Regional Scale Risk-Informed Land-Use Planning Using Probabilistic Coastline Recession Modelling and Economical Optimisation: East Coast of Sri Lanka
<p>Study area: Trincomalee and Batticaloa district coastlines, along the east coast of Sri Lanka from Kuchchaveli to Karaitivu (~200 km).</p> "> Figure 2
<p>Location of measured beach profiles and adopted zonation in (<b>A</b>) Trincomalee and (<b>B</b>) Batticaloa.</p> "> Figure 3
<p>Measured beach Profiles (thin) and computed coastal cell-averaged beach profile (thick) for Coastal cell B-IV (Kalmunai to Karaitivu coastline stretch).</p> "> Figure 4
<p>Regional and local wave model grids for the wave model.</p> "> Figure 5
<p>Wave boundary conditions applied to the model as time series of wave conditions.</p> "> Figure 6
<p>Nearshore transformed wave rose (<b>a</b>), and 30 years of extracted nearshore wave heights (<b>b</b>) in front of the Dutch Bay in Trincomalee at the location with 38 m depth.</p> "> Figure 7
<p>The process of storm detection for the data point in front of Dutch Bay. Storms identified over the 30-year period from 1979–2009 (<b>a</b>), zoom-in plot of storms identified over the 2-year period 1995–1997 (<b>b</b>); Blue: All data, Red: identified storm events.</p> "> Figure 8
<p>Storms characteristics at Dutch Bay, Trincomalee (Cell T-VIII).</p> "> Figure 9
<p>Cumulative distribution function and fitted GEV of maximum significant wave height (H<sub>s</sub>) (<b>a</b>), storm duration (<b>b</b>), and Empirical cumulative distribution function of average wave direction (<b>c</b>) during 30 years of storms at Dutch Bay (Cell T-VIII).</p> "> Figure 10
<p>Dependency between maximum significant wave height and average wave direction—(<b>a</b> panel), and maximum significant wave height and storm duration—(<b>b</b> panel), for Dutch Bay (Cell T-VIII).</p> "> Figure 11
<p>Joint probability model (JPM) of maximum significant wave height (m) and storm duration (Hour) for Dutch Bay (Cell T-VIII).</p> "> Figure 12
<p>Linear fit between maximum significant wave height and average peak wave period during storms.</p> "> Figure 13
<p>Regional RSLR for RCP 8.5 excluding land subsidence by 2100 (relative to 2000).</p> "> Figure 14
<p>The scheme of the P CR model used in this study. H: maximum Hs in one storm, T: peak period associated with H, D: duration of storm, Dir: Mean direction of storm, S: Gap between two storms.</p> "> Figure 15
<p>Schematic illustrates the algorithm used for constructing one long-term record of storms.</p> "> Figure 16
<p>Exceedance probability curves of coastal recession for selected profiles in Trincomalee and Batticaloa districts for different years.</p> "> Figure 17
<p>Increase in the annual probability of coastal recession reaching fixed location at 50 or 70 m distances from the RCL.</p> "> Figure 18
<p>Examples of computed 1% (red) and 50% (orange) exceedance probability coastal recession contours in Trincomalee (<b>a</b>) and Batticaloa (<b>b</b>).</p> "> Figure 19
<p>The annual probability of damage at the EOSL for selected profiles in Batticaloa (continuous lines) and the probability of damage above which retreat is optimal (dashed lines). The optimal investment horizon at the EOSL is found where these lines cross. Results for r = 8% per year in black; results for r = 5% per year in red.</p> "> Figure 20
<p>The annual probability of damage at the EOSL for selected profiles in Trincomalee (continuous lines) and the probability of damage above which retreat is optimal (dashed lines). The optimal investment horizon at the EOSL is found where these lines cross. Results for r = 15% per year in black; results for r = 10% per year in red.</p> "> Figure 21
<p>Examples of estimated economically optimal coastal setback lines in Trincomalee (<b>a</b>) and Batticaloa (<b>b</b>).</p> "> Figure 22
<p>The distance between the EOSL based on the base case parameter values and the EOSL based on different economic constants. Results for profiles in Trincomalee, assuming they all lie in high-value zones. A negative distance implies a position seaward of the base case EOSL.</p> "> Figure 23
<p>The distance between the EOSL based on the base case parameter values and the EOSL based on different economic constants. Results for profiles in Trincomalee, assuming they all lie in lower-value zones. A negative distance implies a position seaward of the base case EOSL.</p> "> Figure 24
<p>The distance between the EOSL based on the base case parameter values and the EOSL based on different economic constants. Results for profiles in Batticaloa, assuming they all lie in high-value zones. A negative distance implies a position seaward of the base case EOSL.</p> "> Figure 25
<p>The distance between the EOSL based on the base case parameter values and the EOSL based on different economic constants. Results for profiles in Batticaloa, assuming they all lie in lower-value zones. A negative distance implies a position seaward of the base case EOSL.</p> ">
Abstract
:1. Introduction
2. Study Site
3. Data Collection and Analyses
3.1. Beach Profiles and Sediment Size
3.2. Wave Data
3.2.1. Off-Shore Wave Data
3.2.2. Wave Model
Grid and Bathymetry
Boundary Conditions and Forcing
Simulation and Results
Storm Detection
3.2.3. Storm Data Analyses
3.3. Relative Sea Level Rise
- SLR is global mean sea level rise (m)
- t is number of years starting from 2000 (year)
- a1 is rate of sea level rise at year 2000 (m/year) (in this case 0.003)
- a2 is factor of the change in the rate of sea level rise (m/year2) (in this case 4.5 × 10−5)
4. Application of Probabilistic Coastal Recession (PCR) Simulations
4.1. Event Generation
4.1.1. Sampling Single Storm Conditions (H,T,D,Dir)
- GEV distributions for Hs,max (H) and Storm Duration (D); (e.g., Figure 10)
- Joint probability model, characterised by single values of dependency factor between H & D; (e.g., Figure 11)
- A linear fit between storm wave peak period (T), which is dependent on H; (e.g., Figure 12)
- An empirical distribution for wave direction (Dir), based on measured data. (e.g., Figure 9)
- Sample a random uniform deviation from [0;1] ‘a’ and use this with the GEV distribution for H to generate a maximum significant for the storm;
- Sample a random uniform deviation from [0;1] ‘b’ as the dependency parameter as dependency value for H and D;
- Use a and b to determine the deviation ‘c’ for storm duration (D) from the joint probability model;
- Use ‘c’ with the GEV distribution for storm duration (D) to generate a storm duration for the storm;
- Use H and the linear fit between H and T to determine storm wave peak period (T);
- Sample a random uniform deviation from [0;1] and use this with the empirical distribution for direction to generate a wave direction for the storm (Dir).
4.1.2. Constructing a Record of Storms Including Seasonality
- Gap between storms during the storm season;
- Duration of the ‘years’, which is the time from start of the first storm in the storm season until the start of the first storm in the next stormy season;
- Duration of ‘storm season’, which is the time from start of the first storm in the storm season until the end of the last storm in the same storm season;
- Sample a random uniform deviation from [0;1] and use this with the Poisson distribution for Duration of the ‘years’ to generate the duration of one ‘year’;
- Sample a random uniform deviation from [0;1] and use this with the Poisson distribution for Duration of the ‘storm season’ to generate the duration of the ‘stormy season’ in that ‘year’;
- Sample a single storm based on the algorithm described in Section 4.1.1;
- Sample a random uniform deviation from [0;1] and use this with the empirical distribution for gap between storms during the storm season to generate one gap corresponding to the storm generated in pervious step;
- Repeat the previous steps until the duration of the ‘storm season’ is filled with storms;
- Repeat all steps to cover the whole simulation length (e.g., 100 years).
4.2. Erosion Model
4.3. Definition of Reference Coast Line (RCL)
4.4. PCR Simulations
4.5. Results and Discussion
5. Economically Optimal Setback Lines
5.1. Economic Model
- Property owners value the risk of coastline recession at expected loss or a multiple thereof. In the presence of efficient insurance markets, the insurance premium would be equal to expected loss, i.e., the product of the probability of damage and potential loss. In that case, every risk-averse individual would purchase insurance. CCD has indicated that the Sri Lankan government is under no circumstances liable for damage caused by coastal erosion. Still, according to the CCD, insurance penetration is low. While this could be due to people’s risk preferences, it could also be due to the unavailability of efficiently priced insurance coverage, mistrust or incorrect risk perceptions. Since people are typically risk-averse, here we value the economic risk as a multiple of expected loss.
- The impact of coastline recession on a property is assumed to be proportional to the footprint of the property being impacted by coastline recession.
- Each property that is impacted by coastline recession is damaged completely.
- Damages are restored to their initial condition following a recession impact. This implies that insurance pay-outs cannot be put to alternative use.
- Restoration takes place in the year in which damage occurs, in the period following the storm season.
- NPV(x)—The net present value of an investment at a distance x from today’s coastline.
- γ—Discount rate.
- pi(x)—The probability of damage at distance x in year i. This is the probability in year i that the coastline recedes up to a point that is at least a distance x from the reference coastline.
- c(x)—The investment that is made at a distance x from the initial coastline.
- a—The ratio of the certainty equivalent to expected loss. The certainty equivalent is the certain loss that is valued the same as the probability of suffering a loss. For a risk-neutral agent, the certainty equivalent is equal to the expected loss. The factor a could thus be perceived as a risk aversion coefficient.
- r(x)—Rate of return on the initial investment without accounting for coastline recession risk. The product r(x)·c(x) equals an annual return on investment measured in money (e.g., dollar) terms.
- n—The time horizon being considered in years. The value of n equals the number of years in which the return on investment exceeds the certainty equivalent of the risk of coastline recession. This is consistent with the assumption that investors do not willingly incur avoidable losses, but walk away when risks become too high. For practical reasons, we have ignored all cash flows beyond the year 2110, which is the last year for which recession estimates are here computed from the probabilistic coastline recession model.
5.2. Economic Constants
5.3. Results: The Position of the EOSL and Optimal Damage Probabilities
5.4. Sensitivity to Variations in Economic Constants
- Decreasing the ratio of the certainty equivalent to expected loss (a) gives the risk of coastline recession less weight and shifts the EOSL seaward. In the sensitivity analyses, we only considered values of a that were smaller than the base case value. This is why the plots only show differences of less than zero. A negative value on the horizontal axis implies the EOSL of the sensitivity analysis lies seaward of the EOSL based on the base case parameter values.
- Increasing the rate of return on investment relative to the discount rate (Δr) increases the economically optimal probability of damage and shifts the EOSL seaward. Conversely, decreasing Δr reduces the economically optimal probability of damage and shifts the EOSL inland.
- Increasing the discount rate (γ) while keeping Δr constant decreases the present value of climate change impacts. At the same time, it increases the present value of future returns on investment. This largely explains why variations in the discount rate influence the position of the EOSL to a lower extent than variations in the rate of return on investment.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Coelho, C.; Silva, R.; Veloso-Gomes, F.; Taveira-Pinto, F. Potential effects of climate change on northwest Portuguese coastal zones. ICES J. Mar. Sci. 2009, 66, 1497–1507. [Google Scholar] [CrossRef] [Green Version]
- Casas-Prat, M.; Sierra, J.P. Trend analysis of wave direction and associated impacts on the Catalan coast. Clim. Chang. 2012, 115, 667–691. [Google Scholar] [CrossRef] [Green Version]
- Bonaldo, D.; Benetazzo, A.; Sclavo, M.; Carniel, S. Modelling wave-driven sediment transport in a changing climate: A case study for northern Adriatic Sea (Italy). Reg. Environ. Chang. 2015, 15, 45–55. [Google Scholar] [CrossRef]
- Ranasinghe, R. Assessing Climate change impacts on Coasts: A Review. Earth Sci. Rev. 2016, 160, 320–332. [Google Scholar] [CrossRef]
- Dastgheib, A.; Reyns, J.; Thammasittirong, S.; Weesakul, S.; Thatcher, M.; Ranasinghe, R. Variations in the wave climate and sediment transport due to climate change along the coast of Vietnam. J. Mar. Sci. Eng. 2016, 4, 86. [Google Scholar] [CrossRef]
- Bruun, P. Sea-level rise as a cause of shore erosion. J. Waterw. Harb. Div. 1962, 88, 117–132. [Google Scholar]
- Vrijling, J.K.; Van Hengel, W.; Houben, R.J. A framework for risk evaluation. J. Hazard. Mater. 1995, 43, 245–261. [Google Scholar] [CrossRef]
- Jongejan, R.B.; Ranasinghe, R.; Vrijling, J.K.; Callaghan, D.P. A risk-informed approach to coastal zone management. Aust. J. Civ. Eng. 2011, 9, 47–60. [Google Scholar] [CrossRef] [Green Version]
- Jongejan, R.; Ranasinghe, R.; Wainwright, D.; Callaghan, D.; Reyns, J. Drawing the line on coastline recession risk. Ocean Coast. Manag. 2016, 122, 87–94. [Google Scholar] [CrossRef]
- Wainwright, D.J.; Ranasinghe, R.; Callaghan, D.P.; Woodroffe, C.D.; Cowell, P.J.; Rogers, K. An argument for probabilistic coastal hazard assessment: Retrospective examination of practice in New South Wales, Australia. Ocean Coast. Manag. 2014, 95, 147–155. [Google Scholar] [CrossRef]
- Ranasinghe, R.; Callaghan, D.P.; Stive, M. Estimating coastal recession due to sea level rise: Beyond the Bruun rule. Clim. Chang. 2012, 110, 561–574. [Google Scholar] [CrossRef]
- Callaghan, D.P.; Nielsen, P.; Short, A.; Ranasinghe, R. Statistical simulation of wave climate and extreme beach erosion. Coast. Eng. 2008, 55, 375–390. [Google Scholar] [CrossRef]
- Callaghan, D.P.; Ranasinghe, R.; Short, A. Quantifying the storm erosion hazard for coastal planning. Coast. Eng. 2009, 56, 90–93. [Google Scholar] [CrossRef]
- Ministry of Mahaweli Development and Environment. Sri Lanka Coastal Zone and Coastal Resource Management Plan—2018. Government of Sri Lanka. 2018. Available online: http://www.coastal.gov.lk/images/stories/pdf_upload/acts_gazettes_czmp/czcrmp_2018_gazette_2072_58_e.pdf (accessed on 5 December 2018).
- Wong, P.-P.; Losada, I.J.; Gattuso, J.P.; Hinkel, J.; Khattabi, A.; McInnes, K.L.; Saito, Y.; Sallenger, A. Coastal Systems and Low-Lying Areas. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects; Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
- Duong, T.M.; Ranasinghe, R.; Luijendijk, A.; Waltsra, D.J.R.; Roelvink, D. Assessing climate change impacts on the stability of small tidal inlets—Part 1: Data poor environments. Mar. Geol. 2017, 390, 331–346. [Google Scholar] [CrossRef]
- Duong, T.M.; Ranasinghe, R.; Thatcher, M.; Mahanama, S.; Zheng, B.W.; Dissanayake, P.K.; Hemer, M.; Luijendijk, A.; Bamunawala, J.; Roelvink, D. Assessing climate change impacts on the stability of small tidal inlets: Part 2-Data rich environments. Mar. Geol. 2018, 395, 65–81. [Google Scholar] [CrossRef] [PubMed]
- Berrisford, P.; Dee, D.; Poli, P.; Brugge, R.; Fielding, K.; Fuentes, M.; Kallberg, P.; Kobayashi, S.; Uppala, S.; Simmons, A. The ERA-Interim archive Version 2.0; ERA Report Series 1; ECMWF: Reading, UK, 2011; Volume 13177. [Google Scholar]
- Holthuijsen, L.; Booij, N.; Ris, R. A spectral wave model for the coastal zone. In Proceedings of the 2nd International Symposium on Ocean Wave Measurement and Analysis, New Orleans, LA, USA, 25–28 July 1993; pp. 630–641. [Google Scholar]
- Ris, R.C. Spectral Modelling of Wind Waves in Coastal Areas. Communications on Hydraulic and Geotechnical Engineering, Report 97-4. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 1997. [Google Scholar]
- Ris, R.; Booij, N.; Holthuijsen, L.A. Third-generation wave model for coastal regions, Part II: Verification. J. Geophys. Res. 1999, 104, 7649–7666. [Google Scholar] [CrossRef]
- Battjes, J.; Janssen, J. Energy loss and set-up due to breaking of random waves. In Proceedings of the 16th International Conference Coastal Engineering, Hamburg, Germany, 27 August–3 September 1978; pp. 569–587. [Google Scholar]
- Hasselmann, K.; Barnett, T.P.; Bouws, E.; Carlson, H.; Cartwright, D.E.; Enke, K.; Ewing, J.; Gienapp, H.; Hasselmann, D.E.; Kruseman, P.; et al. Measurements of wind wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Dtsch. Hydrogr. Z. 1973, 8, 12. [Google Scholar]
- BODC. The GEBCO Digital Atlas” Published by the British Oceanographic Data Centre on Behalf of IOC and IHO. 2003. Available online: http://www.gebco.net (accessed on 15 October 2018).
- Bindoff, N.; Willebrand, J.; Artale, V.; Cazenave, A.; Gregory, J.; Gulev, S.; Nojiri, Y. Observations: Oceanic climate and sea level. In Climate Change 2007: The Physical Science Basis; Contribution of Working Group I to the Fourth Assessment Report of the Intergouvernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007; pp. 385–432. [Google Scholar]
- Church, J.A.; Clark, P.U.; Cazenave, A.; Gregory, J.M.; Jevrejeva, S.; Levermann, A.; Nunn, P.D. Sea Level Change; PM Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
- Christensen, J.H.; Hewitson, B.; Busuioc, A.; Chen, A.; Gao, X.; Held, R.; Jones, R.; Kolli, R.K.; Kwon, W.K.; Laprise, R.; et al. Regional Climate Projections. In Climate Change 2007: The Physical Science Basis; Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007. [Google Scholar]
- Parry, M.; Parry, M.L.; Canziani, O.; Palutikof, J.; Van der Linden, P.; Hanson, C. Coastal Systems and Low-Lying Areas. Climate Change 2007: Impacts, Adaptation and Vulnerability; Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2007; pp. 315–356. [Google Scholar]
- Nicholls, R.; Hanson, S.; Lowe, J.; Warrick, R.; Lu, X.; Long, A.; Carter, T. Constructing Sea-Level Scenarios for Impact and Adaptation Assessment of Coastal Area: A Guidance Document; Supporting Material, Intergovernmental Panel on Climate Change Task Group on Data and Scenario Support for Impact and Climate Analysis; TGICA: Geneva, Switzerland, 2011. [Google Scholar]
- Mendoza, E.T.; Jimenez, J.A. Storm-Induced Beach Erosion Potential on the Catalonian Coast. J. Coast. Res. 2006, 48, 81–88. [Google Scholar]
- Muis, S.; Verlaan, M.; Winsemius, H.C.; Aerts, J.C.; Ward, P.J. A global reanalysis of storm surges and extreme sea levels. Nat. Commun. 2016, 7, 11969. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vousdoukas, M.I.; Mentaschi, L.; Voukouvalas, E.; Verlaan, M.; Jevrejeva, S.; Jackson, L.P.; Feyen, L. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. Nat. Commun. 2018, 9, 2360. [Google Scholar] [CrossRef] [PubMed]
- Jiménez, J.A.; Sánchez Arcilla, A.; Stive, M.J.F. Discussion on prediction of storm/normal beach profiles. J. Waterw. Port Coast. Ocean Eng. 1993, 19, 466–468. [Google Scholar] [CrossRef]
- Viavattene, C.; Jimenez, J.A.; Owen, D.; Priest, S.; Parker, D.; Micou, A.P.; Ly, S. Coastal Risk Assessment Framework Guidance Document. Deliverable No: D.2.3—Coastal Risk Assessment Framework Tool, Risc-Kit Project” (G.A. No. 603458). 2015. Available online: http://www.risckit.eu/np4/file/23/RISC_KIT_D2.3_CRAF_Guidance.pdf (accessed on 15 October 2018).
- Roelvink, D.; Reniers, A.; van Dongeren, A.P.; de Vries, J.V.T.; McCall, R.; Lescinski, J. Modelling storm impacts on beaches, dunes and barrier islands. Coast. Eng. 2009, 56, 1133–1152. [Google Scholar] [CrossRef]
- Roelvink, D.; McCall, R.; Mehvar, S.; Nederhoff, K.; Dastgheib, A. Improving predictions of swash dynamics in XBeach: The role of groupiness and incident-band runup. Coast. Eng. 2017. [Google Scholar] [CrossRef]
- Soulsby, R. Dynamics of Marine Sands, a Manual for Practical Applications; Thomas Telford: London, UK, 1997. [Google Scholar]
- Stockdon, H.F.; Holman, R.A.; Howd, P.A.; Sallenger, A.H., Jr. Empirical parameterization of setup, swash and run-up. Coast. Eng. 2006, 56, 573–588. [Google Scholar] [CrossRef]
- Lin-Ye, J.; Garcia-Leon, M.; Gracia, V.; Sanchez-Arcilla, A. A multivariate statistical model of extreme events: An application to the Catalan coast. Coast. Eng. 2016, 117, 138–156. [Google Scholar] [CrossRef]
- Kunreuther, H.; Pauly, M. Neglecting Disaster: Why Don’t People Insure Against Large Losses? J. Risk Uncertain. 2004, 28, 5–21. [Google Scholar] [CrossRef]
- Slovic, P.; Fischhoff, B.; Lichtenstein, S.; Corrigan, B.; Combs, B. Preference for Insuring against Probable Small Losses: Insurance Implications. J. Risk Insur. 1977, 44, 237. [Google Scholar] [CrossRef]
- Harrington, S.E. Rethinking Disaster Policy; Breaking the cycle of “free” disaster assistance, subsidized insurance, and risky behavior. Regulation 2000, 23, 40–46. [Google Scholar]
- Mishan, E.J. The postwar literature on externalities: An interpretative essay. J. Econ. Lit. 1971, 9, 1–28. [Google Scholar]
Trincomalee | Batticaloa | ||
---|---|---|---|
Coastal Cell | Ave. d50 (μm) | Coastal Cell | Ave. d50 (μm) |
T-I | 213 | B-I | 553 |
T-II | 285 | B-II | 377 |
T-III | 268 | B-V | 441 |
T-IV | 323 | B-IV | 456 |
T-V | 200 | ||
T-VI | 200 | ||
T-VII | 240 | ||
T-VIII | 270 |
Profile | 2050 | 2110 | ||||
---|---|---|---|---|---|---|
Probability of Exceedance | Probability of Exceedance | |||||
1% | 10% | 50% | 1% | 10% | 50% | |
B-4 | 142 | 89 | 48 | 149 | 98 | 56 |
B-43 | 109 | 64 | 28 | 132 | 75 | 32 |
T-18 | 54 | 42 | 29 | 72 | 60 | 47 |
T-37 | 41 | 34 | 26 | 91 | 80 | 67 |
Variable | Trincomalee | Batticaloa | ||
---|---|---|---|---|
High-Value Zone | Lower-Value Zone | High-Value Zone | Lower-Value Zone | |
Rate of return on investment relative to the discount rate (Δr) (per year) | 0.09 0.12 (base case) 0.15 | 0.05 0.07 (base case) 0.09 | 0.03 0.05 (base case) 0.07 | 0.01 0.02 (base case) 0.03 |
Discount rate (γ) (per year) | 0.02 0.03 (base case) 0.04 0.05 | |||
Ratio of the certainty equivalent to expected loss (a) (-) | 1.0 (risk-neutral) 1.5 (moderate degree of risk-aversion) 2.0 (base case) |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dastgheib, A.; Jongejan, R.; Wickramanayake, M.; Ranasinghe, R. Regional Scale Risk-Informed Land-Use Planning Using Probabilistic Coastline Recession Modelling and Economical Optimisation: East Coast of Sri Lanka. J. Mar. Sci. Eng. 2018, 6, 120. https://doi.org/10.3390/jmse6040120
Dastgheib A, Jongejan R, Wickramanayake M, Ranasinghe R. Regional Scale Risk-Informed Land-Use Planning Using Probabilistic Coastline Recession Modelling and Economical Optimisation: East Coast of Sri Lanka. Journal of Marine Science and Engineering. 2018; 6(4):120. https://doi.org/10.3390/jmse6040120
Chicago/Turabian StyleDastgheib, Ali, Ruben Jongejan, Mangala Wickramanayake, and Roshanka Ranasinghe. 2018. "Regional Scale Risk-Informed Land-Use Planning Using Probabilistic Coastline Recession Modelling and Economical Optimisation: East Coast of Sri Lanka" Journal of Marine Science and Engineering 6, no. 4: 120. https://doi.org/10.3390/jmse6040120
APA StyleDastgheib, A., Jongejan, R., Wickramanayake, M., & Ranasinghe, R. (2018). Regional Scale Risk-Informed Land-Use Planning Using Probabilistic Coastline Recession Modelling and Economical Optimisation: East Coast of Sri Lanka. Journal of Marine Science and Engineering, 6(4), 120. https://doi.org/10.3390/jmse6040120