Where to build your next sandcastle

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(1)INAUGURAL LECTURE 10 NOVEMBER 2016. WHERE TO BUILD YOUR NEXT SANDCASTLE PROF. DR. ROSHANKA RANASINGHE.

(2) PROF. DR. ROSHANKA RANASINGHE.

(3) WHERE TO BUILD YOUR NEXT SANDCASTLE. Inaugural lecture marking the commencement of the position as professor of Climate change impacts and Coastal risk at the Faculty of Engineering Technology at the University of Twente on Thursday 10 November 2016 by. PROF. DR. ROSHANKA RANASINGHE.

(4) COLOFON Prof. dr. Roshanka Ranasinghe (2016) © Prof. dr. Roshanka Ranasinghe 2016 All rights reserved. No parts of this publication may be reproduced by print, photocopy, stored in a retrieval system or transmitted by any means without the written permission of the author. November 2016.

(5) 3. WHERE TO BUILD YOUR NEXT SANDCASTLE “Look again at that dot. That's here. That's home. That's us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every hopeful child, inventor and explorer, every teacher of morals, every corrupt politician, every "superstar," every "supreme leader," every saint and sinner in the history of our species lived there – on a mote of dust suspended in a sunbeam.” Carl Sagan, 1997. Pale Blue Dot. PREAMBLE Dear Rectors of the University of Twente and UNESCO-IHE, Professors from the University of Twente, UNESCO-IHE, Utrecht University and Delft University of Technology, colleagues, students and friends, ladies and gentlemen, thank you for being here today. Like many of the coastal engineers here today, I started off my research career with process based coastal modelling. As part of my PhD (University of Western Australia), I developed a quasi 3D coastal morphodynamic model to investigate the seasonal closure of tidal inlets. Following that, as part of my Postdoc (Oregon state University, USA and University of New South Wales, Australia), I added sediment transport and morphodynamic capabilities to an existing 2DH wave/flow model to investigate mediumterm rip morphodynamics. After my Postdoc, in 2000, I joined the New South Wales (Australia) government sector as a coastal modeller, with the primary aim of experiencing first-hand how science was used in frontline coastal zone management and planning..

(6) 4. After a couple of months at my government employer, one of my new colleagues came over to me, showed me a photograph of a precariously positioned house near Byron bay in Northern New South Wales and asked me “Can you use your models and tell me the chance (i.e. probability) of this house falling into the sea if we were to get a 1:100 year storm tomorrow”? Given the capabilities of the then available models and computing power (this was the time of the single Pentium chip PCs, just moving on from 486s), I couldn’t see how I could provide a reliable answer to his question, so I had to send him away disappointed. A few months later, the IPCC AR3 was released and that generated a lot of concern in Australia, and especially in New South Wales (NSW), about potential climate change impacts along the 1000 km NSW coastline. So the same colleague was again back at my desk, this time with a photo of a highly developed embayed beach close to Sydney. This time his question was, given the full range of sea level projections provided by IPCC AR3 whether we could predict the chance (i.e. probability) of the first row of (very expensive) houses along this beach being lost to the sea within the next 50 years. Again, given the technology and resources available at that time I had to give him a negative answer. Mind you, I was using Danish software at the time... These experiences, followed in the next couple of years by a lot of similar discussions with frontline coastal engineers, managers and planners from around Australia and abroad made me realise that a key need for effective on-the-ground coastal zone management and planning was modelling methods and tools that can provide fully probabilistic estimates of coastal hazards, both under present conditions and under future climate change modified conditions. This realization underpinned my research efforts over the last decade or so, leading to a number of interesting developments particularly on the topic of assessing climate change impacts on coasts, and so I am standing here in front of you today..

(7) 5. GLOBAL CLIMATE CHANGE Consistent observations of increasing global average surface and ocean temperatures, sea level rise (SLR), widespread melting of snow and ice, widespread changes in precipitation amounts, ocean salinity, wind patterns and aspects of extreme weather including droughts, heavy precipitation, heat waves and the intensity of tropical cyclones have provided unequivocal evidence that the world’s climate is changing due to the enhanced greenhouse effect (IPCC, 2013). Measurements since the 1960’s have shown that the average temperature of the world’s oceans has increased to depths of at least 3000 m and that the ocean has been absorbing over 80% of the heat added to the climate system, resulting in thermal expansion of seawater which contributes to global sea level rise (SLR). Global averaged sea level rise for the 20th century is estimated to be 0.17 m. Since the early 1990s however, the rate of SLR is estimated to have increased to about 3 mm/yr (Leuliette et al., 2004; Church and White, 2011), while Church and White (2006; 2011) have shown that the rate of SLR continues to accelerate. The projections given by the IPCC (2013) indicate an increase of 1.1°C – 6.4°C in global average surface temperature by 2081-2100 relative to 19862005. For the same period, IPCC SLR projections range from 0.26 m to 0.98 m (Figure 1). However, IPCC (2013) also states that larger values of SLR cannot be excluded. Future tropical cyclones are expected to become more intense, with larger peak wind speeds and heavier precipitation. Extra-tropical storm tracks are expected to move poleward, with associated changes in wind, wave, and precipitation patterns..

(8) 6. Figure 1. IPCC projections of future global average temperature and sea level rise (Source: IPCC, 2013).. Projected climate change driven variations in mean sea level (i.e. SLR), wave conditions, storm surge, and riverflow will affect the coastal zone in many ways (Nicholls et al., 2007; Fitzgerald et al., 2008; Ranasinghe and Stive, 2009; Nicholls and Cazenave, 2010; Cazenave and Le Cozannet, 2013; Wong et al., 2014; Ranasinghe, 2016). On the other hand, the coastal zone is the most heavily populated and developed land zone in the world (Small and Nicholls, 2003; Valiela, 2006; Hallegate et al., 2013; Hinkel et al., 2013). The insatiable human attraction to the coast has resulted in rapid expansion in settlements, urbanisation, infrastructure, economic activities and tourism. Small and Nicholls (2003) estimated that, in 1990, 23% of the global population lived within 100 km and less than 100 m above sea level..

(9) 7. Of the world’s 20 megacities of over 10 million inhabitants each, 15 are in low elevation coastal zones (LECZs) (Figure 2). The economic losses due to flooding alone in coastal cities is expected to be around US $ 1 Trillion by 2050 (Hallegatte et al., 2013). The costs of forced migration due to just SLR driven coastal erosion over the 21st century could also be as high as US $ 1 Trillion (Hinkel et al., 2013). The massive global scale socio-economic impacts of climate change (hereafter CC) effects on coasts are discussed in detail by, among others, Stern (2007), Arkema et al. (2013), Kron et al., (2013), McNamara and Keeler (2013), Johnson et al. (2015), Brown et al. (2016).. Figure 2. Megacities in low elevation coastal zones (Source: World Development Report 2010: Development and Climate Change. Washington, DC: The World Bank)..

(10) 8. CLIMATE CHANGE IMPACTS ON OPEN SANDY COASTS The world's coastlines can be broadly divided into two main sub-systems: Open coasts and Deltaic coasts. Open coasts comprise sandy coasts, cliffed coasts and gravel beaches as well as estuaries (i.e. inlet-interrupted coasts) while Deltaic coasts include estuaries and mostly consist of muddy or silt-sand coasts. My focus is on open sandy coasts, which comprise up to 40% of world's coastline (Bird, 1996) and are subject to a very high level of human utilisation. Sandy beaches are highly dynamic and continually adjust to subtle changes in hydrodynamic forcing, and the feedback between hydrodynamics and morphology (i.e. morphodynamics) is highly non-linear and scale dependent, both temporally and spatially (Stive et al., 2002). The exact response of a particular stretch of the coast to a given set of environmental forcing (e.g. mean water level, storm waves, storm surge) will depend to a large extent on site-specific geomorphic features. Therefore, the composite physical impact of CC at local scale is impossible to determine without a comprehensive local scale study which takes into account site specific nonlinear forcing-response mechanisms and geomorphology. Nevertheless, the potential first order CC driven physical impacts that maybe felt along the world’s sandy coastlines can be summarised as shown in Table 1. Note that the various CC impacts listed in Table 1 will manifest themselves at different time scales. The various impacts are here classified into episodic (time scale ~ hours-days), medium-term (time scale ~ year - decade), and long-term (time scale ~ decades - century)..

(11) 9. Table 1: Potential first order climate change driven physical impacts on open sandy coasts. Potential impact. Process time scale*. Main drivers. More/less frequent and/or more/less severe episodic coastal inundation. Episodic. Sea level rise, changes in intensity and/or frequency of storms, changes in storm surge. Increased/decreased episodic storm erosion of beaches and dunes. Episodic. Changes in intensity and/or frequency of storms, changes in storm surge, changes in storm wave characteristics. More/less frequent (or previously unexperienced) episodic formation and closure of small tidal inlets. Episodic. Changes in storm surge, changes in intensity/frequency of extreme riverflow events, changes in storm wave characteristics. More/less breaching of Barrier islands. Episodic. Sea level rise, changes in intensity and/or frequency of storms, changes in storm surge. Sustained erosion/accretion due to permanent re-alignment of embayed beaches. Medium-term. Changes in mean offshore wave direction. More/less elongation of (updrift) barrier islands and subsequent changes in barrier inlet geometry. Medium-term. Changes in mean offshore wave conditions. Sustained changes in cross-section/ stability of mainland inlets. Medium/ Long-term. Sea level rise, changes in mean offshore wave conditions, changes in annual riverflow. Permanent inundation of low lying land and increased flood height. Long-term. Sea level rise. Chronic coastline recession (uninterrupted coasts). Long-term. Sea level rise, changes in mean offshore wave conditions. Chronic coastline recession (inlet interrupted coasts). Long-term. Sea level rise, changes in riverflow, changes in fluvial sand supply, changes in mean offshore wave conditions. Barrier Island thinning. Long-term. Sea level rise. Barrier rollover. Long-term. Sea level rise, changes in storm surge, changes in storm wave characteristics. * Time scale definitions: Episodic ~ hours-days, medium-term ~ year - decade, and long-term ( ~ decades - century).

(12) 10. As shown in Table 1, CC impacts on sandy coasts will mainly be governed by CC driven variations in mean sea level (i.e.SLR), wave conditions, storm surges and riverflow. A synthesis of what is currently known about the potential impacts of these four environmental drivers is provided below.. SEA LEVEL RISE Due to the very slow nature of SLR, physical impacts due to SLR will manifest themselves as long-term impacts (~50-100 yrs time scale). Along most sandy coasts, SLR is likely to result in permanent inundation of unprotected low-lying land and more frequent/intense episodic coastal inundation when CC modified wave conditions and storm surge act in combination with SLR. Both permanent and episodic inundation will be exacerbated at locations that are subject to land subsidence. Another well known impact of SLR is chronic (i.e. long-term) coastline retreat (recession). The commonly used Bruun rule (Bruun, 1962) predicts an upward and landward movement of the coastal profile (Bruun effect) in response to SLR, suggesting a recession of 50-100 times the SLR amount along most sandy coastlines, depending on the average shoreface slope. However, the exact nature of the local response will be governed by coastal cell sediment budgets (Cowell et al., 2003). Another SLR induced process driving chronic recession along inletinterrupted coasts is the SLR induced infilling of estuaries and lagoons (basin infilling). Basin infilling occurs due to the SLR driven increase in the basin volume below mean water level. This additional volume is known as 'accommodation space'. In response to this geomorphic change, the basin, which always tries to maintain an equilibrium volume, will start importing sediment from offshore to raise the basin bed level such that the basin volume remains at its pre-SLR value. Depending on sediment availability, the basin morphology will reach equilibrium when a sand volume equivalent to the SLR induced accommodation space is imported into the basin (Figure 3). The basin infill volume is usually borrowed from the adjacent coastline and/or the ebb tidal delta, leading to additional coastline recession (on top of the above described Bruun effect) and/or depletion of the ebb delta (Stive and Wang, 2003; Ranasinghe et al., 2013)..

(13) 11. Figure 3. Schematic diagram depicting the concept of SLR induced basin infilling (From: Ranasinghe et al., 2013).. At estuaries/lagoons backed by salt marshes, SLR will increase the estuary/ lagoon surface area and thus the tidal prism. This will inevitably change the inlet cross-section area (O'Brien, 1931) and possibly inlet stability (Bruun, 1978), leading to profound changes in hydrodynamics and sediment transport in the nearshore zone and estuary/lagoon dynamics such as ebb/ flood delta evolution and estuarine mixing, flushing, and water quality. On Barrier island coasts, SLR will promote barrier thinning, and, in combination with enhanced storm waves and/or storm surge, also barrier overwash and subsequent rollover (Fitzgerald et al., 2013; Moore et al., 2014; Duran Vinent and Moore, 2015). SLR may also decrease the effectiveness of existing coastal protection structures (e.g. overtopping of breakwaters, groynes, seawalls, dykes). In extreme cases an existing effective coastal protection structure might turn into a coastal erosion hazard due to SLR. One example of such a situation is when a currently emerged structure (usually placed close to the shoreline for maximum beach widening) becomes submerged due to SLR. While an emerged breakwater placed closed to shoreline will almost guarantee coastal protection (Silvester and Hsu, 1997), a shallow submerged breakwater placed closed to the shoreline could result in significant erosion of the shoreline in the lee of the structure (Ranasinghe et al., 2006, 2010)..

(14) 12. AVERAGE WAVE CONDITIONS Following a comprehensive multi-model ensemble study, Hemer et al. (2013) have shown that CC will result in significant changes in the average annual wave climate around the world (Figure 4). These future variations in average wave climate could lead to significant coastal impacts. Any CC driven variation in average wave direction could lead to increased erosion on the downdrift side of embayed beaches and accretion on their updrift side (Slott et al., 2006, Ratliff and Murray, 2014), resulting in permanent re-alignment of the mean orientation of these beaches (over and above the beach oscillation/rotation due to climate variability commonly experienced at embayed beaches). This impact will most likely manifest itself over a decade or two (medium-term). Of particular relevance to embayed beaches located along the Pacific coast is the ENSO phenomenon which has been firmly linked to the cyclic rotation of these beaches via the annual wave climate (Ranasinghe et al., 2004; Harley et al., 2011; Barnard et al., 2015). Thus, any CC driven variations in the ENSO phenomenon, and the associated variations of wave conditions, are likely to result in changes in the magnitude and frequency of this cyclic rotation phenomenon leading to more intense, more frequent erosion/accretion cycles on the many embayed beaches found on both sides of the Pacific Ocean..

(15) 13. Figure 4a. Projected future changes in significant wave height. (a) annual mean significant Hs for the present (~1979-2009). (b) projected changes in annual mean Hs for the future (~2070-2100) relative to the present (~1979-2009) (% change) (from Hemer et al., 2013).. Figure 4b. Projected future changes in mean wave period. (a) annual mean significant TM for the present (~1979-2009). (b) projected changes in annual mean TM for the future (~2070-2100) relative to the present (~1979-2009) (absolute change (s)) (from Hemer et al., 2013).. Figure 4c. Projected future changes in mean wave direction. (a) annual mean wave direction θ (degrees clockwise M from North) for the present (~1979-2009). (b) projected changes in annual mean wave direction θ for the future M (~2070-2100) relative to the present (~1979-2009) (absolute change, degrees clockwise). Vectors in (b) indicate the directions in the left colour bar. Colours indicate the magnitude of projected change following to the right colour bar (from Hemer et al., 2013)..

(16) 14. Any CC driven variations in average wave direction and/or height would directly be translated into corresponding changes in annual longshore sediment transports. This will result in either enhancing or retarding contemporary downdrift barrier island elongation rates, with a corresponding narrowing or widening of the associated barrier inlets (at least temporarily) (Fitzgerald et al., 2008, 2013). Changes to average wave direction and/or height could also have a medium-term effect on the stability of, especially, the thousands of small bar built tidal inlets (STIs) located in wave dominated, microtidal environments (O’Brien, 1931; Bruun, 1978; Duong et al., 2016). STIs may be permanently open and fixed in location, permanently open and migrating alongshore, or fixed in location but seasonally/intermittently closed. At such systems, if CC driven changes in average wave characteristics are such that the longshore current increases (thus increasing longshore sediment transport rates), a presently permanently open STI may close off or turn into a seasonally/ intermittently open inlet. This is particularly likely at river influenced systems (i.e. systems where mean tidal discharge (m3/s)/river discharge (m3/s) < 20 (Bruun, 1978; Powell et al., 2006) when a CC driven decrease in riverflow into the estuary/lagoon is combined with an increase in longshore current. Conversely, if the longshore sediment transport decreases, a currently seasonally/intermittently open STI could turn into a permanently open STI, particularly if combined with a concurrent CC driven increase in riverflow. CC driven variations in longshore sediment transport could also result in inlet migration and their subsequent relocation (Duong et al., 2016).. STORMS AND STORM SURGE CC is also expected to affect storm wave characteristics and storm surges (Nicholls et al., 2007; Sterl et al., 2009; Hemer et al., 2012). An increase in the frequency of storm occurrence and/or storm intensity could result in more severe episodic coastal erosion. The situation will be further exacerbated by a concurrent increase in storm surge. Indeed, increased storm erosion may well have a more damaging coastal impact than the slow gradual erosion due to SLR. Coastal setback lines that are presently based only on, for example, the 1 in 100 year storm event extrapolated from historical data, will need to be re-evaluated using future projected storm and surge characteristics. The combination of SLR, increased storm.

(17) 15. wave height, and increased storm surge will also result in more instances of episodic inundation due to dune/barrier overwash (either by runup overtopping or dune/barrier overflow). In extreme cases of dune overwash, the dune/barrier may breach and be completely destroyed (Donnelly et al., 2006). This will present major threats to coastal communities located in low lying coastal zones that depend on the stability of coastal dunes/barrier as a primary defense mechanism. Furthermore, increases in storm wave heights, storm occurrence frequency and/or storm surge might render existing coastal protection structures such as offshore breakwaters and seawalls ineffective. CC driven changes in storm occurrence/intensity and/or storm surges may either close existing STIs and/or create new inlets by breaching sand bars that separate the estuary/lagoon from the ocean. Breaching of new inlets is particularly likely when storm surges are combined with extreme riverflow events at river influenced STI systems. As CC is expected to result in intensifying both extreme storm surges and extreme rainfall/runoff events in some parts of the world, breaching of new inlets may become more frequent at such river influenced systems. Closing of an existing inlet and/or breaching of new inlets will have massive implications on the tidal prism and hence water exchange between the ocean and the estuary/ lagoon, which in turn will affect all estuarine processes (mixing, flushing, circulation, water quality).. RIVERFLOW IPCC (2013) projections indicate that CC may result in significant increases/ decreases of annual riverflows around the world, in some places exceeding 40%. At river influenced inlet-estuary systems, when CC results in a decrease (increase) of riverflow and/or fluvial sand supply into the estuary/ lagoon, the long-term recession of the coastline adjacent to the inlet will increase (decrease) due to the additional (reduced) demand of sand by the basin to maintain equilibrium velocities within the estuary/lagoon (Ranasinghe et al., 2013). Furthermore, a decrease in annual riverflow may result in the medium-term effect of stable STIs becoming unstable (alongshore migration and/or intermittent closure) (Duong et al., 2016), while an increase in riverflow may have the opposite effect (Slinger et al., 1994; Ranasinghe and Pattiaratchi, 1999; Duong et al., 2016)..

(18) 16. As is evident from the above discussion, while we do have a reasonably good qualitative understanding of how CC may affect open sandy coasts of the world, assessing the magnitude of these impacts (how much?), especially at the scale of local governance units (LGUs), remains a major challenge. I will focus on this aspect hereon.. QUANTIFYING CLIMATE CHANGE DRIVEN PHYSICAL IMPACTS ON COASTS AT LOCAL SCALE Global and/or national scale assessments of CC impacts on coasts may be undertaken with reduced complexity (or scale aggregated) models forced directly by coarse grid output from IPCC Global Climate Models (GCMs with typical spatial resolutions of about 200 km) (e.g. Hinkel et al., 2013). However, the development of effective CC adaptation strategies at LGU level requires the quantification of CC impacts at much higher spatial resolutions, typically at resolutions of 10 km or higher. Theoretically, a carefully selected and validated suite of mathematical models, operating at various spatio-temporal resolutions, should be able to quantify all of the above mentioned CC impacts at local scale ( < 10 km length scales). However, there are large uncertainties associated with not only the various models, but also with the forcing (i.e. greenhouse gas (GHG) emissions scenario uncertainty). Any conscientious effort to assess CC impacts on coasts should therefore include the quantification of the range of uncertainty associated with model predictions. This can be achieved via ensemble modeling. A thorough local scale coastal CC impacts study would ideally follow the broad structure shown in Figure 5 (Ruessink and Ranasinghe, 2014; Ranasinghe, 2016). This ensemble modelling approach will provide a number of different projections of the coastal impact of interest. The range of projections will account for future GHG scenario uncertainty, GCM (Global Climate Model) uncertainty, and RCM (Regional Climate Model).

(19) 17. uncertainty. If required, regional/catchment scale model and coastal impact model uncertainty can also be included in this approach, albeit at significant computing cost. The range of coastal impact projections thus obtained can then be statistically analysed to obtain not only a best estimate of coastal impacts (i.e. expected value) but also the range of uncertainty associated with the projections. The biggest stumbling block with respect to implementing the above approach lies in the last step: Coastal impact modelling. As highlighted above in Table 1, CC impacts on sandy coasts will manifest themselves at various spatio-temporal scales (~10 m to ~100 km and days to centuries). It should also be noted that impacts manifesting themselves at different spatio-temporal scales may have profound inter-dependencies. For example, the coastal response to a given storm (episodic impact) may be quite different on a year 2100 coastal profile that has already adjusted to ~1m of SLR (long-term impact) to that on a contemporary profile; or the rate of coastline recession (long-term impact) adjacent to an inlet with a depleted ebb delta (medium-term impact) may be different to that adjacent to a contemporary inlet with a large ebb delta. Thus, for CC impact assessment on sandy coasts, a coastal impact model should ideally be able to concurrently simulate the physical processes occurring at different spatio-temporal scales, including inter-scale morphodynamics. However, presently available models are generally only able to simulate processes occurring at one main spatio-temporal scale or the other (Le Cozannet et al., 2014). For example, the profile models SBEACH and XBeach are able to simulate beach/dune response to storms occurring at spatio-temporal scales of metres-days (Larson, 1988; Larson and Kraus, 1989; Roelvink et al., 2009; de Winter et al., 2015); the coastal area model Delft3D (Lesser et al., 2004) is able to adequately simulate morphological change due to concurrent tides, waves and currents at spatio-temporal scales of about 5 km-5 yrs (Lesser, 2009); the coastline models UNIBEST-CL (Szmytkiewicz et al., 2000; Ruggiero et al., 2010) and GENESIS (Hanson, 1989; Hoan et al., 2010) can simulate coastline change due to longshore transport gradients over length scales of ~100 km and time scales of ~ 100 yrs. While there are ongoing efforts to combine these different types of models to seamlessly simulate multi-scale coastal evolution, a generally applicable multi-scale model has not yet been successfully developed. However, even if/when such a multi-scale process based model were to be available, the inevitably heavy computational costs associated will most likely preclude the multiple.

(20) 18. simulations required for robust quantification of the uncertainty cascade associated with CC impact assessments (see Figure 5) and/or quantitative coastal risk assessments.. Figure 5. Modelling framework for a local scale climate change impact quantification study on sandy coasts (From Ruessink and Ranasinghe, 2014)..

(21) 19. THE RISK SIDE OF THE COIN Up to this point, I have only discussed the hazard quantification side of the problem. The other side of the coin is the potential consequence that may result from the hazard. When considered together, the hazard and consequence represents the associated risk. Knowingly or unknowingly, we all make risk based decisions throughout our life. A classic example from our childhood is our quest to build sandcastles that at least last the duration of our visits to the beach. By instinct, we try to build the sandcastle at the highest possible location where wet sand can be found. When we grow older, we still tend to favour risk averse decisions, but the scales (and the stakes) get much bigger. The sandcastle is replaced by multi-million Euro houses or infrastructure, the desired lifespan of the construction changes from a few hours to decades, and the threats posed by surf beat and the rising tide are replaced by that due to storm surges and SLR. The combination of coastal CC impacts and their effect on the ever increasing human utilization of the coastal zone will invariably result in increasing coastal risk in the coming decades. But, while the economic damage (potential consequence) that can be caused by CC driven coastal inundation and erosion (potential hazard) can be very high, foregoing land-use opportunities in coastal regions is also costly (opportunity cost). Thus, a ‘zero risk’-policy could have severe economic consequences, while high risk policies could lead to risks that are unacceptable to society and individuals. Developing appropriate policies and strategies for land-use planning purposes is therefore a delicate balancing act. However, in the past, far reaching coastal management/planning decisions have been made based only a single extreme hazard estimate with no consideration given to the uncertainty in the hazard nor the potential consequences (damage) caused by the hazard. More often than not, this has led to very conservative management/planning decisions which, ultimately, have resulted in forgoing lucrative land-use opportunities (Wainwright et al., 2015; Jongejan et al., 2016). While risk informed management/planning has been common practice in spheres such as flood protection, inexplicably, this way of thinking has only recently emerged in coastal zone management/planning (Kunreuther et al., 2013; Villatoro et al., 2014; Zanuttigh, 2014; PenningRowsell et al., 2014; van Dongeren et al., 2014; Wainwright et al., 2015)..

(22) 20. To avoid future losses, may they be due to coastal hazards or sub-optimal land use, it is imperative that risk informed and sustainable coastal planning/management strategies are implemented sooner rather than later. This requires comprehensive coastal risk assessments which combine state-of-the-art consequence (or damage) modelling and coastal hazard modelling that can be used to produce risk maps or contours along a given coastline (Figure 6). Apart from being of crucial importance to coastal managers/planners, this type of risk quantification will also be invaluable to the insurance and re-insurance industries for determining optimal insurance premiums, which will undoubtedly have a follow-on effect on coastal property values. However, generally applicable coastal risk assessment approaches have not been developed to date. This is mainly due to the lack of numerical models that can provide reliable, probabilistic estimates of potential CC driven coastal hazards at spatio-temporal scales relevant for coastal zone planning/management (tens of kilometres and decades).. Figure 6. Combination of probabilistic coastal hazard estimates and spatial information on potential consequences (value at risk) to develop coastal risk maps (Courtesy: Ruben Jongejan (JongejanRMC))..

(23) 21. RESEARCH PROGRAM ON CLIMATE CHANGE IMPACTS AND COASTAL RISK AT UNESCO-IHE/UNIV. OF TWENTE To address the above mentioned knowledge gaps and needs, over the last few years, I have been implementing a research program on Climate Change impacts and Coastal Risk (CC&CR) at UNESCO-IHE and University of Twente, in collaboration with key Dutch and international partners and stakeholders (http://climate-change-and-coastal-risks.unesco-ihe.org/). The overarching objective of the CC & CR research program is “to generate new fundamental scientific knowledge and formulate theoretical and modelling concepts which will enable the development of innovative CC driven coastal risk assessment methods that are underpinned by interdisciplinary science”. The research program comprises three main research lines: (a) Climate change impacts on coasts: The Science; (b) Numerical modelling of CC driven coastal hazards, and (c) Innovations in coastal risk assessment. Each of these research lines are briefly described below.. RESEARCH LINE #1: CLIMATE CHANGE IMPACTS ON COASTS: THE SCIENCE As discussed earlier, we already have quite a good qualitative understanding of how CC may affect the world’s sandy coasts. But there are still many unknowns. For example: - Will sea level rise (SLR) driven coastline recession on open coasts be negated due to modified CC driven variations in average wave conditions or be enhanced by increased storm erosion? - How will CC driven variations in the catchment (e.g. land cover, rainfall) affect coastlines adjacent to river/estuary entrances? - How will coastal inlets respond to the combined effects of CC driven variations in riverflow, waves and MSL? Will the potentially inlet destabilising effect of one process (e.g. increased wave driven longshore sediment transport) be balanced/overridden by another (e.g. increased riverflow)?.

(24) 22. - Will SLR driven sediment import into large estuaries be sufficient to prevent expansive marginal tidal flats from being completely drowned? - What will govern the orientation of embayed beaches more: CC driven variations in average wave conditions or variations in storm conditions/ frequency? This research line will focus on finding answers to such fundamental questions using a combination of methods including; synthesis and reanalysis of existing data and literature, and strategic application of existing process based and reduced complexity models. Once such fundamental questions are answered, numerical models and/or modelling approaches that can reliably predict coastal CC impacts may be developed. Research we have conducted over the past few years on this topic are already providing some interesting insights. A recently concluded PhD study by Dr. Trang Duong has shown that even under the most extreme CC forcing, small tidal inlets (STIs) are unlikely to change their type (permanently open and locationally stable, permanently open and alongshore migrating, locationally stable but intermittently closing). However, CC will have a significant effect on inlet stability, in most cases reducing inlet stability (Figure 7); and the dominant CC effect governing this variation in inlet stability appears to be future variations in wave direction, not SLR as commonly believed (Figure 8). Another study undertaken in collaboration with Dr. Dave Callaghan and Dr. David Wainwright from University of Queensland (Australia), and Dr. Fan Li from Yangzhou University (China) has shown that while SLR and storm erosion both contribute to long term coastline recession, storm erosion has an increasing effect on recession at lower exceedance probabilities (< 0.3 exceedance at Noordwijk beach and < 0.025 exceedance at Narrabeen beach) (Figure 9). At present, two PhD students, Mr. Janaka Bamunawala and Ms. Jeewanthi Sirisena at UNESCO-IHE and Twente University, in cooperation with Assoc. Prof Shreedhar Maskey (UNESCO-IHE), are investigating (using both process based and reduced complexity modelling approaches) how CC driven and anthropogenic changes in the catchment could affect sediment fluxes into estuaries/ agoons and the subsequent effect that would have on adjacent coastlines..

(25) 23. Figure 7. Predicted increase (doubling) in annual inlet migration distance at an alongshore migrating small tidal inlet (Kalutara lagoon, Sri Lanka: under contemporary forcing conditions (top) and CC modified year 2100 forcing conditions (bottom). The black line indicates the initial shoreline position (From Duong, 2015)..

(26) 24. Figure 8. Relative contributions of CC forcings (SLR, riverflow, wave height and wave period) to the predicted reduction in the stability of a stable small tidal inlet (Negombo lagoon, Sri Lanka). S1 is with all CC driven variations in all forcings included. S2, S5, and S6 are without any CC induced variation in wave direction (while CC driven variations in all other forcings are included). S7 is without SLR, with variations in all other forcings included. (From Duong, 2015).. Figure 9. Ratio between the relative contributions of Storms and SLR to total recession (by 2100, relative to 1990) at Narrabeen beach, Sydney and Noordwijk beach, The Netherlands. Values greater (smaller) than 1 indicate a dominant contribution from storms (SLR).. RESEARCH LINE #2: NUMERICAL MODELLING OF CC DRIVEN COASTAL HAZARDS At present, physical impacts of CC on coasts are commonly estimated via: - Simple and loosely physics based principles such as the Bruun Rule (Bruun, 1962) or the Pelnard-Considère equation (Zacharioudaki and Reeve, 2011) - Highly scale-aggregated models that contain limited process descriptions (e.g. ASMITA (Stive and Wang, 2003), CASCADE (Larson et al., 2002), CASCADE-CS (Larson et al., 2016), and.

(27) 25. - Integration of micro scale processes over long periods of time using process based morphodynamic models (Roelvink, 2006; van der Wegen, 2013). It is now known that simplistic but easy-to-use techniques such as the Bruun rule are unlikely to produce robust results (Pilkey and Cooper, 2004; Cooper and Pilkey, 2004; Ranasinghe and Stive, 2009). Highly aggregated models such as ASMITA and CASCADE do not provide much insight into processes governing morphological evolution. Time and space integration of micro-scale processes via process based models has also failed to produce sufficiently accurate predictions of long term coastal change due to the combined effect of waves, tides, and riverflow (Lesser, 2009). Allow me here to digress a little on the topic of process based modelling of CC impacts on coasts, as there is a strong focus on this type of modelling in The Netherlands. As CC impacts on sandy coasts will manifest themselves at various different spatio-temporal scales (~10 m to ~100 km and days to centuries), ideally what is required for comprehensive CC impact assessments is a multi-scale coastal impact model that concurrently simulates the various physical processes occurring at different spatio-temporal scales, including inter-scale morphodynamics. To simulate coastal hydrodynamics relevant for episodic, medium-term, and long-term morpodynamics, such a model should incorporate both cross-shore (vertically non-uniform) and longshore (mostly vertically uniform) hydrodynamics. Thus, the model should be a coastal area model with at least quasi-3D hydrodynamics. Quasi-3D representation of nearshore hydrodynamics has already been achieved (see for example, Ranasinghe et al., 1999; Reniers et al., 2004). The challenge, however, lies in modelling morphological change due to the combined effect of waves and currents at time scales greater than a few years (De Vriend et al., 1993; Lesser, 2009). Since the 1990s, there have been numerous attempts, using very different approaches, to overcome this problem (De Vriend et al., 1993; Dabees and Kamphuis, 2000; Hanson et al., 2003; Roelvink, 2006). However, all of these attempts have only met with limited success. In the 80s and 90s, computing power was a major limitation for long term simulations. In order to keep the simulations within reasonable time limits, therefore the computational grids (wave/flow) could not be much bigger.

(28) 26. than the study area. Invariably this resulted in the propagation of boundary errors (in the form of instabilities) into the area of interest sooner or later, eventually leading to nonsensical morphodynamic predictions. With the rapid increase of computational power in the last decade or so, now it is possible to have larger and larger computational domains that extend for kilometers beyond the area of interest. While this allows us to undertake decades long wave-tide driven simulation without any instabilities propagating into the study area, still, such long term simulations (with a reasonably reduced wave climate) takes several days to run. A good example is the 3 year simulation of the Sand engine recently undertaken by Ir. Arjen Luijendijk at Deltares/DUT (Figure 10). This simulation, which used 12 different wave conditions, took 3 days to run on the Deltares HP cluster. While this is a huge improvement from what was possible 10 years ago, a 100 year simulation using this straightforward modelling approach, even with reasonable morphological upscaling with the MORFAC approach (Roelvink, 2006; Ranasinghe et al., 2011), still seems very far away.. Figure 10. Successful 3 year morphodynamic simulation of the Sand Engine, The Netherlands (Courtesy: Arjen Luijendijk (Deltares), based on Luijendijk et al., in press, Coastal Engineering)..

(29) 27. Another solution, which is currently being attempted is the ‘automatic gear shift’ approach using the Basic Model Interface (BMI). When undertaking coastal morphodynamic simulations, typically the relevant processes are resolved at the highest level of representation and accuracy. For example, storm effects should typically be modelled in infra-gravity wave mode, but during the fairweather conditions that prevail after the storm, such functionality does not have added value while it consumes significant computational time. With the ‘automatic gear shift’ approach, the model can automatically change ‘transmission’ depending on the forcing conditions (for example, differentiated by a pre-defined wave height threshold). The different ‘gears’ represent the complexity or level of process representation. A high speed gear (5) applies the most simple formulations combined with a large MORFAC, while a low speed gear (1) activates advanced model features (such as infra-gravity waves, temporal and spatial roughness predictor etc.) (Figure 11). This approach will facilitate optimal model accuracy at significantly reduced computational cost.. Figure 11. The currently in-development ‘automatic gear shift’ coastal morphodynamic modelling approach (Courtesy: Arjen Luijendijk (Deltares))..

(30) 28. Another option that Prof. Dano Roelvink and I have recently started to develop is a novel approach for 100 year long process based morphodynamic model simulations that draw from monthly cumulative distribution functions of wave and riverflow forcing (including CC effects), the ‘parallel online’ approach, and the MORFAC approach (with the possible introduction of a “CCFAC”). A key feature of this method will be the preservation of seasonal signals in wave/riverflow forcing that are commonly found in nature (e.g. in monsoonal environments) but are lost in contemporary input reduction techniques (Walstra et al., 2013; Benedet et al., 2016). A totally different solution might be to develop an entirely new morphodynamic modelling concept where a non-gridded approach is adopted to simulate morphological change. For example, in such an approach, quasi-3D hydrodynamics calculated on a traditional grid may be spatially aggregated over the significant morphological features that are of interest (ebb deltas, sand bars, channels, mounds, trenches etc.), and subsequently, the aggregated hydrodynamic forcing may be used with an appropriate scale-factor to 'move' and 'change' those bed features, at say, the time scale of a few tidal cycles. While these new ideas and developments might herald a new era in long term coastal morphodynamic modelling on a laptop computer, still the process based approach will probably preclude the multiple simulations required (for example, within a Montecarlo simulation) to derive fully probabilistic estimates of CC driven coastal change as required by risk based coastal zone management/planning frameworks. An effective approach to circumnavigate this problem is to develop physics based, yet simple and fast numerical models via scale-aggregation (i.e. reduced complexity models). This approach adopts simplified descriptions of fundamental system physics and delivers estimates of system response to forcing. It is a well-grounded and fast approach that lends itself to multiple simulations (thousands of simulations in minutes), enabling probabilistic estimates of system response. It is only very recently that some initial steps have been taken to develop such numerical models for CC impact assessment (Callaghan et al., 2008; Roscoe and Diermans, 2011; Ranasinghe et al., 2012; Ranasinghe et al., 2013; Duong, 2015)..

(31) 29. While this research line will experiment with developing novel ways to extend the application time scales of process based models, it will focus mostly on developing a suite of physics based reduced complexity models to derive probabilistic estimates of at least the 1st order coastal CC impacts (see Table 1), using the fundamental scientific process knowledge gained from Research Line #1. The probabilistic coastal hazard estimates obtained via these models will then facilitate the development of robust coastal risk assessment methods. Efforts over the past few years have already resulted in the development of a few novel reduced complexity models. The Probabilistic Coastline Recession model (Ranasinghe et al., 2012) is able to provide exceedance curves of coastline recession (at any time in the next 100 years) due to the combined effect of SLR and storm erosion, within a few minutes on a standard PC (Figure 12). The Scale-aggregated Model for Inlet-interrupted coasts (SMIC) (Ranasinghe et al., 2013) is able to provide 100 year estimates of coastline change adjacent to small tidal inlets due to the combined effect of SLR and CC driven variations in riverflow and fluvial sediment supply within a few seconds (Figure 13).. Figure 12. Exceedance probability curves of coastline recession over the 21st century obtained from the Probabilistic Coastline Recession (PCR) Model for Narrabeen beach, Australia (from Jongejan et al., 2016)..

(32) 30. Figure 13. Coastline recession adjacent to the Swan river inlet (Western Australia) by the end of the 21st century as predicted by the Bruun Rule (blue line) and the Scale aggregated Model for Inlet interrupted Coasts (SMIC) (red line) (based on Ranasinghe et al., 2013).. Janaka Bamunawala (PhD candidate at UNESCO-IHE/University of Twente), in cooperation with Dr. Ad van der Spek, Dr. Dirk-Jan Walstra, and Dr. Trang Duong (Deltares), is currently developing a more generically applicable version of SMIC (i.e. applicable to different types of inlet-estuary systems) that will also include better representation of the effects that upstream changes in the catchment (due to CC and/or human activities) could have on fluvial sediment loads. Dr. Trang Duong, as part of her PhD research, recently developed a reduced complexity model (RAPSTA) to obtain rapid assessments of CC driven temporal evolution of the stability of small tidal inlets. RAPSTA provides a 100 yr time series of inlet stability evolution within seconds. This model has now been embedded within a probabilistic framework to enable the quantification of forcing related uncertainty (Figure 14)..

(33) 31. Figure 14. Probabilistic assessment of CC driven variations in inlet stability (Negombo inlet, Sri Lanka) by the reduced complexity model RAPSTA. The solid line shows the expected value and 95% confidence limits are indicated by the shaded area (based on Duong, 2015).. RESEARCH LINE #3: INNOVATIONS IN COASTAL RISK ASSESSMENT The main thrust of this research line will be to adapt the approach used in The Netherlands for flood risk management since the 1950's (Van Dantzig, 1956) to suit coastal risk assessments. This approach essentially assumes that, in the presence of efficient risk-sharing arrangements, investments in risk mitigation can be evaluated through net present value computations with the cost of risk mitigation on the one hand, and expected loss (or: the actuarially fair insurance premium) on the other. The probability distribution of the coastal hazard will be derived by the application of appropriate probabilistic, numerical models developed in Research Line #2. This information can then be considered in conjunction with the cost distribution of the potential loss due to the hazard to optimise coastal risk; the target being the minimisation of the total cost curve, which is the sum of the value of expected loss and the cost of increasing coastal protection levels (Figure 15)..

(34) 32. Figure 15. The optimisation of flood protection: total cost equals the cost of dike heightening plus the present value of expected loss (after van Dantzig, 1956).. Coastal protection may be achieved via engineering structures such as breakwaters, groynes etc or via soft options such as beach or shoreface nourishments. Another commonly used approach to protect coastal communities and developments is the establishment of coastal setback lines. While setback lines have historically been determined using deterministic approaches, the emergence of probabilistic, reduced complexity models of coastal hazards now enables the determination of risk informed setback lines. The way in which risk optimisation can be used to define economically optimal coastal setback lines is schematically shown in Figure 16. One of the main objectives of this research line is to develop a user-friendly, generically applicable model which adopts the above mentioned risk quantification and optimisation principles to produce risk maps and optimal setback line positions at beaches around the world. In close collaboration with Dr. Dave Callaghan (University of Queensland), Dr. Ruben Jongejan (Jongejan RMC) and Dr. Ali Dastgheib (UNESCO-IHE), a research version (i.e. non-user friendly!) of such a model has now been developed. The model has been successfully demonstrated at Narrabeen beach, Sydney Australia (Figure 17) (Jongejan et al., 2016). Through an Asian Development.

(35) 33. bank funded project, we are now applying this modelling approach to assess coastal risk and determine EOSLs along the east coast of Sri Lanka for on-the-ground decision making. Also, Prof. Suzanne Hulscher (Univ. of Twente), Prof. Dano Roelvink (UNESCO-IHE) and I have recently submitted an EU proposal to apply this model in Senegal and Kenya via a consortium comprising University of Twente, Deltares and UNESCO-IHE, and several other European partners.. Figure 16. Determination of the Economically Optimal Coastal Setback Line (OESL) using risk optimisation. (Courtesy: Ruben Jongejan (JongejanRMC)).. Figure 17. Economic Risk map and OESL for Narrabeen beach, Sydney (for 2100) (from Jongejan et al., 2016)..

(36) 34. Apart from economic risk, this research line will also attempt to develop generic methods to quantify CC driven environmental risk in coastal areas. The complexity of economic valuation and quantifying ecosystem services in monetary terms has been a challenging issue for both economists and ecologists for a long time. Although, environmental loss and risk are generally scale dependent, most studies to date have opted for specific case studies and therefore the methods used and results of such studies are not directly applicable elsewhere. With the emergence of risk based coastal zone management frameworks, there is now an urgent need for robust and generic approaches to quantify the economic value of the environmental damage due to coastal flooding/erosion and associated risk. Mr. Abdi Mehvar, in cooperation with Dr. Ali Dastgheib and Dr. Erik de Ruyter (UNESCO-IHE) and Dr. Tatiana Filatova (Univ. of Twente), is making a valiant attempt at addressing this need within his PhD at UNESCO-IHE/ University of Twente by using a range of ecosystem service valuation methods such as the Market price method, contingent choice method (CCM), and Hedonic pricing at diverse coastal locations in Sri Lanka, Bangladesh and Indonesia. Once economic and/or environmental risk at a given location is quantified, informed options for risk reduction can be developed. The risk quantification approaches and tools developed in this research line could then be further utilised to assess the risk-reduction (in terms of Euros per year) a certain design measure may afford. This could be achieved by reexecuting the model train with the design measure in place. In this way, the cost/benefit of design risk-reduction measures can be compared and contrasted to arrive at an optimal solution. To be effective, however, the determination of coastal protection measures should take into account not only risk reduction, but also site specific geomorphic/ environmental conditions and social preferences. For example, it is unlikely that a Sand Engine type mega nourishment, which appears to disperse its sand during high energy storms (de Schipper et al., 2016; Luijendijk et al., in press), will work well in low wave energy environments. Similarly, in very high energy environments, such as monsoonal coasts where a large number of high energy storms occur in rapid succession over a matter of months, shoreface nourishments may simply disappear within a few months. Social acceptance of design coastal protection measures is another very important aspect that needs to be taken into account during the decision making process..

(37) 35. In the modern world, regardless of how successful a certain measure might be in practical terms, its “perceived success” will depend to a large extent on its social acceptance. While in some countries, ‘invisible’ measures such as shoreface nourishments may be socially more acceptable than unaesthetic engineering structures, in other countries, there might be a social preference for highly visible measures regardless of any resulting aesthetic impairment or even negative physical impacts in the long run.. CONCLUDING REMARKS Developments in numerical modelling over the last decade or so have given us the ability to obtain at least first-pass assessments of potential climate change driven physical impacts on coasts. However, when it comes to deriving fully probabilistic estimates of CC driven coastal hazards and quantitatively reliable assessments of coastal risk, while we have made a good start, there is still a long way to go. The bad news is it will take some time to fully crack this. The good news is that we seem to be on the right track. At this point in time, however, I am very happy to be in a position to answer “Yes” to both of the questions that my New South Wales government colleague asked me over a decade ago..

(38) 36. ACKNOWLEDGMENTS There are so many people who have over the years, knowingly or unknowingly, shaped my thinking, beliefs, knowledge and skill development, all of which have influenced the way in which my academic career evolved. These include family members, teachers and friends at Trinity College, Sri Lanka (where I obtained my entire school education), fellow students and lecturers at University of Peradeniya, Sri Lanka (where I obtained my undergraduate education), fellow postgraduate students and academics at the Centre for Water Research, University of Western Australia (where I obtained my PhD), colleagues at the Dept. of Environment and Climate Change, Australia (where I worked for 8 years), and postgraduate students and colleagues in Netherlands (where I have been since 2008). It is unpractical to thank all of these people here, and therefore, here, I will limit these words of thanks to those who have helped, guided and inspired me during my time in The Netherlands. First, I would like to thank Dano Roelvink and Marcel Stive for getting me to leave my (too) comfortable life in Australia and move to The Netherlands in 2008. It has been a mostly fun, and very fast, ride since then. Dano – thank you for encouraging me to pursue my somewhat unconventional modelling approaches and for your great enthusiasm to discuss (especially, over a few beers!) new modelling concepts and the inescapable details that keep getting in the way of innovation! But my biggest thanks to you is for sticking up for me when sometimes the chips were down, and for going way out of your way to ‘bat for me’. And Marcel, thank you for your hospitality when I first arrived in The Netherlands. Thank you also for introducing me to ‘big’ proposals, the many esoteric discussions on long term coastal evolution, and the opportunities to pursue my scientific dreams. Next, I would like to thank the Nomination Advisory Committees (NACs) of UNESCO-IHE (headed by Prof. Arthur Mynett) and the University of Twente (headed by Prof. Geert Dewulf), the Executive Board of the University of Twente (UT) and the Rectorate of UNESCO-IHE for approving my appointment as Professor of Climate change and Coastal Risk at both institutions. A very special thank you to Prof. Suzanne Hulscher for being the prime mover behind my appointment at University of Twente..

(39) 37. Thank you also to the members of the Academic board at UNESCO-IHE for welcoming me into your midst and for your cooperation over the last couple of years. Thank you to the AXA Research Fund (Paris) for granting my proposal for a Chair in Climate change and Coastal Risk. Everything else followed from that. Merci Beaucoup Isabelle Jubellin for all your help and guidance related to (and beyond) the AXA Chair. My heartfelt gratitude also to Greet Vink and Stefan Uhlenbrook for your excellent feedback when I was preparing the proposal. Dirk-Jan Waltsra, Ruben Jongejan, Arjen Luijendijk, and Ad van der Spek: thank you for your longstanding collaboration, friendship, and unwavering solidarity. DJ – your generosity, dependability and readiness to ‘fight the good fight’ enhances my belief in the general goodness of human beings. Thank you for being there, especially in trying times – at Moodz! Ruben – thank you for introducing me to your LSE brand of economic jargon and for your incredible wit. You can make me laugh even when I am in the darkest of moods. I look forward to continuing our unique tradition of only watching the worst American movies. Arjen – thank you for showing me that, in the right hands, (almost) anything is possible with Delft3D, and that Chocomel could in fact be a good chaser for beer, under some special circumstances! Ad – you have convinced me that “Holocene” is a not a bad word, and that indeed it is possible to find a common ground for a geologist and an engineer to work together effectively and amicably! Arthur Mynett – thank you for your rock solid support during the time you were Head of Water Science and Engineering (WSE) at UNESCO-IHE, and for the many interesting discussions over a late night Remy Martin! Your tenacity in ‘doing the right thing’ and ‘sticking up for the underdog’ inspires me. Ali Dastgheib – your readiness and ability to find workable solutions for management problems as Deputy Head of WSE, is refreshing and is the reason why many things have become possible over the last few years. Thank you for your ever-dependable support and quick actions..

(40) 38. PhD students past (Trang, Matthieu, Sierd, Joao, Lu, Fan, Dissa) and present (Abdi, Janaka, Jeewa and Hieu), thank you for giving me the opportunity to contribute to your passionate quest for new knowledge and insights. Your youthful idealism and curiosity keeps the fire burning in me, and your loyalty humbles me. Special thanks to Matt for accommodating my weakness for MJ, to Trang for embarrassing me into (reluctantly) embracing new technologies, and to Sierd for being my Merc advisor! To my immediate colleagues within the Chair groups of Coastal Science Engineering and Port Development (UNESCO-IHE) and Marine and Fluvial systems (UT) – a big thank you for the stimulating discussions, cooperation and the collegial atmosphere. Thanks also to the wonderful support staff at both organisations – nothing would be possible without your support. A special thank you to Gaetano Casale and Robert de Bruijn for helping me find a way to get past (may it be over, around, or under!) contractual and financial issues whenever they arose, and a very special thank you to Ewout Heeringa and Joyce Membre, for making every interaction an enjoyable one. To my colleagues from other Dutch Universities: Ad Reniers, Bas Jonkman, Mathijs Kok, Zheng Wang, Gerben Ruessink, Stefan Aarninkhof – thank you for your continued cooperation and collaboration. Thank you Klass Jan Bos and the Harbour, Coastal and Offshore Engineering section of Deltares for giving me the opportunity to contribute to very interesting R&D initiatives and international projects. Finally, a very big thank you to the two people who were instrumental in arranging this inaugural event with perfect precision: Anke Wigger-Groothuijs (UT) and Anique Karsten (UNESCO-IHE). Your professionalism and attention-to-detail are beyond awesome!.

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