Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation

2016 ◽  
Vol 94 ◽  
pp. 103-119 ◽  
Author(s):  
S. Pathiraja ◽  
L. Marshall ◽  
A. Sharma ◽  
H. Moradkhani
Keyword(s):  
Data Assimilation ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Paired Catchment ◽  
Using Data

scholarly journals Evaluating prior predictions of production and seismic data

2019 ◽  
Vol 23 (6) ◽  
pp. 1331-1347 ◽  
Author(s):  
Miguel Alfonzo ◽  
Dean S. Oliver
Keyword(s):  
Data Assimilation ◽  
Seismic Data ◽  
History Matching ◽  
Simulated Data ◽  
Model Parameters ◽  
Model Diagnostic ◽  
Diagonal Approximation ◽  
Data Coverage ◽  
Reservoir Simulation Model ◽  
Using Data

Abstract It is common in ensemble-based methods of history matching to evaluate the adequacy of the initial ensemble of models through visual comparison between actual observations and data predictions prior to data assimilation. If the model is appropriate, then the observed data should look plausible when compared to the distribution of realizations of simulated data. The principle of data coverage alone is, however, not an effective method for model criticism, as coverage can often be obtained by increasing the variability in a single model parameter. In this paper, we propose a methodology for determining the suitability of a model before data assimilation, particularly aimed for real cases with large numbers of model parameters, large amounts of data, and correlated observation errors. This model diagnostic is based on an approximation of the Mahalanobis distance between the observations and the ensemble of predictions in high-dimensional spaces. We applied our methodology to two different examples: a Gaussian example which shows that our shrinkage estimate of the covariance matrix is a better discriminator of outliers than the pseudo-inverse and a diagonal approximation of this matrix; and an example using data from the Norne field. In this second test, we used actual production, repeat formation tester, and inverted seismic data to evaluate the suitability of the initial reservoir simulation model and seismic model. Despite the good data coverage, our model diagnostic suggested that model improvement was necessary. After modifying the model, it was validated against the observations and is now ready for history matching to production and seismic data. This shows that the proposed methodology for the evaluation of the adequacy of the model is suitable for large realistic problems.

scholarly journals Simultaneous state-parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, applying classical prediction method will result in significant error. Data assimilation method offers a new way to merge multi-source data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide system. In this paper, simultaneous state-parameter estimation (SSPE) using particle filter-based data assimilation is applied to predict displacement of the landslide. Landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan province, China as experiment site to test SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of SSPE assimilation strategy in short-term landslide displacement estimation.

Estimation of FEM Model Parameters Using Data Assimilation and Its Application to an Electrical Machine

2016 ◽  
Vol 52 (3) ◽  
pp. 1-4 ◽  
Author(s):  
A. Bacchus ◽  
A. Tounzi ◽  
J.-P. Argaud ◽  
B. Bouriquet ◽  
M. Biet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Parameters ◽  
Electrical Machine ◽  
Fem Model ◽  
Using Data

scholarly journals Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

2006 ◽  
Vol 7 (3) ◽  
pp. 548-565 ◽  
Author(s):  
Jasper A. Vrugt ◽  
Hoshin V. Gupta ◽  
BreanndánÓ Nualláin ◽  
Willem Bouten
Keyword(s):  
Data Assimilation ◽  
Simultaneous Optimization ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimization Approach ◽  
Streamflow Forecasting ◽  
Time Data ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Using Data

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships.

scholarly journals Estimating model parameters of liquid rocket engine simulator using data assimilation

2020 ◽  
Vol 177 ◽  
pp. 373-385
Author(s):  
Daiwa Satoh ◽  
Seiji Tsutsumi ◽  
Miki Hirabayashi ◽  
Kaname Kawatsu ◽  
Toshiya Kimura
Keyword(s):  
Data Assimilation ◽  
Rocket Engine ◽  
Model Parameters ◽  
Liquid Rocket Engine ◽  
Using Data ◽  
Liquid Rocket

scholarly journals Assimilation of wide-swath altimetry observations to correct large-scale river routing model parameters

2019 ◽  
Author(s):  
Charlotte M. Emery ◽  
Sylvain Biancamaria ◽  
Aaron Boone ◽  
Sophie Ricci ◽  
Mélanie C. Rochoux ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Global Scale ◽  
Hydrologic Model ◽  
Model Parameters ◽  
True Value ◽  
Input Parameters ◽  
River Routing

Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages but not without non-negligible uncertainties; among which inexact input parameters have a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch schedule for 2021, will be dedicated to measure water surface elevations, widths and surface slopes of rivers larger than 100 meters at global scale. SWOT will provide a significant amount of new data for river hydrology and they could be combined, through data assimilation, to global-scale models in order to correct their input parameters and reduce their associated uncertainty. The objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that assimilates synthetical SWOT observations of water elevations to correct the input parameters of a large scale hydrologic model over a 21-day time window. The study is applied on the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm, tested through a set of Observing System Simulation Experiments (OSSE), is able to retrieve the true value of the Manning coefficients within one assimilation cycle most of the time and shows perspectives in tracking the Manning coefficient temporal variations. Ultimately, in order to deal with potential bias between the observed and the model bathymetry, the assimilation of water elevation anomalies was also tested and showed promising results.

Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation

2014 ◽  
Vol 15 (1) ◽  
pp. 359-375 ◽  
Author(s):  
Gift Dumedah ◽  
Jeffrey P. Walker
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Open Loop ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Model Parameter ◽  
High Soil Moisture ◽  
Using Data ◽  
Parameter Values ◽  
Moisture Estimation

Abstract Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation (EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.

A Direct Application of Data Assimilation to Multi-Scale Regionalized Parameters

2020 ◽  
Author(s):  
Lennart Schüler ◽  
Sabine Attinger
Keyword(s):  
Data Assimilation ◽  
Transfer Functions ◽  
Computing Time ◽  
Cosmic Ray ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Multi Scale ◽  
The One ◽  
Global Parameters ◽  
Local Parameters

<p>Streamflow observations are integrated signals of a catchment. This data is only weakly correlated to local observations (e.g. soil moisture and groundwater heads) or local parameters (e.g. hydraulic conductivity) of the catchment. On the one hand, this makes it next to impossible to estimate model parameters from streamflow observations alone. On the other hand, local observations only make parameter estimation possible in their immediate proximity. With data scarcity in mind, this multi-variate data assimilation alone has limited potential to solving the problem of estimating model parameters.<br>Therefore, we propose to not apply data assimilation to the model parameters directly, but to the global parameters of the multi-scale regionalization (MPR, Samaniego et al. 2010) approach. This approach relates a very limited number of global parameters through transfer functions to the model parameters. By doing so, the number of parameters to be estimated can be drastically reduced, saving computing time and with robust transfer functions, the local parameters can be estimated not only in the proximity of observations, but also throughout the catchment.<br>Using the DA-MPR approach, we investigate different experiment setups for estimating model parameters, e.g. a stationary cosmic ray sensor vs. a mobile one or how many local observations are actually needed in order to uniquely identify the model parameters.</p><p>Samaniego L., R. Kumar, S. Attinger (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46</p>

scholarly journals Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Vol 19 (7) ◽  
pp. 1387-1398 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, and applying a classical prediction method will result in significant error. The data assimilation method offers a new way to merge multisource data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide systems. In this paper, simultaneous state and parameter estimation (SSPE) using particle-filter-based data assimilation is applied to predict displacement of the landslide. A landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan Province, China, as the experiment site to test the SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of the SSPE assimilation strategy in short-term landslide displacement estimation.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

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