The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method

Abstract Improving flood forecasting performance is critical for flood management. Real-time flood forecasting correction techniques (e.g., proportional correction (PC) and Kalman filter (KF)) coupled with the Muskingum method improve the forecasting performance but have limitations (e.g., short lead times and inadequate performance, respectively). Here, particle filter (PF) and combination forecasting (CF) are coupled with the Muskingum method and then applied to 10 flood events along the Shaxi River, China. Two indexes (overall consistency and permissible range) are selected to compare the performances of PC, KF, PF and CF for 3 h lead time. The changes in overall consistency for different lead times (1–6 h) are used to evaluate the applicability of PC, KF, PF and CF. The main conclusions are as follows: (1) for 3 h lead time, the two indexes indicate that the PF performance is optimal, followed in order by KF and PC; CF performance is close to PF and better than KF. (2) The performance of PC decreases faster than that of KF and PF with increases in the lead time. PC and PF are applicable for short (1–2 h) and long lead times (3–6 h), respectively. CF is applicable for 1–6 h lead times; however, it has no advantage over PC and PF for short and long lead times, respectively, which may be due to insufficient training and increase in cumulative errors.

Download Full-text

Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽

10.3390/w11081571 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1571 ◽

Cited By ~ 4

Author(s):

Song ◽

Park ◽

Lee ◽

Park ◽

Song

Keyword(s):

Decision Making ◽

Water Level ◽

Real Time ◽

Lead Time ◽

Flash Flood ◽

Warning System ◽

Flood Forecasting ◽

Urban Stream ◽

Forecasting And Warning ◽

Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

Download Full-text

Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

Remote Sensing ◽

10.3390/rs13214459 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4459

Author(s):

Aline Falck ◽

Javier Tomasella ◽

Fabrice Papa

Keyword(s):

Data Collection ◽

Lead Time ◽

Flood Forecasting ◽

Lead Times ◽

Latency Time ◽

Distributed Hydrological Model ◽

Satellite Altimeter ◽

Operational Characteristics ◽

Potential Benefits ◽

Forecasting System

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times.

Download Full-text

Accuracy assessment of real-time flood forecasting of coupled hydrological and mesoscale meteorological models

10.5194/nhess-2017-447 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aida Jabbari ◽

Jae-Min So ◽

Deg-Hyo Bae

Keyword(s):

Real Time ◽

Spatial Resolution ◽

Lead Time ◽

Hydrological Model ◽

Weather Prediction ◽

Atmospheric Model ◽

Flood Forecasting ◽

Coupled Models ◽

Temporal And Spatial ◽

Meteorological Models

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the temporal resolution did not affect the inherent inaccuracy of the results. Lead time variation demonstrated that lead time dependency was almost negligible below 36 h. With reference to our case study, comparisons of model performance provided quantitative knowledge for understanding the credibility and restrictions of hydro-meteorological models.

Download Full-text

Near-real-time flood forecasting for an urban coastal catchment: An approach in combination of numerical weather and 3-way coupled hydrodynamic flood modelling

10.5194/egusphere-egu21-12834 ◽

2021 ◽

Author(s):

Mousumi Ghosh ◽

Supantha Paul ◽

Subhankar Karmakar ◽

Subimal Ghosh

Keyword(s):

Real Time ◽

Wrf Model ◽

Urban Canopy ◽

Flood Management ◽

Flood Forecasting ◽

Hazard Mapping ◽

Urban Canopy Model ◽

Numerical Weather ◽

Flood Model ◽

Canopy Model

<p>The rapid increase in heavy precipitation flooding events highlights the need for efficient flood forecasting techniques to facilitate flood hydrological research and effective flood management by civic bodies. The current study aims to develop a near-real-time flood forecasting framework by integrating a 3-way coupled hydrodynamic flood model framework with numerical weather modelling based rainfall forecasts. The proposed framework has been demonstrated over Mumbai city in India, which is subjected to flooding every year during the monsoon months. A fine-resolution atmospheric simulation with the Weather Research and Forecasting (WRF) model has been performed for rainfall forecasts, which serve as an input to the flood model. To access the impact of urbanization on rainfall extremes, three scenarios are considered in the WRF simulations, i.e., WRF model: (1) without Urban canopy model (WRF-NoUCM), (2) coupled with a single-layer Urban canopy model (WRF-SUCM), and (3) coupled with a multi-layer Urban canopy model (WRF-MUCM). Further, a three-way coupled flood model has been developed where the MIKE 11 model (streamflow) with the drainage network (stormwater drains) and the MIKE 21 model (overland flow) have been considered for flood inundation and subsequently hazard mapping. In addition, the tidal elevation is provided along the coastline in the model setup. The flood maps developed by three WRF forecasted rainfall scenarios have been compared with that of the maps developed with observed rainfall. The extent to which the scenarios have been able to imitate the pattern and extent of flooding generated by observed rainfall has been investigated to decide the best scenario to be adapted in the comprehensive flood forecasting network. This state-of-art flood forecasting approach may be implemented in other flood-prone coastal regions as a major non-structural flood management strategy to reduce flood risk and vulnerabilities for the people dwelling in those regions.</p>

Download Full-text

Integrated real-time flood forecasting and inundation analysis in small-medium streams

10.20944/preprints201904.0113.v1 ◽

2019 ◽

Author(s):

Byunghyun Kim ◽

Seung-Yong Choi ◽

Kun-Yeun Han

Keyword(s):

Real Time ◽

Fuzzy Inference ◽

Flood Forecasting ◽

Water Levels ◽

Subtractive Clustering ◽

Lead Times ◽

Hydrodynamic Models ◽

Inference System ◽

Neuro Fuzzy ◽

Input Variables

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small-medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed data without abundant physical, and can be performed in real time by integrating point- and section flood forecasting and spatial inundation analysis.

Download Full-text

Lead Time for Cities of Northern India by Using Multiparameter EEW Algorithm

International Journal of Geophysics ◽

10.1155/2018/9086205 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8

Author(s):

Rakhi Bhardwaj ◽

Mukat Lal Sharma

Keyword(s):

Real Time ◽

Early Warning ◽

Lead Time ◽

P Wave ◽

Mitigation Measures ◽

Lead Times ◽

S Wave ◽

Central Himalayas ◽

Northern India ◽

Indian Cities

Earthquake early warning (EEW) is considered one of the important real-time earthquake damage mitigation measures. The presence of seismogenic sources generating high seismicity in Himalayas and the cities of concern lying at appropriate distances makes Northern India a perfect case to be monitored using EEW systems. In the present study, an attempt has been made to estimate the lead times for Northern Indian cities for issuing early warning by using the EEW system deployed by IIT Roorkee in Central Himalayas. The instrumentation deployed at 100 locations between Uttarkashi and Chamoli has been used to estimate the lead time at six cities. The estimated lead time includes the time to reach S-wave after subtraction of the sum of P-wave arrival time at the station, time taken by EEW algorithm, transmission and processing delay. The study reveals that for Dehradun, Hardwar, Roorkee, Muzaffarnagar, Meerut, and Delhi the minimum calculated lead time is 5, 11, 20, 35, and 68 sec while the maximum lead time is 37, 36, 47, 59, and 90 sec, respectively. Such larger estimated lead times to these densely populated cities show that EEW can successfully work as one of the important real-time earthquake disaster reduction measures in Northern India.

Download Full-text

Comparison of wavelet and machine learning methods for regional drought prediction

10.5194/egusphere-egu21-218 ◽

2021 ◽

Author(s):

Gilbert Hinge ◽

Ashutosh Sharma

Keyword(s):

Machine Learning ◽

Lead Time ◽

Early Warning Systems ◽

Support Vector ◽

Lead Times ◽

Model Parameters ◽

Coupled Models ◽

Drought Prediction ◽

Forecasting Performance ◽

Grid Points

<p>Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations. &#160;ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis. &#160;Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models. &#160;All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.&#160; These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.</p>

Download Full-text

The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

Entropy ◽

10.3390/e22111264 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1264

Author(s):

David A. Rhoades ◽

Sepideh J. J. Rastin ◽

Annemarie Christophersen

Keyword(s):

New Zealand ◽

Lead Time ◽

Active Regions ◽

Earthquake Forecasting ◽

Scaling Relations ◽

Lead Times ◽

Medium Term ◽

Trade Off ◽

Forecasting Performance ◽

Precursory Seismicity

‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.

Download Full-text

Improving efficiencies of flood forecasting during lead times: an operational method and its application in the Baiyunshan Reservoir

Hydrology Research ◽

10.2166/nh.2018.051 ◽

2018 ◽

Vol 50 (2) ◽

pp. 709-724 ◽

Cited By ~ 2

Author(s):

Pan Liu ◽

Xiaojing Zhang ◽

Yan Zhao ◽

Chao Deng ◽

Zejun Li ◽

...

Keyword(s):

Objective Function ◽

Conventional Method ◽

Lead Time ◽

Flood Control ◽

Flood Forecasting ◽

Lead Times ◽

Forecast Performance ◽

Reservoir Basin ◽

Hydrological Parameter

Abstract Accurate and reliable flood forecasting plays an important role in flood control, reservoir operation, and water resources management. Conventional hydrological parameter calibration is based on an objective function without consideration for forecast performance during lead-time periods. A novel objective function, i.e., minimizing the sum of the squared errors between forecasted and observed streamflow during multiple lead times, is proposed to calibrate hydrological parameters for improved forecasting. China's Baiyunshan Reservoir basin was selected as a case study, and the Xinanjiang model was used. The proposed method provided better results for peak flows, in terms of the value and occurrence time, than the conventional method. Specifically, the qualified rate of peak flow for 4-, 5-, and 6-h lead times in the proposed method were 69.2%, 53.8%, and 38.5% in calibration, and 60%, 40%, and 20% in validation, respectively. This compares favorably with the corresponding values for the conventional method, which were 53.8%, 15.4%, and 7.7% in calibration, and 20%, 20%, and 0% in validation, respectively. Uncertainty analysis revealed that the proposed method caused less parameter uncertainty than the conventional method. Therefore, the proposed method is effective in improving the performance during multiple lead times for flood mitigation.

Download Full-text

Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0212.1 ◽

2015 ◽

Vol 16 (3) ◽

pp. 1171-1183 ◽

Cited By ~ 38

Author(s):

Phu Nguyen ◽

Andrea Thorstensen ◽

Soroosh Sorooshian ◽

Kuolin Hsu ◽

Amir AghaKouchak

Keyword(s):

Real Time ◽

A Priori ◽

Flood Forecasting ◽

Bias Reduction ◽

Probability Of Detection ◽

Wide Field ◽

Precipitation Data ◽

Inundation Maps ◽

Precipitation Estimation ◽

Better Than

Abstract Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. This research applied the high-resolution coupled hydrologic–hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UCI was forced with the near-real-time Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps using USDA’s flood extent maps derived from Advanced Wide Field Sensor (AWiFS) 56-m resolution imagery. The results show that the PERSIANN-CCS simulation tends to capture the observed hydrograph shape better than Stage 2 (minimum correlation of 0.86 for PERSIANN-CCS and 0.72 for Stage 2); however, at most of the stream gauges, Stage 2 simulation provides more accurate estimates of flood peaks compared to PERSIANN-CCS (49%–90% bias reduction from PERSIANN-CCS to Stage 2). The simulation in both cases shows a good agreement (0.67 and 0.73 critical success index for Stage 2 and PERSIANN-CCS simulations, respectively) with the AWiFS flood extent. Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.93) is slightly lower than that of the Stage 2 simulation (0.97). As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.23) is better than that of the Stage 2 simulation (0.31).

Download Full-text