (RCP8.5) emissions scenario, the West Coast, the Southwest, and the Deep South (South-Central US and the Deep South) have the highest risk of municipal water shortages. Across the whole domain, the risk is the highest in the summer season when demand is high. We find that the uncertainty in projected changes to the water demand is substantially lower than the uncertainty in the projected changes to the supply. Regions with the highest risk of water shortages should begin to investigate mitigation and adaptation strategies.
... Un exemple de générateur stochastique pour éviter l'utilisation d'années typiques est montré dans Ailliot et al. (2020). L'utilisation d'un générateur aléatoire de conditions météorologiques en couplage avec une modèle hydraulique a été fait par exemple dans Caron et al. (2008) ou encore Breinl (2016). Toutefois ces références utilisent des modèles hydrauliques simplifiés qui permettent de faire tourner un grand nombre de séries. ...
Dans les zones urbaines, où une part importante des réseaux d’assainissement est unitaire, des déversements d’eaux usées en milieu naturel peuvent avoir lieu lors d’événements pluvieux. Le projet MEDISA vise à établir une méthodologie de dimensionnement des réseaux d’assainissement afin de mieux les gérer. La pluviométrie est un forçage central pour les modèles hydrauliques qui décrivent les réseaux d’assainissement. Le dimensionnement dépend fortement de ce forçage, il est donc nécessaire d’étudier la sensibilité du modèle à différents facteurs afin de déterminer les forçages à utiliser. Les modèles hydrauliques nécessitent des données à un pas de temps de quelques minutes. A cette échelle la distribution marginale de la pluie est caractérisée par 1) une forte occurrence de temps sec, 2) une discrétisation non négligeable et 3) une forme asymétrique à queue lourde. Un modèle méta-Gaussien basé sur ces caractéristiques est développé, pouvant convenir à des données allant de quelques minutes à l’échelle mensuelle et présentant l’avantage de lier chaque paramètre à une partie différente de la distribution. Une analyse des données de pluie disponibles dans la zone d’étude est réalisée. Les pluviomètres et les radars météorologiques contiennent des erreurs de mesure et ne sont pas représentatifs de la vraie pluie, aussi une méthode de fusion de données est développée pour tirer profit des deux sources de données et tester la sensibilité du modèle hydraulique.
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In reservoirs with relatively low capacity-inflow (C/I) ratios, real-time daily operations provide the possibility of minimizing the risk of failure and meeting downstream demands sustainably even during extreme droughts. In the present study, it has been attempted to generate synthetic daily precipitation and maximum and minimum temperature data using the widely applied weather generator Long Ashton Research Station weather generator (LARS-WG). The synthetic meteorological data were then inserted into the Arno River (ARNO) daily rainfall-runoff (R-R) model to reproduce long-term daily streamflow scenarios and subsequently used to determine optimal daily releases and operating policies. The daily R-R model can also produce appropriate predictions of future inflows to the reservoir, which can be applied to implementing real-time operations in daily time steps. The methodology used in this study provides two main advantages: (1) the capability to determine optimal daily releases for streamflow sequences, which can be applied to make optimal real-time decisions even by considering the unregulated downstream flow regime, and (2) the ability to predict future inflows and determine real-time decisions, which can be used to decrease predictive uncertainties of future releases.
... Hence, for each parameter, a reasonable parameter range needs to be defined in which the true parameter value is located (Seibert 1997). Finally, input uncertainties exist due to missing and/or uncertain input data (Breinl 2016;McMillan et al. 2012) as well as due to simplifications of the processes that finally represent the model input, e.g. the groundwater recharge (Kavetski et al. 2006;Patil et al. 2011;Vrugt et al. 2008). ...
Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.
... The snow component is important for catchments with significant snow processes, i.e., for two out of three studied here. HBV has been frequently applied to rainfall-and snow-dominated catchments (e.g., Breinl, 2016;Griessinger et al., 2016;Sikorska & Seibert, 2018;Westerberg et al., 2020). ...
A novel framework, an ensemble-based conceptual-data-driven approach (CDDA), is developed that integrates a hydrological model (HM) with a data-driven model (DDM) to simulate an ensemble of HM residuals. Thus, a CDDA delivers an ensemble of ‘residual-corrected’ streamflow simulations. This framework is beneficial because it respects hydrological processes via the HM and it profits from the DDM’s ability to simulate the complex relationship between residuals and input variables. The CDDA enables exploring different DDMs to identify the most suitable model. Eight DDMs are explored: Multiple Linear Regression (MLR), k Nearest Neighbours Regression (kNN), Second-Order Volterra Series Model, Artificial Neural Networks (ANN), and two variants of eXtreme Gradient Boosting (XGB) and Random Forests (RF). The proposed CDDA, tested on three Swiss catchments, was able to improve the mean continuous ranked probability score by 16-29% over the standalone HM. Based on these results, XGB and RF are recommended for simulating the HM residuals.
... Other studies have also looked into using WGs as constituting a downscaling tool in climate change impact studies because of the relative ease with which their parameters can be modified to represent climate variability (Semenov and Barrow 1997, Kilsby et al. 2007, Zhuang et al. 2016, Keller et al. 2017. Despite this advantage, only a few studies have looked at the potential of using stochastic WGs for long-term streamflow forecasting (Li et al. 2013, Breinl et al. 2015, Shield and Dai 2015, Breinl 2016, and an even smaller number implemented a WG into streamflow forecasts. Of note, Šípek and Daňhelka (2015) used a WG for ESP based on a limited number of observed years selected on the basis of large-scale climate indices, and Hwang et al. (2011) generated several precipitation scenarios to study the impact of uncertainty on streamflow simulations. ...
... It performs generally well but often fails at generating extreme precipitation that follows a distribution with a heavier tail, such as the generalized extreme value distribution (GEV) or Generalized Pareto distributions. Accordingly, several studies have compared various distribution functions to be used in WGs (Li et al. 2013, Breinl 2016, Breinl et al. 2017, Li and Shi 2019. These studies typically find that the more complex distribution functions, with three or more parameters, outperform the simpler ones, especially for larger quantiles of the observed distributions. ...
Resampling historical time series remains one of the main approaches used to generate long-term probabilistic streamflow forecasts, while there is a need to develop more flexible approaches taking into account non-stationarities. One possible approach is to use a modelling chain consisting of a stochastic weather generator and a hydrological model. However, the ability of this modelling chain to generate adequate probabilistic streamflows must first be evaluated. The aim of this paper is to compare the performance of a stochastic weather generator against resampling historical meteorological time series in order to produce ensemble streamflow forecasts. The comparison framework is based on 30 years of forecasts for a single Canadian watershed. Forecasts resulting from the two methods are evaluated using the continuous ranked probability score (CRPS) and rank histograms. Results indicate that while there are differences between the methods, they nevertheless perform similarly, thus showing that weather generators can be used as substitutes for resampling the historical past.
... Rainfall observations (daily, sub-daily, maximum annual values for different durations) and time series generations are available for as fundamental input for continuous models. Data availability and science advancements stimulated a variety of investigations on continuous frameworks for hydrological applications (Ormsbee, 1989;Blazkova and Beven, 2002;Chu and Steinman, 2009;Camici et al, 2011;Pathiraja et al, 2012;Breinl, 2016;Lamb et al., 2016;Blazkova et al, 2017;Davtalab et al., 2017;Rowe and Smithers, 2018;Winter et al, 2019). It is our opinion that continuous model evolution is mature enough to support its development for ungauged basins (Boughton and Droop, 2003). ...
The design hydrograph estimation in small and ungauged basins represents one of the most common practices and, yet, a challenging open research topic for hydrologists. When discharge observations are not available, the practitioner is compelled to apply empirical approaches. The rational formula is slowly disappearing, while event-based approaches are more and more widespread. A step forward is represented by continuous models that have the potential to address the major drawbacks of event-based approaches. In this work we applied a continuous model specifically designed for ungauged basins (COSMO4SUB) and tested its use in conditions where typically the rational formula and the event-based approaches are applied. Results confirm that the continuous modelling is suitable for rapid and effective design simulations supporting flood hazard modelling and mapping studies.
