8th IAHR ISHS 2020 Santiago, Chile, May 12th to 15th 2020
DOI: 10.14264/uql.2020.517
Towards a multi-hazard analysis of infrastructure in a seismic coast subjected to climate change, with a focus on the Chilean coastline
P. Winckler1 1Escuela de Ingeniería Civil Oceánica Universidad de Valparaíso Centro de Observación Marino para estudios de Riesgos del Ambiente Costero (COSTAR) Centro Nacional de Investigación para la Gestión Integrada de Desastres Naturales (CIGIDEN) Valparaíso, Chile E-mail: [email protected]
ABSTRACT
The physics of some of the most relevant climatic and geophysical drivers affecting the Chilean coasts are reviewed. Some relevant figures about the country are introduced, and recent findings and research gaps on extreme waves, meteotsunamis, storm surge, sea level rise, earthquakes and tsunamis along the country are analyzed. A discussion on how to combine these phenomena to assess the level of flooding from a multi-hazard perspective is proposed, and a conceptual framework for the integration of short-term meteorological effects, long-term climate driven hazards and geophysical drivers into a multi-hazard analysis is discussed. Finally, the identification of gaps and future research is proposed. The multi-hazard, probabilistic and nonstationary design approach proposed herein, where uncertainty is at stake, can be extended to the design of coastal infrastructure, such as the stability of breakwaters, overtopping and forces on slender elements.
Keywords: Multi-hazard analysis, coastal flooding, climate change
1. INTRODUCTION
The physical impacts in the coastal territory stemming from a combination of short-term meteorological effects (coastal storms, meteotsunamis and storm surge), long-term climate driven hazards (sea-level rise) and astronomical tides include, among others, flooding of coastal areas; erosion of beaches, cliffs, river outlets and dunes; changes in the dynamics of wetlands and estuaries; increased downtime and operational delays in commercial and minor ports and damage to coastal infrastructure. These climate driven impacts can be enhanced by other fast-occurring geophysical hazards (earthquakes, tsunamis), slow seafloor changes of anthropogenic source (e.g. subsidence due to the compaction of sediment by buildings or groundwater extraction) or shortage of sediment supply during the design life of a structure. In the most general sense, the flooding level 𝐶 with respect to a present-day lowest astronomical tide (LAT) can be computed as
𝐶 𝑍 ∆𝑍 𝑍 𝑍 𝑍 𝑅 𝑅 𝑍 , 1 where 𝑍 is the astronomical tide, ∆𝑍 is the sea-level rise between a reference and a target period, 𝑍 is the storm surge, 𝑍 is the meteotsunami, 𝑍 is the wave setup, 𝑅 is the wave runup, 𝑅 is the tsunami runup and 𝑍 considers ingragravity waves and other effects, such as river flooding. Naturally, these variables occur at very different spatial and time scales and with very different amplitudes depending on the local conditions of a site. Equation (1) is based on a simple additive model where variables are assumed independent and nonlinear interactions among them are disregarded. It can either be understood from both the deterministic or probabilistic points of view (e.g. using Monte Carlo simulations), requiring in both cases long term records for each variable and correlations to understand their dependency. Additionally, it can be computed from a traditional approach based on historical data, or considering climate projections (Toimil et al. 2019). From an applied standpoint, coastal structures in seismic-prone regions are usually designed by separating short and long-term climate driven variables (Figure 1) from earthquakes and tsunamis. Under this simplification, Equation (1) reduces to
𝐶 𝑍 ∆𝑍 𝑍 𝑍 𝑍 𝑅 , (2) where waves and sea level are obtained from hindcasts (e.g. Beyá et al 2017; Winckler et al, 2019) or from Global Climate Models (GCMs) in case climate change is considered. If only geophysical phenomena are considered, Equation (1) becomes: 𝐶 𝑍 𝑅 𝑍 , (3) where the coseismic uplift/subsidence is considered as a change in the LAT (Figure 2). In Equations (2) or (3), the astronomical tide are usually included as a static level above the LAT.
Figure 1: Climate-driven and astronomical tide used to define the flooding level.
During the design life of a structure, a combination of short and long-term climate with geophysical phenomena can occur, thus altering the expected risks and possible economic consequences of failure. Despite this, there are few studies proposing methodologies to combine short-term meteorological effects and long-term climate driven hazards in the design of coastal structures (e.g. Vousdoukas et al. 2017) and even less studies combining tsunami flooding with sea level rise (e.g. Li et al. 2018). The long-term projection and combination of climate-driven hazards, on the other hand, is relatively novel. Mori et al. (2016), for example, combined sea level rise, storm surge and extreme waves to evaluate the influence of future climate change on caisson breakwaters while Lee et al. (2013) conducted a reliability analysis for wave run-up and armor stability of inclined coastal structures for various scenarios of long-term sea level rise due to climate change. To the author’s level of knowledge, which is obviously limited, no comprehensive studies have been conducted to include all variables in Equation (1) in the context of risk analysis of coastal structures.
Figure 2: Coseismic seafloor changes, tsunamis and astronomical tide used to define the flooding level.
2. THE LOCAL CONTEXT
Chile has an extensive coastline of ~4200 km facing the southeast Pacific Ocean (Figure 3A). The territory is characterized by strong latitudinal gradients, with a climate ranging from the most arid desert worldwide in the north to rainforests in Patagonia. Its geomorphology is shaped by the Chile-Peru trench, formed by the subduction of the Nazca Plate beneath South America. Between 18.4°S and 41.5°S, an almost rectilinear coastline is comprised by cliffs, dune fields, coastal wetlands, peninsulas and few bays where major ports cities are situated. This coastline has one of the narrowest continental shelves worldwide (Paris et al. 2016) showing an intense
seismicity and tsunami generation potential. In contrast, the south (41.5°S to 55.6°) represent the most extensive fjord region in the world (Cameron and Pritchard 1963). These features provide unique atmospheric, oceanographic and geologic conditions which make the country a natural laboratory to investigate climate trends and coastal hazards, with similarities with countries found the Pacific coasts of South, Central and North America, Alaska, Japan and Indonesia. Indeed, between 1980 and 2011, Chile recorded losses of nearly 1.2% of its GDP due to natural disasters, a significant part of which is due to meteorological drivers (floods, winds, and storm surge), earthquakes and tsunamis (UNISDR 2015). Figure 3B depicts the offshore mean wave climate while Figure 3C shows the population in coastal municipalities. Winckler et al. (2019) indicates that nearly 972 thousand inhabitants lived in less than 10 meters above sea level in 2017, figure which is lower than the global and Latin American average, with 10% and 6%, respectively (McGranahan et al. 2007). According to Church et al. (2013), the rapid increase in coastal population will boost the risk of climate related disasters.
A) B) C)
Figure 3: A) Typical atmospheric pressure pattern including the Southeast Pacific Anticyclone and an westward extratropical cyclone L (orange), tectonic setting (pink) and tide gauges with records of more (blue triangles) or less (red dots) than 30 years of data. B) Mean wave direction and significant wave height (𝐻𝑠) offshore Chile (adapted from Beyá et al. 2017). C) Population of coastal municipalities, according to 5 censuses (INE 2017).
3. MULTI-HAZARDS
3.1. Extreme waves
Wave climate is controlled by swells emerging from westward extratropical cyclones (Figure 3A) with trajectories over almost 2000 km, following a latitudinal belt between 40°S and 60°S (Beyá et al. 2017). The intense surface winds associated with extratropical cyclones transfer energy to waves, which propagate from the South Pacific Ocean towards South American coasts. Due to Chile’s length, there are strong latitudinal gradients in mean wave climate (Figure 3B), ranging from highly energetic western swells in the south to a relatively mild wave climate in the far north due to the greater distance from the wave generation zone.
A) B)
Figure 4: A) Number and B) Rate of change of extreme wave events along the Chilean coast, based on a wave hindcast (Beyá et al., 2017) for the period 1980-2015 (Winckler et al. 2019).
Based on a wave hindcast (Beyá et al., 2017) for the period 1980-2015, Winckler et al. (2019) shows a significant increase in the number extreme wave climate throughout the entire coastal zone (Figure 4), while the number of extreme wave events increased between 4 to 12 events since the early 80’s in the northern and southernmost extremes of the country, respectively. As for the future, the frequency and intensity of extreme waves is expected to increase by 2045 in the main 9 ports which are exposed to the Pacific Ocean (Figure 5).
A) B) C) D)
Figure 5: Projected extreme values of 𝐻𝑠 computed as the median of six wave models in 9 ports (Winckler et al 2019). Thick circles correspond to the analysis in Valparaíso. A) Historical period (1985-2004), B) projection (2026-2045), C) absolute difference and D) relative difference between both periods are presented.
3.2. Meteotsunamis and storm surges
Meteotsunamis and storm surges are long waves caused by storms, the former usually ranging between 5 minutes and 2 hours and the later between 2 hours and days. Meteotsunamis seldom occur along the west coast of South America and, when combined with other oceanographic conditions (extreme waves and storm surge), may cause damage levels comparable to those resulting from Mw 8 earthquake generated tsunamis (Carvajal et al., 2017). Storm surges are relatively minor along the Chilean coasts basically due to the presence of the Perú-Chile trench and the relatively small continental shelf (Paris et al. 2016) which inhibit its formation. As shown in Figure 6, these atmospherically induced tsunami-like oscillations were first instrumentally recorded in local tide gauges and analyzed for an intense storm that affected central Chile on August 8th, 2015 (Winckler et al., 2017). The storm was characterized by strong winds, a locally unprecedented atmospheric low pressure and intense sea-level oscillations which caused six casualties and severe damage to infrastructure along 500 km of coastline. A
comprehensive long-term statistical analysis and the future projections of these phenomena, however, are yet to be understood in the Pacific margin of South America.
A) B)
C) D)
Figure 6: A) Meteotsunami and storm surge of August 8th, 2015, as recorded in tide gauges. The storm path is depicted in red circles every 6 h. B) Time series of 1-min sampling for the original time record, C) 2-h low-pass detided and filtered time series (storm surge) and D) residual time series (meteotsunami) at various stations.
3.3. Mean sea level
Changes in the relative mean sea level (RMSL) are caused by a combination of changes in both the ocean surface and the seafloor. This variable results from a combination of long-term climate drivers, climate variability, short term meteorological phenomena, astronomical tides and vertical changes in the earth’s crust acting at different spatio-temporal scales. Secular subsidence (i.e. slow process occurring after a load is applied) due natural and anthropogenic causes, as well as changes caused in the seismic period (Wesson et al. 2015), also play an important role in subduction zones. As shown by Winckler et al. (2020), RMSL trends at tide gauges (Figure 3A) differ along the continental margin, with some stations revealing an increase of up to 4.30 mm/yr (San Antonio) or reductions of up to -4.83 mm/yr (Puerto Montt). Except for Caldera, northern stations covering an area of nearly 1600 km show a drop of -1.56 to -0.45 mm/year, probably due coastal uplift built up since the 1877 megathrust earthquake (Métois et al. 2013). The central region shows opposing trends, with maximum and minimum values in San Antonio and Talcahuano, respectively. Considerable differences between the neighboring stations could be attributable to differential effects in seismicity on a relatively large scale (e.g. Comte et al., 1986) or local effects; while the tidal gauge in Valparaíso is located in a ~100-year breakwater built on a rocky formation, in San Antonio the instrument is sited near a river outlet. Toward the south, Corral and Puerto Montt show the largest RMSL reductions throughout the country, which may still be affected by post seismic relaxation of the earth crust after the 1960 Valdivia Earthquake. In Puerto Williams, the RMSL seems to be less affected by seismicity, since tectonic motions occur at a lower speed compared with northern Chile; the region is also affected by glaciation- induced sea-level change during the Holocene (Perucca et al. 2016).
Being one of the most seismically active margins on the planet (Giesecke et al. 2004), RMSL changes due to climate drivers seem to be minor when compared to coseismic deformations. Indeed, vertical displacements of various meters triggered during the 1960 Valdivia (Plafker and Savage 1970) and 2010 Maule (Farías et al. 2010) earthquakes are comparable with centuries of absolute mean sea level (AMSL) change due to climate change. Indeed, Winckler et al 2019 show that the AMSL is expected to rise between 0.1 and 0.2 m by the period 2026- 2045 (Figure 7A) and roughly 0.6 m in Chile by the end of the 21st century (Figure 7B), range which is relatively small compared to coseismic displacements. Additionally, ENSO events are responsible for significant changes in AMSL (Enfield and Allen 1980) causing a rise (drop) of up to 30~40 cm during strong El Niño (La Niña) years (Reguero et al. 2015). The distinctive features of this coast, where the AMSL rise may be overshadowed by coseismic deformation and ENSO, can be used to understand the long-term effects in coastal environments due to climate change. For example, the subsidence of nearly 2 m caused by the 1960 Valdivia earthquake (Plafker and Savage 1970), which transformed low lying areas used for agriculture into extensive wetlands (Saint-Amand 1963), is comparable to nearly 600 years of climate driven AMSL rise, according to global trends of 3.2 mm/yr estimated for 1993-2010 (Church et al. 2013). While coseismic subsidence due to megathrust earthquakes in combination with AMSL rise may be catastrophic consequences in lowlands, coastal uplift may reduce the impacts of long-term changes in sea level.
A) B)
Figure 7: A) Absolute mean sea level rise, in [m] between the projection (2026-2045) and an historical period (1986-2005), obtained from the median of 21 models (CMIP5, AR5). Red dots are the numerical nodes where time series are computed. B) AMSL projections in some of the main ports (Winckler et al. 2019). Both plots correspond to medians. The range (in other words, multi-model uncertainty) considering all models is in the order of ±30 cm).
3.4. Earthquakes and tsunamis
Since the arrival of the Spaniards, Chile has been affected by nearly 25 tsunamis (Figure 8). As mentioned, the triggering earthquakes may cause uplift/subsidence of various meters while the consecutive tsunami flooding may extend for kilometers. Since the country joined the TIME Project (Tsunami Inundation Modeling Exchange) in 1995, tsunami flooding charts based on worst case scenario analysis have been produced for 61 coastal cities in the country (SHOA 2015). These charts provide pretty conservative flooding lines which are adequate to define evacuation safe zones but are insufficient for structural design, as they do not include velocities, momentum, nor exceedance probabilities (Figure 9) which could be used in combination with fragility curves (Suppasri et al. 2012) to assess risk. One of the main problems to characterize tsunamigenic earthquakes if the long span associated to the seismic cycle, which makes the use of different techniques inevitable to capture recurrence intervals (Figure 8), thus resulting in heterogeneous type of data.
Still, the quality and quantity of data is insufficient to have a profound understanding of the seismic cycle. As an example, though the local seismic network was installed in 1908, during the M9.5 1960 Valdivia Earthquake, the largest ever recorded in human history (Kanamori 1977), it was not functioning. Furthermore, the first tide gauge was installed in Valparaíso in 1944, the first extensive post-tsunami survey was conducted after the 2010 Maule Earthquake (Fritz et al. 2011) while the Antofagasta earthquake of July 30, 1995 was first characterized with GPS (Ruegg et al. 1996) and InSar (Pritchard et al. 2002). Recent advances have been achieved in probabilistic tsunami hazard assessments PTHA (e.g. González et al. 2009, Sepúlveda et al. 2019); however, to the author’s level of
knowledge, vertical deformations during the coseismic stage of the seismic cycle are yet to be studied. Overall, these data is and will be insufficient to fully characterize megathrust earthquakes and the corresponding tsunamis.
A)
B)
Figure 8: A) Time series (1543-2016) of rupture lengths associated to M > 7.5 earthquakes occurring in the subduction zone, as compiled form Lomnitz (2004) and Carena (2011). Red bars represent tsunamigenic earthquakes. Existing dart buoys and the tide gauge network are depicted in black stars and red dots. B) Time span where different techniques use to constrain the earthquake source are shown.
Figure 9: Tsunami flooding chart obtained by SHOA for a deterministic scenario (1868) for Arica, Chile.
4. NATION-WIDE ASSESSMENT OF COASTAL FLOODING
An example of a simplified multi-hazard analysis is based on the study Risk Assessment of the impacts of Climate Change on the coasts of Chile (MMA, 2019). The study sought to generate information on projections of the hazard, exposure and vulnerability of human and natural systems of the coastal zone at 104 municipalities in continental Chile, Easter Island and Juan Fernández Archipelago. Among other issues, the study computes the flooding hazard stemming from the combination of extreme waves, sea level rise and storm surge for a historical period (1985-2004) and a projection (2026-2045) under the RCP 8.5 scenario.
The first step was to build a Digital Elevation Model (DEM) from satellite data in rural areas (ASTER GDEM-2, ALOS WORLD 3D and ALOS PALSAR) and topographic surveys in coastal cities. To characterize the hazard, the flooding level with respect to the present day chart datum (LAT) for the historical period was computed with the expression
𝐶 𝑍 𝑍 𝑍 𝑅 | 4 where the astronomical tide (𝑍 ) was computed conservatively as the annual maxima from tidal charts (SHOA 2017), a storm surge (𝑍 ) corresponding to a 50-years return period was obtained from (CEPAL, 2013) and the wave setup (𝑍 ) and runup (𝑅 ) were computed from the ensemble of 6 different wave models of the period 1985-2004. The calculation of local wave conditions deserves a bit more detail. Offshore wave spectra were propagated using SWAN (Booij et al. 1997) for each sea state following Massel’s (1989) approach where high resolution bathymetry was available (e.g. ports and urbanized bays); for rural areas with low quality bathymetry, a simplified method combining Snell’s law of refraction and the small amplitude wave theory was used. In the example of Quintero bay, where various industrial ports exist, a high resolution bathymetry (Figure 10A) is accurate enough to use Massel’s approach (Figure 10B). Once all sea states of the historical period are obtained within the bay, the 99% percentile of the significant wave height (𝐻𝑠) is arbitrarily chosen to compute the wave setup and the runup which is exceeded 2% using Stockdon’s (2006) empirical formula. This calculation is conducted for each state and then the median of all the historical period for each model is computed.
Finally, an ensemble for all 6 models is computed and used in Equation (4). Fort the projection, the flooding level is computed as
𝐶 ∆𝑍 𝑍 𝑍 𝑍 𝑅 | 5 where the increment of the mean sea level between the projection and the historical period (∆𝑍 ) is computed as the median of 21 models from the CMIP5 (Church et al. 2013) and the other variables are computed as explained above. The flooding lines are depicted in Figure 10D and Figure 10E.
To evaluate the exposure, 76 types of systems, with more than 18.000 elements, were identified within Low Elevated Coastal Zones (LECZs) located below 10 m above sea level (McGranahan et al. 2007). The inventory was built in 71 of the 106 municipalities where the DEM was sufficiently accurate; in the southern fjords, the combination of satellite data and a macro-tidal regime provided poor results to define the coastline. Figure 10C depicts the area where the inventory of exposure is computed in Quintero bay.
The vulnerability in LECZs is computed for various systems. In the 71 municipalities, for example, the study concludes that 46.357 people and 18.338 homes currently live in areas which were not flooded in the historical period but will be flooded in the projection. Similar fate will undergo 17 bridges, 4245 segments of coastal roads, 11 power facilities and 53 water treatment plants, among others.
Though the above computation is incomplete and somewhat inconsistent in terms of the combination of the probabilities and methods used for different hazards, it nevertheless provides a first order approach which may be valuable to rank municipalities of smaller portions of coastline in terms of its hazard, exposure vulnerability and/or risk, depending on the availability of information required to compute each of these variables. At this level, the type of information produced herein may be valuable for spatial planners and public services, but it is certainly insufficient for design of coastal structures. In the above case, Equations (4) and (5) exclude meteotsunamis (𝑍 ) due to the lack of sufficient historical data and projections and disregards geophysical phenomena, such as tsunamis (𝑅 ), infragravity waves (𝑍 ) and vertical displacement of the seafloor during the seismic cycle or other sources. Additionally, the projections are limited to mid-century but can be extended to the end of the century (e.g. Camus et al. 2017).
A) B)
C) D)
E)
Figure 10: A) Bathymetry and B) Spatial pattern of the annual mean of the significant wave height of Quintero Bay (Beyá et al. 2016). C) Low elevated coastal zone where the inventory of exposure was built, D) and E) Flooding lines for the historical period (1985-2004) and projection (2026-2045) in Quintero bay (MMA 2019).
5. DISCUSSION
The implementation of multi-hazard analysis in a seismic coast subjected to climate change is challenging due to several aspects, namely:
Different physics of the variables at stake (short-term meteorological hazards, long-term climate driven hazards, geophysical hazards, astronomical tides and human-induced changes in the coastline and immediate basin), which in turn demand expertise from researchers from different backgrounds and skills (e.g. seismologists, geophysicists, engineers, oceanographers, climatologists, among others). Different level of understanding of the physical processes. While AMSL is relatively well understood and projections are constrained, RMSL remains poorly understood due to the lack of understanding of the crustal motions during the seismic cycle. Waves are fairly well characterized on an historical basis, but the projections still require to account for the local effect of bias in global climate models. The insufficient understanding of the physical mechanisms triggering meteotsunamis and storm surges on Chilean waters, the lack of knowledge on how they interact with other types of waves, the inexistence of future projections or the limited existing ones (CEPAL, 2015) calls for further research. As for earthquakes and tsunamis, one of the main problems in their characterization if the long span associated to the seismic cycle, which makes the use of different techniques inevitable to capture recurrence intervals. Different spatial and time scales at which they occur and different amplitudes depending on the local conditions of a site, which in turn demand diverse statistical, mathematical and modelling approaches. Different type and availability of historical data from in situ records, hindcasts and/or satellite data (format, time ranges, spatial coverage, spatial and temporal resolution, existence of gaps and outliers gridded vs unevenly distributed), as well as for projections. Different methods used to build the probability density functions, especially between variables with continuous records or hindcasts (e.g. waves, AMSL) and those depending on geophysical factors which result in different levels and nature of the uncertainty in their characterization. Limitations in the modeling approaches, e.g. additive model in Equation (1), to model interdependencies, non-linear interactions, non-stationarity, accumulation and cascade effects, which may lead to increased structural failure of structures or an upraise on the episodic or chronic flooding or beach erosion.
Regardless of these limitations, planning and design decisions cannot be postponed until improved scenarios, methodologies or data are available (Toimil et al. 2019). In terms of the risk reduction of life and coastal infrastructure, a multi-hazard, probabilistic, nonstationary design approach where uncertainty is at stake has to be pushed forward, regardless of the lack of complete knowledge. Consequently, in a seismic coast subjected to climate change, the conjunction of short-term meteorological effects, long-term climate driven hazards and geophysical phenomena should be addressed by means of a multi-hazard analysis, extending existing studies (e.g. Li et al. 2011, Reguero et al. 2015, Vousdoukas 2017) to include local estimations of subsidence/uplift due to sediment compaction, isostatic rebound and earthquakes. Specifically, an extension of current probabilistic earthquake hazard assessments (including uncertainties of slip distribution and location of earthquakes) to estimate coseismic vertical deformation, in combination with PTHA, should be crossed with sea level changes, waves, storm surges and meteotsunamis. These efforts are needed based on new evidence where, for example, sea level rise also increase both the frequency and the intensity of tsunami-induced flooding (Li et al. 2018).
In other terms, the current design process, based on the assumption of nature as a stationary process (i.e. use of time-invariant probability distribution functions built from observations), should also shift to a paradigm where non-stationary statistical methods account for a changing climate during the design life, with uncertainty at the core of the process. The expected sea level rise in combination with an increase in the frequency and intensity of coastal storms (Winckler et al. 2019), will change the probability density functions of relevant variables, such as flooding of lowlands (Figure 11), downtime and operational delays of ports, overtopping and damage of coastal infrastructure as the century evolves. Non-stationary hazard analysis should also be extended to exposure, vulnerability, thus risk (Toimil et al. 2019).
Figure 11: Idealized shift of probability distribution functions for flooding level, as affected by sea level rise and increased frequency and intensity of storms. The case of milder conditions is also included. The shift in functions as a consequence of coseismic uplift/subsidence remains to be explored.
In parallel, the lack of data has to be urgently addressed. Though Chile’s coastal zone is economically, culturally and socially dependent on the ocean, there is a lack of long-term and real-time observations to rapidly respond to climate change and fast nearshore perturbations. This also affects countries located in the Pacific coasts of South and Central America. Indeed, the Chilean Navy Hydrographic and Oceanographic Service operates only 45 tide gauges, on average one every 100 km (SHOA, 2020), and has no permanent offshore wave buoy network. Efforts should be carried on a nationwide level to develop oceanographic networks and open databases following examples elsewhere (e.g. Hamilton 1980, Clemente 2001), and enhance international collaboration following examples such as the Pacific Tsunami Warning System, operating for more than 50 years. Recently, the local scientific community proposed an Integrated Observing System for the Southeast Pacific Ocean, consisting of an array of instruments for monitoring and forecasting several variables (CC-COP25 2019). Its implementation would require integrating existing observational systems, acquisition of state-of-the art equipment (oceanic, atmospheric, computational, real-time transmission and database management systems), development of data management protocols, periodic maintenance, calibration facilities and qualified human resources. Aside from multi-hazard analysis, such a network could aid in the development of other relevant issues such as climate change monitoring, early warning systems (e.g. storm surges, tsunamis) and operational weather systems for port and aquaculture.
To finalize, an idealized conceptual model where the spatial planning of a coastal city is defined in terms of the vulnerability of buildings and infrastructure to multiple hazards is shown in Figure 12. Different zones are proposed in terms of the vulnerability of buildings or infrastructure (importance, use and risk of failure to casualties), following the national code for earthquake resistant design of buildings (Table 4.3; INN, 2012). As a tentative proposal, which can be adapted to local constrains, the following four zones are proposed:
Zone 1 allows green belts, recreational facilities and green infrastructure. Zone 2 allows small commercial buildings, as well as those included in Zone 1. Zone 3 allows households, mid-size commercial buildings, and those included in Zones 1 and 2. Zone 4 allows critical services (e.g. schools, police stations, post offices, media headquarters, hospitals, fire stations and garages for emergency vehicles), infrastructure (e.g. power plants, chemical plants, water treatment plants, sewage treatment plants and tsunami safe zones) , and those included in Zones 1, 2 and 3.
Other coastal typologies such as wetlands, dunes or river outlets would need special planning instruments. This proposal could extend the existing building code for areas of flooding due to tsunamis (INN, 2015), where elevated structural types and design guidelines are suggested (the code is not yet official). Naturally, this type of proposal should be validated through community engagement (Lawrence et al. 2018) and considered from a dynamic planning approach, where flooding levels should be reviewed in light of new emerging evidence, dynamic projections of spatially-distributed hazard, exposure and vulnerability (Toimil et al. 2019) as the century advances.
Figure 12: Conceptual framework for the integration of multiple hazard in land use.
6. CONCLUSIONS
The implementation of multi-hazard analysis in a seismic coast subjected to climate change is challenging due to aspects such as the different physics of the variables at stake, different level of understanding of the physical processes, different amplitudes, spatial and time scales at which they occur, different type and availability of historical data and projections, different methods and limitations in the modeling approaches, among others. Additionally, lack of sufficient knowledge is yet to be unveiled to model interdependencies, non-linear interactions, non-stationarity, accumulation and cascade effects, which may in turn lead to increased structural failure of structures, an upraise on the episodic or chronic flooding and beach erosion. Another issue is the lack of long-term and real-time observations to rapidly respond to climate change and fast nearshore perturbations. Still, planning and design decisions cannot be postponed until improved scenarios, methodologies or data are available. The multi-hazard, probabilistic and nonstationary design approach, where uncertainty is at stake, has to be pushed forward among practitioners, based on the new findings stemming from scientific research. The approach can be extended to the design of coastal infrastructure, such as the stability of breakwaters, overtopping and forces on slender elements (USACE 2006). In other terms, the proposal for an (idealized) land zoning of potentially flood areas could aid in reducing the exposure of infrastructure, beach conservancy, preservation of wetlands, land use plan, coastal zone management and adaptation strategies.
7. ACKNOWLEDGEMENTS
The author would like to acknowledge Profs. Rodrigo Cienfuegos and Patricio Catalán, from CIGIDEN, for aiding in the production of Figures 8 and 11. Ocean engineers Javiera Mora and César Esparza, geographer Cristian Larraguibel and Prof. Manuel Contreras worked on the example included in section 4.
8. REFERENCES
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