Satellite Remote Sensing in Hydrological Data Assimilation

2020 ◽  
Author(s):  
Mehdi Khaki
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Hydrological Data

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Khaki ◽  
H.-J. Hendricks Franssen ◽  
S. C. Han
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Mission Satellite ◽  
Model Predictions

Abstract Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period ($$\sim $$ ∼  32% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.66 to $$\sim $$ ∼  0.85) but also during the forecast period ($$\sim $$ ∼  14% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.69 to $$\sim $$ ∼  0.78) due to the effective impacts of the approach on both state and parameters.

scholarly journals Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

2019 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Yifei Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Three Dimensional ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Temporal Scales ◽  
Localization Scheme ◽  
Spatio Temporal

Abstract. The understanding of lakes physical dynamics is crucial to provide scientifically credible information for ecosystem management. We show how the combination of in-situ data, remote sensing observations and three-dimensional hydrodynamic numerical simulations is capable of delivering various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we present a flexible framework for DA into lakes three-dimensional hydrodynamic models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraints of operational systems and near real-time operations (e.g. integration into http://meteolakes.ch).

scholarly journals A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

2015 ◽  
Vol 12 (5) ◽  
pp. 4857-4878 ◽  
Author(s):  
Z. N. Musa ◽  
I. Popescu ◽  
A. Mynett
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Flood Mapping ◽  
Research Topics ◽  
Hydrological Data ◽  
Rain Gauges ◽  
Physical Infrastructure ◽  
The World

Abstract. Hydrological data collection requires deployment of physical infrastructure like rain gauges, water level gauges, as well as use of expensive equipment like echo sounders. Many countries around the world have recorded a decrease in deployment of physical infrastructure for hydrological measurements; developing countries especially have less of this infrastructure and where they exist, they are poorly maintained. Satellite remote sensing can bridge this gap, and has been applied by hydrologists over the years, with the earliest applications in water body and flood mapping. With the availability of more optical satellites with relatively low temporal resolutions globally, satellite data is commonly used for: mapping of water bodies, testing of inundation models, precipitation monitoring, and mapping of flood extent. Use of satellite data to estimate hydrological parameters continues to increase due to use of better sensors, improvement in knowledge of/and utilization of satellite data, and expansion of research topics. A review of applications of satellite remote sensing in surface water modelling, mapping and estimation is presented, and its limitations for surface water applications are also discussed.

China land soil moisture EnKF data assimilation based on satellite remote sensing data

2011 ◽  
Vol 54 (9) ◽  
pp. 1430-1440 ◽  
Author(s):  
ChunXiang Shi ◽  
ZhengHui Xie ◽  
Hui Qian ◽  
MiaoLing Liang ◽  
XiaoChun Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

2015 ◽  
Vol 19 (9) ◽  
pp. 3755-3769 ◽  
Author(s):  
Z. N. Musa ◽  
I. Popescu ◽  
A. Mynett
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Surface Water ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Research Topics ◽  
Hydrological Data ◽  
Rain Gauges ◽  
Physical Infrastructure ◽  
The World

Abstract. Hydrological data collection requires deployment of physical infrastructure like rain gauges, water level gauges, as well as use of expensive equipment like echo sounders. Many countries around the world have recorded a decrease in deployment of physical infrastructure for hydrological measurements; developing countries especially have less of this infrastructure and, where it exists, it is poorly maintained. Satellite remote sensing can bridge this gap, and has been applied by hydrologists over the years, with the earliest applications in water body and flood mapping. With the availability of more optical satellites with relatively low temporal resolutions globally, satellite data are commonly used for mapping of water bodies, testing of inundation models, precipitation monitoring, and mapping of flood extent. Use of satellite data to estimate hydrological parameters continues to increase due to use of better sensors, improvement in knowledge of and utilization of satellite data, and expansion of research topics. A review of applications of satellite remote sensing in surface water modelling, mapping and parameter estimation is presented, and its limitations for surface water applications are also discussed.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

Sign in / Sign up

Export Citation Format

Share Document