Abstract
Abstract. Forecast uncertainties are unfortunately inevitable when conducting a 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, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. In practice, the proper interpretation of the predictive uncertainties and model outputs will also have a crucial impact on risk-based decisions. In this study, the performance of evolutionary multi-objective optimization (i.e., non-dominated sorting genetic algorithm II – NSGA-II) as a hydrological ensemble post-processor was tested and compared with a conventional state-of-the-art post-processor, the affine kernel dressing (AKD). Those two methods are theoretically/technically distinct, yet share the same feature in that both of them relax the parametric assumption of the underlying distribution of the data (the streamflow ensemble forecast). Both NSGA-II and AKD post-processors showed efficiency and effectiveness in eliminating forecast biases and maintaining a proper dispersion with increasing forecasting horizons. In addition, the NSGA-II method demonstrated superiority in communicating trade-offs with end-users on which performance aspects to improve.
Highlights
Hydrologic forecasting is crucial for flood warning and mitigation (e.g., Shim and Fontane, 2002; Cheng and Chau, 2004), water supply operation and reservoir management (e.g., Datta and Burges, 1984; Coulibaly et al, 2000; Boucher et al, 2011), navigation, and other related activities
The issue of member interchangeability is central to this study, since, for affine kernel dressing (AKD), each raw ensemble will be considered as a whole, whereas for non-dominated sorting genetic algorithm II (NSGA-II) a weight matrix is sought, which implies that different weights are assigned to each candidate members
Both the kernel ensemble dressing and the evolutionary multi-objective optimization approaches are tested in this study to estimate the probability density directly from the data over five single-model hydrologic ensemble prediction systems (H-EPSs)
Summary
Hydrologic forecasting is crucial for flood warning and mitigation (e.g., Shim and Fontane, 2002; Cheng and Chau, 2004), water supply operation and reservoir management (e.g., Datta and Burges, 1984; Coulibaly et al, 2000; Boucher et al, 2011), navigation, and other related activities. Hydrologic models are typically driven by dynamic meteorological models in order to issue forecasts over a medium-range horizon of 2 to 15 d (Cloke and Pappenberger, 2009). These kinds of coupled hydrometeorological forecasting systems are used as effective tools to issue longer lead times. Inherent in the coupled hydrometeorological forecasting systems are some predictive uncertainties, which are inevitable given the limits of knowledge and available information (Ajami et al, 2007) Those uncertainties occur all along the different steps of the hydrometeorological modeling chain (e.g., Liu and Gupta, 2007; Beven and Binley, 2014). These different sources of uncertainty are related to deficiencies in the meteorological forcing, misspecified hydrologic initial and boundary conditions, inherent hydrologic model structure errors, and biased estimated parameters (e.g., Vrugt and Robinson, 2007; Ajami et al, 2007; Salamon and Feyen, 2010; Thiboult et al, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
