scholarly journals Gridded Flood Depth Estimates from Satellite Derived Inundations

2021 ◽  
Author(s):  
Seth Bryant ◽  
Heather McGrath ◽  
Mathieu Boudreault
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Cost Allocation ◽  
Remote Sensing Data ◽  
Flood Events ◽  
Sensing Data ◽  
Flood Depth ◽  
Emergency Managers ◽  
Minimal Data ◽  
Allocation Algorithms

Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

scholarly journals Hydrological real-time modeling using remote sensing data

2010 ◽  
Vol 7 (6) ◽  
pp. 8809-8835
Author(s):  
P. Meier ◽  
A. Frömelt ◽  
W. Kinzelbach
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Real Time ◽  
River Basin ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Modeling Framework ◽  
Sensing Data ◽  
Soil Storage ◽  
Time Modeling

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. Sequential data assimilation using the Ensemble Kalman Filter provides a both efficient and robust tool for a real-time modeling framework. One key parameter in a hydrological system is the soil moisture which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by the radar scatterometer on board the ERS satellite. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modeling framework yields good results in watersheds where the soil storage is an important factor. For the largest watershed a forecast over almost six weeks can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In watersheds with little soil storage and a quick response to rainfall events the performance is relatively poor.

scholarly journals Remote radiological assessment in the marine environment: A pilot study based on Cs-137 measurements and satellite observations in the Aegean Sea

2019 ◽  
Vol 24 ◽  
pp. 191
Author(s):  
G. Mavrokefalou ◽  
H. Florou ◽  
O. Sykioti
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Data ◽  
Sea Water ◽  
Remote Sensing Data ◽  
Aegean Sea ◽  
137Cs Activity ◽  
Sea Surface ◽  
Sensing Data ◽  
Activity Concentrations

A program concept has been developed to utilize sea parameters like sea surface temperature (SST), ocean colour (OC) and sea surface salinity (SSS), in order to explore their potential relations with 137Cs activity concentrations in sea water. These relations are expected to lead to the creation of an innovative tool based on remote sensing data and in real time 137Cs measurements, for the remote radioactivity detection of the Greek marine ecosystem both for routine control and emergency recordings. The presented results are a preliminary effort of the tool’s development. Remote sensing data have been acquired from MIRAS and MODIS instruments on-board ESA-SMOS and NASA-TERRA/AQUA satellites respectively. Satellite data comprise of SST and OC measurements. The ERL’s data of 137Cs activity concentrations (204 measurements) in seawater have been used for the period March 2012 to February 2015. Therefore, a) map analyses in a GIS including interpolation and integration of 83 real time measurements corrected with the effective half live of 7.2 y according to the monthly data of satelites and spatial linear regression have been implemented for the Aegean Sea, b) additional temporal analyses using linear and polynomial regression have been performed for the area of Souda- Crete, for which the most frequent measurements of 137Cs activity concentration in sea water have been measured in ERL. In this study, the first derived results on the correlation between SST measurements with 137Cs activity concentrations are presented, whereas the respective correlation with OC is being under invstigation. Further investigations include multivariate polynomial analyses into the Geographic Information System (GIS) platform with more extensive sampling and satellite data from new systems, whereas comparative correlations of 137Cs with seawater parameters derived by conventional means will be performed.

scholarly journals Estimation of antecedent wetness conditions for flood modelling in northern Morocco

2012 ◽  
Vol 16 (11) ◽  
pp. 4375-4386 ◽  
Author(s):  
Y. Tramblay ◽  
R. Bouaicha ◽  
L. Brocca ◽  
W. Dorigo ◽  
C. Bouvier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Data Availability ◽  
Flood Events ◽  
Catchment Scale ◽  
Ungauged Catchments ◽  
Sensing Data ◽  
Event Based

Abstract. In northern Morocco are located most of the dams and reservoirs of the country, while this region is affected by severe rainfall events causing floods. To improve the management of the water regulation structures, there is a need to develop rainfall–runoff models to both maximize the storage capacity and reduce the risks caused by floods. In this study, a model is developed to reproduce the flood events for a 655 km2 catchment located upstream of the 6th largest dam in Morocco. Constrained by data availability, a standard event-based model combining a SCS-CN (Soil Conservation Service Curve Number) loss model and a Clark unit hydrograph was developed for hourly discharge simulation using 16 flood events that occurred between 1984 and 2008. The model was found satisfactory to reproduce the runoff and the temporal evolution of floods, even with limited rainfall data. Several antecedent wetness conditions estimators for the catchment were compared with the initial condition of the model. Theses estimators include an antecedent discharge index, an antecedent precipitation index and a continuous daily soil moisture accounting model (SMA), based on precipitation and evapotranspiration. The SMA model performed the best to estimate the initial conditions of the event-based hydrological model (R2 = 0.9). Its daily output has been compared with ASCAT and AMSR-E remote sensing data products, which were both able to reproduce with accuracy the daily simulated soil moisture dynamics at the catchment scale. This same approach could be implemented in other catchments of this region for operational purposes. The results of this study suggest that remote sensing data are potentially useful to estimate the soil moisture conditions in the case of ungauged catchments in Northern Africa.

scholarly journals Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

2020 ◽  
Vol 12 (14) ◽  
pp. 2337
Author(s):  
Wonsook S. Ha ◽  
George R. Diak ◽  
Witold F. Krajewski
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Numerical Weather Prediction Model ◽  
Sensing Data ◽  
Numerical Weather ◽  
Spatio Temporal

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

scholarly journals Estimation of antecedent wetness conditions for flood modelling in Northern Morocco

2012 ◽  
Vol 9 (8) ◽  
pp. 9361-9390 ◽  
Author(s):  
Y. Tramblay ◽  
R. Bouaicha ◽  
L. Brocca ◽  
W. Dorigo ◽  
C. Bouvier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Data Availability ◽  
Flood Events ◽  
Catchment Scale ◽  
Ungauged Catchments ◽  
Sensing Data ◽  
Antecedent Precipitation

Abstract. In Northern Morocco are located most of the dams and reservoirs of the country, while this region is affected by severe rainfall events causing floods. To improve the management of the water regulation structures, there is a need to develop rainfall-runoff models to both maximize the storage capacity and reduce the risks caused by floods. In this study, a model is developed to reproduce the flood events for a 655 km2 catchment located upstream of the 6th largest dam of the Morocco. Constrained by data availability, a standard event-based model was developed for hourly discharge using 16 flood events that occurred between 1984 and 2008. The model was found satisfactory to reproduce the runoff and the temporal evolution of floods, even with limited rainfall data. Several antecedent wetness conditions estimators for the catchment were compared with the initial condition of the model. These estimators include the discharge of the previous days, the antecedent precipitation index and a continuous daily soil moisture accounting model (SMA). The SMA model performed the best to estimate the initial conditions of the model, with R2=0.9. Its daily output has been compared with ASCAT and AMSR-E remote sensing data products, both were able to reproduce with accuracy the daily soil moisture dynamics at the catchment scale. This same approach could be implemented in other catchments of this region for operational purposes. The results of this study indicate the potential usefulness of remote sensing data to estimate the soil moisture conditions in the case of ungauged catchments in Northern Africa.

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