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Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method

Y.R. Fan, G.H. Huang, B.W. Baetz, Y.P. Li, K. Huang, Z. Li, X. Chen, L.H. Xiong
2016 Environmental Modelling & Software  
In this study, a hybrid sequential data assimilation and probabilistic collocation (HSDAPC) approach is proposed for analyzing uncertainty propagation and parameter sensitivity of hydrologic models.  ...  A probabilistic collocation method (PCM) is further employed to show uncertainty propagation from model parameters to model outputs.  ...  Acknowledgments This research was supported by the Natural Science Foundation of China (Nos. 51190095 and 51225904) and the Program for Innovative Research Team in University (IRT1127).  ... 
doi:10.1016/j.envsoft.2016.09.012 fatcat:4wwteeubuzdwho4yjv4z6bpjc4

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

Y. R. Fan, Department of Civil and Environmental Engineering, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
2019 Journal of Environmental Informatics Letters  
In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average  ...  This study provides a brief review for uncertainty quantification in hydrological predictions.  ...  The author is very grateful for the editor's and the anonymous reviewers' insightful and constructive comments. They are critically helpful for improving this manuscript.  ... 
doi:10.3808/jeil.201900019 fatcat:zknswmxcezaobcxiei5d5qacey

Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction

Gopal Saha, Jianbing Li, Ronald Thring
2017 Hydrology  
This study presents the understanding of temporal dynamics of groundwater-surface water (GW-SW) interaction due to parameter uncertainty by using a physically-based and distributed gridded surface subsurface  ...  flow varied monthly and annually, respectively, due to the uncertainties of the sensitive model parameters.  ...  The authors would also like to thank Peter Caputa, Faye Hirshfield, Siddhartho Shekhar Paul, Malyssa Maurer, Reg Whiten, and Chelton van Geloven for their help and support.  ... 
doi:10.3390/hydrology4020028 fatcat:hkk3r6cpwjh43bulzyxihjwbqu

Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE

Pierre-Emmanuel Kirstetter, Y. Hong, J. J. Gourley, S. Chen, Z. Flamig, J. Zhang, M. Schwaller, W. Petersen, E. Amitai
2012 Journal of Hydrometeorology  
A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States.  ...  Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements  ...  This work was funded by a postdoctoral grant from the NASA Global Precipitation Measurement Mission Ground Validation Management.  ... 
doi:10.1175/jhm-d-11-0139.1 fatcat:qzljkqj4mbeyfhmc3kn4jncwnq

Cross-Covariance Models [chapter]

2017 Encyclopedia of GIS  
The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns.  ...  Cross-References Indexing, Hilbert R-tree, Spatial Indexing, Multimedia Indexing The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery  ...  Tracking Feature Detection and Tracking in Support of GIS Collocation Pattern Co-location Pattern Collocation, Spatiotemporal Movement Patterns in Spatio-Temporal Data Co-location Patterns,  ... 
doi:10.1007/978-3-319-17885-1_100240 fatcat:2ojzb7es7rhofinw4abol6dgc4

Online coupled regional meteorology-chemistry models in Europe: current status and prospects

A. Baklanov, K. H. Schluenzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss (+27 others)
2013 Atmospheric Chemistry and Physics Discussions  
Topics discussed include a survey of processes relevant to the interactions between atmospheric physics, dynamics and composition; a brief overview of existing online mesoscale models and European model  ...  Online access models use independent meteorology and chemistry modules that might even have different grids, but exchange meteorology and chemistry data on a regular and frequent basis.  ...  Several authors of this article are grateful for the support received from the following projects: EC FP7 TRANSPHORM and PEGASOS, Nordic En-sCLIM, the RF SOL No 11.G34.31.0078 and No 14.B37.21.0880 at  ... 
doi:10.5194/acpd-13-12541-2013 fatcat:b2lfzd5el5bwpdcohumjda4ctu

Online coupled regional meteorology chemistry models in Europe: current status and prospects

A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss (+27 others)
2014 Atmospheric Chemistry and Physics  
Topics discussed include a survey of processes relevant to the interactions between atmospheric physics, dynamics and composition; a brief overview of existing online mesoscale models and European model  ...  This review will be of particular interest to model developers and users in all three fields as it presents a synthesis of scientific progress and provides recommendations for future research directions  ...  Several authors of this article are grateful for the support received from the following projects: EC FP7 TRANSPHORM and PEGASOS, Nordic En-sCLIM, the RF SOL No 11.G34.31.0078 and No 14.B37.21.0880 at  ... 
doi:10.5194/acp-14-317-2014 fatcat:4thjulvrlrbpho36w7at7nkcie

Meshless techniques for anisotropic diffusion

Annamaria Mazzia, Giorgio Pini, Flavio Sartoretto
2014 Applied Mathematics and Computation  
The latter would allow better use of parallel computers, since time-stepping is essentially a serial process. Moreover, it would be good for the methods to be of high order accuracy.  ...  A good numerical method would be locally mass conservative, produce no or minimal over/under-shoots, produce minimal numerical diffusion, and require no CFL time-step limit for stability.  ...  We show how to solve the equations using a global implicit approach in an efficient way, and we present the derived computational results.  ... 
doi:10.1016/j.amc.2014.03.032 fatcat:c527226gyfgbffnq4p67qxd7wi

Machine Learning in Heterogeneous Porous Materials [article]

Marta D'Elia, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, George Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu (+12 others)
2022 arXiv   pre-print
modeling in heterogeneous porous materials via ML, and Discovery of materials constitutive laws and new governing equations.  ...  Within the scope of ML and materials research, the goal of the workshop was to discuss the state-of-the-art in each community, promote crosstalk and accelerate multi-disciplinary collaborative research  ...  a wholly owned subsidiary of Honeywell International, Inc., for the U.S.  ... 
arXiv:2202.04137v1 fatcat:tuhghvcifnebzo2pcwifeek4vu

Streamflow and Soil Moisture Assimilation in the SWAT model Using the Extended Kalman Filter

Leqiang Sun, Université D'Ottawa / University Of Ottawa, Université D'Ottawa / University Of Ottawa
2016
Data Assimilation (DA) is a promising technology that uses real-time observations to modify a model's parameters and internal variables to make it more representative of the actual state of the system  ...  Numerical models often fail to accurately simulate and forecast a hydrological state in operation due to its inherent uncertainties.  ...  Introduction Data Assimilation (DA) is a method that tries to estimate the true state of a system by optimally merging observations with dynamic model simulations based on analysis of their uncertainties  ... 
doi:10.20381/ruor-4934 fatcat:zjhjklljvbdk7jdypidc3q6tv4

Table of Contents

2016 Australian Journal of Primary Health  
This study explores a sequential data assimilation method to overcome the challenge of using data from heterogeneous sources for improving the model performance.  ...  A sensitivity study of the model parameters will be used to test the robustness of the numerical results.  ... 
doi:10.1071/pyv22n4toc fatcat:hrwhnxdpjreslhuwi7y4edqmo4

Spatiotemporal enabled Content-based Image Retrieval

Mariana Belgiu, Martin Sudmanns, Tiede Dirk, Andrea Baraldi, Stefan Lang
2016 International Conference on GIScience Short Paper Proceedings  
For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation.  ...  In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment.  ...  Future Work Future research will extend our machine learning approach on Spark in the following four directions: (1) scanning a wider parameterization space and further optimizing search methods for the  ... 
doi:10.21433/b311729295dw fatcat:fulw4pw3kfh5nmfzcsy3pkisvm

Surrogate Groundwater Models [article]

Michael Asher, University, The Australian National
2021
A faster model enables more model runs, critical for understanding models through methods such as sensitivity and uncertainty analysis.  ...  Surrogate modelling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model as a function of its inputs and parameters.  ...  Understanding the benefits of surrogate methods is made challenging by the wide range of reported efficiency gains and the breadth of modelling contexts (e.g. data assimilation, sensitivity analysis, uncertainty  ... 
doi:10.25911/h1km-0s27 fatcat:7fs76l4swnf6bpzrq4likbhkpu

Efficient modeling of environmental systems in the face of complexity and uncertainty [article]

Sergey Oladyshkin, Universität Stuttgart, Universität Stuttgart
2015
Moreover, environmental data is hardly available and expensive to acquire. Overall, this leads to limited observability, and an inherent uncertainty in all modeling endeavors.  ...  The current thesis contains research in the field of environmental modeling in the face of complexity and uncertainty.  ...  Chapter 10 provided a flexible and efficient framework for global sensitivity analysis for quantification of the effects of modeling parameters on the overall model uncertainty.  ... 
doi:10.18419/opus-615 fatcat:cegmjx73zraprnj3fm2ytig5aa

Data Assimilation in high resolution Numerical Weather Prediction models to improve forecast skill of extreme hydrometeorological events [article]

MARTINA LAGASIO
2019
radar and lightning data.  ...  , as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, for the back-building Mesoscale Convective System which hit Genoa city (Italy) in 2014  ...  ETKF-Variational data assimilation The hybrid data assimilation techniques aim to combine the benefits of the variational data assimilation (dynamical and physical constraints, simultaneous treatment  ... 
doi:10.15167/lagasio-martina_phd2019-05-22 fatcat:yt2lzrkqovfk7gqhehihhhtqsi
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