of a few centimeters along the center line of the estuary and the northern parts of Tampa Bay are not affected at all.

Besides the EVA results, the maximum differences between the control and the scenario run and also the relative increases of wave heights have been calculated for each grid point (see **Figures 10A,B** respectively). Both plots confirm that changes in wave heights are only found in the Lower Tampa Bay area. Maximum differences between 1.5 and 2.0 m around the location of the barrier island could have a significant impact on the navigability since these increases are a doubling of the today's wave heights in this area. The eastern coast of Lower Tampa Bay would also see significant increases in wave heights (up to 300%). For the city Anna Maria, located on the barrier island south of Egmont Key, wave height increases of up to 13 cm are found.

Regarding the change in wave period increases are limited to the area described above. The loss of sheltering and the increased depth directly behind today's location of Egmont Key result in periods of 9 to 12 s in Lower Tampa Bay under scenario conditions where control runs show periods of 3 to 5 s. Wave directions are affected by the removal of Egmont Key as well, but only in close vicinity to the barrier island.

# 5. DISCUSSION AND CONCLUSIONS

The simulation of water levels and waves in the Tampa Bay with and without the barrier island Egmont Key shows that the island provides significant natural coastal protection. For extreme still

water levels the removal of Egmont Key from the model's bathymetry yielded higher return water levels in the northern parts of the estuary. The absence of the barrier that blocks about a third of the mouth of Tampa Bay allows westerly winds to generate more wind set-up in the estuary. The northern bay sections as well as the Manatee River are affected most due to the very shallow waters and narrow connections to Middle Tampa Bay that only allow a limited near-ground back flow. The extreme water level increases in the northern bay sections are expected to be larger than a decimeter in case of a 100-year event which is a change of about 10%. The affected areas are densely populated and developed with residential, commercial, and industrial infrastructure close to the waterfront. Smaller events that occur more frequently (e.g., 25-year return period) are also expected to increase when Egmont Key disappears. From a coastal protection perspective, it is thus reasonable to put effort in the maintenance of Egmont Key since large parts of the adjacent mainland is low-lying and therefore already vulnerable to extreme water levels and waves. An increase in extreme events, adding up on the existing level, would increase the ecological and economical risk.

Middle Tampa Bay shows only small increases in 25-year return levels and even a decrease in the 100-year water level. This can primarily be attributed to a change of the shape parameter of the extreme value distribution. In this area, under scenario conditions, relative increases of smaller events are larger than those of the most extreme events. Therefore, the EVA leads to smaller return levels, especially for longer return periods. We speculate that the most extreme events do not increase as much as the smaller events due to an increased back flow since the

FIGURE 10 | Maximum difference in significant wave height considering all simulated events (A) and the difference expressed as ratio between scenario and control run simulation (B).

FIGURE 11 | Comparison of the 15 largest events (by total still water level under control run conditions) in northern Tampa Bay. Yellow bars indicate storm surges during the tropical season (June through November), blue bars indicate all other events (e.g., winter storms). The tidal contribution to the water level is depicted as gray overlay. The total water level under scenario conditions is shown as dotted bar edges.

removal of the barrier island also enlarges the outflow crosssection.

The wave height assessment shows that the potential loss of Egmont Key has a significant impact on the waves in Lower Tampa Bay but overall the influence occurs locally. The effect of Egmont Key is limited to an area which extends approximately 10 km into the Bay, measured from today's location of the island. Increasing wave heights in this area can be attributed to the missing barrier which today shelters Lower Tampa Bay directly, and to the increased depth around the location of Egmont Key. Areas in the northern Tampa Bay are protected by the shallow waters of the estuary, which would dissipate most wave energy in case that Egmont Key completely disappears. The very local change in wave directions is also attributable to the increased depth. With the loss of Egmont Key and the increase in water depth local shallow water effects like refraction and diffraction do not appear anymore. Waves from the Gulf of Mexico enter Tampa Bay unchanged until the depth limitation decreases wave heights significantly.

The EVA has been conducted without distinguishing between event types. Tampa Bay is located in an area where tropical cyclones occur during summer and fall months. Therefore, separate analyses of tropical and extra-tropical events would be a reasonable approach, as described e.g., by Haigh et al. (2014). Furthermore, this would be necessary in case that the study aims at estimating absolute heights for return periods. The simplified method used in this study is feasible since no major tropical cyclone directly hit Tampa Bay within the time period of interest. **Figure 11** shows that tropical and extra-tropical events in Tampa Bay led to similar total still water levels. Possible changes from other extreme events that occured beyond the period of observation are not considered in this particular study but could also have an effect on the shape of the underlying extreme value distribution. Additionally the return levels are only used for the A-B-comparison and therefore the chosen simplified method leads to suitable results.

**Figure 11** also shows the tide-surge-ratio of the top events in Tampa Bay. Haigh et al. (2010) detected an underestimation for large return periods when using direct methods (i.e., conducting an EVA with the total water level signal instead of modeling and examining tide and surge components separately) in case that the tidal component is larger than twice the size of the non-tidal component. In this context, the large contribution of the surge

#### REFERENCES

component to the total still water levels in Tampa Bay confirms the applicability of the direct approach.

Regarding the analyzed processes, this study focuses on water levels and waves. Changes in estuarine circulations have been neglected but may also be significantly affected by a loss of Egmont Key. In particular, changes in the tidal prism, tidal currents, exchange circulation and flushing could occur. These changes are associated with alterations in the entire ecosystem and should be investigated in further studies.

Overall the complete removal of Egmont Key is a simplified approach and disregards other morphologic changes in the barrier island system which would probably occur simultaneously with an erosion of the island extending over several decades. Examples are coastline changes in Tampa Bay or at the Gulf coast near the mouth of the estuary, changes in depth due to sediment displacement, and the impact of sea level rise. Albeit using a worst-case scenario, the presented results show that Egmont Key significantly alters extreme events in Tampa Bay and that a detailed investigation of realistic scenarios is needed. Further studies could include the above-mentioned morphologic and hydrodynamic changes to improve the results. Furthermore, inundation of the low-lying islands and of the mainland during storm surges should be considered. Authorities and coastal managers could benefit from the results and use the findings to develop appropriate protection strategies for the Tampa Bay area.

#### AUTHOR CONTRIBUTIONS

AA, TW, and MU developed the concept. MU set up and ran the models, analyzed output data, and drafted the paper with the guidance of AA. SM provided fundamental model data. All authors revised and finalized the draft. ML and JJ supervised the research project and finally approved the paper.

#### ACKNOWLEDGMENTS

All analyses presented in this paper were part of the project The effect of eroding barrier islands on coastal flood risk and estuarine health (project ID 57052194), supported by the German Academic Exchange Service (DAAD) with funds of the German Federal Ministry of Education and Research (BMBF).

Paper. Gainesville, FL: Florida Sea Grant College Program, University of Florida.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Ulm, Arns, Wahl, Meyers, Luther and Jensen. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Uncertainties in Sandy Shorelines Evolution under the Bruun Rule Assumption

Gonéri Le Cozannet 1, 2 \*, Carlos Oliveros 1 , Bruno Castelle3 , Manuel Garcin1 , Déborah Idier 1 , Rodrigo Pedreros 1 and Jeremy Rohmer 1

1 Coastal Risks and Climate Change Unit, Risk and Prevention Department, BRGM (French Geological Survey), Orléans, France, 2 Laboratoire de Géographie Physique, Meudon, France, 3 Université de Bordeaux, CNRS, UMR EPOC, Pessac, France

In the current practice of sandy shoreline change assessments, the local sedimentary budget is evaluated using the sediment balance equation, that is, by summing the contributions of longshore and cross-shore processes. The contribution of future sea-level rise induced by climate change is usually obtained using the Bruun rule, which assumes that the shoreline retreat is equal to the change of sea-level divided by the slope of the upper shoreface. However, it remains unsure that this approach is appropriate to account for the impacts of future sea-level rise. This is due to the lack of relevant observations to validate the Bruun rule under the expected sea-level rise rates. To address this issue, this article estimates the coastal settings and period of time under which the use of the Bruun rule could be (in)validated, in the case of wave-exposed gently-sloping sandy beaches. Using the sedimentary budgets of Stive (2004) and probabilistic sea-level rise scenarios based on IPCC, we provide shoreline change projections that account for all uncertain hydrosedimentary processes affecting idealized low- and high-energy coasts. Hence, we incorporate uncertainties regarding the impacts of longshore processes, sea-level rise, storms, aeolian, and other cross-shore processes. We evaluate the relative importance of each source of uncertainties in the sediment balance equation using a global sensitivity analysis. For scenario RCP 6.0 and 8.5 and in the absence of coastal defenses, the model predicts a perceivable shift toward generalized beach erosion by the middle of the 21st century. In contrast, the model predictions are unlikely to differ from the current situation in case of scenario RCP 2.6. Finally, the contribution of sea-level rise and climate change scenarios to sandy shoreline change projections uncertainties increases with time during the 21st century. Our results have three primary implications for coastal settings similar to those provided described in Stive (2004) : first, the validation of the Bruun rule will not necessarily be possible under scenario RCP 2.6. Second, even if the Bruun rule is assumed valid, the uncertainties around average values are large. Finally, despite these uncertainties, the Bruun rule predicts rapid shoreline retreat of sandy coasts during the second half of the 21st century, if greenhouse gas concentration in the atmosphere are not drastically reduced (scenarios RCP 4.5, 6.0, and 8.5).

Keywords: sea-level, shoreline, erosion, Bruun, uncertainties

Edited by:

Sönke Dangendorf, University of Siegen, Germany

#### Reviewed by:

Athanasios Thomas Vafeidis, Christian-Albrechts-Universität zu Kiel, Germany João Miguel Dias, University of Aveiro, Portugal

> \*Correspondence: Gonéri Le Cozannet<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Coastal Ocean Processes, a section of the journal Frontiers in Marine Science

Received: 08 December 2015 Accepted: 30 March 2016 Published: 19 April 2016

#### Citation:

Le Cozannet G, Oliveros C, Castelle B, Garcin M, Idier D, Pedreros R and Rohmer J (2016) Uncertainties in Sandy Shorelines Evolution under the Bruun Rule Assumption. Front. Mar. Sci. 3:49. doi: 10.3389/fmars.2016.00049

# 1. INTRODUCTION

One of the most important challenge for coastal adaptation is the rise of sea-level caused by anthropogenic climate change (Slangen et al., 2014b; Dangendorf et al., 2015). At present, there are already evidences that extreme water levels are becoming higher and more frequent (Marcos et al., 2009; Menéndez and Woodworth, 2010; Woodworth et al., 2011; Woodworth and Menéndez, 2015). At longer timescales, contributions from polar ice-sheets largely exceeding one meter are now recognized possible (Golledge et al., 2015; Hansen et al., 2015; Winkelmann et al., 2015). Meanwhile, increased rates of shoreline retreat and particularly of sandy beaches — are expected to take place (Nicholls and Cazenave, 2010).

A common approach to address sub- to multi-decadal shoreline variability on wave-exposed sandy coast with infinite sand availability is to use the sediment balance equation, which can be written as follows (e.g., Cowell et al., 2003b; Stive, 2004; Yates et al., 2011; Aagaard and Sørensen, 2013; Anderson et al., 2015):

$$
\Delta \mathcal{S} = \Delta \xi / \tan(\beta) + f\_{cross-share} + f\_{longshort} \tag{1}
$$

where:

Equation (1) applies to the upper shoreface of the coastal tract (Cowell et al., 2003a). It assumes that the upper shoreface keeps the same profile and translates seaward or landward depending on the sediment budget. In particular, the term 1ξ/tan(β) represents the impacts of sea-level change and corresponds to the Bruun rule (Bruun, 1962). While this term has been subject of debate over the last decades (Cooper and Pilkey, 2004; Ranasinghe and Stive, 2009; Woodroffe and Murray-Wallace, 2012; Passeri et al., 2015), there is no clear recommendation to leave it out. Recent results regarding the global impact of sealevel rise on shoreline change are largely based on the Bruun rule (Hinkel et al., 2013). Alternative approaches exist, but they are more complex and they require more data (Ranasinghe et al., 2012; Wainwright et al., 2015). Finally, even if the concepts of the Bruun rule are abandoned, the formula 1Ssea−level−rise = 1ξ/tan(β) will remain. Indeed, other conceptual models have been developed and finally came up with the same formula (Davidson-Arnott, 2005). In this paper, we leave aside the key question of the relevance of Equation (1) as a modeling tool to evaluate future shoreline change. Instead, considering that the Bruun rule will continue to be widely used in the future, we evaluate the type of results and the uncertainties that can be expected by using it.

To evaluate the expected impacts of present day and future sea-level rise to sandy beaches erosion, Stive (2004) considered typical values for each term in Equation (1). These values are shown in **Tables 1**, **2**. They are based on observations in the Netherlands and Australia. Stive (2004) showed that under present sea-level rise rates, the contribution of the Bruun effect to sediment losses and shoreline changes is of the same order of magnitude or lower than other effects. Because of the lack of longterm [O (10 years)] coastal data, firmly (in)validating the Bruun rule is difficult (e.g., Leatherman et al., 2000a,b; Sallenger et al., 2000), with the impacts of present-day sea-level rise on shoreline changes being challenging to observe (Stive, 2004; Le Cozannet et al., 2014). However, Stive (2004) showed that for higher rates of sea-level rise, the Bruun effect will not be neglectable any more, as it will significantly impact shoreline change. Hence, Stive (2004) not only helps understanding the behavior of Equation (1) in most general cases, but also provides a general background consistent with present-day observations.

The periods of time by which one will be able to (in)validate the use of Bruun rule in Equation (1) is a critical unknown. The question is complex, because all terms in Equation (1) are uncertain, and because the duration by which sea-levelrise-induced erosion is decipherable likely depends on climate change scenario and regional coastal settings. Ultimately, this effect might never be observed in some regions, if the impacts of sea-level rise remain smaller than those of other sedimentary processes causing shoreline change. To investigate this question, we consider the case of idealized wave-exposed sandy beaches with infinite sand availability, and we adapt an approach developed to quantify uncertainties in future flooding occurrence (Le Cozannet et al., 2015): we first define realistic probability density function for each uncertain parameter in Equation (1), using values from Stive (2004) and the IPCC (Church et al., 2013) (Section 2). In Section 3, we propagate these uncertainties through Equation (1) to provide shoreline change projections for different idealized coastal settings and climate change scenarios. Using a global sensitivity analysis (Sobol', 2001; Saltelli et al., 2008), we evaluate the contribution of each uncertain parameter to the variance of future shoreline changes. We use these results to assess where and when the Bruun effect should become observable. Finally, in Section 4, we discuss the results with respect to previous work and question the representativeness of the idealized sandy shorelines considered.

TABLE 1 | Order of magnitude for cross-shore sedimentary processes contributing to shoreline change for typical coastal settings in the Netherlands and Australia and for a closure depth of 10 m (data from Stive, 2004).

TABLE 2 | Order of magnitude for the annual longshore sedimentary processes contributions to shoreline changes for typical coastal settings in the Netherlands and Australia and for a closure depth of 10 m (data from Stive, 2004).

# 2. MATERIALS AND METHODS

The method proceeds in three steps, which are detailed below.

# 2.1. Modeling Uncertainties of Input Parameters

We model the uncertainties of each input variable in Equation (1) using the probability density functions indicated in **Table 3** and the values shown in **Tables 1**, **2**. These tables consider different types of idealized coastal sites, based on the values provided by Stive (2004) for representative beaches in Australia and the Netherlands. For example, **Table 2** indicates that if a groin extending far offshore is implemented on a high energy beach, it will act as a trap for the large longshore sediment transport and will contribute to shoreline change at a rate soundly exceeding 10 m/year. These orders of magnitudes can have a large site to site variability. This point is further discussed in the Section 5. Overall, in the absence of any other contribution similar to that of Stive (2004), we rely on the large uncertainties related to the contribution of each driving process shown in **Tables 1**, **2** to argue that they are applicable for a large number of sandy coasts.

When defining probability distributions representing the uncertainties of input parameters (**Table 3**), the information available is often limited. As a general guidance, it should be avoided to introduce arbitrary constraints besides what is known already. In other words, the statistical entropy of the selected probability distribution functions should be maximized (Mishra, 2002). For example, a uniform probability density function should be selected when only boundaries are known. This case is met for most of the parameters given in **Table 3**, including the beach slopes and the contributions of cross-shore and longshore processes to the sedimentary budget. This choice reflects the fact that the data provided by Stive (2004) define boundary values for each uncertain input parameter (see **Table 1**).

More complex distribution can be elaborated when sufficient information is available. This is the case here for future sea-level rise. This source of uncertainties increases with time, and several components must be distinguished: (1) the selection of a climate change scenario, (2) the uncertainties of future sea-level rise for each climate change scenario, and (3) the regional variability of sea-level rise.

The IPCC provides likely range and median values for four climate change scenarios (RCP 2.6, 4.5, 6.0, and 8.5), corresponding to different trajectories of greenhouse gas emissions. To select one of these sets of value, we use a uniform discrete probability distribution, that is, a random application with a probability of 1/4 to select each climate change scenario. This choice assumes that we do not know what will be future greenhouse gas emissions.

Even if the true climate change scenario was known, future sea-level rise would still remain uncertain, in particular because of insufficient knowledge on future ice-sheets melting and the Earth energy imbalance (Church et al., 2013; Kopp et al., 2014; von Schuckmann et al., 2016). Modeling results combined with expert judgements have suggested that this source of uncertainties can be adequately modeled by a skewed distribution with a finite support (Ben Abdallah et al., 2014; Jevrejeva et al., 2014). As in our previous article (Le Cozannet et al., 2015), we use a Beta distribution to represent this source of uncertainties. While many scientists estimate that the actual rates sea-level rise over the 21st century could exceed those anticipated by the IPCC (Horton et al., 2014), we strongly rely on their likely range and median values to elaborate our sea-level rise scenarios (Church et al., 2013). The upper bound of the distribution is a critical unknown, due to uncertainties in the dynamics of polar icesheets melting, which is currently accelerating (Rignot et al., 2011). Referring to recent modeling results of the Antarctic icesheet instability (Ritz et al., 2015), we use a conservative estimate of 1.5 m for the upper bound of sea-level rise by 2100. To our knowledge, there is presently no evidence that the lowprobability and high impact event of an ice-sheet instability can be avoided, even for the climate change scenario RCP 2.6. Therefore, we use the same upper bound of 1.5m by 2100 for all climate change scenarii. This results in probabilistic sea-level rise scenarios as indicated in **Figure 1**.

Due to spatially non-uniform warming of the ocean and the response of the Earth to ice-sheets melting, sea-level rise will display regional variability (Slangen et al., 2014a; Carson et al., 2016). This introduces an additional source of uncertainties. We assume that it includes a large part of natural randomness, which we evaluate using Church et al. (2013) and Carson et al. (2016). These references suggest that in addition to the boundaries of the distribution, its most likely value is known. Hence, the principle of maximum entropy leads to the choice of a triangular distribution (Mishra, 2002).

Extreme events are generated here assuming their occurrence follows a Poisson distribution. While this assumption is commonly used (Ranasinghe et al., 2012; Wainwright et al., 2015), it is challenged by some storm sequences, such as the winter 2013–2014 events in western Europe (Masselink et al., 2016). Moreover, as we use a Poisson distribution with fixed parameters, only the interannual variability of storms is captured, and the natural randomness is assumed unaffected by climate change (Plant et al., 2014). This hypothesis could be justified, as it is still difficult to assess where and to which extent climate change will affect extreme winds and waves (Planton et al., 2008; Mori et al., 2010; Hemer et al., 2013). However, we consider here that the return periods of storms are uncertain as well. The range of values considered for this uncertain input parameter is sufficiently large to exceed any plausible change of storminess due to climate change and any other limitation related to the Poisson assumption (see **Table 1**). Finally, we assume that the shoreline retreats after each extreme event ranges from 0 to 20 m. The first value can correspond to a storm occurring at

#### TABLE 3 | Modeling of the different sources of uncertainties.

Note that human impacts refer here to the presence of groins, which are coastal engineering structures acting as a trap for the longshore sediment transport to locally accrete beaches.

low tide. Field surveys shows that the second value can result from a collision regime after exceptional storms, and can even be exceeded in case of overwash or breaching, which are not considered in this study. **Figure 2** shows examples of shoreline retreats observed in western France after the storm Xynthia in February 2010 (Pedreros et al., 2010; Garcin et al., 2011). The return period of water levels for this storm is estimated to be approximately 200 year in the harbor of La Rochelle (Bulteau et al., 2015). Such return periods are consistent with our working hypothesis, and we note that shoreline retreat ranged from a few meters to 20 m, except where breach occurred. Similar values can be found in many other studies (e.g., Forbes et al., 2004; Mendoza and Jiménez, 2006; Roelvink et al., 2009; Loureiro et al., 2014).

As a summary, our approach relies on two major assumptions: (1) sea-level rise scenarios are based on IPCC; (2) we consider sites with gently sloping upper shorefaces, i.e., which are the most impacted by the Bruun effect. Other assumptions can be mentioned: some input parameters here are assumed independent, although they are not in practice. For example, for the same sediment size, beach slopes are expected to be steeper for low-energy beaches (Wright and Short, 1984). However, given the range of uncertainties provided in **Table 3**, such limitations can be left aside in a first attempt to propagate uncertainties through Equation (1).

# 2.2. Propagation of Uncertainties Through the Sediment Balance Equation

The second step of the approach consists in propagating these uncertainties through Equation (1).

The computational approach starts with defining auxiliary variables with uniform values in the interval [0,1]. Then we apply inverse cumulative distribution functions to these auxiliary variables, in order to generate distributions with the desired shapes (e.g., Beta distribution for sea-level rise). This computational approach ensures the independence of the different input parameters considered in **Table 3**, so that the Sobol' parameters (see next section) can be computed using a classical procedure. To reduce the computation time, we use a quasi-Monte-Carlo approach, where we assign values following a quasi-random Sobol' sequence (Sobol', 1967) to the auxiliary variables with values in [0,1]. We empirically determine the number of computations required to converge, by comparing purely random against quasi-random simulations in a test case corresponding to a situation of the end of the 21st century.

This procedure allows to produce generic probabilistic shoreline change projections based on the coastal tract assumptions. Importantly, all input parameters vary simultaneously during these simulations. Therefore, the probability density function of future shoreline change projections integrates the complete variability.

# 2.3. Quantifying and Ranking the Sources of Uncertainties in Shoreline Change Projections

Once uncertainties of all input parameters have been propagated through Equation (1), we obtain a distribution for future shoreline change as a function of time. To understand what this distribution means, we analyse the contribution of each uncertain input parameter to the variance of the model outcome using a global sensitivity analysis (Sobol', 2001; Saltelli et al., 2008).

The principle of variance-based global sensitivity analysis is to decompose the variance of the model outcome Y into several terms that relate to each uncertain input parameter Xi . These terms are called the Sobol' indices. Two subsets of terms are of particular interest for the evaluation of uncertainties: the first-oder and total order Sobol' indices.

compatible with the random sample of storms considered in this study.

The first-order Sobol' indices (Si) correspond to the contribution of a given input parameter alone to the variance of the model outcome:

$$S\_i = \frac{\text{Var}(E(Y|X\_i))}{\text{Var}(Y)} \tag{2}$$

They represent the expected proportion of the variance of the model outcome that would be removed if Xi was known.

The total order Sobol' index (STi) represents the contribution of a given input parameter and all its interactions with other parameters to the variance of the model outcome. It is defined as:

$$S\_{T\bar{\imath}} = 1 - \frac{Var(E(Y|X\_{-i}))}{Var(Y)}\tag{3}$$

with X−i the set of all Xj except Xi . This index is used to identify which parameters can be set to any possible value without much impact to the variance of Y.

All computations are done using R (R Core Team, 2014), using the same quasi Monte-Carlo approach as in the previous section. The values of the Sobol' indices are very similar for two consecutive years. Therefore, to reduce the computing time, we calculate these indices each 5 year and interpolate them over the 21st century. We use the codes developped by the Joint Research Center of the European Commission to compute the first and total order Sobol' indices (see Jansen, 1999; Saltelli et al., 2010, for the related algorithms and formulations).

#### 3. RESULTS: WHEN WILL WE BE ABLE TO (IN)VALIDATE THE BRUUN RULE?

**Figure 3** shows the probabilistic shoreline change projections 1S(t) at year t, once uncertainties from the input parameters (**Table 3**) have been propagated through Equation (1). This represents 1S(t) if no prior information is available, besides values in **Tables 1**–**3**. **Figures 4**–**6** show 1S(t) for high and low energy beaches, with and without groins, and considering the different climate change scenarios separately.

As expected, a slight shift toward erosion is observed over the 21st century (**Figure 3**). However, in practice, the response is very different depending on the climate change scenario and the type of coast considered. In case of RCP 2.6, we can not identify any clear separation between present and future probability density functions of shoreline change rates. Conversely, for other climate change scenarios and for beaches without groins, the likely range of future shoreline change rates separates or nearly separates (case of RCP 4.5) from present days values (**Figures 4**, **5**). For example, in case of RCP 8.5 and coasts without groins, it is more than likely that observations will separate from their present values by the end of the 2060s for low-energy beaches, and in the early 2070s for high-energy beaches. Hence, using **Figures 4**, **5**, it becomes possible to identify times of emergence for a more than likely observable shift toward erosion of sandy beaches under the assumptions listed above. As a practical implication, it should be possible to assess the Bruun rule validity by the mid-21st century on beaches without groins with gently sloping shoreface, if efforts to reduce greenhouse gas concentrations in the atmosphere fail.

If groins are present on the beach, the longshore processes have a larger impact on the sedimentary budget (**Table 2**): on the one hand, large volumes of sediments can be trapped in protected areas, but on the other hand, these sediments are missing in adjacent locations. This does not prevent from observing a shift toward erosion in case of low-energy beaches and climate change scenarios RCP 4.5, 6.0, and 8.5 (**Figure 4**). In these cases, not enough sediments can be trapped to compensate the losses induced by sea-level rise. Conversely, if groins are built on high-energy beaches, the transport of sediment is severely altered. This results in high variability of shoreline advance and retreat, reaching ±10 m/year (see Stive, 2004, and **Table 2**), which may compensate the impacts of sea-level rise theoretically (**Figure 6**). However, it is widely acknowledged that groins are not a sustainable solution because they typically fix the erosion issue, but shift it nearby. Hence, it should not be concluded that groins are an efficient adaptation strategy for high-energy sandy coasts.

Drawing on these results, present day and future rates of shoreline change could remain indistinguishable if greenhouse gas emissions are reduced importantly (RCP 2.6 scenario). In addition, for coastal sites similar to those presented by Stive (2004), the times of emergence of a shift toward erosion induced

FIGURE 3 | Shoreline retreat rate projections for the 21st century, with no prior information regarding the type of sandy coast considered, the impacts of man-made infrastructures and the climate change scenario. Positive values corresponds to shoreline retreat. For each date, the intensity of the color represents the probability density function, and the solid, dashed and dotted black lines represent the median, likely, and very likely ranges. The gray lines represent present days rates: they are extended toward the whole 21st century for comparison. They represent a virtual case in which sea-level remains at its present level, whereas the black lines take into account uncertain sea-level rise scenarios of Figure 1 using the Bruun rule.

by sea-level rise should occur during the second half of the 21st century, if sea-level rise follows the IPCC projections.

# 4. FURTHER INSIGHTS FROM THE GLOBAL SENSITIVITY ANALYSIS

**Figure 3** reflects a situation where nothing is known regarding local coastal settings, future climate change and sea-level rise, except their probability density functions. To obtain this figure, 12 uncertain parameters were propagated in Equation (1), resulting in shoreline change projections with very large uncertainties. Indeed, the standard deviation of shoreline change projections rises from about 4 m/year now, to 4.5 m/year by 2050 and 5 m/year by 2100. These values are large, and the true values might be closer to 1 m/year (Bird, 1985). This suggests that the uncertainties in shoreline change projections can be reduced importantly with more knowledge regarding local coastal processes. Hence, the ensuing question is to understand what drives the variability of shoreline change projections in **Figure 3**. In this section, we investigate this issue using the results of the global sensitivity analysis. In other words, we separate the variance of 1S(t) into several components corresponding to the contribution of the 12 uncertain input parameters, in order to gain further insight into the understanding of the upper shoreface sediment balance equation outcome under **Table 3** constraints.

**Figures 7**, **8** show the first and total order Sobol' indices obtained by the global sensitivity analysis. Using these figures, it becomes possible to classify input parameters according to their contribution to the variability of shoreline change rates: the first order Sobol' indices are used to rank input parameters, whereas the variability of parameters with total order Sobol' indices close to zero can be neglected without much impacts to the variability of the final results (Saltelli, 2004). In the following subsections, the uncertain input parameters of Equation (1) are classified according to their first and total order Sobol' indices, which reveal research priorities to better anticipate future shoreline change under the Bruun rule assumption.

# 4.1. Critical Unknowns for the Present Days

The random variables used to model the interannual variability of storms and the impacts of longshore sedimentary processes display large first and total order indices: **Figure 7** shows that

the value of the first order indices is of about 0.2. **Figure 7** indicates that the total order indices decreases from about 0.6–0.3 for longshore processes (0.4–0.3 for the interannual variability of storms). Therefore, these parameters account for a large part of the uncertainties in shoreline change projections. As a consequence, knowing more about these processes should be a priority for reducing the uncertainties resulting from the application of the sediment balance equation for sandy upper shorefaces with gentle slopes. For example, our results implies that a perfect knowledge of longshore processes would reduce the variance of shoreline change projections by a factor of nearly 20%. Many studies in the field of coastal research have studied these sources of uncertainties (Allen, 1981; Inman and Dolan, 1989; Slott et al., 2006; Roelvink et al., 2009; Castelle et al., 2015). However, it remains difficult to quantify precisely all the phenomena involved (see Section 5). Here, the results of the global sensitivity analysis just highlight that sandy shoreline change projections are more accurate if the impacts of storms and longshore sedimentary processes are taken into account. While integrating these processes at regional to global scales is a challenge, it is increasingly addressed in recent assessments of climate change impacts (e.g., Anderson et al., 2015).

different IPCC climate change scenarios. The meaning of gray and black lines is the same as in Figure 3.

# 4.2. Uncertain Parameters with Minor Impacts

At the opposite, the Sobol' first and total order indices representing the aeolian processes, other cross-shore effects (e.g., due to waves asymmetry) and the regional variability of sealevel rise are small. Therefore, the variability of these parameters can be neglected without any large impact to the variability of shoreline change projections. This result can seem surprising. In fact, several studies have highlighted the importance of cross-shore sedimentary processes in explaining seasonal and interannual shoreline changes (e.g., Yates et al., 2009; Splinter et al., 2014). To understand how ourresults can be related to these findings, we need to point out that Stive (2004) distinguishes the rapid erosive impacts of highly energetic events from the slow accretion due to the net onshore contributions from mild waves (**Table 1**).

The regional variability of sea-level rise also plays a minor role, whereas it can be much more important for strongly non-linear impact models. For example, urban coastal flooding generally becomes most damaging once a critical threshold has been exceeded (Hallegatte et al., 2013; Idier et al., 2013b; Miller et al., 2014). In this case, knowing more about the regional variability of sea-level rise would be one of the priorities for coastal managers interested in understanding how risk will evolve over the coming decades (Le Cozannet et al., 2015).

According to **Table 1**, aeolian processes taking place on coastal beaches and dunes are expected to induce net sediment losses and shoreline retreat. However, these processes do not appear as a critical unknown either, in spite of their complexity (Arens, 1996; Hesp et al., 2013; Bauer et al., 2015). Again, our results highlight that when few data are available regarding other coastal processes, the values provided in **Table 1** are sufficiently narrow to anticipate the impacts of the slow recovery of beaches satisfactorily. Finally, as this result comes directly from the order of magnitude provided in **Tables 1**–**3**, it can be invalidated if new observations demonstrate that these processes have much larger impacts. This question of the representativeness of the values of **Tables 1**, **2** is addressed in details in the discussion section.

# 4.3. Interactions between Processes: Why is a Global Sensitivity Analysis Recommended?

**Figures 7**, **8** also reveal that for two random variables, the Sobol' first order indices are close to zero, whereas the total order indices are large. This is the case for example for the random variables indicating if groins are built on the beach, or if the beach is exposed to low- or high- energetic conditions: their total order Sobol' indices ranges from 0.1 to 0.2 (**Figure 8**). In this case, the principles of the global sensitivity analysis implies that the variability of the input parameters cannot be neglected, because it interacts with other random variables (Saltelli, 2004). In other words, this result indicates that, for instance, groins will have little impacts on shoreline change if longshore drift is weak.

**Figure 7** shows that the sum of the first order Sobol' indices ranges from 0.45 to 0.6 depending on the period of time considered. This means that the interaction term is large. Hence, to a significant extent, the overall variability is driven by the combined variation of the parameters (joint effect). A very common way to perform a sensitivity analysis consists in evaluating the model response to each uncertain input factor independently. Our results show that despite the apparent simplicity of Equation (1), such an approach could fail to rank the impact of each uncertainty source: the total uncertainty on shoreline change can not be considered as the sum related to each uncertainty sources taken alone. On the contrary, the combined effect among them should be accounted for: here the interaction terms represent about the half of the total variance of shoreline change projections.

sea-level rise, (2) local site characteristics, (3) impacts of storms, and (4) of coastal defenses (groins).

# 4.4. Critical Unknowns for the Next Decades

**Figures 7**, **8** shows two distinct periods, during which the relative importance of sea-level rise, climate change scenarios, and beach slopes increase successively. This result primarily comes from the propagation of uncertain sea-level rise scenarios (**Figure 1**) in the Bruun term of Equation (1).

First, the climate change scenarios have small Sobol' indices during the 1st part of the 21st century. Therefore, as expected, the next decades do not appear as a relevant period of time to differentiate future shoreline change according to the future climate change scenarios. However, sea-level will be rising, and Equation (1) implies that this will induce a slight shift toward erosion. By 2050, uncertainties on global sea-level rise account for 20% of the variance of future shoreline change projections.

Conversely, there are large differences between e.g., RCP 2.6 and RCP 8.5 during the second part of the 21st century. **Figure 7** shows that if a decision is taken to follow a given climate change scenario, the variance of shoreline change projections is reduced by 20%. This is almost twice the maximum reduction of variance that we could expect, if we were able to predict accurately future sea-level rise given greenhouse gas emissions by 2100. Hence, even if nothing is known regarding coastal processes besides **Table 1**, the benefits of climate change mitigation and greenhouse gaz emissions reductions appear significant, as suggested by Nicholls and Lowe (2004) and Pardaens et al. (2011). Here, **Figures 7**, **8** constitute an other way to communicate this result, which was already identified in Section 3 and **Figure 3**.

# 4.5. Appropriate Coastal Settings for Validating the Use of the Bruun Rule in the Sediment Budget Equation

**Figures 3**–**6** have indicated that if a large number of coastal sites are considered, (in)validating the use of the Bruun rule in Equation (1) will be straightforward by the end of the 21st century for RCP 6.0 and 8.5 scenarios (see Section 3). In a more general case, the results of the global sensitivity analysis (**Figures 7**, **8**) enable to identify which types of coastal sites are potential candidates for testing the use of the Bruun term in Equation (1): beaches without human impacts, little exposed to storms and where longshore drift can be quantified are among such sites. This result is not a surprise: previous studies that tried to validate the Bruun rule and detect an observable impact of sea-level rise on shoreline change actually focused precisely on this type of coasts (Leatherman et al., 2000b; Zhang et al., 2004). In practice however, the selection of such sites is not straightforward, and

there remains question regarding the possibility to remove the impacts of longshore drift in the analysis (Leatherman et al., 2000a; Sallenger et al., 2000). Nevertheless, these results provide a perspective for coastal observatories currently collecting coastal evolution observations with the aim of better understanding coastal impacts of climate change.

### 5. DISCUSSION: REPRESENTATIVENESS OF THE SELECTED IDEALIZED COASTAL SITES

Our study relies on the orders of magnitudes provided by Stive (2004) for typical coastal settings in the Netherlands and Australia. Though there is no study providing orders of magnitude at other representative sites, it is hypothesized that the respective contribution of each process in **Tables 1**, **2** vary considerably for other coastal settings. For instance, on open and straight sandy beaches exposed to high-energy oblique ocean waves, longshore sediment transport rate can be high [e.g., O(106 m3 /year)] but without any alongshore gradients. In such a situation, longshore sedimentary processes do not contribute to shoreline change. This is the case of the southern Gironde Coast, SW France, where gradients in longshore sediment transport are negligible (Idier et al., 2013a) and where the observed shoreline variability on the timescales from hours to years can be explained by cross-shore processes only (Castelle et al., 2014). Yet, even in this simplified coastal setting, there is to date no detailed quantification of the contribution of aeolian processes and other cross-shore processes to the overall shoreline change. As far as extreme storms are concerned, it is impossible to quantify maximum storm wave erosion for given return period exceeding a few decades simply because there is no such long time series of storm-driven erosion. At the same beach, the cumulative impact of a series of outstanding storms during the winter 2013/2014 only drove a 15 m dune retreat over 3 months (Castelle et al., 2015). The same outstanding severe storms drove highly variable erosion patterns along Western Europe (Masselink et al., 2016), revealing the complexity of quantifying the erosion driven by multi-decadal or multi-centennial return period storms.

While providing numbers for this simple case of open beaches with no alongshore sediment transport gradients is arguably challenging, this is even worst for embayed beaches. Embayed beaches are ubiquitous worldwide given that nearly half of the world's coast consists of hilly or mountainous coastline. In addition, beaches artificially bounded by groins or jetty can behave as embayed beaches. Shoreline variability along embayed beaches is dominated by rotation signal (Ranasinghe et al., 2004). However, the respective contributions of cross-shore and longshore processes to the embayed beach rotation signal is still a subject of debate (Ranasinghe et al., 2004; Harley et al., 2011, 2015).

Overall, although we do not pretend to address all sandy coasts worldwide, we advocate that the values given by Stive (2004) are sufficiently large to be representative of many coastal settings. It is obvious that further work is required (1) to decipher the respective contributions of the different driving processes to shoreline change in different coastal settings and (2) to develop alternative model framework in which the different processes can be accounted for explicitly. In this framework, reducedcomplexity modeling approaches (e.g., Splinter et al., 2014; Reeve et al., 2015) appear as a relevant avenue.

Noteworthy, Stive (2004) refers to coastal sites with gentle slopes, ranging from 0.001 to 0.002, which corresponds to very gentle slopes actually (Hinton and Nicholls, 1998; Marsh et al., 1998). Shoreface slopes are often larger. For instance in SW France, high-energy beaches display coastal slopes of about 0.015 (Castelle et al., 2014), whereas they reach about 0.012 for low-energy Mediterranean beaches in Languedoc (Yates et al., 2011). In practice, however, the use of the Bruun rule in Equation (1) is more questionable for steep coasts. Other models would be required in such coastal sites, for example considering the sediment losses and gains during and after each storm (Larson et al., 2004; Ranasinghe et al., 2012). Nevertheless, we acknowledge that beach slopes values that can be calculated from **Table 3** correspond to coastal sites where the Bruun effect will have the strongest impacts.

The approach presented here could be applied to other medium-complexity geomorphic evolution modeling approaches (French et al., 2015), in order to finally identify research priorities. However, in the case of complex coastal settings with limited sand availability, the constraints imposed by the geology, the continental sedimentary supply or the role of coastal lagoons should be considered as well.

#### 6. CONCLUSION

In this article, we examined where and by which period of time the Bruun effect should become observable on waveexposed sandy coasts. To do this, we refered to Stive (2004) to evaluate the order of magnitude of each physical process contributing to shoreline change in the case of coastal uppershoreface with gentle slopes. We also considered probabilistic sea-level rise scenarios based on the IPCC (Church et al., 2013). We propagated these uncertainties through the sediment balance equation, that sums the Bruun effect with other drivers such as longshore and cross-shore processes. This allowed to identify a time of emergence of an observable Bruun effect by the middle of the 21st century in the case of beaches with gentle slopes without groins, and for climate change scenarios RCP 4.5, 6.0, and 8.5 only. Under the assumptions above, the absence of generalized sea-level-rise-driven beach erosion by the end of the 21st century is only achieved for RCP 2.6 climate change scenario.

Rather than predictions of future shoreline changes, our results can be viewed as an attempt to better understand where and when the Bruun rule can be (in)validated. Using a global

REFERENCES

sensitivity analysis, our results confirm that low-energy gently sloping beaches with little human impacts and small gradients in longshore drift and sheltered from storms are the most relevant to assess the validity of the Bruun rule in the sediment balance equation. These results could be further improved through a better integration of the primary driving processes of shoreline change in the sediment balance equation, and refined probability distribution functions. This also implies collecting site-specific and more accurate values than those listed in **Tables 1**, **2**. Whatever the amount of data collected, part of the uncertainties in future shoreline change projections will remain. In addition, it should be noted that new knowledge may also illuminate some "not-yet envisaged" complex processes, of which our understanding may still be poor, and would require new research efforts in the future. Overall, and regardless the shortcomings discussed above, our approach is a relevant modeling framework to further improve communication to stakeholders and general public on uncertainties in sea-level rise impact assessments.

# AUTHOR CONTRIBUTIONS

Overall design of the study: GC. Detailed design and implementation of the study: GC, CO, BC, MG, DI, RP, JR. Interpretation and discussion: All. Writing and correcting the paper: All.

## ACKNOWLEDGMENTS

This project has been funded by BRGM through the "Adaptation to Climate Change Risks" project. BC was funded by the CHIPO project (ANR-14-ASTR-0004-01), financially supported by the Agence Nationale de la Recherche (ANR). The field mission following Xynthia (**Figure 2**) was funded by BRGM, and conducted by MG and RP, Daniel Monfort, Julie Mugica, Yann Krien, and Benjamin Francois. We thank Thomas Bulteau, Anny Cazenave, Franck Desmazes, Franck Lavigne, Cesca Ribas, and Marcel Stive for useful discussions and comments that finally resulted in this study. Finally, we thank the two reviewers for their very useful comments.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Le Cozannet, Oliveros, Castelle, Garcin, Idier, Pedreros and Rohmer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Assessing Future Flood Hazards for Adaptation Planning in a Northern European Coastal Community

Carlo Sorensen1, 2 \*, Niels H. Broge3 , Mads R. Molgaard4 , Charlotte S. Schow5 , Peter Thomsen5 , Karsten Vognsen3 and Per Knudsen1

1 DTU Space, Department of Geodesy, Technical University of Denmark, Kongens Lyngby, Denmark, 2 Coast and Climate, Danish Coastal Authority, Lemvig, Denmark, 3 Danish Geodata Agency, Copenhagen, Denmark, 4 Geo, Kongens Lyngby, Denmark, 5 Ramboll, Viborg, Denmark

From a transdisciplinary approach in the town of Thyboron, Denmark, we investigate couplings between sea state (i.e., mean and extreme) and flooding hazards today and ahead. This includes analyses of change and variability in the groundwater table, precipitation, land motion, geotechnical ground properties, sewerage systems and other infrastructure to outline a more complete platform for the integration of knowledge into climate adaptation schemes at this highly vulnerable coastal location. It involves the engagement of the main stakeholders who, although having different responsibilities, interests, needs of knowledge and data, and different timeframes for investment and planning, must join in a common appraisal of the challenges faced ahead to provide for better adaptation measures. Apart from obvious adverse effects from future storm surge events, knowledge about the coupled effects of the abovementioned parameters needs to be taken into account to reach optimal mitigation and adaptation measures. Through stakeholder interviews it becomes clear that an enhanced focus on transdisciplinary research is a viable way forward to develop such measures: it will bring in more knowledge, a broader scope, and it will provide for more holistic solutions that both serve to protect the town and allow for business development and better municipal planning ahead.

Keywords: sea level rise, local impact, extremes, land motion, geotechnical properties, integrating tools, stakeholder collaboration

# INTRODUCTION

The future impact of climate change to people and the environment in coastal regions due to floods, inundation and erosion is substantial (Nicholls et al., 2007; IPCC, 2012). Furthermore, the groundwater table may be affected on varying time scales as may ground stability and the overall morphodynamics off the coastal locations. Climate research has advanced our knowledge of current and potential future sea level changes at global and regional scales (IPCC, 2013; Grinsted et al., 2015), of extreme water levels (Arns et al., 2015), natural variability and the anthropogenic footprint in sea level (Dangendorf et al., 2015), and of methods to evaluate sea level change from satellite (Nerem et al., 2010) and tide gauge data (Wahl et al., 2013; Visser et al., 2015; Watson, 2015).

Knowledge of climate change effects on society is in many ways still fragmented regarding approaches to adaptation, however. Updates on sea level rise projections and real-time monitoring

#### Edited by:

Marta Marcos, University of the Balearic Islands, Spain

#### Reviewed by:

Goneri Le Cozannet, Bureau de Recherches Géologiques et Minières (BRGM), France Ivica Vilibic, Institute of Oceanography and Fisheries, Croatia

> \*Correspondence: Carlo Sorensen<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Coastal Ocean Processes, a section of the journal Frontiers in Marine Science

Received: 11 December 2015 Accepted: 22 April 2016 Published: 11 May 2016

#### Citation:

Sorensen C, Broge NH, Molgaard MR, Schow CS, Thomsen P, Vognsen K and Knudsen P (2016) Assessing Future Flood Hazards for Adaptation Planning in a Northern European Coastal Community. Front. Mar. Sci. 3:69. doi: 10.3389/fmars.2016.00069 of change need better integration in planning tools, and consequences should be looked at in a more holistic way. For several reasons gaps exist between scientific knowledge and societal measures to mitigate climate change effects: First, the translation of climate change evidence to concrete impact measures is difficult. Second, the dissemination of uncertainty in projections and climate scenarios is certainly not a "one size fits all." Depending on the user, the information on uncertainty must be transformed to concrete impact variables used for e.g., adaptation and mitigation. Third, different and shifting agendas and opinions across levels of governance challenge the ways we address, perceive, and act on climate change issues in society. In order to bridge gaps, we thus need to address the translation of climate change parameters like sea level rise (SLR) and extreme events to actual effect measures; we must evaluate other contributors affecting climate change impact in time and space, and we must consider the stakeholders that may be affected as well as the actual assets that become threatened.

Many studies deal with current and future coastal hazards, exposure and vulnerability, risk and risk reduction on global (e.g., Nicholls, 1995; Hallegatte et al., 2013), regional (e.g., Du et al., 2013; Gibbs, 2015; Kuklicke and Demeritt, 2016) or local scales (e.g., Hallegatte et al., 2011; Kortenhaus and Oumeraci, 2014) that use different methodologies and modeling practices (Bosello and De Cian, 2014; Gallina et al., 2016; Nguyen et al., 2016). Often the research shows that a comparison between locations (or methodologies) is difficult as "the impacts of climate change on the coastal zone are expected to be largely site specific, due to the influence of local factors" (Linham and Nicholls, 2010). In relation to climate change adaptation the conclusion may even be that "coastal communities are unique and defy generalization" (Lane et al., 2015).

The quantification of potential hazards from climate change is governed by uncertainty and simplified assumptions about the impacts, and this affects the calculated exposure, vulnerability and risk of the area of investigation. Through independent and interdependent natural processes and potential interactions from multiple hazards—that may act differently through space and time, climate change impact studies gain complexity. In Denmark, for instance, flood risk mapping of the EU Flood Directive (EU, 2007; DCA, 2013a) uses a source-pathwayreceptor approach with simplified assumptions made regarding the hazards and pathway of potential flooding. Also, a multitude of local national flood studies, typically carried out by consulting companies, tend to be "off the shelf " work that only look into one source of flooding (i.e., either sea floods, precipitation and runoff, or, the sewerage system) depending on the contracting entity. This often causes the decision-making process regarding mitigation measures to be uncoordinated between sectors and local governance administrations which lead to suboptimal solutions.

The starting point of the present study is to investigate couplings between sea state (mean and extremes) and flooding hazards today and ahead for a small coastal community in Denmark. We seek to include land motion, precipitation, geology, geotechnical properties, the sewerage system, and changes in groundwater level to reveal a more detailed picture of the climate impact. Through interviews with main stakeholders we investigate their roles, responsibilities and incitements to act. From this we discuss the added value of using existing data together with ongoing and future data provision for climate adaptation, and we address the diversified needs for knowledge and tools among the stakeholders. In doing so, we move from a cross-disciplinary scientific platform to a transdisciplinary research approach (using the definition by Jahn et al., 2012, pp. 8–9) to address local future practiceoriented adaptation collaboration where "transdisciplinarity is a gradual process of conceptual and methodological articulation" (Visser, 2001).

As such, we present ongoing research and also include data and preliminary results that may become focal points of collaboration between stakeholders. Not all scientific aspects are treated in detail and the paper rather addresses general links between disciplines, and it provides an overview of locally coupled climate change impact factors that may prove useful to advance climate change research and adaptation. The main research focus is thus on the identified coastal hazards to provide managers and decision-makers with a more complete common platform for the integration of knowledge in climate adaptation and planning.

# DATA AND METHODS

History is an important but often neglected factor to the understanding of current and future hazards. As such, potentially important knowledge about an area may not be included in adaptation planning. We start by providing a fairly detailed historical overview of the area of interest, thereby also identifying hazards not included in the present study and factors that affect the future impact from climate change—apart from climate change itself. We then proceed to present the legislative framework and main stakeholders, and the methods applied dealing with land motion, sea state, groundwater, geological and geotechnical modeling, and hydrological modeling in relation to coastal hazards.

#### The Study Area

The town of Thyboron (population 2015: 2104), **Figure 1**; belongs to the Lemvig Municipality. A breach of the sandy barrier during a storm surge in 1862 led to the formation of Thyboron Channel. Since 1900 the channel has been maintained by groins along the channel banks and on the North Sea coast north and south of the channel, respectively (Sorensen et al., 1996; Knudsen et al., 2011). The coastal protection works and opening of a harbor in 1914 led to population growth. From the 1930s the ongoing coastal erosion, and doubts about whether it was possible protect the town, led the Danish Parliament to pass an act to close the Thyboron Channel with large dams and a sluice (MPW, 1942; DP, 1946). Dams were built on the fjord side of the barriers both north and south of the channel, but the decision to close the channel and the scientific reasoning for doing so was soon disputed (Bruun, 1954; MPW, 1968) and in 1970 a decision

was made to maintain an open channel and let the Danish Coastal Authority monitor the evolution (DP, 1970; DCA, 1975).

The "Thyboron question" is still controversial, however (e.g., Larsen and Beck, 2009; Norgaard et al., 2014). An expanding channel cross section (Bruun and Gerritsen, 1960; Knudsen et al., 2011) leads to increased water transport into and higher surge levels in the fjord during storms. Between 1958 and 2005 a statistical 100-year extreme water level in the town of Lemvig has increased from 1.73 to 1.99 m DVR90, and it is expected to increase further to 2.38 m DVR90 by 2060 excluding effects of sea level rise, if the channel is allowed to develop naturally (Christensen, 2011a,b; Knudsen et al., 2012; Sorensen et al., 2013). All heights refer to the Danish Datum DVR90 which corresponds to Danish mean sea level around 1990 (Schmidt, 2000) unless otherwise stated. The Danish Coastal Authority has sketched several possible solutions to mitigate future storm surge levels inside the fjord (Ingvardsen et al., 2012) but no efforts have been made toward reaching a solution. The decision (or lack of such) on how to handle Thyboron Channel is a large, unknown factor in flood protection and adaptation planning in Thyboron and other communities located at the shores of the Limfjord.

A large sea dike and a transverse dike toward south protect Thyboron against flooding from the North Sea for a 1000 year storm surge event according to the Danish Coastal Authority, who is in charge of the coastal protection along the 110 km central part of the Danish North Sea coast (DCA, 2013b). Lemvig Municipality is in charge of protection against floods from the channel and fjord. Coastal erosion challenges (Jensen and Sorensen, 2008; DCA, 2013b) and dike strength assessments are not within the scope of this paper but are, of course, important in relation to the future safety of Thyboron. Today, sand nourishments on the North Sea coast are used for protection.

A small part of Thyboron, locally known as "old town," in the south-west dates back to the nineteenth century and is the remnant of the pre-1862 settlement not lost to coastal erosion (see Sorensen, 2015a). Aerial photography provides a visual impression of town growth, **Figure 2**. The road and railway track to Thyboron are both toward west in 1945—just east of the "dike trenches" seen as dark patches in the lower left of the photo. Most houses are situated between the main road and the North Sea, and houses and infrastructure have been built in connection with the port. In the 1950s, the large dam/dike is being finalized in conjunction with a new road and railway along the fjord to the east. In Thyboron, an area between the railway and the road shows marshlands to the west and shallow water with deeper parts draining under the road to the fjord. The very light-colored appearance of the town is indicative of a very sandy environment. In 1965 the harbor is expanding and the north-east part of the town has developed. In addition, a new development is forming on sandy landfill on the aforementioned marshland (middle of photo). Gradually, between 1965 and 1992 the town development continues in this area in 4–5 major stages and with the local heating plant in the south-eastern most part opening in late 1992. Since 1992 the development has slowed down and the largest changes are witnessed in the north-west corner of the town and in connection with the harbor. A harbor expansion is currently being developed on new landfill areas southeast of the area shown.

© COWI 1954, 2002, 2014), flyfotoarkivet.dk (© Geomidt/Lemvig Municipality 1965, 1978, 1981, 1992).

# Governance and Main Stakeholder Interviews

Denmark has three levels of governance: national, regional (5 regions) and local (98 municipalities). Regions have a small role in facilitating climate adaptation and mitigation processes. The European Union plays a role at the coast e.g., through the implementation of the Floods Directive in member states (EU, 2007). The Danish Coastal Protection Act (MT, 2009) is one of several laws regulating the coastal zone (see Figure 15 in Sorensen, 2015b). Through national legislation passed (DP, 2012a,b) the Danish municipalities have made climate adaptation plans and are in the process of preparing measures to mitigate future events. The plans only focus on water related issues and with a strong emphasis on flash floods, however. The Lemvig Municipality adaptation plan points out Thyboron as a focus area (Lemvig Municipality, 2014) and, although initially unrelated to the municipal work, the present research relates to the challenges faced by the municipality and other stakeholders to address coastal hazards in their adaptation planning.

The Port of Thyboron, Lemvig Municipality, Lemvig Water and Wastewater, and the Danish Coastal Authority (DCA) are identified as the four main stakeholders in relation to climate change adaptation in Thyboron. The local population is not considered in the present study. The institutions have formal responsibilities and are all a part of a political system governed by legislation and rules. Although, the Port of Thyboron and Lemvig Water and Wastewater (LWW) both operate as independent corporations, they are either owned entirely or partly by and have board members from Lemvig Municipality. The municipality itself follows strict rules regarding their expenditure, and the Danish Coastal Authority needs to act according to national legislation with reference to the Minister for the Environment and Food. As mentioned the Danish Coastal Authority is responsible for the flood protection toward the North Sea. Lemvig Municipality is responsible for the flood protection toward the Thyboron Channel and the fjord, or, at least the Danish Coastal Authority is not.

Semi-structured interviews were held in September 2015 with Port of Thyboron Director, Mr. Jesper Holt Jensen, and Technical Director, Mr. Christian Vrist; the Director at Lemvig Water & Wastewater Inc., Mr. Lars Noergaard Holmegaard; the Head of Nature and Environment Department at Lemvig Municipality, Mr. Thomas Damgaard, and Danish Coastal Authority Director Ms. Merete Loevschall. Interviews were held in Danish and an English translation of the interview summaries enters the research.

The scope of the interviews was to bring out opinions and reflections on the current level of preparedness in relation to coastal floods (from all sources); positions in relation to current legislation; potential future or already planned actions in relation to climate change adaptation; collaboration issues, and the need of information regarding climate change and e.g., data and tools for adaptation. Previous to the interviews the stakeholders were consulted in late 2014 for the provision of data relevant to the study presented. In the course of research Lemvig Municipality has provided spatial data from house registers etc. and local knowledge, and Lemvig Water and Wastewater has financed the groundwater monitoring setup.

Although, potentially biased from knowledge of and varying interests in the ongoing research by the stakeholders, the information provided and main learnings from the interviews are included to discuss the concrete use and integration of research and data in adaptation planning.

#### Ethics Statement

The scientific data use fulfills the Danish Ethical legislation as authorized in writing by the responsible institution Legal Affairs at the Technical University of Denmark (DTU), 15 April 2016, regarding the legal and ethical handling of personal data. The Data Protection Act implementing Directive 95/46 was passed on in Law no. 429 of 31/05/2000. The Act faithfully transposes the provisions of the EC Directive into Danish law. The University (DTU) follows the Danish Statutory Order on Security on Data processing of Personal data, no. 528 of 15/06/2000 when processing Personal Data. According to § 10.2 in The Danish Statutory Order on Security on Data processing of Personal data, public universities may handle personal data without the permission from the subject (person) where the processing takes place for the sole purpose of carrying out statistical or scientific studies of significant public importance and where such processing is necessary to carry out these studies.

#### Ground Deformation and Topography

The NE part of Denmark experiences larger absolute national scale glacio-isostatic uplift rates than SW (Mertz, 1924; Duun-Christensen, 1990). Several studies deal with surface deformation on regional and local scale in Denmark due to sedimentary processes and tectonics (Andersen et al., 1996; Lykke-Andersen and Borre, 2000; Hanssen and Perski, 2007; Gregersen and Voss, 2010, 2015; Pedersen et al., 2011; Hansen et al., 2012; Jakobsen et al., 2013). In European collaboration projects (PanGeoProject.eu; SubCoast.eu; Terrafirma.eu.com, and Absrate, Knudsen et al., 2009) Danish issues of coastal hazards from land subsidence have been addressed. Here, we investigate the local pattern of vertical ground movement from a combination of methods to relate it to climate change impact.

#### Glacio-Isostatic Uplift

The glacio-isostatic land uplift rates within the study area, and for Denmark in general, are found from uplift model calculations (Knudsen et al., Supplementary Material 1 [S1]) related to the Milne et al. (2004) uplift model, three national leveling campaigns over the past century, long tide gauge series (Holgate et al., 2013; Permanent Service for Mean Sea Level, 2015), and to vertical deformation rates from permanent GPS station data in Denmark (Khan, 2014). The model is used to evaluate the overall rates of land uplift currently experienced.

#### Precision Leveling

Repeated motorized, geometric precision leveling (Vognsen et al., 2013a,b) to a few existing and +60 newly established height benchmarks, typically bolts, along the Limfjord Barriers and in Thyboron was carried out in 2006, 2009, 2012, and 2015, respectively, **Figure 3**. Across the Thyboron Channel, the height determination is carried out in relation to the geometric leveling by simultaneous-reciprocal trigonometric leveling under favorable weather conditions. All campaigns were carried out in late summer/early autumn of the corresponding year. Leveling was performed between two assumedly stable benchmarks north and south of the barriers, respectively, and with the northern benchmark kept at a fixed height (Id. 72- 12-9021; determined in 1995). Differences in the calculated heights at the southern benchmark (Id. 125-06-9026; determined 2006) between individual leveling campaigns, which is ascribed to small differences in general glacio-isostatic land uplift between the two points, are small and within the allowed uncertainty for the individual leveling campaigns. Therefore, no subsequent corrections of individual benchmarks are made, and the uncertainty in their height determination over this 9 year period is evaluated at ±0.003 m (Vognsen et al., 2013b). Ahead, however, a recalculation of the benchmark heights covering the area should be performed when either point is connected to the national leveling network again. Older leveling data were digitized, recalculated, transformed from the former Danish Ordnance Datum (DNN) to DVR90, and quality assessed for calculations of vertical velocities extending back in time.

#### Elevation of Sewers

In 2012, elevations of the bottom of the sewers were measured beneath man-hole covers at 57 accessible locations to compare these with elevations measured between 1980 and 1990 (in parts of Thyboron this was not possible due to floating manhole covers that could not readily be removed). So far, it has not been possible to date the older measurements more precisely. The uncertainty in the 2012 height determination is evaluated to be better than ± 0.013 m; refer to Vognsen et al. (2013b) for more information on methods. The 1980–1990 measurements are believed to represent the bottom level of sewers well although the uncertainty is unknown and probably somewhat larger.

#### Land Motion from Satellite Interferometry

Imagery from the ERS radar satellite has been used as an alternative method to reveal land deformation at Thyboron. The methodology is based on analysis of time series of co-registered radar satellite images known as persistent scatterer interferometry, PSI (Ferretti et al., 2007). The method has been applied to monitor land deformation from a number of geo-hazards like landslides, earthquakes, volcanism, and coastal flooding.

In the present study time series of ERS radar images from 1995 to 2001 [ERS SAR: Track 108, frame 2457: 48 scenes (period: 1995–2001)] were collected by courtesy of the European Space Agency (Levinsen et al., 2015). The images were acquired and processed by the Geological Survey of Norway. After a detailed quality assessment of the results, a threshold measure of 0.55 is set on the "coherence" parameter, which is a measure of reliability of each PSI point with a calculated vertical velocity. Although, newer satellite imagery is desirable, this has not been possible due to lack of coverage, data series not being sufficiently available for calculations, or, due to inhibitive costs for inclusion in the present study. No subdivision of the PSI data set has been attempted and the data thus provides information about the averaged relative rates of vertical land deformation 1995–2001 in Thyboron relative to a point near the northern benchmark mentioned above for the leveling. Thus, the PSI calculated vertical velocities do not include the contribution from the glacial isostatic adjustment.

#### Digital Terrain Model

The Digital Terrain Model (DTM) from the Danish 2007 Digital Elevation Model (DK-DEM) used throughout this study has a 1.6 ∗ 1.6 m resolution, a horizontal accuracy of 0.67 m RMSE and an averaged vertical accuracy of 0.059 m RMSE (Hawa et al., 2011; Rosenkranz and Frederiksen, 2011). Data are available free of charge through the Danish "basic data program" (eng.gst.dk). In addition to the DTM (DK-DEM), a hydrological adaptation layer

Groundwater monitoring (B1–B10), tide gauge (TG C & TG H) and meteorological (MET) station positions are plotted (right) together with lines (A\*, B\*) along which groundwater stations are approximately aligned.

has been produced and is also available for download. A stretch of quay that has been heightened by 0.5 m since the acquisition of the DEM is not included but is unlikely to affect the overall results. According to Thyboron Port Authorities and confirmed in our own investigations, the new quay height corresponds well to the initial height when the original quay was constructed approximately 50 years ago indicating land subsidence rates in the order of 10 mm/year.

# Tide Gauge Data and Sea State

Two continuous tide gauge (TG) digital data series exist for Thyboron and are operated by the Danish Coastal Authority. Thyboron Harbor TG (Id: 4201; from 1974) is in the northern part of the harbor, and Thyboron Coast TG (Id: 4203; from 1975) is placed in a groin on the open coast, **Figure 3**. In addition, analog data exist back to c. 1935 for the harbor TG but still await digitization. Historical extreme water levels and storm events have been digitized in the past. No systematic metadata have been collected and kept for the central Danish North Sea West Coast TGs and data series (service & maintenance, benchmark and water level corrections etc.) and no concise manual for their operation exists. Correction of data, services, and benchmark leveling has been performed in the past, however, and can be traced in the data series (e.g., linear corrections in time and datum shifts). From a close manual scrutiny of the data, the TG series (1975–2012) have been corrected and validated as far as possible, e.g., by recalculating of the TG benchmarks and omitting erroneous data. Furthermore, tide gauge data from the Limfjord are available from a number of stations. 2015 TG measurements are used in relation to the groundwater level observations. Regarding extreme water levels and statistics, numbers are adopted from Sorensen et al. (2013).

# Groundwater and Precipitation Data

Ten stations measuring the groundwater level in Thyboron were established in august 2015, **Figure 3**. Their current spatial distribution reflects a plan to monitor variations N-S and W-E, interests in certain areas in the town, and that the deployment should be on municipal land. Although covering a mere 3 months only, the data logging in 5 min intervals are included to, at least, discuss their potential value ahead in assessing climate impacts in Thyboron. The measured groundwater levels are related to DVR90 from GPS measurements at time of deployment and validated in the DTM. Precipitation is measured in Thyboron (10 min intervals) and is cumulated on a daily basis. Wind is also recorded but these data are not used in the present study (Id: 4200).

# Geological Modeling and Geotechnical Data

A geological model is a spatial representation of the local subsurface conditions and its quality depends on the accuracy and availability of geological information. A geological model rarely accounts for the entire geology of an area but it can focus on specific layers such as the shallow geology or the pre-Quaternary conditions (Binzer and Stockmarr, 1994; Nielsen et al., 2007). We use standard methods (Jorgensen et al., 2008; Sandersen, 2008) to define layers which potentially play a role for land subsidence in Thyboron, and to preliminarily discuss issues related to the groundwater level and coastal hazards. These layers are generated in GeoScene3D (I-GIS, 2015) accordingly and appear as continuous near-surface layers (0–30 m depth) in the resultant geological model.

#### Incorporation of Data

Various subsurface information from public and private archives, such as geological (Geo, 2015; GEUS, 2015a), geophysical (GEUS, 2015b), hydrological (GEUS, 2015c,d), and geotechnical data (Geo, 2015) are integrated into the model. A combination of these data can considerably increase the model's accuracy. The local geological knowledge primarily results from public and private well data as well as from a soil map of the upper 1 m (GEUS, 2015e), where according to Jakobsen et al. (2015) the Thyboron area is among the 10% of the country which is still not included in the map. Geophysical information for the area exists in the form of DUALEM-421s data collected in the framework of this study. No additional geophysical data have been located except for a single geophysical log in well no. 44.571 located SE of Thyboron (Petersen et al., 2008). Hydrologic data are from groundwater level measurements, hydraulic tests in wells and from a groundwater mapping campaign previously conducted in the area.

#### Digitization of Borehole Logs

A review of the Geo (2015) geotechnical archive located +200 geotechnical boreholes, all of which are geo-referenced based on the old site plans and digitized based on the old borehole reports to enter the GeoGIS database. The boreholes were mainly conducted in relation to construction works and, unlike most of the publicly available drilling from Thyboron (GEUS, 2015a), contain a high level of detail that describes the geological and geotechnical parameters such as water content and strength; e.g., Geoteknisk Institut (1978) and Geoteknisk Institut (1983), Figure S1. Groundwater levels are often registered, too, if established below the groundwater level. The Geo (2015) geotechnical boreholes are mainly concentrated in the eastern and southern part of the town facing the harbor and are not evenly distributed. At the same time, the exact location of the boreholes from the old site plans is uncertain. By combining this data set with boreholes from the GEUS (2015a) database, although in less detail, it seems possible to achieve a fairly good coverage in major areas of the town, **Figure 4**. The model of the geological conditions is expected to be more accurate around the harbor, however.

#### Geophysical Data (DUALEM-421s)

Geophysical data were collected in Thyboron during May 2015. The ground conductivity meter DUALEM-421s was applied for detailed mapping of the electrical resistivity of the upper 6 meters of the ground. Data have been collected at all available green areas, parking lots and on some roads with the DUALEM-421s deployed to an ATV (max. speed 30 km/h) with simultaneous video and GPS recordings. Data is inverted using a constrained multi-layer model (Vignoli et al., 2015). **Figure 4** shows the inverted resistivity in depth interval 1–1.5 m. Green colors indicate clay, whereas red and purple colors indicate the presence of more coarse sediments like sand and gravel. The results from the DUALEM-421s survey will together with borehole information represent data input for the geology of the upper few meters of the soil.

#### Hydrologic Flow Modeling

To gain insights about hydrological conditions in Thyboron today as well as in the future modeling is performed using the MIKE software (dhigroup.com). MIKE FLOOD couples the models MIKE URBAN and MIKE 21. MIKE URBAN is a hydraulic 1D-model used to simulate the flow and thereby stowage levels in the sewers. The calculation in MIKE URBAN is divided into two parts: first the runoff on terrain and thereby hydrography (flow over time) which runs into the individual wells in the sewer system is calculated. Secondly, the flow and backflow into sewers, wells, sump pumps, water outlet drains, basins etc. are calculated. A detailed digital mapping of the sewer system was kindly provided by the Lemvig Water and Wastewater Company and used in the model. Connecting this 1D calculation with 2D calculations in MIKE 21 via MIKE FLOOD water stowage from the sewers can propagate across the terrain indicated to the model by the aforementioned DTM including the hydrological adaptation layer. Thereby MIKE FLOOD calculates dynamic flood levels at any given precipitation event. Similarly, storm surge events are modeled. The results indicate a potential flood from various scenarios, sources, and durations by showing the maximum water depth in each cell (1.6 ∗ 1.6 m) of the DTM/DK-DEM. This may not necessarily occur simultaneously, however, and results thus show maximum potential flooding.

Hydrological conditions are calculated for the following precipitation events:

Values of rain intensity including a climate factor are adopted from Gregersen et al. (2014).

The combined effects of precipitation, land subsidence (projected from 2006 to 2012 leveling results and general uplift in the DEM) and sea level rise/extreme water levels have been calculated for various future scenarios, **Table 1**.

Subsidence rates are assumed constant in time in the calculations. The sea level rise scenario is adopted from Grinsted (2015) and Olesen et al. (2014) and modified to 2065 and 2115, respectively. Extreme water levels are adopted from Sorensen et al. (2013) using the Thyboron Harbor statistics. Linearity in 100 year event extreme water levels with SLR is assumed and e.g., changes in climate conditions (storm frequency and intensity), tides, morphodynamic conditions, construction works etc. are not considered. The effect of wave overtopping on flooding is not considered in the present study. The 2015 mean water level (0.05 m) accounts for the relative SLR since determination of the DVR90 datum around 1990. The Thyboron tide gauge statistics are assumed to be representative of extreme water levels in the channel and fjord thus disregarding potential gradients in the extreme water levels along the coastline. Furthermore,

FIGURE 4 | Available subsurface data for Thyboron. The green dots indicate wells from Geo's well database and black dots show wells available from the public Jupiter database. The blue line indicates the position of the geological profile A-A′ . DUALEM-421s data coverage is exemplified by resistivity measurements at 1–1.5 m depth (right).

TABLE 1 | Scenarios modeled in MIKE for a range of combinations of precipitation, sea level rise, and extreme water levels with and without land movement, and looking 50 and 100 years ahead.

considerably higher extreme storm surge levels on the North Sea coast, which may potentially lead to breaches of the sea dike, are not considered.

# RESULTS

Results from in situ and satellite measurements are presented and joined in modeling results. Main stakeholder interview results are briefly presented.

# Water Levels

The central Danish North Sea coast TGs' data are of a varying quality, **Figure 5**, Figure S2. The Thyboron Coast TG data are not as complete as the Harbor TG and data quality is lower mainly due to the harsher physical environment and more difficult access. From around 1990 the Thyboron series show a good correlation to the long tide gauge series in Esbjerg and Hanstholm (from Hansen, 2013) in annual mean water levels, Figure S3. Calculated rates of change in mean water levels from linear regression, used only here to evaluate datum/benchmark corrections of the TG's, seem reasonable and within the expected range for Thyboron, Table S1. Results imply that the Thyboron TG's can be useful to regional sea level studies and, on the other hand, that the data represent well the overall measured annual mean sea level variations in the eastern North Sea over the past decades. Results from relevant regional sea level studies may thus be representative for consideration at Thyboron at the current stage of research. Especially in the Hvide Sande TG data apparent local land subsidence effects still need to be accounted for in a more thorough TG benchmark and leveling reanalysis, if possible. Considering the usefulness of the DK North Sea TGs, an effort should be made, in general, to upgrade TG maintenance, benchmarking, and data collection to international standards.

Storm water levels can get significantly higher on the coast than in the harbor and Thyboron Channel and often with a strong gradient in water levels. In the vane of storms or after prolonged periods of windy conditions, the water level inside the fjord may be significantly higher than on the coast with a

general water level in the fjord of c. 0.6–1.0 m DVR90, Figure S4. Regarding extreme water levels, a 100-year event at Thyboron Harbor is 1.93 m DVR90 (Thyboron Coast, 2.64 m) according to Sorensen et al. (2013) who also provide a list of the 40 highest water levels recorded. These show some variability in numbers and intensity over time at Thyboron Harbor, Figure S5.

# Vertical Ground Motion

Observations of land deformation investigated from repeated benchmark leveling in 2006, 2009, 2012, and 2015 relative to an assumed stable point; from leveling the bottom of sewers in 1980/1900 and 2012, and from persistent scatterer interferometry (data from 1995 to 2001) all reveal the same pattern of subsidence, **Figure 6**, Figure S6. The subsidence rates of the bottom of sewers are up to 10 mm/year, from PSI up to 9 mm/year, and from leveling up to 7 mm/year, respectively, and results are thus consistent. Especially the independent results from PSI serve to validate the calculated leveling benchmark subsidence rates and vice versa. The two methods represent data acquired at different time periods points for the calculation of subsidence rates. Regarding PSI the measured rates of individual points are relative to the movement of a stable point that is only influenced by glacial uplift.

The PSI uncertainty is somewhat larger than from the leveling, but the spatial coverage (number of points measured) is larger and the risk of human error influencing calculations and thus the results is smaller. The separation in time points to the fact that subsidence is not a new phenomenon, and the subsidence does not seem to be restricted to individual buildings but is more widespread throughout the area (although differences may exist between building and ground subsidence rates). The apparent subsidence of the sewer system substantiates this. Also, there is a close connection between the year of construction of houses and infrastructure and rates of subsidence. This is, of course, not surprising considering the history of Thyboron (e.g., **Figure 2**). The area in SW which shows no/little sign of subsidence holds the oldest housing in town. Furthermore, the older parts of the harbor has probably been constructed on what was then believed to be the most stable location along the Thyboron Channel. Unfortunately, the digitization and investigation of historic leveling yielded little information that could be connected directly to the present study through "surviving" benchmarks. A few benchmarks have reliable results dating back to 1954, Figure S7, showing large subsidence rates on the then new-build dam north of Thyboron Channel, small but consistent subsidence rates of 1–2 mm/year in the more stable areas of Thyboron and on the Limfjord barrier, and stability north and south of the barrier, respectively.

The relative subsidence measured from leveling and PSI does not take into account the overall land movement from glacial

FIGURE 6 | The DTM of Thyboron shows the high dikes and dunes toward the North Sea. Also, the road into Thyboron is elevated above the general land surface of the town at or below 2.0 m (top left). Repeated leveling of benchmarks (colored dots) show subsidence rates of 1–7 mm/year (2006–2015) and from ordinary kriging a small area toward N and a large area toward S experience large subsidence rates, whereas areas toward SW and N are relatively more stable. Also, the total change (1980/1990–2012) of the bottom of sewers (circles) shown a pattern that seems to closely resemble that from leveling (top right). A spatial-temporal analysis of constructions years (bottom left) shows that most houses built after 1971 (blue dots) are located on a former part of the fjord (map from c. 1930). PSI-results show the same pattern of subsidence where the SW part of town is stable and rates of subsidence are increasing from N toward SE with rates of up to 9 mm/year (1995–2001).

effects. From the modeling an uplift value of +0.6 ± 0.2 mm/year is found for Thyboron and slightly more for the stable benchmark at Vestervig, **Figure 7**. Here, +0.8 mm/year is used and added in the hydrologic modeling to the subsidence rates measured from leveling at individual points (to yield slightly smaller subsidence rates than from leveling alone).

# Geological Modeling

The geologic and geotechnical information are combined in the geological model. Results of the preliminary work to render all available geo-information are presented at an N-S profile (position is shown in **Figure 4**) in the eastern part of Thyboron, **Figure 8**. The surface is generally at an elevation of 1.6–2.0 m DVR90 and the upper three meters (+2 – −1 m) consist of landfill (incl. reworked marine sand). The marine sand fill may include layers or lenses of varying thickness of organic material that contains plant remains and root and wood fragments.

Beneath the fill, a gyttja layer of a varying thickness (0.5– 3.0 m) is found. Gyttja layers can be highly compressible. Below the gyttja is marine sand to −7 m, where marine clay is found. The model suggests that the surface of the marine sand and clay forms ridges, which may be interpreted as several former

geoid .

marsh areas; some shallower than others. Along parts of the profile results are enhanced by inclusion of the DUALEM-421s results for the upper 1–2 m, Figure S8 (as **Figure 8** but showing DUALEM-421s results as well). The DUALEM-421s data, Figure S9, are difficult to interpret in most places due to the shallow groundwater level. The geophysical logging in well no. 44.571 SE of Thyboron (Petersen et al., 2008) indicates a high salinity in the groundwater. The DUALEM-421s resistivity measurements corroborate these findings but need verification from additional salinity measurements to e.g., map the salinity profile of the groundwater.

#### Hydrological Modeling and Groundwater

Although, covering less than 4 months (Aug 15–Nov 15), initial results from the 10 groundwater monitoring stations are included but do not offer sufficient data for a thorough analysis of the spatial-temporal variations in the groundwater level due to individual factors like ocean water level and waves, precipitation, temperature and saturation, infrastructure etc. Still, observations are worthy of an inspection following a dry period in October and a wet in November, **Figure 9**, including a minor storm event in late November with water levels of 1.5 m DVR90 in Thyboron Harbor. To separate effects from high (or low) water levels and wave run-up at the North Sea coast, and very high/low water level in the fjord, and in the sluice regulated lagoon south of Thyboron, more data is needed. Results show, however, that groundwater levels vary by at least up to 0.5 m in a few months, **Figure 10**, Figure S10. Also, at some locations along the profile lines and elsewhere the groundwater is likely to be present just below the ground surface. Whereas the average groundwater level is lowering toward south in profile A∗ , the picture is more unclear in profile B∗ and needs further investigation including effects from roads/railroad, sewers and drains etc. Some attenuation of the signal is apparent in B7. This may be due to the proximity to the sluice regulated lagoon experiencing smaller variations in water level. When longer coincident time series become available from the groundwater monitoring stations, from the Thyboron TGs, and from newly established gauges on either side of the sluice, groundwater level variations will be investigated further and the profiles integrated in the geological model as well as the hydrological model.

In the MIKE modeling it is demonstrated that future subsidence will lead to an increased risk of flooding for both a 5 year and a 100 year precipitation event, **Figure 11**. As a result of the subsidence the sewer system will, and to some extent already does, experience stagnant water, sludge deposits, reversed slopes, and broken pipes leading to a dysfunctional sewer system accompanied with more frequent floods. Rainwater is pumped from the sewer and drainage system to the sea

FIGURE 9 | Water levels at Thyboron Harbor tide gauge (top), cumulated precipitation for 2015 at Thyboron (middle), and daily precipitation (bottom) showed together with groundwater levels in borehole B7 for the period 5 Aug–30 Nov 2015 .

and a general rise in sea level has a minimal impact on the sewer ability to divert to recipients, whereas problems may occur during storm surges. As Thyboron is at the coast there is a clear relationship between groundwater and sea levels. Increased sea levels will lead to increased groundwater levels. This will increase the risk of seepage to leaking parts of the sewer system.

Rising groundwater stocks due to climate change (rising sea level and increased rainfall) can lead to a high water table where seepage is not possible all year round, especially during extreme water levels in the sea. At the same time an increased water table leads to the drainage system being filled with groundwater. This will significantly increase the risk of flooding in these areas as the existing drainage system is not working and a new sewage system must be constructed. A range of combinations of the modeled future scenarios may cause flooding at Thyboron, and many of the processes are coupled to enhance their effects on the hydrological system. As such, the hydrological modeling suggests that it is not the sea level rise itself that yields the largest impact but derived effects of prolonged periods of high ocean water

even if the groundwater level is raised (left). Today Thyboron is potentially at risk of getting flooded from a 100 year event. A similar event in 2115 including sea level rise and land subsidence will "drown" the town (a similar scenario potentially can occur in 2065) (middle). The sewer system will experience increased stow up from precipitation with a raised groundwater level, or, due to subsidence (right).

levels, extreme events, changing groundwater levels, and with subsidence being an important factor to increase flooding in both time and space e.g., through the degradation of the sewer system.

### Stakeholder Approaches to Climate Change and Adaptation

The four main stakeholders identified and interviewed in relation to this study are each playing an important role in relation to climate change adaptation in Thyboron, **Table 2**. The Port of Thyboron feels a responsibility toward to their customers (e.g., ships berthing and companies residing on port property) but as stated: "This is Thyboron, and this is a harbor. We occasionally get flooded and then we clean up." Whereas most other business infrastructure and private housing in Denmark is partly covered by the "Danish national storm surge relief fund" for floods exceeding a 20 year water level event, this is not the case for harbor areas (DP, under revision). Individual business insurances may cover some losses but, in general, e.g., damages to buildings and production time losses are not covered. The Port of Thyboron does take actions to make buildings flood resilient to optimize their business, however, but does not perceive storm surges and climate change related floods as any immediate threat to their business and future development. The harbor accounts for a projected sea level rise related to an average life expectancy of 50–100 years for new quay areas, however. In recent years the harbor areas has experienced flooding on a couple of occasions but only in the case of much more frequent and severe storm surge events will the Thyboron Harbor act. The Thyboron Harbor is aware that the "underground is difficult" and is interested in more dynamic representations of the subsurface conditions. Finally, the harbor relies on a functioning sewerage system and, of course, is aware that sea water is most likely to pass harbor areas in order to flood the town.

LWW seeks to keep a service level to avoid flooding exceeding 10 cm on street level due to heavy precipitation up to a 10 year event, and through their large investments in the water and wastewater pipe systems and costs for maintenance; they have a strong economic interest in the future. House owners and other businesses (otherwise not considered here) are responsible for their own protection against flooding.

The Lemvig Municipality, of course, has an interest in keeping their citizens happy and to develop the community for the benefit of all. To a large extent this is done through the municipal planning act. Also, the abovementioned municipal climate adaptation plan, which is to be integrated in the municipal plan, serves as an instrument for mitigation efforts in relation to climate change and in the development of the community. Climate change adaptation is ranked fairly high on the municipal agenda—at least in the future, and is something that the municipality will have to deal with. But, as pointed out, there are many other and more immediate challenges that the

#### TABLE 2 | Tabulated summary of information provided from semi-structured interviews held with main stakeholders in September 2015.

deviations and extremes.

municipality has to tackle and climate change adaptation has hitherto not been the most prominent. That said the municipality still feels that it is ahead of many other Danish municipalities regarding hazard mapping and addressing future challenges, e.g., by initiating investigative work in relation to Thyboron. The citizens of Thyboron have a reputation of coping and not complaining, an impression shared with the DCA and Thyboron Harbor, and the municipality has so far not invited the citizens to participate in the broader adaptation work. The reason for this is that the municipality wants a more elaborate scientific background and ideas about where they are heading before engaging the community. The municipality does, however, inform the citizens individually when needed and takes interest in local water related challenges. The Lemvig Municipality sees the continuous expansion of the Thyboron Channel and its effect on current and future storm surge water levels as the overarching future adaptation challenge to Thyboron. The town's mitigation and adaptation measures will very much depend on whether and which solution to decrease the amount of water entering the fjord is implemented. Besides looking into local actions, the municipality is therefore active both politically and in a cross-municipal project funding search with other municipalities bordering the Limfjord to reach a future solution regarding Thyboron Channel.

The DCA is in charge of the implementation of the EU Flood Directive and is responsible for preliminary national flood hazard assessments and for the hazard, vulnerability, and risk assessment and mapping for areas appointed under the Directive. As mentioned before, DCA also is responsible for the erosion and flooding protection of Thyboron from the sea, currently in 5 year plans with economic contributions from Lemvig and other municipalities along the central Danish North Sea coast. DCA thus has a strong focus on the protection level toward the sea in Thyboron and, more general, a focus on tools for risk reduction and decision-making to e.g., municipalities.

Common to the stakeholders is the acknowledgment that coastal floods and climate change are factors that must be addressed and taken into account in future planning. Although, the perception of the timescale needed for action differs between the stakeholders according to their core business areas (and depending on potential future flooding experiences!), they all agree that collaboration is important to achieve the best solutions. Except for some co-work between the LWW and the municipality, no such collaboration is operational yet when it comes to coastal hazards and climate change adaptation. The stakeholders have quite varied needs for data, information, knowledge and tools. Whereas the Danish Coastal Authority sees itself as a provider of tools etc., as experts on coast protection and coastal climate adaptation and does give legal and coastal engineering advice, the LWW and Lemvig Municipality have fairly detailed but different demands for updated information and "dynamic results." Port of Thyboron basically wants "a number to plan from."

Although, direct questions about economy and funding were deliberately omitted—this will soon enough become a matter of attention, the interviews still provide some information on stakeholders' positions and ability to engage in collaborative efforts for climate change adaptation. The LWW essentially only has its customers to pay for adaptive measures, but they are forced to safeguard their investments in water supply and sewerage in relation to future conditions. The municipality is financed by the tax-payers and has to prioritize their initiatives very strictly in a time of budget-cuts due to a decreasing population, and the Port of Thyboron basically sees itself as a private enterprise with no formal responsibility toward climate adaptation and mitigation of the town of Thyboron. The three stakeholders to some extent all point to the national government to take a lead in adaptation and to assist economically. Opposed to this, DCA takes the economic responsibility given by the legislation and lets politicians make decisions regarding the national economic engagement at Thyboron and elsewhere in Denmark. However, through its scientific work DCA continuously aims at providing knowledge and better tools for decision support.

#### DISCUSSION

The investigated coastal climate hazards from sea level rise and associated effects from storm surges, prolonged periods of a raised water level, and a general rise in the groundwater level will in combination with precipitation and land subsidence adversely impact the local community of Thyboron, Denmark. The future coastal erosion, erosion protection, and the effect of morphodynamic changes on storm surge levels in Thyboron due to an ongoing expansion of the cross-sectional area of the Thyboron Channel are additional challenges not considered in the present study. They, too, are significant in the evaluation of future options for adaptation and planning. Some results are preliminary and need further work. They are included to exemplify potential links between processes and impacts to the different sectors and main municipal stakeholders with interests and responsibilities in relation to adaptation in Thyboron. As such both scientific and societal aspects of hazards and impacts are parts of ongoing investigations into how coastal climate change issues are addressed to stakeholders to reveal their motivation, ideas, and needs in relation to knowledge and tools for adaptation management in each of their areas, and to create a local platform for transdisciplinary collaboration for mitigation and adaptation.

### Toward Transdisciplinary Research and Collaboration

The development of a common language and to move from consulting to participatory transdisciplinary research (Mobjörk, 2010) and management may prove viable in Thyboron in relation to climate change adaptation. Serrao-Neumann et al. (2015) reviewed transdisciplinary literature and approaches, and provided learnings about sectorial and cross-sectorial climate change research and management of climate change impacts in SE Queensland, Australia. Particularly relevant to this study are the authors' reflections and conclusions regarding the concept of learning-by-doing and doing-bylearning concerning the development of theoretical knowledge from practice, and the development of practical knowledge from theory, respectively, proposed by Loorbach and Rotmans (2011, cf. Serrao-Neumann et al., 2015): learning-by-doing was pursued by engaging stakeholders representing all sectors and various scales (local, regional and state), and, the research team was able to integrate the practice/corporate/agency knowledge across sectors when formulating and refining adaptation options. In parallel, doing-by-learning was carried out by reframing adaptation options proposed in the literature to improve their capacity of being adopted and implemented in a pragmatic way based on stakeholders' feedback. In addition, a solutionoriented transdisciplinary research collaboration (opposed to a problem-oriented) was an advantage; and a selected sector can successfully be used as a platform to (re)integrate and apply created knowledge according to Serrao-Neumann et al. (2015).

At our local study location, the implementation of concrete adaptation measures is a matter of (preliminiary) discussion but cross-sector and transdisciplinary co-work is only slowly forming. Still, inasmuch as learnings are transferrable across scales, our work corroborates the findings of Serrao-Neumann et al. (2015) that transdisciplinary research is conducive to climate adaptation research. The research and cross-sector collaboration presented here has gradually evolved from a scientific interest in flooding hazards, data provision from leveling by national authorities, and an interest in these results by the local municipality. In relation to the work ahead several factors may prove advantageous: The currently involved stakeholders represent both public and private actors across levels of governance and legislation, and a common understanding of the legislative frameworks is already established. To some extent the political and/or economic standpoints of the individual actors have been clarified, and an initial platform for collaboration is "under construction" based on the research presented with positive (and realistic??) expectations from the stakeholders. Still, methods for the integration of knowledge need to be defined (e.g., Hinkel, 2008).

The research thus grows from a combined natural science and science-governance perspective. As the Thyboron Harbor, DCA and LWW mainly are technically founded, a technology/science base may well serve as the platform for transdisciplinary work and integration—gradually bringing in more social science oriented work. Although, there is a political agenda for adaptation actions, the "slow and small start" with no imminent need for actions may become a great advantage toward transdisciplinary work to decide upon optimal adaptation and mitigation measures. One major threat may be impatience among actors, including academia, to reach results, and another is a lack of focus on the process by the involved actors.

As climate change impact manifests itself in ways that do not consider administrative borders the identified main stakeholders must, although working in different areas and on different timescales for e.g., investment and maintenance, work together in a common appraisal of the challenges faced. By somehow deflecting scenarios of sea level rise, which can be difficult to relate to and address, into very concrete results on the natural processes this provides an alternative way of addressing climate change impact. For instance, the ongoing land subsidence is very concrete and easy to relate to for all stakeholders although the subsidence rates ahead may be governed by the same level of uncertainty as sea level rise. The measured land subsidence will almost certainly increase the vulnerability toward flooding, however. Here, the transformation of scientific knowledge into applicable measures can optimally provide a platform for this common work. In addition, the research presented seeks to explore how existing and new data can be utilized for this purpose. As such the work mainly addresses the transformation of current scientific knowledge for use in decision-making to create a joint platform for data, knowledge (sharing), and planning that satisfies the individual needs of the stakeholders, and at the same time optimizes the decision-making process to provide better common solutions for adaptation and planning in Thyboron and elsewhere.

The presented research implicitly intends to address the added-value of cross-sectoral collaboration in climate change adaptation from private consulting and local companies, municipal and national government levels agencies, and academia. As these entities have different scopes of work that all must be satisfied within such a framework, a large effort must be made to clarify scopes, agendas, and perspectives of engagement to reach a consensus for collaboration. Equally, the research and development work must bear implications of advancement in a wider sense (e.g., economically through the transferability of results and methods developed, added value of data, business and research advancement, and optimal local solutions). This is no easy task as such co-work necessitates excursions into the unknown and at the same time it requires continuous attention to provide concrete results to the local politicians and decisionmakers. Also, all material must be shared in a clear and concise way to be comprehendible to all actors. The research and local adaptation work has not yet addressed and engaged the local inhabitants. This is one of the forthcoming challenges that also call for enhanced inclusion of social sciences as well as someone to take responsibility of the process work ahead. So far, addressing common challenges between stakeholders seems a good starting point for collaboration.

#### The Probability of Sea Floods

Quality reassessment and analysis of two digital tide gauge series (late 1970s–2012/2015) from Thyboron have provided reliable results of the water level variations. Especially the harbor TG series (with additional data extending back to 1935 still in an analog form, and some daily recordings dating back to 1874) seems robust. A comparison of the harbor TG with the long TG series from the Danish North Sea coast at Esbjerg and Hanstholm shows a good correlation in the annual means. This potentially provides sea level research with additional data from the North Sea coast, and at this stage of investigation justifies that regional projections of water levels can be used for Thyboron, too. In addition, extreme water levels at Thyboron show a marked decadal variability. This variability is to be investigated further e.g., in a comparison with other TG series in Denmark and from the countries bordering the North Sea. The extremity of events in the Danish North Sea TG data has to be evaluated very carefully due to local effects in both open coast and harbor locations. For Thyboron Harbor a 100 year event has previously been calculated to 1.93 m DVR90 which is used in this study. This level is according to the digital elevation model just above the threshold where sea water may potentially flood large parts of Thyboron. As the three most extreme recordings at Thyboron Harbor TG are between 1.85 and 1.90 m DVR90, and the harbor area is flooded at c. 1.80 m DVR90, special attention to the most extreme events must be given ahead in relation both to the statistics, and to the actual probability of flooding. So far, the town has only experienced minor floods except on harbor areas. The 100 year event has been used in the hydrologic flow modeling, and scenarios of future extremes are based on Grinsted et al. (2015) adding SLR only to current statistical extreme water levels.

## Land Deformation and Perspectives

Thyboron is subsiding. As the surface elevation generally is between 1.4 and 2.5 m DVR90 subsidence is relevant to consider in relation to the current and future vulnerability to flooding. Repeated precise leveling campaigns in 2006, 2009, 2012, and 2105 provide a detailed and quite unique dataset, at least in a Danish context, to show subsidence rates of 2–7 mm/year and with the largest rates toward SE. A recalculation of the leveling and corrections of the height benchmarks should be performed the next time the assumed stable points are connected to the national network. This will not change the overall results substantially but will connect the local, relative measurements to regional and national scale vertical land motion.

Considering the historic development of Thyboron over the past 100 years, subsidence may not come as a surprise as large parts of the town rests on landfill on the former fjord bottom. Only the oldest part of the town—residing on a previous fjord holm, does not experience any substantial subsidence, except perhaps c. 1 mm/year as witnessed on the barrier N and S of Thyboron, respectively. The leveling results are consistent and rates are, to a first approximation, considered linear between the measurements (9 years). As witnessed from aerial photographs and house registers there is a strong correlation between subsidence rates and time of landfill, and it is assumed that the thickness of fill material is larger toward SE. As the bottom of sewer wells show a similar pattern of subsidence (up to 10 mm/year) it is also assumed that the entire area is subsiding. The differentiated subsidence between N and S has led to broken pipes and to reverse sloping of the pipes at a considerable cost to the Lemvig Water and Wastewater Company over the past years.

Although, local land subsidence is a well-known phenomenon, data from Denmark are scarce. Detailed leveling is costly and densification and/or renovation of the Danish height system have not been carried out in many areas. Benchmarks were established by the Danish Geodata Agency in assumedly stable positions to avoid e.g., organic soils susceptible to land movement. However, local land subsidence issues have gained attention recently due to their adverse effect on flooding vulnerability, and other local areas of subsidence are currently being identified (Broge et al., 2013). In a Danish perspective a geographic focus of investigation may be related to coastal towns

where low-lying former meadows have been urbanized in recent decades. Like in Thyboron, a screening that combines historical landscape information, house registers, and satellite imagery may be a viable way to proceed.

The PSI Insar mapping of Thyboron from 1995 to 2001 ERS satellite data provides results that are consistent with the leveling in terms of both subsidence rates and spatial variation. Although, separated in time, the leveling and PSI methods and results compliment and validate each other. Despite the lowresolution imagery compared to newer satellites' coverage, the PSI results appear to reflect the differentiated subsidence pattern in Thyboron very well. The difference in subsidence rates within the small area of Thyboron is a main reason for the good results. A combination of the two methods, e.g., by normalizing the PSI rates to the benchmark rates from leveling can be attempted ahead to enhance the spatial resolution and provide a more rightful map of subsidence than applying an ordinary kriging method from leveling as performed here.

PSI methods are used in many studies for detection of land subsidence in relation to coastal flooding hazard mapping. However, it is the first time convincing results are presented and validated by detailed leveling in Denmark. Knudsen et al. (2011) did not reach univocal conclusions regarding the method's applicability in a coastal setting (Esbjerg). Pedersen et al. (2011) initially set out to relate to coastal flooding in their work but the quality of mapping was not sufficient to address this further. Here PSI methods are applied directly in relation to the investigation and evaluation of coastal climate impact. When sufficient Sentinel-1 satellite data are available for subsidence mapping in mid-2016 (expectedly), this will provide access to near real-time (repeat cycle 12 days) subsidence mapping in Thyboron and the rest of Denmark. This will potentially become a strong tool to the local stakeholders provided that a service is set up to monitor the changes and deliver the results. With the launch of the twin Sentinel-1 satellite in April 2016, data acquisition frequency will double to the benefit of future updates of satellite based subsidence maps.

The rates in Thyboron are liable to decrease over time depending on the causes of subsidence. This is to be investigated further. However, we assume linearity (until 2065) in the rates from leveling in a first appraisal of the combined effect on flooding from land movement and sea level rise. This allows a dynamic modeling of the cumulated elevation changes in the DTM. As both the leveling and PSI methods show relative values, 0.8 mm/year uplift from GIA in the Danish uplift model [S1] is added to the rates and used in the hydrological model (using the ordinary kriging interpolation). The results show that the relative change in sea level also bears implications on sea flooding inland and not solely at the coast/at tide gauges.

GIA rates are small (≈ 0 – +2 mm/year) as Denmark lies at the margin of the last glaciation and thus also at the edge of most Fennoscandian GIA and uplift models. With the increased accessibility to land movement information ahead, this will allow for improvement of both the uplift model on a national scale (better fit to data) and yield a more detailed picture of regional (e.g., due to tectonics and basin compaction) and local deviations from the overall pattern. As the results from Thyboron show, land subsidence is, indeed, a factor that must be accounted for locally in Denmark.

#### Subsurface Interactions

Consideration must be given to sea level—groundwater interactions. Permanent or temporary changes in the groundwater level may affect the coastal flooding risk. Also, the geology plays a role. Although, the short data series (Aug– Nov 2015) from the 10 stations are too short for a detailed interpretation, variations in the groundwater level exceeding 0.5–0.8 m are witnessed, and by the end of the investigated period the groundwater levels is less than 0.6 m below terrain at low-lying stations, and likely less at some unmonitored locations. Groundwater is already a challenge in Thyboron. The groundwater information will enter the ongoing 3D geological model setup to couple to the geologic and geophysical mapping of the upper layers, which is expected to provide a more solid basis to interpret causes and magnitudes of subsidence and provide information that can be rendered useful for the hydrologic flow modeling and interpretation. With the increased repeat cycle of e.g., the Sentinel 1 satellites' imagery, seasonal variation in subsidence rates and the potential relation to the groundwater level can be resolved.

The coupling in a model of engineering geological and geotechnical analyzes and satellite borne subsidence mapping opens up a range of options, too. In and around Thyboron a large amount of geological and geotechnical parameters have been collected from decades of experience in construction works. By comparing these data with other available data in a 3D model, spatial analyzes of the underground conditions in relation to subsidence mapping can be performed. This allows for a comparison of cumulated layers of filling and observed compressible layers with actually recorded subsidence rates. Analyzes may also include geotechnical strength parameters, water content of sediments, and historic hydrological observations of water levels. Since the data coverage of the geotechnical information in Thyboron area is not evenly distributed spatially, the possible relation between subsidence rates and thickness of filling layers etc. from areas of good coverage may be used to predict thicknesses expected in areas which are not covered by geotechnical information. Satellite based subsidence mapping will then become a very useful source of data to support the establishment of engineering geological models for the area to be used by the individual stakeholders with a spatial interest in the area. The models can then be used in conditional assessments and climate proofing of existing facilities and infrastructure, in future design and planning, as well as for economic risk analysis of future investment conditions. This also means that subsidence attenuation better can be estimated and used in planning.