scholarly journals Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1612 ◽  
Author(s):  
Minghong Chen ◽  
Juanjuan Pang ◽  
Pengxiang Wu
Keyword(s):  
Data Assimilation ◽  
Water Level ◽  
Real Time ◽  
Hydrodynamic Model ◽  
Yellow River ◽  
Flood Forecasting ◽  
Water Levels ◽  
Flood Routing ◽  
Micro Model ◽  
Assimilation Model

Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particle filter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.

Assimilation of near real-time radar altimetry data into a hydrodynamic model for streamflow and water level forecasting

2020 ◽  
Author(s):  
Liguang Jiang ◽  
Youjiang Shen ◽  
Dedi Liu ◽  
Henrik Madsen ◽  
Peter Bauer-Gottwein
Keyword(s):  
Data Acquisition ◽  
Water Level ◽  
Real Time ◽  
Hydrodynamic Model ◽  
Water Levels ◽  
Observation Error ◽  
Altimetry Data ◽  
Radar Altimetry ◽  
Virtual Station ◽  
Time Critical

<p>Satellite radar altimetry has been widely used in hydrological studies, such as monitoring of lakes and reservoirs, retrieving water level and discharge of rivers, calibration of river models, etc. Sentinel-3 SAR altimeter delivers data at three levels of latency, i.e. near real-time (less than 3 hours after data acquisition), slow time critical (within 48 hours after data acquisition), and non-time critical (typically one month after data acquisition). However, most studies use final products, i.e. non-time critical products of altimetry data for inland water monitoring or hydrological simulations. So far, to the best of our knowledge, no study has been exploiting the value of near-real time satellite altimetry data for hydrological research.</p><p>In this study, we first investigate data quality of Sentinel-3 near real-time data against non-time critical product and in-situ data over the Han River in China. Then, we assimilate these data into a 1-D hydrodynamic model, i.e. MIKE Hydro River, to exploit the near-real time altimetry dataset for hydrological forecasting. Specifically, we use the Ensemble Kalman Filter to assimilate altimetry-derived water surface elevation data into MIKE Hydro River model. The model state variable that is updated is the water level defined on the numerical grid of the 1D hydrodynamic model. Observation error estimates are generated from the standard deviations of water levels at each virtual station. Applying this operational forecasting system retrospectively over historical periods, the effect of updating water level at multiple virtual stations on forecast performance is investigated.</p><p>Through this study, we gain new knowledge about near real-time altimetry products for hydrological studies. This will be informative for both the hydrology community and satellite data providers.</p>

scholarly journals Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 919 ◽  
Author(s):  
Byunghyun Kim ◽  
Seng Yong Choi ◽  
Kun-Yeun Han
Keyword(s):  
Water Level ◽  
Real Time ◽  
Fuzzy Inference ◽  
Flood Forecasting ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Lead Times ◽  
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 rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis.

scholarly journals Integrating a 2D Hydraulic Model and GIS Algorithms into a Data Assimilation Framework for Real Time Flood Forecasting and Mapping

10.29007/29nd ◽  
2018 ◽  
Author(s):  
Antonio Annis ◽  
Noemi Gonzalez-Ramirez ◽  
Fernando Nardi ◽  
Fabio Castelli
Keyword(s):  
Data Assimilation ◽  
Water Level ◽  
Real Time ◽  
Satellite Images ◽  
Early Warning Systems ◽  
Hydraulic Model ◽  
Water Levels ◽  
Data Types ◽  
Flow Measurements ◽  
The Impact

The intensification of flood-related damages and fatalities is challenging Early Warning Systems (EWS) to always better perform in predicting flood levels allowing decision makers to take the most effective decisions for mitigating the impact of extreme events. EWS require hydrologic and hydraulic modelling that are usually affected by uncertainties that can be extremely significant in data scarce regions. This work presents the implementation and application of a Data Assimilation (DA) framework, based on the Ensemble Kalman Filter, integrating the hydraulic model FLO-2D and geospatial algorithms for data post-processing and mapping. The hydraulic model is forced by both flow gages and simulated flow data produced by a simplified GIS-based hydrologic modelling for flood wave analysis tailored for small ungauged basins. The hydraulic code is adapted to assimilate different observation data types: flow measurements taken along the channel, water level observations captured within the floodplain, such as water signs on vegetation and buildings pictures by human sensors, and inundation extents obtained by processing satellite images. This DA framework required the development of significant novelties for incorporating the 2D hydraulic model and for integrating the different types of measurements considering the heterogeneous specifications and uncertainty of the various assimilated data types. Advanced GIS algorithms are implemented for improving the real time flood mapping taking advantage of the distributed output provided by the 2D inundation model. Results show improved model performances in terms of water level simulations and reduced uncertainties. The integrated hydraulic and geospatial modelling allows to empower the water levels correction on the flood extension prediction. Additionally, the capability of using the different available observations, from satellite images to crowdsourced data, is promising for the development of a flexible and scalable flood EWS model overcoming the limitations of standard DA working generally with 1D hydraulic models and traditional sensors.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Forecasting System

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

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
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.

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 Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

2013 ◽  
Vol 10 (3) ◽  
pp. 2879-2925 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M.-P. Bonnet ◽  
L. G. G. de Gonçalves ◽  
S. Calmant ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrodynamic Model ◽  
Stream Flow ◽  
Large Scale ◽  
Water Levels ◽  
Large Rivers ◽  
Altimetry Data ◽  
Radar Altimetry ◽  
Prediction Approach

Abstract. In this work we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites and also transferring information to ungauged rivers reaches. Altimetry data assimilation improves results at a daily basis in terms of water levels and discharges with minor degree, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterization of model errors and spatial localization techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. > 90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote sensing information.

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