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 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 On the spatio-temporal representativeness of observations

2017 ◽  
Vol 17 (16) ◽  
pp. 9761-9780 ◽  
Author(s):  
Nick Schutgens ◽  
Svetlana Tsyro ◽  
Edward Gryspeerdt ◽  
Daisuke Goto ◽  
Natalie Weigum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Measurement Errors ◽  
Remote Sensing Data ◽  
Temporal Sampling ◽  
Representation Error ◽  
Temporal Averaging ◽  
Carbon Mass ◽  
Spatio Temporal

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

scholarly journals On the spatio-temporal representativeness of observations

2017 ◽  
Author(s):  
Nick Schutgens ◽  
Svetlana Tsyro ◽  
Ed Gryspeerdt ◽  
Daisuke Goto ◽  
Natalie Weigum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Measurement Errors ◽  
Remote Sensing Data ◽  
Temporal Sampling ◽  
Representation Error ◽  
Temporal Averaging ◽  
Carbon Mass ◽  
Spatio Temporal

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of time and length-scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site remote sensing or in-situ (PM2.5, black carbon mass or number concentrations), satellite remote sensing with imagers or LIDARs (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. Temporal collocation of data (only possible in the context of evaluating model data with observations) can be very effective at reducing representation errors even when spatial sampling issues remain (e.g. when using ground-sites). We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce even with substantial temporal averaging.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa

2010 ◽  
Vol 7 (4) ◽  
pp. 4785-4816 ◽  
Author(s):  
S. I. Khan ◽  
P. Adhikari ◽  
Y. Hong ◽  
H. Vergara ◽  
T. Grout ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Water Cycle ◽  
Lake Victoria ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
East African ◽  
Catchment Scale ◽  
Distributed Hydrologic Model

Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.

Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France

2021 ◽  
Author(s):  
Kuei-Hua Hsu ◽  
Laurent Longuevergne ◽  
Annette Eicker ◽  
Mehedi Hasan ◽  
Andreas Güntner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Freshwater Resources ◽  
Sensing Data

<p>The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.</p><p>The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.</p><p>To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF).  Over 85% variances can be explained by the first 3 EOFs for both data sets.</p>

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