A rain-on-snow mixed flood forecast model and its application

2009 ◽  
Vol 3 (4) ◽  
pp. 440-444
Author(s):  
Jian Wu ◽  
Lan Li
2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.


2020 ◽  
Vol 51 (6) ◽  
pp. 1312-1331
Author(s):  
Qian Li ◽  
Caisong Li ◽  
Huanfei Yu ◽  
Jinglin Qian ◽  
Linlin Hu ◽  
...  

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.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammed Moishin ◽  
Ravinesh C. Deo ◽  
Ramendra Prasad ◽  
Nawin Raj ◽  
Shahab Abdulla

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 312
Author(s):  
Robert Cifelli ◽  
Lynn E. Johnson ◽  
Jungho Kim ◽  
Tim Coleman ◽  
Greg Pratt ◽  
...  

Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users.


1998 ◽  
Vol 43 (2) ◽  
pp. 215-242 ◽  
Author(s):  
H. GÖPPERT ◽  
J. IHRINGER ◽  
E. J. PLATE ◽  
G. MORGENSCHWEIS

Author(s):  
Jurgita Simaityte Volskiene ◽  
Eugenijus Uspuras ◽  
Juozas Augutis

Author(s):  
Wei Ming Wong ◽  
Mohamad Yusry Lee ◽  
Amierul Syazrul Azman ◽  
Lew Ai Fen Rose

The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.


2006 ◽  
Vol 20 (7) ◽  
pp. 1525-1540 ◽  
Author(s):  
Shen-Hsien Chen ◽  
Yong-Huang Lin ◽  
Li-Chiu Chang ◽  
Fi-John Chang

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