post-processing

human intervention in nature and the land-use change of rivers and floodplains, and climate change, the risk of flood has increased in the world. Controlling and reducing the damages flood is done in two methods, structural and non-structural. Research conducted in different parts of the world shows that the use of non-structural methods such as flood forecasting and warning systems along with structural methods reduces flood damage. Flood forecasting and flood notification are logical tools to reduce flood risks in flood-prone areas. Precipitation forecasting is very important due to its use in flood and water resources studies. Numerical weather predictions have been extensively developed in recent years. Today, the world's leading meteorological centers have used these models in their meteorological forecasts. The ECMWF is one of the centers in global meteorological forecasts. The present study was conducted to evaluate the performance of the ECMWF center for precipitation forecasting in the Poldokhtar watershed. Numerical prediction models have systematic errors. Therefore, the purpose of this study was to investigate the effect of 7 bias correction methods (Delta, EQM, EZ, QM, LS, STB, TVSV) on the ECMWF rainfall forecasting model.

The studied area is the Poldokhtar watershed from the subbasins of the Karkheh basin. This watershed is located in Lorestan province. The information from 7 rain gauges station was used in this research. In this study, the comparison of ECMWF center forecasted rainfall with rain gauge station data was made point by point. 7 rain gauge stations in the Poldokhtar watershed were selected for point evaluation. The distance of these stations from the center of gravity of the network points was less than 10 km. The period used in this study is from 2016 to 2020. In the first step, the predicted rainfall was downloaded from the TIGGE database website. The data of this database is in GRIB2 format, which was extracted with QGIS software in Excel format. In the next step, predictions were evaluated before bias correction. Then due to the bias in the predictions, corrections were made with 7 bias correction methods. Finally, the bias correction forecast was evaluated. Bias correction methods that were used in this study include the Delta method, Elevation Zone (EZ), Quantile Mapping based on Empirical Distribution (EQM), Quantile Mapping (QM), Linear Scaling (LS), Spatio-Temporal Bias correction (STB), and the TVSV method. In this study, the daily precipitation forecast of the ECMWF center was used. In the used methods, 70% of the data were considered for the control period and 30% of the data for the prediction period. In the Delta method, the changes between the average observations and the simulation are added to the daily observed precipitation. In the LS method, the correction of daily values is based on the difference between the observed control period and the uncorrected data. The elevation zone (EZ) bias correction method, corrects the forecasted precipitation at the high altitude. The EQM method is a statistical-empirical base-quantile method, which is based on the experimental transformation and bias correction of the simulated precipitation by the regional climate model. The QM method removes biases by using cumulative distribution functions (CDF) for observed and predicted values at any time scale. In the STB method, the predicted daily precipitation is calculated for the relevant time window, and the values of the ground stations and the corrected prediction values are replaced and calculated bias. The TVSV method is based on the 7-day time window. In this study, two types of statistical indicators continuous and classified were used to evaluate the numerical model of precipitation forecasting. The continuous index includes RMSE, ME, and MAE and the classified indicators include POD, FAR, and BIAS.

The results showed that the ECMWF model had a low RMSE in most parts of the Poldakhtar watershed and had acceptable accuracy. Also, the corrected precipitation evaluation showed that the Delta method had a good performance in all the rain gauge stations under study. The STB method in two stations corrected 90% of the precipitation data. The EZ and QM methods in about 4 stations corrected about 85% of the predictions. These two methods had similar performance. LS and EQM methods had poor performance in all 7 points studied. In other words, they could not correct the bias of the predictions. The TVSV method also did not have acceptable performance. The Delta method improved the predicted precipitation in most parts of the RMSE and the STB method was able to correct the low estimate of the predicted precipitation. The results of this study showed that altitude is involved in the accuracy of predicted precipitation. If the low altitude forecast model had the lowest RMSE and at high altitudes, the highest value of this index. Biased correction methods improved the predicted precipitation values to an acceptable level, which increases the application of the predicted precipitation model in the flood warning system. According to the ME index, the underestimation is higher in the upper elevations of the basin. The main reason for this difference can be not correct the effect of altitude on the value of precipitation. At the ECMWF center, no significant change was observed in the POD index after bias correction. The small change in the POD index at the ECMWF center can be due to the good performance and structure of the numerical model at this center. The POD index at high altitudes performed better than this index at low altitudes. The bias correction methods improved the predicted precipitation values to an acceptable level, therefore increasing the effectiveness of the precipitation forecasting model in the flood warning system.

Forecasting of river discharge is a important aspect of efficient water resources planning and management. In this study, time series pre and post-processing methods along with support vector machine (SVM) and Gaussian process regression (GPR) kernel based approaches were used to estimate flow discharge of two natural river in the United States with two consecutive hydrometric stations. The first river contained about 2 years of data and in the second river 4 years of daily discharge data was used. Different models were defined based on hydraulic characteristics and the capability of integrated pre and post-processing methods in two states of inter-station and between-stations was investigated. For data pre-processing, the Discrete Wavelet Transform (DWT) method was first used. Then, the high-frequency sub-series were selected and re-decomposed using the Ensemble Empirical Mode Decomposition (EEMD). Finally, sub-series with higher energy were imposed as inputs for kernel-based models. Non-linear neural average (NNA) model was also used for data post-processing. The obtained results from the defined models showed the high accuracy of the integrated methods used in the research in estimating flow discharge. At both stations, the error percentage was reduced by approximately 20 to 25% using the integrated pre-post-processing methods compared to the intelligent kernel based models. It was observed that in the case of river flow prediction based on the station's own data, the RMSE error value of the model decreased from approximately 0.3 to 0.26 and in the case of using the previous station data decreased from 0.44 to 0.33. Due to the high capability and accuracy of the pre-processing methods used in this study, similar studies are recommended in other rivers of the country.