Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

Abstract

Ensemble streamflow predictions (ESPs) offer great potential benefits for water resource management, as they contain key probabilistic information for analyzing prediction uncertainty. Ensemble weather forecasts (EWFs) are usually incorporated into ESPs to provide climate information. However, there is no simple way to combine both of them, since EWFs are generally biased and under-dispersed. This study presents a new short-term (1 to 7 lead days) probabilistic streamflow prediction system combining stochastic weather generation and EWFs. The bias and under-dispersion of EWFs were first corrected using a weather generatorbased post-processing approach (GPP). The corrected weather forecasts were then coupled with a hydrological model for streamflow forecasts. The proposed GPP forecast was compared against two other forecasts, one using the raw EWF (GFS), and the other using a stochastic weather generator (WG). The comparison was carried out over two Quebec watersheds, using a set of deterministic and probabilistic verification metrics. The deterministic metrics showed that the GPP forecast is consistently the best at predicting the ensemble mean streamflow for both watersheds and for all the leads ranging between 1 and 7 days, followed by the WG forecast. The probabilistic metrics showed negative or near zero skill retained by the GFS forecast for the first 7 lead days. The WG system was much more skillful than the GFS. The GPP forecast consistently displayed the highest skill and reliability in terms of all the metrics applied. With increasing lead days, the skill and reliability of the GPP forecast tend to converge toward that of the WG forecast, indicating that the short-term GPP forecast could easily be linked to a pure WG forecast to extend the forecast horizon.

Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

“…This approach represents a variant of post-processing bias-correction with the use of a stochastic weather generator and the change-factor method [43,44]. This method is efficient in generating weather timeseries for future projections, but constrained by the assumption that the weather cumulative distribution function retains properties of the historical dataset.…”

Long‐term streamflow forecasting using artificial neural network based on preprocessing technique

“…A number of research studies have been reported on hydrological forecasting in past decades, mainly including physical models and mathematical methods (X. L. Zhang, Peng, Zhang, & Wang, 2015). Physical models explore the hydrological dynamic process of watershed combing weather processes (Chen & Brissette, 2015;Smiatek, Kunstmann, & Werhahn, 2012;Ye et al, 2017), meteorological conditions (Hanna et al, 2013;Ralph, Coleman, Neiman, Zamora, & Dettinger, 2013) and underlying surface conditions (Rosenberg, Clark, Steinemann, & Lettenmaier, 2013;Sinha, Sankarasubramanian, & Mazrooei, 2014). However, due to the complexity of the hydrological processes affected by various factors such as runoff, rainfall and human activity, not only a large number of data and parameters are needed, but also it is difficult to obtain streamflow forecasting with high accuracy.…”

Hydrology Forecasting Model of Upper-lower Bound Estimation Based on Projection Pursuit Regression

“…applied GLUE (Generalized Likelihood Uncertainty Estimation) method to conduct multiple-scale uncertainty analysis and improved the sampling process [16] . Ye L et al used multiple-target optimization algorithm to construct the forecasting intervals of ensemble hydrological forecasting model [17] . By using atmosphere mode to do rainfall forecast and then applying rainfall runoff model to do runoff forecast can help achieve ensemble forecast.…”