A hydrologic post-processor for ensemble streamflow predictions

Abstract

Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are wellcalibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equallikelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions.Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of Correspondence to: Q. Duan (qyduan@bnu.edu.cn) experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

“…The resulting probability distribution is deseribed by a complete density funetion (e.g., Krzysztofowiez 1999; Seo et al 2006;Montanari and Grossi 2008;Todini 2008;Bogner and Pappenberger 2011) or several thresholds of the distribution (e.g., Solomatine and Shrestha 2009;. Examples of postproeessing teehniques for hydrologie ensemble predietion systems include error correction based on the last known forecast error (Velazquez et al 2009), an autoregressive error correction using the most recent modeled error Hopson and Webster 2010; in the latter, postprocessing is also applied to multimodel ensembles), bias correction similar to the MEFP temperature methodology for long-term ESP streamflow ensembles (Wood and Schaake 2008), a Bayesian postprocessor for ensemble streamflow forecasts (Reggiani et al 2009), error correction for multiple temporal scales based on wavelet transformation (Bogner and Kalas 2008;Bogner and Pappenberger 2011), and a generalized linear regression model using multiple temporal scales (Zhao et al 2011). To help establish the reliability of different statistical postprocessors and predictors under varied forecasting conditions, the HEPEX project includes an initiative to intercompare postprocessing techniques in order to develop recommendations for their operational use in hydrologie ensemble prediction systems (van Andel et al 2012).…”

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

“…Within this study, post-processing encompasses a model for correcting the errors of historical simulations and real-time forecasts, as well as the estimation of the model and forecast uncertainty. Especially in the field of hydro-meteorological Ensemble Predictions Systems (EPS), the importance of post-processing has been acknowledged in order to remove systematic bias and increase forecast skill (see for example, Brown and Seo [1], Zhao et al [2] and Hemri et al [3], to name a few). It is also one of the major themes of the international initiative called HEPEX (Schaake et al [4]).…”

Using multiple watershed models to assess the water quality impacts of alternate land development scenarios for a small community

“…Ensemble model averaging provides alternatives in addition to a single model, especially when there is not enough information to identify the best model or when the data do not favor a particular model (Kadane and Lazar, 2004). Several studies have applied the MME approach to flow prediction or flood forecasting (Renner et al, 2009;Zhao et al, 2011) and one study demonstrated that combining nitrogen predictions of five models gave better predictions than the individual models (Exbrayat et al, 2010). In addition, the LUCHEM study applied an ensemble of 10 watershed models to assess the effects of land use and land cover (LULC) change on hydrology and water quality Huisman et al, 2009;Viney et al, 2009).…”