scholarly journals Data Assimilation Application to the Subsurface Flow and Solute Transport

10.5772/56705 ◽  
2013 ◽  
Author(s):  
Bill X. ◽  
Juxiu Tong
Keyword(s):  
Data Assimilation ◽  
Solute Transport ◽  
Subsurface Flow

Microtopographical and hydrophysical controls on subsurface flow and solute transport: A continuous solute release experiment in a subarctic bog

2018 ◽  
Vol 32 (19) ◽  
pp. 2963-2975 ◽  
Author(s):  
Nicole Elizabeth Balliston ◽  
Colin Patrick Ross McCarter ◽  
Jonathan Stephen Price
Keyword(s):  
Solute Transport ◽  
Subsurface Flow ◽  
Release Experiment ◽  
Solute Release

Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

2016 ◽  
Vol 20 (5) ◽  
pp. 929-952 ◽  
Author(s):  
Mohammadali Tarrahi ◽  
Siavash Hakim Elahi ◽  
Behnam Jafarpour
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Subsurface Flow ◽  
Flow Data

Selected HYDRUS modules for modeling subsurface flow and contaminant transport as influenced by biological processes at various scales

Biologia ◽  
2009 ◽  
Vol 64 (3) ◽  
Author(s):  
Jirka Šimůnek ◽  
Diederik Jacques ◽  
Navin Twarakavi ◽  
Martinus Genuchten
Keyword(s):  
Solute Transport ◽  
Contaminant Transport ◽  
Water Flow ◽  
Subsurface Flow ◽  
Subsurface Water ◽  
Biological Processes ◽  
The Past ◽  
Software Packages ◽  
Saturated Media

AbstractA large number of modifications or special modules of the HYDRUS software packages have been developed during the past several years to evaluate the effects of a range of biohydrological processes on subsurface water flow and the transport of various chemicals and contaminants. The objective of this manuscript is to briefly review the different modules that were included, and to present various applications illustrating the effects of biological processes on water flow and solute transport and reactions in variably-saturated media.

Comparison of Data Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow

2009 ◽  
Vol 8 (4) ◽  
pp. 837-845 ◽  
Author(s):  
Matteo Camporese ◽  
Claudio Paniconi ◽  
Mario Putti ◽  
Paolo Salandin
Keyword(s):  
Data Assimilation ◽  
Subsurface Flow ◽  
Coupled Model ◽  
Surface And Subsurface Flow

Data assimilation of subsurface flow via iterative ensemble smoother and physics-informed neural network

2020 ◽  
Author(s):  
Nanzhe Wang ◽  
Haibin Chang
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Surrogate Model ◽  
Flow Problem ◽  
Subsurface Flow ◽  
High Accuracy ◽  
Training Data ◽  
Model Parameters ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

<p>Subsurface flow problems usually involve some degree of uncertainty. For reducing the uncertainty of subsurface flow prediction, data assimilation is usually necessary. Data assimilation is time consuming. In order to improve the efficiency of data assimilation, surrogate model of subsurface flow problem may be utilized. In this work, a physics-informed neural network (PINN) based surrogate model is proposed for subsurface flow with uncertain model parameters. Training data generated by solving stochastic partial differential equations (SPDEs) are utilized to train the neural network. Besides the data mismatch term, the term that incorporates physics laws is added in the loss function. The trained neural network can predict the solutions of the subsurface flow problem with new stochastic parameters, which can serve as a surrogate for approximating the relationship between model output and model input. By incorporating physics laws, PINN can achieve high accuracy. Then an iterative ensemble smoother (ES) is introduced to implement the data assimilation task based on the PINN surrogate. Several subsurface flow cases are designed to test the performance of the proposed paradigm. The results show that the PINN surrogate can significantly improve the efficiency of data assimilation task while maintaining a high accuracy.</p>

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