scholarly journals Assimilating SAR-derived water level data into a hydraulic model: a case study

2011 ◽  
Vol 8 (1) ◽  
pp. 2103-2144 ◽  
Author(s):  
L. Giustarini ◽  
P. Matgen ◽  
R. Hostache ◽  
M. Montanari ◽  
D. Plaza ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Cross Sections ◽  
Hydraulic Model ◽  
Water Levels ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data ◽  
Assimilation Scheme

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction to the model forecast uncertainty. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

scholarly journals Assimilating SAR-derived water level data into a hydraulic model: a case study

2011 ◽  
Vol 15 (7) ◽  
pp. 2349-2365 ◽  
Author(s):  
L. Giustarini ◽  
P. Matgen ◽  
R. Hostache ◽  
M. Montanari ◽  
D. Plaza ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Cross Sections ◽  
Hydraulic Model ◽  
Water Levels ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data ◽  
Assimilation Scheme

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

scholarly journals Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

2010 ◽  
Vol 14 (9) ◽  
pp. 1773-1785 ◽  
Author(s):  
P. Matgen ◽  
M. Montanari ◽  
R. Hostache ◽  
L. Pfister ◽  
L. Hoffmann ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Temporal Frequency ◽  
Hydraulic Model ◽  
Water Levels ◽  
Model Errors ◽  
Time Step ◽  
Hydraulic Models ◽  
Satellite Radar ◽  
Assimilation Scheme

Abstract. With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

scholarly journals Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept

2010 ◽  
Vol 7 (2) ◽  
pp. 1785-1819 ◽  
Author(s):  
P. Matgen ◽  
M. Montanari ◽  
R. Hostache ◽  
L. Pfister ◽  
L. Hoffmann ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Temporal Frequency ◽  
Coupled Model ◽  
Water Levels ◽  
Model Errors ◽  
Time Step ◽  
Hydraulic Models ◽  
Satellite Radar ◽  
Assimilation Scheme

Abstract. With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydrodynamic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

From SAR-derived flood mapping to water level data assimilation into hydraulic models

2012 ◽  
Author(s):  
Laura Giustarini ◽  
Patrick Matgen ◽  
Renaud Hostache ◽  
Jacques Dostert
Keyword(s):  
Data Assimilation ◽  
Water Level ◽  
Flood Mapping ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data

scholarly journals Transfer Entropy as a Tool for Hydrodynamic Model Validation

Entropy ◽  
2018 ◽  
Vol 20 (1) ◽  
pp. 58 ◽  
Author(s):  
Alicia Sendrowski ◽  
Kazi Sadid ◽  
Ehab Meselhe ◽  
Wayne Wagner ◽  
David Mohrig ◽  
...  
Keyword(s):  
Water Level ◽  
Numerical Models ◽  
Water Levels ◽  
Transfer Entropy ◽  
Spatial And Temporal Scales ◽  
River Deltas ◽  
Level Data ◽  
Water Level Data ◽  
Wax Lake Delta ◽  
Validation Tool

The validation of numerical models is an important component of modeling to ensure reliability of model outputs under prescribed conditions. In river deltas, robust validation of models is paramount given that models are used to forecast land change and to track water, solid, and solute transport through the deltaic network. We propose using transfer entropy (TE) to validate model results. TE quantifies the information transferred between variables in terms of strength, timescale, and direction. Using water level data collected in the distributary channels and inter-channel islands of Wax Lake Delta, Louisiana, USA, along with modeled water level data generated for the same locations using Delft3D, we assess how well couplings between external drivers (river discharge, tides, wind) and modeled water levels reproduce the observed data couplings. We perform this operation through time using ten-day windows. Modeled and observed couplings compare well; their differences reflect the spatial parameterization of wind and roughness in the model, which prevents the model from capturing high frequency fluctuations of water level. The model captures couplings better in channels than on islands, suggesting that mechanisms of channel-island connectivity are not fully represented in the model. Overall, TE serves as an additional validation tool to quantify the couplings of the system of interest at multiple spatial and temporal scales.

scholarly journals Multi-objective optimization of typhoon inundation forecast models for water-level gauging network by integration of ARMAX with a Genetic Algorithm

2016 ◽  
Author(s):  
Huei-Tau Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Water Level ◽  
Water Levels ◽  
Time Shift ◽  
Multi Objective ◽  
Cumulative Rainfall ◽  
Level Data ◽  
Water Level Data ◽  
Forecast Models ◽  
Gauging Station

Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real-time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water level data makes it possible to obtain an approximation as to the cumulative duration of rainfall and time lags associated with each gauging station. Analyses on water levels as well as on cumulative rainfall enable the identification of associate sites pertained to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water level data from associate sites is used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). We used a multi-objective genetic algorithm to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan in which optimal water-level forecast models were established for each of the four water-level gauging stations in the area. Test results demonstrate that the model best able to satisfy PE exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

scholarly journals Deteksi Level Ketinggian Air Dengan Nikelin, Encoder dan Sensor Tekanan Untuk Sistem Peringatan Dini Banjir

2021 ◽  
Vol 1 ◽  
pp. 45-49
Author(s):  
Latiful Hayat ◽  
Dian Nova Kusuma Hardani
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Pressure Sensor ◽  
Pressure Sensors ◽  
Warning System ◽  
Water Levels ◽  
Nickel Wire ◽  
High Data ◽  
Level Data ◽  
Water Level Data

Floods and their problems show an increasing indication when rainfall is high. Data from BNPB shows that floods, landslides and tornadoes contributed to the total disasters in Indonesia in a decade. The existence of an early warning flood disaster can help evacuate before a disaster strikes. The system requires a water level detector as the basic data for determining flood predictions. In order to get the water level value, a touch water method can be used using electrodes or without touching the water with the help of pressure sensors, ultrasonic and imaging. Each method has advantages over the other. In this study, the effectivity and accuracy of detecting water levels were investigated using 3 methods: the direct touch of water through nickel wire, buoys with encoder, and pressure sensors. Detection of water levels can be used as a reference to obtain river water level data which is then connected via an IoT or internet connection as a reference for the Early Warning System for the arrival of floods. This study found that changes in water level of less than 30 cm can utilize buoys and encoders with an accuracy of detecting 5 to 6 counts per 1 mm increase in water level. Meanwhile, the measurement of less than 30 cm water level using nickel wire resulted in a non-linear value. The utilization of nickel wire can be used for a height of more than 30 cm where the change in resistivity has started to be linear. ADC change value is 2.93 mV/cm using 10 bit ADC at 5 Volt reference voltage. For water level heights of 50 cm and above, a pressure sensor can use a pressure sensor that can detect changes in pressure of 0.002 in Hg/mm or 0.05 mmHg/mm.

scholarly journals Particle Tracking Using Dynamic Water-Level Data

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2063
Author(s):  
Yuan Gao
Keyword(s):  
Groundwater Flow ◽  
Water Level ◽  
Water Table ◽  
Flow Direction ◽  
Water Levels ◽  
Subsurface Water ◽  
Pressure Transducers ◽  
Level Data ◽  
Water Level Data ◽  
Fluid Particles

The movement of fluid particles about historic subsurface releases is often governed by dynamic subsurface water levels. Motivations for tracking the movement of fluid particles include tracking the fate of subsurface contaminants and resolving the fate of water stored in subsurface aquifers. This study provides a novel method for predicting the movement of subsurface particles relying on dynamic water-level data derived from continuously recording pressure transducers. At least three wells are needed to measure water levels which are used to determine the plain of the water table. Based on Darcy’s law, particle flow pathlines at the study site are obtained using the slope of the water table. The results show that hydrologic conditions, e.g., seasonal transpiration and precipitation, influence local groundwater flow. The changes of water level in short periods caused by the hydrologic variations made the hydraulic gradient diversify considerably, thus altering the direction of groundwater flow. Although a range of groundwater flow direction and gradient with time can be observed by an initial review of water levels in rose charts, the net groundwater flow at all field sites is largely constant in one direction which is driven by the gradients with higher magnitude.

scholarly journals Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

2016 ◽  
Vol 16 (8) ◽  
pp. 1897-1909 ◽  
Author(s):  
Huei-Tau Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Water Level ◽  
Water Levels ◽  
Time Shift ◽  
Cumulative Rainfall ◽  
Level Data ◽  
Water Level Data ◽  
Forecast Models ◽  
Gauging Station ◽  
Cross Site

Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multi-objective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four water-level gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

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