Improved tidal and non tidal representation of numerical models through data model integration

IMPROVED TIDAL AND NON-TIDAL REPRESENTATION OF NUMERICAL MODELS THROUGH DATA MODEL INTEGRATION ALAMSYAH KURNIAWAN NATIONAL UNIVERSITY OF SINGAPORE 2014 IMPROVED TIDAL AND NON-TIDAL REPRESENTATION OF NUMERICAL MODELS THROUGH DATA MODEL INTEGRATION ALAMSYAH KURNIAWAN (B.Eng., Institut Teknologi Bandung) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2014 DECLARATION I hereby declare that this thesis is my original work and it has been written by me its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. ----------------------------------------------------------ALAMSYAH KURNIAWAN 24 February 2014 Acknowledgments In the name of Allah, the Most Gracious, the Most Merciful. All praises and thanks be to Allah who has provided the knowledge and guidance to the author in finishing this research work. This thesis is a result of four years of research work since I was admitted into the PhD programme in the Department of Civil and Environmental Engineering, the National University of Singapore. I have worked with a great number of people whose contributions in the research deserved special mention. It is a pleasure to convey my gratitude to them all in this acknowledgment section. In the first place, I want to show my utmost gratitude to Assoc. Prof. Vladan Babovic for his supervision, advice, guidance, and above all, for his patience from the very early stage of this research. I am indebted to him more than he knows. He gave me the opportunity to work with other researchers in Singapore-Delft Water Alliance. I would like to extend my sincere gratitude to Dr. Joost Buurman and Mr. Deepak Vatvani for their kind recommendation and support of the initial stage of my study in Singapore. I also want to record my sincere gratitude to Dr. SK Ooi and Dr. Herman Gerritsen whose vast knowledge and experience have triggered and nourished my intellectual maturity that I will benefit from for a long time to come. I thank them for all the valuable suggestions that they made to help me shaping up my ideas and research. I learned lots of things along the way, Sirs. Thank you very much  I would also like to thank my examiners, Prof. Cheong, Dr. Bai Wei and Prof. Palmer for their helpful suggestions and comments. My special thanks to MHBox project team member, Dr. Raghu, Dr. Abhijit, Dr. Rama, Dr. Yabin, Dr. Wang Xuan, Mr. Zemskyy from Singapore side. As well as Dr. Ann, Prof. Stelling, Mr. Firmijn, Mr. Daniel, Mr. Stef, Dr. Martin, Dr. Ghada and Dr. Julius from Deltares side, for their advice and their willingness to share their bright thoughts with me. It i was great to collaborate with them. Special thanks to Ms. Serene for all fruitful discussions, all the best with PhD  It is a pleasure to pay tribute also to the administrative staffs. To Ms. Sally, Ms. Ivy, Ms. Juli, Ms. Sae’dah, Ms. Cecialia, Ms. Rila and Ms. Sau Koon and Ms. Charu, I am thankful for their assistance in dealing with administration matters during my study in National University of Singapore. I gratefully thank my PhD colleagues, Dr. Arunoda, Mr. Albert, Mr. Abhay, Mr. Ali, Mr. Kalyan and Ms. Jayashree for their cheerful discussions. All the best guys  I am thankful to SDWA colleagues, Assoc. Prof. Obbard, Mr. Mark, Dr. Jahid, Dr. Desmond, Dr. Stephane, Dr. Petra, Dr. Jingjie, Dr. Umid, Dr. Sheela, Dr. Carol, Dr. Samuel, Dr. David Burger, Ms. Hongjuan, Dr. Bui, Dr. Stefano for their kind support and encouragement. I was extraordinarily fortunate in having Dr. Muslim Muin as my adviser in Institut Teknologi Bandung. I could never have embarked and started all of this without his support. His teachings have encouraged me to grab this challenging research opportunity. My parents deserve a special mention for their inseparable support and prayers. My father, Alimin Umar, is the person who always reminds me the importance of learning. My mother, Nurbaya Kaco, is the one who sincerely raised me with her never ending caring and love. Big brother, Rahmansyah Dermawan and lovely little sister, Nielma Auliah, thanks for being supportive and caring siblings. My guardian, Mr. Razak Latang, is the person who always gives support and encouragements. It is unfair if I did not express my appreciation to Ms. Euis Komariah, my lovely wife and Ms. Dzikra Zahratun Nisa, my lovely daughter. I am grateful for all the prayers, patience and support that they have given. I would like to thank everybody who has helped me, as well as expressing my apology that I could not mention personally one by one. Finally, I would like to thank the Singapore-Delft Water Alliance for providing the scholarship that enabled me to study here in Singapore. ii Table of Contents Acknowledgments i Table of Contents iii Summary . vii List of Tables ix List of Figures x List of Abbreviations .xvi List of Publications . xviii Chapter Introduction 1.1 Background . 1.2 Data-Model Approaches in Numerical Model 1.3 Motivation . 1.4 Objectives . 1.5 Outline of Report Chapter Literature Review 2.1 Earlier Tidal Studies in the Region . 2.2 Non-Tidal Phenomena 2.2.1 Earlier Non-Tidal Studies in the Region . 2.2.2 Tide-Surge Interaction 2.3 Research gaps and Significance of the Study . Chapter Methodologies – Building Blocks 10 3.1 Review of the Backbone Models 10 3.1.1 Delft3D-FLOW Software . 10 3.1.2 Singapore Regional Model (SRM) . 15 3.1.3 South China Sea Model (SCSM) 18 3.2 Data-Model Integration . 19 iii 3.2.1 Introduction . 19 3.2.2 Model Calibration and Calibration Techniques 20 3.2.3 Data Assimilation and Data Assimilation Techniques . 26 Chapter Sensitivity Analysis of Tidal Representation in Singapore Regional Waters . 32 4.1 Introduction . 32 4.2 Building Blocks: Tidal Data, Tidal Model and Assimilation Approach 33 4.2.1 Tidal Observational Data – Along Track Data and Long Term In-situ Data Sets . 33 4.2.2 Numerical Model – Uncertainties, Coarse Model 34 4.2.3 OpenDA and Multiple Parameter Variation . 36 4.3 Evaluation Criteria for Assessing Model Representation of Tide 37 4.4 Design of the Sensitivity Experiments 38 4.4.1 Ranking of Uncertainties – Sequence of Simulations . 38 4.5 Results and Discussion . 40 4.5.1 Sensitivity of Tidal Representation to Variation of Ocean Forcing . 40 4.5.2 Sensitivity of the Region to Malacca Strait Bathymetry (and Friction) . 42 4.5.3 Final Sensitivity Analysis of SRW to Incoming Tide 44 4.5.4 Overall Evaluation of the Sensitivity of the SRW 45 4.5.5 Coarse and Original Model Performance . 46 4.6 Conclusions on the Improved Tidal Study 47 Chapter On Improving Sea Level Anomalies (Surge) Modelling in Singapore Regional Waters Using Multi-Scale Modelling Approach . 66 5.1 Introduction . 66 5.2 Sea Level Anomalies in the Study Area . 67 5.2.1 Preparation of SLA Data . 67 5.2.2 Results and Discussion on SLA data 68 iv 5.3 Numerical Models, Input Data and Methodology 70 5.3.1 Finer Resolution Model 70 5.3.2 Basin Scale Model 70 5.3.3 Meteorological Dataset . 71 5.3.4 Methodology . 72 5.3.5 Evaluation Criteria for Non-Tidal Barotropic Models 72 5.4 Results and Discussion . 74 5.4.1 Non-Tidal Barotropic Modelling 74 5.4.2 Effect of Tide-Surge Interaction (Nonlinearity) and Multi-Scale Approach . 75 5.5 Conclusions on the Improved Non-Tidal Study . 78 Chapter Data Relationship Analysis on Sea Level Anomalies 98 6.1 Introduction . 98 6.2 Data Availability, Preparation and Basic Statistics of the Study Area . 100 6.2.1 Predicted Pressure and Wind Components . 101 6.2.2 Observed and Predicted Astronomical Tide . 101 6.2.3 Observed Sea Level Anomalies 101 6.2.4 Predicted Sea Level Anomalies (SLA) and SLA Prediction Errors . 101 6.2.5 Statistic of the Data at Singapore Strait (UH699-TG-PAGAR) . 102 6.3 Data Relationship Analysis of Sea Level Anomalies Prediction Errors . 102 6.3.1 Uncertainties and Information 102 6.3.2 A Measure of Information 103 6.3.3 Interpretation of the Correlation Coefficient and AMI Values . 106 6.4 Results and Discussion . 108 6.4.1 Observed Sea Level Anomalies at Singapore Strait . 108 6.4.2 Sea Level Anomalies Prediction Errors at Singapore Strait . 112 6.5 Conclusions on Data Relationship Analysis . 115 v Chapter Improving Sea Level Anomalies Prediction using Genetic Programming 131 7.1 Introduction . 131 7.2 Evolutionary Computing . 132 7.2.1 Evolution Principle . 133 7.2.2 Evolutionary Computing Techniques . 134 7.3 Evolutionary Computing in Non-tidal Barotropic Modelling: An Overview . 135 7.4 Genetic Programming and its Scope in Non-Tidal Barotropic Modelling . 136 7.4.1 Modelling the Observations: GP as a Modelling Tool . 137 7.4.2 Modelling the Model Error: GP as a Data Assimilation Tool 138 7.5 Important Issues Pertaining to Genetic Computing 139 7.6 Preparation of Data and Genetic Programming Implementation 140 7.6.1 Forecast Horizon . 142 7.6.2 Selection on Predictive Parameters . 142 7.6.3 Evaluation Criteria for GP Output 143 7.7 Results and Discussion . 144 7.7.1 SLA Prediction Errors Modelling for Direct Forecasting . 144 7.7.2 Mathematical Nature of SLA Prediction Errors Dynamics 145 7.7.3 GP model as Data Assimilation (Error Correction) Tools 145 7.7.4 Accuracy of Error Correction Tools . 146 7.8 Conclusions on Genetic Programming . 148 Chapter Conclusions and Future Works 164 8.1 Conclusions . 164 8.2 Future Works 165 Chapter References . 168 vi Summary The strategic importance of Singapore regional waters (SRW) has led to numerous studies to understand the physical processes that drive and are driven by the hydrodynamics in this region. However, due to geo-political realities and its highly complex tidal and nontidal variation, relatively few studies encompass the region as a whole. Currently, characteristic of non-tidal water levels and currents in terms of spatial and temporal and also its driving mechanism in the Singapore Strait and Malacca Strait regions are still not well understood. In view of those, research has been carried out to understand, examine and develop effective and efficient methods to improve tidal and non-tidal representation in SRW through data model integration (DMI) approaches. The first research work corresponds to a structured approach to study the sensitivity of tidal propagation and interactions to parameters like the prescription of tidal forcing at the open ocean boundaries, local depth information and seabed roughness using the open-source software environment OpenDA for sensitivity analysis and simultaneous parameter optimisation. The second research work corresponds to a physical analysis of the non-tidal barotropic or sea level anomalies (SLA) which includes a multi-scale approach, and addresses amongst others hydrodynamic model grid resolution and the importance of resolving non-linear tide-surge interaction. The third research work corresponds to data assimilation to improve the SLA forecast using average mutual information (AMI) and Genetic programming (GP). Overall, it is found that in a user-controlled way, the vector difference error in tidal representation could so effectively be reduced by ~50%. The results confirm the benefit of using OpenDA in guiding the systematic exploration of the modelled tide and reducing the parameter uncertainties in different parts of the SRW region. The study of non-tidal effects or sea level anomalies (SLA) in this region has shown that the water level and current anomalies phenomena in a complex region like SRW can be effectively modelled using an approach combining non-tidal barotropic and multi-scale numerical modelling. The results of combining both approaches suggest that the finer grid resolution improves the accuracy of water level and current anomalies simulations. Furthermore, the results also indicate that for the simulations of non-tidal barotropic flows in this area, non-linear surge interaction is important and should be taken into account. vii Chapter Conclusions and Future Works The research carried out in this study has indicated that information theorybased techniques help to determine how errors in the results of physically based computational models are related to the input and output data for the models. In particular the techniques enable information to be obtained about which data can be used to assist in the recovery of the errors, and to generate insight into the time dynamics inherent in the relationship between the data and model errors. It has been shown that errors from several types of computational models share varying degrees of information with their respective input data, output data and the state variables. This insight has helped to identify particular time series that share a maximum amount of information with the residual errors. In turn, these time series are used subsequently to construct a complementary data-driven model, which can be used to forecast the expected errors of the model. In particular, the average mutual information (AMI) analysis has helped not only in the development of data-driven models but also in analysing and determining the drawback of the numerical model. The thesis has also demonstrated that data-driven modelling is much more than simply predicting the errors of simulation models. It is one way of making sense out of the historical errors of a model. The approach opens up a range of possibilities from improving model predictions to obtaining valuable information in order to help understand the behaviour of the simulation model. The historical errors of the simulation model are treated as any other data 'observed' from this other 'process'. Genetic Programming enables a stereoscopic view of the physical processes in the sense that it allows the modeller to see the physical domain. A successful application of GP model essentially involves an understanding of the principles on the basis of which both data-driven models and physically based models represent a particular physical system. 8.2 Future Works For tidal study, a properly designed coarser grid has been shown to be dynamically equivalent and that it can suitably replace the finer grid model for multiple parameter variation and sensitivity analysis purposes. However it has been also shown that the minor differences that exist between the coarser and the original grids restrict the coarser grid to serve only as an indicator of which is the correct scenario to run the sensitivity analysis with the original grid. Future work is 165 Chapter Conclusions and Future Works recommended to extend the role of the coarser grid to be a more direct form of the finer grid model. The present study did not address the dominant local balances such as friction versus barotropic forcing or the geostrophic balance in the deep parts of the area. For proper calibration however this seems important knowledge. While a higher resolution numerical hydrodynamics model improves the prediction of sea level anomalies (SLA), it also incurs a higher computational cost with regards to time which is a potential issue in an operational forecasting system. For forecasting operations requiring quick but reasonably accurate results, it is suggested that the same non-tidal barotropic modelling approach to be tested on the newly developed SCS model that could represent tide more accurately than the SCSM and the SCSRM and is computationally less expensive than SCSRM, to see if the same SLA result could be obtained. The results of non-tidal barotropic numerical modelling suggests that the SLA in the Malacca Strait region are not induced by atmospheric forcing mainly acting on the waters within the South China Sea, while the applied pressure-based water level correction at the boundary also insufficiently captures externally generated non-tidal water level variation. This indicates that simulation of externally atmospherically induced events requires an oceanographic model that covers the region where the key generation of these water level contributions takes place, which is the Andaman Sea and part of Indian Ocean. Therefore, the future work is to study the effect of the model extension of the non-tidal barotropic model. The data relationship analysis has indicated that the meteorological variables are related to the SLA prediction errors, therefore finer meteorological data resolution may improve the representation of the simulated SLA. Furthermore, as meteorological forces (i.e. pressure, wind magnitude and wind direction) are physically related to the SLA, there should not be any relationship between the meteorological forcing and the SLA prediction errors if the non-tidal barotropic weather modelling properly represents this relationship. This suggests that the way the SLA are simulated using the meteorological forcing and tidal effect needed to be improved. The focused of the future work is to study the effect of the meteorological data resolution as well as the way this meteorological data is transferred into the local model from the basin scale model. As present work used offline nesting to capture the surge forerunner from the 166 Chapter Conclusions and Future Works basin scale model into the local model, the future work should study the impact of online nesting which is domain decomposition. 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Hazards, 62, 207–219, doi:10.1007/s11069-011-9989-z. 180 [...]... understand, examine and develop effective and efficient methods to improve tidal and non -tidal representation in Singapore Regional Waters through DMI approach The specific objectives are to:  review the hydrography (tidal and non -tidal observation data set, bathymetry, land boundary) representation in the domain of interest  propose a DMI approach to study the sensitivity of tidal propagation and interactions... been carried out i.e the proposed numerical models and concept of data- model- integration as used in the present tidal and non -tidal studies 3.1 Review of the Backbone Models Depth-integrated hydrodynamic modelling is a practical means to verify and quantify the various phenomena that contribute to the currents and water levels in the Singapore Region Waters Good knowledge of the driving hydrodynamic processes... parameters of the numerical model These available observation datasets are then used to tune numerical model results by adapting some of the uncertainties to obtain a better fit of measurement tidal water level and current This process in which measurement or observation data and numerical models results can be combined in a structured way in order to reduce errors or uncertainties is known as Data Model Integration. .. narrow straits and numerous small islands 1.4 Objectives The main objective of the research presented in this thesis is to understand, examine and develop effective and efficient methods to improve tidal and non -tidal representation in Singapore Regional Waters through data model integration approaches 1.5 Outline of Report This chapter serves as an introduction and gives a description of the background... Kurniawan et al (2013) re-examined the nonlinearity with a higher resolution model and found that the magnitude of the nonlinear tide-surge interaction cannot be simply neglected 2.3 Research gaps and Significance of the Study Research gaps for the tidal and non -tidal studies in the Singapore Region Waters (SRW) through depth-integrated hydrodynamic modelling and data model integration (DMI) are summarized... techniques of DMI have been successfully developed and implemented to improve hydrodynamic numerical model performance and to better understand:  the behaviour of the tide in the region and its sensitivities to changes in tidal boundary forcing and to local depth and friction variation in the narrow regions of the Malacca Strait  the physics of the non -tidal barotropic water levels, currents and their... ship navigation, offshore operations and water quality modelling) accurate data and maps of sea elevation and current are often of prime importance According to Robinson and Lermusiax, (2000), the fundamental problem of sea elevation and current can be simply described as prediction, meaning given the state of the sea elevation and current at one time, what is the state at a later time? Of the two components... rise and fall of sea levels caused by the combined effects of the gravitational forces exerted by the Moon and the Sun and the rotation of the Earth, tides is typically dominant and deterministic Although the basic equations of tidal dynamics (e.g Hendershott, 1977) are comparatively simple and have been understood since the time of Laplace (Egbert and Erofeeva, 2002) and a tremendous amount of research... region of interest, these models, however, cover a small domain and apply tidal open boundary forcing that is interpolated from data from nearby stations, while the dynamics of the large-scale tidal interaction would require the consideration of a much larger domain Tidal data analysis is hampered by the lack of reliable coastal stations with long-term water level records while numerical tidal modelling... study of both parts is approached using data- model integration 1 Chapter 1 Introduction 1.2 Data- Model Approaches in Numerical Model Numerical model predictions contain errors or uncertainties due to various reasons including the limited insight into physical mechanisms, simplifying assumptions, unknown sub-processes, numerical approximations, model parameterization and the fact that a part of any model . IMPROVED TIDAL AND NON -TIDAL REPRESENTATION OF NUMERICAL MODELS THROUGH DATA MODEL INTEGRATION ALAMSYAH KURNIAWAN NATIONAL UNIVERSITY OF SINGAPORE. SINGAPORE 2014 IMPROVED TIDAL AND NON -TIDAL REPRESENTATION OF NUMERICAL MODELS THROUGH DATA MODEL INTEGRATION ALAMSYAH KURNIAWAN (B.Eng., Institut Teknologi Bandung) . Blocks: Tidal Data, Tidal Model and Assimilation Approach 33 4.2.1 Tidal Observational Data – Along Track Data and Long Term In-situ Data Sets 33 4.2.2 Numerical Model – Uncertainties, Coarse Model