scholarly journals System response curve correction method of runoff error for real-time flood forecast

2020 ◽  
Vol 51 (6) ◽  
pp. 1312-1331
Author(s):  
Qian Li ◽  
Caisong Li ◽  
Huanfei Yu ◽  
Jinglin Qian ◽  
Linlin Hu ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Correction Method ◽  
System Response ◽  
Flood Forecast ◽  
Feedback Correction ◽  
Underlying Surface Conditions ◽  
Source Of Information

Abstract Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios.

The Research of Rainfall Error Correction Based on System Response Curve

2013 ◽  
Vol 368-370 ◽  
pp. 335-339
Author(s):  
Wei Si ◽  
Wei Min Bao ◽  
Hong Yan Wang ◽  
Si Min Qu
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Response Curve ◽  
Flood Forecasting ◽  
Good Effect ◽  
System Response ◽  
Error Feedback ◽  
Model Tracing ◽  
Curve Method ◽  
Source Of Information

The rainfall error affects the accuracy of flood forecasting directly, and the error correction is very important to improve the accuracy of real-time flood forecasting. The system response curve was introduced into the real-time flood forecast updating system, and the error feedback updating model tracing the source of information was established in this paper. In order to certificate the feasibility, rationality and effectiveness of the method multi-directionally, the system response curve method and the second order autoregressive error correction were used in updating the 13 flood events of the Wangjiaba sub-basin of Huaihe River Basin respectively. The results show that the system response curve (SRC) method has physical conception, reasonable structure and good effect. The method will not lose the forecasting period and without increasing the parameters. In this study, the average NS of system response curve method was larger than 0.920. So, this method can be widely used in rainfall error correction for real-time flood forecasting.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

2019 ◽  
Vol 55 (9) ◽  
pp. 7493-7519 ◽  
Author(s):  
Wei Si ◽  
Hoshin V. Gupta ◽  
Weimin Bao ◽  
Peng Jiang ◽  
Wenzhuo Wang
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Response Curve ◽  
System Response ◽  
Flood Forecast ◽  
Curve Method ◽  
Forecast Updating

Real-time flood forecast and flood alert map over the Huaihe River Basin in China using a coupled hydro-meteorological modeling system

2008 ◽  
Vol 51 (7) ◽  
pp. 1049-1063 ◽  
Author(s):  
GuiHua Lu ◽  
ZhiYong Wu ◽  
Lei Wen ◽  
Charles A. Lin ◽  
JianYun Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Flood Forecast ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Flood Alert ◽  
The Huaihe River ◽  
Meteorological Modeling

scholarly journals Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method

2011 ◽  
Vol 42 (2-3) ◽  
pp. 150-161 ◽  
Author(s):  
Muthiah Perumal ◽  
Tommaso Moramarco ◽  
Silvia Barbetta ◽  
Florisa Melone ◽  
Bhabagrahi Sahoo
Keyword(s):  
Real Time ◽  
Forecast Error ◽  
Central Italy ◽  
Variable Parameter ◽  
Warning System ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Time Interval ◽  
Tiber River ◽  
Routing Method

The application of a Variable Parameter Muskingum Stage (VPMS) hydrograph routing method for real-time flood forecasting at a river gauging site is demonstrated in this study. The forecast error is estimated using a two-parameter linear autoregressive model with its parameters updated at every routing time interval of 30 minutes at which the stage observations are made. This hydrometric data-based forecast model is applied for forecasting floods at the downstream end of a 15 km reach of the Tiber River in Central Italy. The study reveals that the proposed approach is able to provide reliable forecast of flood estimate for different lead times subject to a maximum lead time nearly equal to the travel time of the flood wave within the selected routing reach. Moreover, a comparative study of the VPMS method for real-time forecasting and the simple stage forecasting model (STAFOM), currently in operation as the Flood Forecasting and Warning System in the Upper-Middle Tiber River basin of Italy, demonstrates the capability of the VPMS model for its field use.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

2017 ◽  
Vol 130 (6) ◽  
pp. 635-647 ◽  
Author(s):  
Jianzhong Zhou ◽  
Hairong Zhang ◽  
Jianyun Zhang ◽  
Xiaofan Zeng ◽  
Lei Ye ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Wrf Model ◽  
Flood Forecasting ◽  
Precipitation Simulation

scholarly journals Flood Forecasting with Uncertainty Using a Fully Automated Flood Model Chain: a Case Study for the City of Kulmbach

10.29007/jb27 ◽  
2018 ◽  
Author(s):  
Md Nazmul Azim Beg ◽  
Jorge Leandro ◽  
Punit Bhola ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
The Public ◽  
Uncertainty Sources ◽  
Flood Model ◽  
The City ◽  
Model Chain

Real time flood forecasting can help authorities in providing reliable warnings to the public. This process is, however, non-deterministic such that uncertainty sources need to be accounted before issuing forecasts. In the FloodEvac project, we have developed a tool which takes as inputs rainfall forecasts and links a hydrological with a hydraulic model for producing flood forecasts. The tool is able to handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecast with associated uncertainty of flood discharges and flood extents. In this case study, we focus on the linkage with the hydrological model and on the real-time discharge forecasts generated.

scholarly journals Real-time correction method of Muskingum model based on Kalman filter

2020 ◽  
Vol 165 ◽  
pp. 06002
Author(s):  
Li qian ◽  
Yu huanfei ◽  
Hu linlin ◽  
Ge hangjian ◽  
Zheng hongri
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Correction Method ◽  
Actual Situation ◽  
Simulation Test ◽  
Muskingum Model ◽  
Optimal Value ◽  
Time Correction ◽  
Kalman Filter Algorithm

In flood forecasting, general flood forecasting models or empirical forecasts reflect the average optimal value or relationship curve under the previous data. However, in the operation forecast, the forecast plan value often deviates from the actual situation. This paper takes Muskingum model as an example, and uses the Kalman filter algorithm to correct the forecast results. The algorithm structure and principles were described detailed, and the numerical simulation test was set to verify the efficiency of the Kalman filter algorithm. The correct results with corrected method were compared. The results indicated that the efficiency of the updating system using Kalman filter algorithm was improved. Conclusively, the proposed method could be widely applied in real-time flood forecast updating.

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