Testing hydrological models under changing conditions is essential to evaluate their ability to cope with changing catchments and their suitability for impact studies. With this perspective in mind, ...a workshop dedicated to this issue was held at the 2013 General Assembly of the International Association of Hydrological Sciences (IAHS) in Göteborg, Sweden, in July 2013, during which the results of a common testing experiment were presented. Prior to the workshop, the participants had been invited to test their own models on a common set of basins showing varying conditions specifically set up for the workshop. All these basins experienced changes, either in physical characteristics (e.g. changes in land cover) or climate conditions (e.g. gradual temperature increase). This article presents the motivations and organization of this experiment—that is—the testing (calibration and evaluation) protocol and the common framework of statistical procedures and graphical tools used to assess the model performances. The basins datasets are also briefly introduced (a detailed description is provided in the associated Supplementary material).
The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as ...forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the post-processing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts.
Challenges of Operational River Forecasting Pagano, Thomas C.; Wood, Andrew W.; Ramos, Maria-Helena ...
Journal of hydrometeorology,
08/2014, Letnik:
15, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex ...coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1)making the most of available data, 2)making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
In this paper, we analyze how our evaluation of the capacity of a rainfall-runoff model to represent low or high flows depends on the objective function used during the calibration process. We ...present a method to combine models to produce a more satisfactory streamflow simulation, on the basis of two different parameterizations of the same model. Where we previously had to choose between a more efficient simulation for either high flows or low flows (but inevitably less efficient in the other range), we show that a balanced simulation can be obtained by using a seasonal index to weigh the two simulations, providing good efficiency in both low and high flows.
► River bed geometry modifications change the stage-discharge relationship. ► Uncertainty from river bed modifications is estimated with a semivariogram. ► Temporal uncertainty can be handled by the ...proposed variographic approach.
A rating curve provides an estimation of river discharges based on stage (water level). This estimation contains a level of uncertainty. Initial uncertainty occurs at the time of establishment of the rating curve. This may be due, for example, to the randomness of natural processes or to the inaccurate measurement of the stage. Temporal uncertainty is related to the well-known processes of erosion and deposition that modify the geometry of the river bed and, consequently, the relationship between the stage and discharge. As time goes by, temporal uncertainty of the estimated discharge from a rating curve increases. Due to the widespread use of rating curves by scientists and water resource managers, it is important to assess these related uncertainties. Several studies have taken into account initial uncertainties but none, to our knowledge, has considered temporal uncertainties. The aim of this paper is to develop a methodology to estimate the temporal uncertainty of the discharge that is estimated by the rating curve. The proposed approach is based on a variographic analysis. At the beginning of rating curve validity period, the estimated discharge is believed to be distributed as a normal distribution centered on the rating curve’s estimation. The initial variance of the normal distribution, according to the initial uncertainty, is fixed so that the relative uncertainty is less than 5%. A temporal variance term, estimated using a variographic analysis, is then added to the initial variance to take into account the temporal uncertainty. This term corresponds to the mean of semi-variance between estimations separated by a given time. The proposed method has been applied to 1803 gaugings from 19 hydrometric stations located in the French Alps. The 95% confidence intervals cover 90% of 1803 gaugings. This result shows that the confidence intervals are too short. However, this may be due to an underestimation of the initial variance. The method is efficient and robust since it can be adapted to various station characteristics, such as trends in discharge series or stability of the river bed.
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