Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data

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.

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

HNPS Proceedings ◽

10.12681/hnps.1864 ◽

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.

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Fusion of hyperspectral remote sensing data for near real-time monitoring of microcystin distribution in Lake Erie

10.1117/12.2026933 ◽

2013 ◽

Author(s):

Benjamin Vannah ◽

Ni-Bin Chang

Keyword(s):

Remote Sensing ◽

Real Time ◽

Lake Erie ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Real Time Monitoring ◽

Sensing Data

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

Remote Sensing ◽

10.3390/rs12142337 ◽

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.

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Quasi-real time estimation of distributed precipitation using EOS/MODIS remote sensing datasets

10.1117/12.775458 ◽

2007 ◽

Author(s):

Qiuwen Zhang ◽

Cheng Wang ◽

Fumio Shinohara ◽

Tatsuo Yamaoka

Keyword(s):

Remote Sensing ◽

Real Time ◽

Time Estimation ◽

Real Time Estimation ◽

Eos Modis

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Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Mathematical Problems in Engineering ◽

10.1155/2016/6489165 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14

Author(s):

Chao Gao ◽

Jianhua Lu ◽

Guorong Zhao ◽

Shuang Pan

Keyword(s):

Remote Sensing ◽

Real Time ◽

Time Estimation ◽

Finite Horizon ◽

Navigation Systems ◽

Robust Performance ◽

Stochastic Parameters ◽

Real Time Estimation ◽

New Applications ◽

Jump Markov System

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

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