Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

doi:10.3390/w8040115

Cited by 41 publications

(46 citation statements)

References 82 publications

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“…Secondly, a slight increase begins from time order 5 that smoothly concludes in time order 13. This continuous and differentiated behavior (from 1 up to 13) may indicate a temporal dependence, persistent in the short and medium term, which is not detected by a correlogram (see Figure 4b, independent behavior in the intervals [3,4] and [10][11][12]). However, in a correlogam, there is also a lag (Time order) range where dependence raises between lags 5 and 9, as shown in Figure 4b, that noticeably coincides with a slight raise in dependence analysis though the BN approach (Figure 7b).…”

Section: Porma Rivermentioning

confidence: 99%

“…Traditional temporal and spatial behavior patterns are being ever more modified over the world. In this sense, extreme events such as floods or droughts, among others, are more frequent in recent times [1][2][3][4]. There is a consequent strong necessity for powerful and reliable tools to build accurate models that reproduce the past and present hydrological behavior and forecast the future hydrological behavior of a river system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

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Hydrological series are largely characterized by a strong random component in their behavior. More noticeable changes in the behavior patterns of rainfall/runoff temporal series are recently being observed. These modifications are not a trivial issue, especially in regards to peculiarities, non-linearities, diffused influences or higher time orders of dependence. This study mainly aimed to analyze the temporal dependence of an annual runoff series dynamically. This approach comprises a coupling between classic techniques (Autoregressive Moving Average Model, ARMA) and novel ones, based on Artificial Intelligent for hydrological research (Bayesian Networks, BNs). An ARMA model was built to provide reliable data to populate BNs. Then, causal reasoning, through Bayes's theorem, allows the identification of the logic structure of temporal dependences within time series. Furthermore, the resultant conditional probability permits the quantification of the relative percentage of annual runoff change, and provides the right time order of dependence. This research introduces an original methodology able to build a logic structure for a stochastic analysis of temporal behavior. This approach also aimed to provide a powerful and graphic modeling method for improving the understanding of the dynamic runoff series temporal behavior. This was successfully demonstrated in two unregulated river basin stretches, belonging to the Duero river basin which is the largest basin in Spain.

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Section: Porma Rivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

View full text Add to dashboard Cite

show abstract

“…that a certain threshold will be exceeded) and the latter is given by a quantile for a particular probability level of interest (Bouallègue et al, 2015). Since the outputs of the QRNN model are quantiles, it is reasonable to evaluate the performance with a skill score which has been developed for predictive quantiles (Koenker and Machado, 1999;Friederichs and Hense, 2007), known as the quantile score (QS).…”

Section: Verificationmentioning

confidence: 99%

“…Whereas quantile regression (QR) methods (Koenker, 2005) and modifications of them will lead to predictions of quantiles, a predictive pdf can be derived for example by the recently developed waveVARX method (Bogner and Pappenberger, 2011) directly. For more details of these post-processing methods, the reader is referred to Bogner et al (2016), whereas the objective of this paper will be the analysis of combination methods of forecasts. In the next section the three combination methods and the applied verification measures will be described.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Combining quantile forecasts and predictive distributions of streamflows

Bogner¹,

Liechti²,

Zappa³

2017

Hydrol. Earth Syst. Sci.

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Abstract. The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the nonhomogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

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“…Other studies have assessed the efficiency of postprocessing streamflow forecasts only (Bogner et al, 2016;Madadgar et al, 2014;Ye et al, 2015;Zhao et al, 15 2011;Wood and Schaake, 2008). To our knowledge, only Roulin and Vannitsem, (2015); Yuan and Wood, (2012) and Zalachori et al, (2012) have compared the additional gain in skill of doing both pre-and postprocessing.…”

mentioning

confidence: 99%

Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

Lucatero¹,

Madsen²,

Refsgaard³

et al. 2017

Preprint

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Abstract.In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological 10 seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface -subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the 15 different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of 20 accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

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Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Cited by 41 publications

References 82 publications

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Technical note: Combining quantile forecasts and predictive distributions of streamflows

Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

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