Abstract
AbstractThe estimation of predictive uncertainty and its application as a post‐processor of hydrological model output, such as water level, can provide additional information useful for short‐term hydrological forecasting. In this study, We applied quantile regression models for estimating predictive hydrological uncertainty and used it to derive probabilistic hydrological forecasts. Forecast water levels and associated forecast errors were used as predictor and predictand, respectively, to develop three regression models: (a) linear quantile regression (LQR), (b) weighted LQR and (c) LQR in Gaussian space using Normal Quantile Transformation. These different models for hydrological forecasting were developed for, and applied to, the operational flood forecasting system in the Upper Chao Phraya River, Thailand. The quality of these forecasts in terms of reliability, sharpness and overall skill were assessed using various graphical and numerical verification metrics. Results show that the improvement of forecast in terms of either reliability or sharpness depends upon the configurations used. With comparable overall performance, weighted LQR provided a relatively simple configuration, which can be used for estimating uncertainty in hydrological forecasting.
Highlights
Hydrological forecasting is used to predict the future state of a hydrological system to support water management
The quantile regression (QR) models generated for the three different stations NAN007, NAN008 and PIN004 are shown in Figures 2–4 respectively
The plots consist of scatter of forecasted water levels and corresponding forecast errors with regression lines obtained from QR
Summary
Hydrological forecasting is used to predict the future state of a hydrological system to support water management. Accurate and reliable hydrological forecasts can reduce the impacts from waterrelated hazards and provide the basis for more effective water resources management In this context, information on predictive uncertainty is important for forecast-based decision making This study applied and compared three different configurations of QR: (a) Linear quantile regression (LQR), (b) Weighted LQR that gives larger weights to higher water levels in the regression and (c) LQR in Gaussian space using Normal Quantile Transformation We evaluated these QR models with the aim of investigating whether the non-linearity introduced in the regression lines in the original space by applying QR in Gaussian space provide a good representation of the actual error and increases the performance of the QR model in comparison to other configurations. The discharge of Ping, Wang and Nan rivers are mainly
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