Novel Modelling Approaches and Data Sources in Socio-hydrology Peter Blair A thesis presented for the degree of Master of Philosophy Department of Civil and Environmental Engineering Imperial College London I certify that work contained within this thesis is my own, and that all else is referenced appropriately. The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. An Engineer's Haiku Engineers make things Sometimes, the things they make work Sometimes, they don't i Abstract Increasing interactions between anthropogenic and hydrological systems have prompted the development of a new way of thinking in water resource management: socio-hydrology. Socio- hydrology explicitly considers the two-way interactions and feedbacks between humans and water and includes these in models to explore trajectories of development. Expanding sys- tem boundaries to include human behaviour and interactions with natural systems brings significant challenges. This study was largely divided into two parts, modelling in socio-hydrology and data in socio- hydrology. The modelling section focuses on investigating the utility of different modelling techniques in socio-hydrology, and then covers the development and use of models in socio- hydrology, while the data section investigates the use of freely available data sets to investigate an aspect of socio-hydrological systems and in a context of determining land-use. In the modelling section, a detailed review of modelling in socio-hydrology was initially carried out which found that work should focus on further conceptual modelling, as well as developing calibration/validation methodologies. A conceptual model was then developed, focusing on drought in a socio-hydrological context; this model was able to produce some archetypal system behaviours. In the section focusing on data in socio-hydrology, a learning approach was used to try to determine the impact of flooding on house prices. Unfortunately, the data available was not detailed enough to allow for a model with predictive power, and so this learning approach was not able to give more insight than other investigations that have been carried out in this area. In the chapter exploring the use of satellite data in determining land-cover, the model which was learnt had success in determining land-cover at the time and place it was trained ii on, but had limited success in determining land-cover in other times and places, and so does not represent an improvement on what techniques already exist. iii Acknowledgements I would like to give my sincerest thanks to my supervisors, Dr Wouter Buytaert and Dr Rob Ewers. Their support and advice throughout this project has been very valuable and is much appreciated. Thanks also go to the Natural Environment Research Council, whose funding made this project possible. I would also like to thank all of the staff in the Civil and Environmental Engineering Department and Grantham Institute at Imperial College London. Final thanks go to my friends and family who have provided a great deal of support and love during this process, for which I am deeply grateful. iv Contents 1 Introduction 1 2 Background & Literature Review - Socio-hydrological modelling: Why, What and How? 3 2.1 Introduction . 3 2.1.1 Some Background to Socio-hydrology . 5 2.2 Why? . 8 2.2.1 System Understanding . 11 2.2.2 Forecasting & Prediction . 13 2.2.3 Policy & Decision-making . 14 2.2.4 Current & Future Applications . 16 2.3 What? . 22 2.3.1 Socio-hydrology and Other Subjects . 22 2.3.2 Concepts . 27 2.3.3 Human-Water System Representations . 28 2.3.4 Space and Time in Socio-hydrological Modelling . 35 2.3.5 Data . 37 2.3.6 Complexity . 39 2.3.7 Model Resolution . 41 v 2.3.8 Uncertainty . 42 2.4 How? . 44 2.4.1 Model Classifications . 45 2.4.2 Approaches . 46 2.4.3 The Importance of Model Conceptualisation . 48 2.4.4 Agent-Based Modelling (ABM) . 49 2.4.5 System Dynamics (SD) . 53 2.4.6 Pattern-oriented Modelling (POM) . 58 2.4.7 Bayesian Networks (BN) . 60 2.4.8 Coupled Component Modelling (CCM) . 61 2.4.9 Scenario-Based Modelling . 63 2.4.10 Heuristic/Knowledge-Based Modelling . 64 2.5 Conclusions . 65 3 A Socio-hydrological System Dynamics Model for Anthropogenic Drought 70 3.1 Introduction . 70 3.2 Modelling approach . 73 3.2.1 System dynamics modelling . 73 3.2.2 Socio-hydrological system dynamics modelling of anthropogenic drought 74 3.2.3 Anthropogenic Drought . 75 3.2.4 Study Cases . 76 3.3 Model design . 80 3.3.1 System components . 80 3.3.2 Causal loop diagram . 84 3.3.3 Stocks and flows diagram . 85 vi 3.3.4 The numerical model . 88 3.4 Application to idealised cases and evaluation . 108 3.4.1 Frequent drought . 109 3.4.2 Occasional drought . 115 3.4.3 Impending vulnerability . 118 3.4.4 Different resources . 123 3.5 Discussion . 130 3.6 Conclusions . 131 4 Socio-hydrology & Risk: Concepts with Implications for Modelling of One Another 133 4.1 Introduction . 133 4.2 The Implications of Socio-hydrology for Risk Estimation and Modelling . 136 4.2.1 Risk in Hydrology . 136 4.2.2 Appropriate Simplifications When Modelling & Estimating Risk in Socio-hydrological Systems . 138 4.3 The Implications of Risk in Socio-hydrology . 145 4.4 Case Study: A Risk-based Approach to Human-Flood Interactions . 146 4.4.1 Description of Situation & Model . 147 4.4.2 Evolution of Stationary Risk Profile . 151 4.4.3 Different Views of Socio-hydrological Risk . 152 4.4.4 Measuring the Stationarity in Risk Using Different Methods . 162 4.5 Conclusions . 164 5 Does Flooding Have an Impact on House Prices in England? A Tree-based Statistical Learning Approach 167 vii 5.1 Introduction . 167 5.2 Methods . 169 5.2.1 Data . 169 5.2.2 Statistical Learning Methods . 170 5.3 Results . 177 5.3.1 Model Validation and Performance . 177 5.3.2 Impact of Flooding on House Prices . 178 5.4 Discussion . 179 5.5 Conclusions . 184 6 Applying Machine Learning Techniques to Determine Land-cover in Bor- neo 186 6.1 Introduction . 186 6.2 Methods . 189 6.2.1 Study Area . 189 6.2.2 Input Data . 191 6.2.3 Machine Learning Techniques . 192 6.3 Results . 199 6.3.1 Random Classification: Context for Model Performance . 199 6.3.2 Tuning Parameters . 200 6.3.3 Filtering . 202 6.3.4 Overview of Model Performance . 203 6.3.5 Test Application: Sabah, Malaysia . 205 6.4 Discussion . 208 6.5 Conclusions . 209 viii 7 Conclusions 210 7.1 Overall Conclusions & Recommendations for Further Work . 210 7.1.1 Modelling in socio-hydrology . 211 7.1.2 Data in socio-hydrology . 212 References 214 A Tables From Applying Machine Learning Techniques to Determine Land- use in Borneo 256 B Socio-hydrology & Risk: Explanation of Di Baldassarre's Model 260 ix List of Figures 2.1 c Elshafei et al. (2014), reproduced with permission under the CC Attribu- tion License 3.0. A conceptual representation of a socio-hydrological system (Elshafei et al., 2014) . 27 2.2 Temporal and spatial scales at which different research approaches are ap- propriate (Adapted with permission from Reyer et al. (2015), c Reyer et al. (2015), used under the CC Attribution License 3.0) . 36 2.3 c Fernald et al. (2012), reproduced under the CC Attibution License 3.0. An example of a complex CLD (this is approximately one quarter of the complete diagram) . 52 2.4 c Di Baldassarre et al. (2013a), reproduced with permission under the CC Attribution License 3.0. An example of a simple CLD from Di Baldassarre et al. (2013a) . 53 2.5 An example of a Stocks and Flows Diagram (SFD) developed from a Causal Loop Diagram (CLD) . 53 3.1 Different characterisations of drought: (a) drought seen as caused by nature (b).