Hydrological post-processing of streamflow forecasts issued from single-model and multimodel ensemble prediction systems
|Advisor:||Anctil, François; Boucher, Marie-Amélie|
|Abstract:||Hydrological simulations and forecasts are subject to various sources of uncertainties. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, necessarily imperfect hydrologic modeling, and the inconsistent stationnarity assumption in a changing environment. Ensemble forecasting is a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. Thiboult et al. (2016) constructed a 50,000-member great ensemble that accounts for meteorological forcing uncertainty, initial conditions uncertainty, and structural uncertainty. This large ensemble can also be separated into sub-components to untangle the three main sources of uncertainties mentioned above. In asimilar experiment, another multimodel hydrological ensemble forecasting system implemented for different catchments was produced by Emixi Valdez. However,in the latter case, model outputs were simply pooled together, considering the members equiprobable. Although multimodel hydrological ensemble forecasting systems can be considered very comprehensive, they can still underestimate the total uncertainty. For instance, the meteorological forecasts in there search of Thiboult et al. (2016) were pre-tested on some watersheds. It was found out that the forecasting performance of data assimilation fades away quickly as the lead time progresses. In addition, operational forecasts users may not able to perfectly utilize all the forecasting tools (i.e., meteorological ensemble forcing, data assimilation, and multimodel) jointly. Therefore, there is still room for improvement to enhance the forecasting skill of such systems through proper statistical post-processing.The global objective of this research is to explore the proper use and predictive skill of various statistical post-processing algorithms by testing them on single-model and multimodel ensemble stream flow forecasts. First, we tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II) over single-model H-EPSs. Those two methods are theoretically/technically distinct yet are both non-parametric. They do not require the raw ensemble members to follow a specific parametric distribution.AKD-transformed ensembles and the Pareto fronts generated with NSGA-II demonstrated the superiority of post-processed ensembles compared to raw ensembles. Both methods where efficient at eliminating biases and maintaining a proper dispersion for all forecasting horizons. For multimodel ensembles, two post-processors, namely Bayesian model averaging (BMA) and the integrated copula-BMA, are compared for deriving a pertinent joint predictive distribution of daily streamflow forecasts issued by five different single-model hydrological ensemble prediction systems (H-EPSs). BMA assign weights to different models. Forecasts from all models are then combined to generate more skillful and reliable probabilistic forecasts. BMA weights quantify the level of confidence one can have regarding each candidate hydrological model and lead to a predictive probabilistic density function (PDF) containing information about uncertainty. BMA improves the overall quality of forecasts mainly by maintaining the ensemble dispersion with the lead time. It also improves the reliability and skill of multimodel systems that only include two sources of uncertainties compared to the 50,000-member great ensemble from Thiboult et al (2016). Furthermore, Thiboult et al. (2016) showed that the meteorological forecasts they used were biased and unreliable on some catchments. BMA improves the accuracy and reliability of the hydrological forecasts in that case as well.However, BMA suffers from limitations pertaining to its conditional probability density functions (PDFs), which must follow a known parametric distribution form (e.g., normal, gamma). On the contrary, Copula-BMA predictive model fully relaxes this constraint and also eliminates the power transformation step. In this study, eleven univariate marginal distributions and six copula functions are explored in a Copula-BMA framework for comprehensively reflecting the dependence structure between pairs of forecasted and observed streamflow. Results demonstrate the superiority of the Copula-BMAcompared to BMA in eliminating biases and maintaining an appropriate ensemble dispersion for all lead-times.|
|Document Type:||Thèse de doctorat|
|Open Access Date:||5 July 2021|
|Collection:||Thèses et mémoires|
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