Mangroves can provide protection against wind damage during storms

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Mangroves can provide protection against wind damage duringstorms

Saudamini Das a,*, Anne-Sophie Crépin b,c

a Institute of Economic Growth, University of Delhi Enclave, Delhi 110007, Indiab The Beijer Institute of Ecological Economics, S-10405 Stockholm, Swedenc Stockholm Resilience Centre, Stockholm University, S-10691 Stockholm, Sweden

a r t i c l e i n f o

Article history:Received 13 June 2013Accepted 27 September 2013Available online 7 October 2013

Keywords:mangrovesmodellingsimulationstorm protectionwind damagewind directionIndiaOdisha

a b s t r a c t

Research has established that mangroves can protect lives and property from storms by buffering theimpacts of storm surges. However, their effects in attenuating wind velocity and providing protectionfrom wind damage during storms are not known. This study examined whether mangroves attenuatedamage from cyclonic winds and found that they provide substantial protection to properties, evenrelatively far away from mangroves and the coast. We devised a theoretical model of wind protection bymangroves and calibrated and applied this model using data from the 1999 cyclone in the Odisha regionof India. The model predicted and quantified the actual level of damage reasonably accurately andshowed that mangroves reduced wind damage to houses. The wind protection value of mangroves inreducing house damage amounted to approximately US$177 per hectare at 1999 prices. This providesadditional evidence of the storm protection ecosystem services that mangroves supply in the region andan additional reason to invest in mangrove ecosystems to provide better adaptability to coastal disasterssuch as storms.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Mangroves limit the impacts of storm surges and protect coastalareas from storm impacts (UNEP-WCMC, 2006; Barbier et al., 2008;Das and Vincent, 2009; McIvor et al., 2012b). Mangroves also seemto have attenuated waves from the 2004 Indian Ocean tsunami(Kathiresan and Rajendran, 2005; Dahdouh-Guebas and Koedam,2006; Alongi, 2008; Yanagisawa et al., 2009). The wave attenuationthat mangroves provide has been studied by others (Wolanski,1992; Mazda et al., 1997; Massel et al., 1999; Mendez and Losada,2004). Mangroves seem to reduce surge-related damage in theleeward side of the forest, and reduce wind and swell waves incoastal areas (McIvor et al., 2012a, 2012b). However it remainsunclear whether mangroves can reduce wind velocity and thuswind damage from storms. The aim of this study was to shed lighton this issue.

Storms generate a maximumwind in the eye wall region (a fewkilometres surrounding the eye of the storm) and a radial ortangential wind beyond the eyewall that varies with radial distance

from the centre of the eye. Themaximumwind velocity depends onthe air pressure gradient at the eye of the cyclone. It decreasesexponentially after landfall due to soil conditions, topography, etc.1

The tangential/radial wind depends on the maximum wind andmoves anti-clockwise around the storm centre in the Northernhemisphere.2 Das et al. (1974) and Roy Abraham et al. (1995) sug-gest that the velocity of the tangential/radial wind depends on noother variable than the radial distance from the storm eye centre, sowind velocity is the same in a given circle surrounding the eye wall.However, field observations indicate greater wind damage near thecoast than inland on the same circle.3 Vegetation and topographycan also reduce wind erosion and wind speed (Youssef et al., 2012).For example, trees like thick coconut palms can attenuate cyclonewind speed by 50e80% (Joseph et al., 2012). Hence, tangential windvelocity can be assumed to decreasewith distance from the coast ina similar way to the maximumwind velocity as it encounters roughareas on its path. The multi-level canopy cover of mangroves on the

* Corresponding author. Room 004, Institute of Economic Growth, University ofDelhi Enclave, Delhi 10007, India. Tel.: þ91 11 27667101x204, þ91 11 27875427.

E-mail addresses: [email protected], [email protected] (S. Das), [email protected] (A.-S. Crépin).

1 See for example the studies by Basu and Ghosh (1987); Singh andBandyopadhyay, 2004; Joseph et al., 2012; and Kishtawal et al., 2012.

2 See for details: http://www.imd.gov.in/section/nhac/dynamic/faq/FAQP.htm,accessed on 25th August 2013.

3 Evidence from the first author’s field visit after the October 1999 cyclone inOdisha.

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Estuarine, Coastal and Shelf Science 134 (2013) 98e107


path of the wind could also further reduce wind velocity. If so,wind-inflicted damage to villages on the leeward side of mangrovesshould be lower than in villages facing the same potentialtangential wind velocity (measured from radial distance) withoutmangrove protection.

In this study we tested two hypotheses: (H1) Tangential windvelocity declines exponentially relative to potential wind velocityas the wind moves away from the coast4 and (H2) wind barriersat the coast, such as mangroves, further accelerate the rate ofdecline of tangential wind. We built a theoretical model andtested its prediction ability using data from the 1999 stormdamage in the state of Odisha5 in India. We modelled stormdamage using various simulation methods and data and assessedthe simulation procedures and results by comparisons againstactual data.

2. Model of storm damage

Many factors influence storm damage to structures: housequality, storm surge, waves, wind direction and velocity, etc. Inaddition, the effects of storm surge, waves and wind typicallyinteract in a nonlinear way, which may vary with local topography,bathymetry and vegetation (Howes et al., 2010; Wamsley et al.,2010; Gedan et al., 2011; Shepard et al., 2011; Pinsky et al., 2013).The model developed in the present study focuses only on thecombined role of wind, storm surge and mangrove. We usedregion-specific parameters in the model to control for other factorsthat may be highly relevant in some regions (see Section 3.1).Hence, we considered a fictive region with low and near flattopography (small variation in elevation), both wind and surgemoving in same direction (east to west), vegetation layer consistingonly of patches of mangroves, homogeneous house structure andpreparedness against storms etc. Without any access to detaileddata on surrounding landscape, we assumed that these featureshave the same influence on wind and surge in the whole region,6

though research shows the rate of surge attenuation by wetlandsis influenced by topography and bathymetry (Wamsley et al., 2010).

Thus, the model can be applied to regions with similarcharacteristics.

The horizontal structure of a tropical storm consists of the eyeand eye wall, covering an area with a maximum radius of 50 km,while a spiral rain band occurs within the next 100e200 km radius(IMD, 2000). Wind velocity is at its maximum at the eye wall anddeclines over the spiral rain band (Das et al., 1974; Roy Abrahamet al., 1995; Pielke and Pielke, 1997; IMD, 2000). Thus, we distin-guished between villages in the eye and eye wall and villages in thespiral rain band, which receive different wind impacts. Fig. 1 il-lustrates the storm structure in relation to the coastline andmangrove sites.

Following common practice in meteorology, we assumed thattangential wind is constant on a given circle around the storm eye.We then developed a general model of storm damage for a fictivevillage C in the spiral rain band during landfall, which wewere ableto modify to suit other locations within the storm. An accuratemodel of the nonlinear interactions between wind speeds, waveheights and water depth would be very valuable in this context.However, such a model would be difficult to test in the resource-poor economies where most of the mangrove regions exposed totropical storms are located. Hence we traded complexity for a moretractable model that assumes a simple interaction between windand surge and does not explicitly model the role of waves. Wecontrolled to some extent for the separate effects of wind andsurge/waves by including regions inland not reached by surge andwaves, but affected by wind. For a given house quality, we assumedthe percentage of damaged houses (Y) in village C to be a function(f) of the impact of the storm (I), which depends on wind velocity(VW) and velocity of the storm surge (VS) (Farber, 1987; Pielke andPielke, 1997) as follows:

Y ¼ f ðIðVS;VWÞÞ (1)

As storm winds move anticlockwise in the Northern hemi-sphere, mangroves can only protect villages north of landfall thatreceive wind coming from the sea. If the stormmakes landfall pointA in Fig. 1, a village C north of landfall will receive tangential windentering the coast at point D, where d ¼ AC ¼ AD denotes the radialdistance between the cyclone eye and village C. To reach the village,we assumed that wind travels distance c ¼ CD.7

Fig. 1. Model of storm impacts.

4 Assuming that potential wind velocity is equal to wind velocity at the coast.5 Named Orissa before the 113th amendment to the Indian Constitution on 24

March 2011.6 Miller et al. (2013) and to some extent Jackson and Hunt (1975) suggest that

surface roughness does not seem to play any role in variations in hurricane damagefrom wind, whereas variations in topography can have substantial impacts.

7 Distance c is an approximation of the length of the arc between D and C, whichis quite accurate if the radius is large compared with the length of the cord.

S. Das, A.-S. Crépin / Estuarine, Coastal and Shelf Science 134 (2013) 98e107 99


In accordance with hypotheses H1 and H2, we assumed thatwind velocity (VW) over village C depends on the potential radialwind (R) and the exponential reduction in wind velocity due towind barriers (WB) on the wind’s path. Given the maximum windvelocity (i.e. at landfall VMax), the radial distance from landfall to thevillage (d), the radius of the cyclone eye (r) and the decay parameterof tangential wind velocity across radial distances of spiral rainbands (m), the term R(d) in Equation (2) provides a reasonableexpression for the potential radial wind (Das et al., 1974; RoyAbraham et al., 1995). The modelled wind velocity in a specificvillage differs from the potential velocity as the wind coming fromthe coast meets barriers on the way. We assumed distance from thecoast c andmangrovewidthm1 to be the only wind barriers and thewind to decrease exponentially at rate l for every km distance itmoves inland, and additionally at rate d for every km of mangrovesit passes on the way.8 See term WB in Equation (2). Although thesheltering effect of mangroves may be local, we assumed that windspeed does not pick up after a certain distance from the mangroveboundary.9

VWðd; c;m1Þ ¼ VMax

�dr

��m

|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}RðdÞ

e�ðlcþdm1Þ|fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl}WBðc;m1Þ

(2)

The velocity of storm surge, VS, in village C depends on surgeheight (Sh). While radial tangential wind follows a fixed directionand path, the surge can enter a village from different points. Weassumed that surge damage is a function of themaximum surge in avillage. Surge decreases as it moves inland. We expected themaximumsurge at C from the sea elevation Sb at the nearest coast (atdistance a¼ CB, B being the coastal point nearest to village C), unlessthe surgewas substantially higher at another point on the coast. Thiscould occur when the nearest coast is far away from landfall10 andthus has a much lower sea elevation than a coastal point close tolandfall. If so, themaximum surgemay come from, say, sea elevationSd at the coastal point, D, from where the village is receiving thewind. Hence, we compared surge effects from these two points toobtain the maximum surge. The radial distance of points B and Dfrom landfall determines their sea elevation (Kalsi et al., 2004). Thus,we assumed storm damage at C to be proportional to the maximumof the surge impact from each of these points.

Assuming that distance from the coast and mangroves are theonly surge barriers, we assumed storm surge height to decrease atan exponential rate of a per km distance traveled inland and by arate of h per km of mangroves crossed. Equation (3) gives the stormsurge height at village C given the surge heights Sd and Sb, with Dbeing c km away and with m1 km of mangroves between C and D;andwith B being a km away andwithm2 km of mangroves betweenC and B.

ShðSd; Sb; a; c;m1;m2Þ ¼ maxnSde

�ðacþhm1Þ; Sbe�ðaaþhm2Þ

o(3)

Given the lack of knowledge about howwind and surge damageinteract, we calculated the aggregated damage from surge andwind as the sum of storm impacts from surge and wind.11 New

research on the aggregated impact of storms could help estimatethis relationship better.

Aggregating damage fromwind and surge requires both impactsto have the same units. While damage to structures is reported tobe roughly proportional to the square of wind velocity (Pielke andPielke, 1997; Miller et al., 2013), the relationship between stormsurge velocity, surge height and structural damage is unclear. TheSurge Envelope Curve (surge height at different coastal points; Kalsiet al., 2004) was the only storm surge data available for the studyarea. We overcame this issue by converting storm surge height overvillages into a wind equivalent measure We(Sh). We assumed thatstorm surge velocity is proportional to the square root of surgeheight12 and thus to the square root of wind equivalent.13 Equation(4) links storm surge velocity to wind equivalent, with s being agravity parameter.14

VSðSd; Sb; a; c;m1;m2Þ ¼�sWe

�max

nSde


o��12

(4)

Next, we assumed structural damage due to the storm to beproportional to power r of the total measure of storm velocity fromwind and surge (expressed in terms of wind velocity). Equation (5)explains house damage in villages located in the outer eye area,with damage parameter b describing how much damage a givenstorm impact causes. This parameter can be calibrated for aparticular homogeneous region and thus capture regional varia-tions in topography, house structures, etc. (see Table S1 inSupplementary material).

Y ¼ b

�VMax

�dr

��m

e�ðlcþdm1Þ

þ�sWe

�max

nSde


o��12

�r

(5)

In contrast, a village falling under the eye area faces maximumwind (VMax), with anti-clockwise direction before the eye passesand clockwise afterwards. Thus the decline in maximum winddepends on time rather than distance from the coast (Singh andBandyopadhyay, 2004). The entire eye area faces the maximumstorm surge (SMax). Letting n denote the exponential rate of declineof maximumwind per hour and t the number of hours after landfallwhen the storm reaches the village, then Equation (6) explainshouse damage for a village in the eye area.

Y ¼ b VMaxe�vt þ sWe max SMaxe

� aaþhm2ð Þn o� �

12

� �r(6)

3. Methods and data

To verify and quantify hypotheses H1 and H2, we determinedthe values of l and d empirically, while we calibrated other pa-rameters using values either from the literature or specific to ourcase study, the 1999 cyclone in Odisha. If hypotheses H1 and H2 areboth false, l and d should both be 0. We should then be able topredict the extent of house damage in the region using Equation (5)for villages in the rain band and Equation (6) for villages in the eyearea with l ¼ d ¼ 0. Thus, we simulated storm impacts assuming8 If tangential wind velocity did not decline with distance from the coast and

mangrove width, then l ¼ d ¼ 0 and actual wind velocity would coincide withpotential wind velocity in the village.

9 A more complex model of the sheltering effect of mangroves would be usefulbut difficult to test, as detailed data about tree height are difficult to obtain.10 E.g. if the nearest point was E in Fig. 1 instead of B.11 Multiplying the wind and surge velocities would have given obviously wrongestimates in regions beyond the surge as simulated damage would have been equalto zero, which contradicts the data.

12 ‘Water travels at a velocity of roughly the square root of gH, where g ¼ gravity,and H ¼ depth of water.’ http://oceanworld.tamu.edu/resources/environment-book/stormsurgesE.html, accessed on 25th August 2013.13 Gravity is identical in all villages and they suffered storm surge for the samelength of time, in the unique storm event analysed.14 sz

ffiffiffig

p

S. Das, A.-S. Crépin / Estuarine, Coastal and Shelf Science 134 (2013) 98e107100


l ¼ d ¼ 0 to predict expected damage in villages corresponding tothese impacts and compared the results against data on actualdamage.

We compared actual and simulated damage separately for theeye and outer eye areas because the eye area could not receivemangrove protection (Fig. 2) and faced the maximum wind, forwhich we did not expect distance from the coast to be a windbarrier (see Section 2). The outer eye area faced tangential windand had mangroves, which may have reduced wind velocity. Asl¼ 0 and d¼ 0 (asm1¼0) for the eye area, we tested the predictionaccuracy and validity of our model and other parameter values inthis area. We expected actual damage to be similar to damagepredicted in the model simulation for the eye area. We calculatedpotential impacts on villages in the outer eye area assumingl¼ d¼ 0 and compared actual house damage in these villages withpredicted house damage for such impacts. If actual damage cor-responded to model-simulated damage with l ¼ d ¼ 0, then dis-tance from the coast and mangrove presence had no impact ontangential wind velocity. If they did not match, we would havesupport for our hypotheses. We estimated values of l and d bycalculating the actual storm impacts corresponding to the actualdamage, then took the difference between actual and potentialimpact levels and on the basis of this difference measured the l

and d values.15 Furthermore, using the difference in house damagebetween villages with and without mangroves, we estimated themonetary value of the wind protection services of mangroves forthe study area.

Table S1 in Supplementary material provides data on otherparameter values, conversion of surge height to wind equivalent,calculation of aggregate (potential) storm impacts withthe assumption l ¼ d ¼ 0, and Table S2 shows the derivation ofexpected damage corresponding to different levels of stormimpact.

3.1. Study area

We studied house damage in 801 communities16 in the neigh-bouring districts of Jagatsinghpur and Kendrapada in Odisha, whichwere hit by a super cyclone in October 1999 (Fig. 2). The area isnearly flat, the elevation of the entire area being less than 10m a.s.l.(NATMO, 2000) and the average elevation of human habitationpoints just 3.7 m a.s.l.17 Mangroves were the only forest in thedistricts (DES, 2001).18 In general, all the characteristics of the studyarea make it particularly appropriate for our study because ourmodel assumptions hold true there.

The storm made landfall in Jagatsinghpur and moved north-west for approximately 30 km, before turning south (see thecyclone path in Fig. 2). The storm eye had a radius of 15 km (IMD,2000) and covered 174 of the 801 communities studied, while627 fell under the outer eye area (Fig. 2). The maximum wind was256 km h�1 during landfall and was likely to have reduced to190 km h�1 later.19 The storm surge measured 6 m at landfall.

Mangroves were located in two major blocks north of landfalland all areas that could potentially receive protection fell under thecyclone outer eye (Fig. 2). Even mangroves close to landfall resistedwell and probably just lost their foliage during the storm (Nayaket al., 2001). The people in the area are poor fishers or farmersand more than 80% of the houses struck by the cyclone were kutcha(mud) structures (DCOO, 2001). Villages suffered multiple damage(Gupta and Sharma, 2000). During landfall, the wind direction wasperpendicular to the mangroves (see the arrows in Fig. 2) and thewind reached the villages after passing through mangroves, whichcould have provided protection. However, the wind direction

Fig. 2. Villages and gram panchayats studied, areas under cyclone eye and mangroves. Point A, B, C etc shows the different radial distances from storm landfall that match thecategories shown in Table 3. The distances are 15 km (A), 28 km (B), 34 km (C), 41 km (D), 50 km (E), 59 km (F), and 82 km (G).

15 These measures only indicate the role of mangroves for coastal protection,provided for example, that coastal topographical effects on surge propagation donot outweigh the effect of mangroves. However, we expected low results biases asour sea elevation measures for different coastal points accounted for local topog-raphy (Kalsi et al., 2004).

16 We studied different units (727 villages and 76 gram panchayats) because housedamage data were available in that format. Our sample covered the whole ofKendrapada, a district with mangroves, and some house damage data for thelandfall area of Jagatsinghpur.17 Based on authors’ own calculations.18 There were a few patches of Casuarina plantation on some parts of the coast-line, at an average spacing of 200e400 m, before the storm. Eye witnesses reportthat these trees broke down with the first few strokes of cyclonic wind, but thatthere was not much loss to mangroves even after many hours of storm fury.19 Landfall took nearly 3 h and n¼ �0.0991 km h�1 (Singh and Bandyopadhyay,2004).



changed when the storm entered the interior track and becameparallel to the mangrove-fringed coastline. Thus, mangroves couldnot have provided any storm protection during this period, becausethe wind reached the communities without passing through themangrove forest first.

3.2. Data and measurement of variables

We analysed two categories of fully damaged houses, sweptaway (SA) and fully collapsed (FC), assessed by the state govern-ment to calculate compensation payments for damage to houses.20

The SA houses, which were damaged mainly by the storm surge,were located near the coast, whereas FC houses, which weredamagedmainly bywind, were located at the coast and up to 50 kminland. We controlled for the fact that storm surge could havecaused some of the damages to FC houses and analysed the sum ofFC and SA houses and only FC houses separately. We also studiedvillages at different distances up to 40 km from the coast separately,as no surge damage could have occurred to houses beyond somekm from the sea.

We used information from many different sources. We ob-tained house damage data from government records kept at tehsiloffices on compensation paid to families. We had no accurate dataon the number of families in each village. However, familiespredominantly had a male head or at least one working member,so we used the total number of workers and the total number ofadult males recorded in the census as two proxies for the numberof families in each village. As storm impacts above 248 km/hdestroyed all non-engineered (kutcha) structures, we expectedthe correct measure of number of families to show nearly 100%house damage for such villages. In the eye area, the total numberof workers and in the outer eye area, the total number of adultmales gave such estimates, so we used these two different mea-sures of number of families for the eye and outer eye areas,respectively. However, mangroves could only have providedshelter against the wind in the outer eye area, so villages with andwithout mangroves were all compared on the basis of totalnumber of adult males.

We combined the GIS files of villages, coastline and man-groves and used the GIS software to measure mangrove width,m1 and m2, and other distances shown in Fig. 1. Sea elevationmeasures at the minimum distance from the coast, Sb, and at thecoast on the wind path, Sd, for each village were taken from Kalsiet al. (2004). LISS-III data from the Indian satellite IRS-1D takenon 11 October 1999 helped determine mangrove presence beforethe cyclone.

To measure distance d and the eye area, we used cyclone pathinformation from IMD (2000) and Gupta and Sharma (2000). Thelandfall took nearly three hours and the maximum stormwind wasreduced when it entered the interior track after landfall. To find thehighest wind velocity in each village, we calculated two sets ofmeasures of wind velocity: one during the landfall, called veloc-ity_landfall and the other during the interior movement of thestorm, called velocity_interior. Velocity_landfall was higher fornearly 62% of the villages. We approximated the cyclone’s spiralmovement by a series of circles to measure wind velocity atdifferent radial distances. For distance c, we drew n circles with agap of 1e1.5 km each covering the distance from landfall to thefarthest village in the study area, using landfall point as the centre(some are shown in Fig. 2). The circles show the path of thetangential wind during landfall. We measured distance c and

mangrove width on this distance (m1) for villages lying on a circleor just adjacent to it from the point where the circle crossed thecoastline.

While the housing census suggested that house type was nearlyhomogeneous (predominantly mud structures) in the study area,the age of the structures, type of pillars used, etc. may have differedat the village level. We did not have complete information on housequality and other specific features of each village for the pre-cyclone period, so we compared the averages of actual and ex-pected house damage by grouping villages either on the basis ofcyclone impact or location, so that village-specific features werecancelled out.

4. Simulation procedures and results

4.1. Eye area

We used Equation (6) with parameter values: VMax ¼ 256,SMax¼6,a ¼ �0.1407, h ¼ �0.29214,n ¼ �0.0991; t ¼ 3 hrs (Table S1) tosimulate expected house damage for the eye area (15 km radiusaround the eye). Villages in this zone received maximum windwhen the eye passed over them and tangential wind at other times.With damage to houses being proportional to the highest windspeed, we compared tangential and maximum wind velocity forthese villages and retained the highest measure. In the eye area, thehighest wind speed was 256 km/h for 34 villages, 190 km/h for 97villages and between 255 and 199 km/h for the remaining 43 vil-lages. The maximum storm surge was 6 m at the coast and wasestimated to be between 0.0033 and 5.04 m (Equation (3)) forinland villages. In villages with calculated surge height below 0.4m,no houses were swept away.

Thus, to calculate potential cyclone impacts using Equation (6),we only accounted for storm surge heights �0.4 m. Given the lowvariation in storm impacts in the eye area, we placed villages inthree categories to compare expected and actual damage. Wedefined the percentage of actual house damage as the ratio ofFC þ SA to total number of workers in the villages (the more ac-curate measure of number of families in these villages). Table 1displays the results. We found that predicted damage accuratelymatched actual damage in all three categories of storm impacts.Our baselinemodel assumeswind damage to be proportional to thesquare of wind velocity. However, we performed a sensitivityanalysis using power values 1.5, 2.25 and 2.5 and found the bestmatch for r ¼ 1.5, supporting findings in the literature.21

Table 1Storm impact, expected and actual house damage in villages coming under the eyearea of the storm.

Storm impact overthe villages (km/h)

Expectedhouse damage (%)b

Actual housedamage (%) ¼ (FC þ SA)/totalworkers

r ¼ 1.5 r ¼ 2

>248 92.9e100 90.7e100 100 (54)a

217 < I < 248 81.9e92.8 76.7e90.6 84.7 (15)a

190 < I < 217 65.1e81.8 56.4e76.6 78 (105)a

a Figures in brackets show number of villages.b House damage is assumed to be proportional to wind velocity to the power ‘r’,

with the model calibrated for r ¼ 1.5 and r ¼ 2.

20 A ‘partially collapsed’ category included a broad range of minor damage tohouses and was too heterogeneous to be included.

21 The general rule in fluid dynamics states that the force that wind exerts on anobject (i.e. the damage due to wind) is proportional to the square of its velocity.Pielke and Pielke mention that the damage potential of 135 mph wind is 9 timeshigher than the damage potential of 45 mph wind velocity (1997, p. 124), whichsupports our assumption. However, Farber (1987) estimated a storm damagefunction and found an exponent value of 1.487 rather than 2.



4.2. Outer eye area

For 417 villages out of the 627 located in the outer eye area,wind velocity was highest during landfall (velocity_landfall >

velocity_interior), and we expected mangroves to have protectedthese villages against wind, as the wind direction was perpendicularto mangroves when these villages received the strongest wind. For210 villages the highest wind velocity occurred during the interiormotion of the storm (velocity_landfall< velocity_interior). Mangrovescould not have protected these villages (coloured red in Fig. 2),because wind direction was parallel to the mangrove-fringed coast-line during this period, so we excluded them from the simulation. Inthis area, males older than 15 years were used as a proxy for the totalnumber of families to calculate percentage of damaged houses. Tomeasure cyclone impacts, we added wind equivalent to the windvelocity for those villages that had storm surge height �0.3 m (theminimum surge height in villages with at least one SA house in thisarea). Cyclone impacts in the remaining villages were due to wind.Equation (5) gives the expected damage for these villages usingparameter values: VMax ¼ 256, r ¼ 15, m ¼ �0.6, a ¼ �0.1407,h ¼ �0.29214, l ¼ d ¼ 0 (Table S1).

If H1 and H2 are incorrect, model-simulated damage whenl¼ d¼ 0 should not differ significantly from the actual damage, anddamage in mangrove-protected and non-protected areas should bethe same for similar levels of cyclone impact. If mangrove-protected areas had lower damage than expected, or if areasaway from coast had less damage than areas near the coast, then l

and d are not zero and the values of these two parameters can becalibrated so that modelled damage matches actual damage.

The cyclone impact on these 417 villages varied between 96 and250 km/h, but it reached above 201 km/h for only six villageswithout mangrove shelter. Therefore we put these six villages inone storm impact category, with range 201e250 km/h (Table 2) andput the remaining 411 villages into categories as defined in Table S2.We compared simulated and actual percentage damage separatelyfor two different definitions of actual damage: (1) (SA þ FC)/adultmales (damage due to wind and water) and (2) FC/adult males(mainly wind damage) to determine whether these two measuresdiffered substantially. Substantial differences would indicate thatwaves and other coastal features ignored in themodel (which causemore SA houses) seriously biased the results, whereas small dif-ferences would indicate no serious bias.

Table 2 shows themodelled storm impacts assuming l¼ d¼ 0 incolumn 1, the expected damage corresponding to these impacts incolumn 2 (for r ¼ 1.5) and 3 (for r ¼ 2) and then the actual per-centage damage to houses for the whole sample, and for villageswithout and with mangroves. Columns 4, 5, and 6 show thesepercentages for the first definition of actual damage (SA þ FC/adultmales) and columns 7, 8, and 9 for the second definition (FC/adultmales). We find that 6 villages with more than 201 km/h stormimpact suffered 90.7% FC þ SA houses or 90% FC houses. Mangrovevillages witnessed less average damage compared to the no-mangrove villages in all categories, except the 124e140 km/hcategory where these 27 villages behind mangrove suffered 1.8%more average damage than the 62 no-mangrove villages.

Next, we grouped the villages with regard to their distance fromlandfall and the shortest distance from the coast and compareddamage in these different categories (Table 3). We employed the

Table 2Storm impact, expected and actual house damage in villages with and without mangrove coming under the storm outer eye area.

Cyclone impactover villagesin km h�1

Expected damage (%)b Actual damage (%) ¼ SA þ FC/Adult males(figures in parenthesis are number of villages)

Actual damage (%) ¼ FC/Adult malesa

r ¼ 1.5 r ¼ 2 Wholesample

Villages withoutmangrove

Villages withmangrove

Wholesample

Villages withoutmangrove

Villages withmangrove

>201 >73 >65.8 90.7 (6) 90.7 (6) No village 90 90 No village186e201 65.1e73 56.4e65.7 61.8 (13) 71.6(7) 50.3 (6) 59.6 70.6 46.8176e186 59.9e65 50.4e56.4 58.5 (10) 65.4(5) 50.3 (5) 58.1 64.8 50.0155�176 49.5e59.8 39.1e50.5 46.6 (58) 51.0 (6) 46.1 (52) 46.6 51.0 46.1140e155 42.5e49.4 32e39 41.0 (69) 44.7 (14) 40.0 (55) 40.0 42.2 39.2124e140 35.5e42.4 25.1e31.9 19.0 (89) 18.4 (62) 20.2 (27) 19.0 18.4 20.2112e124 30.4e35.4 20.5e25 10.9 (105) 12.1 (76) 7.7 (29) 10.8 12 7.793e112 23.1e30.3 14.1e20.4 6.2 (66) No village 6.2 (66) 6.1 No village 6.1

a Number of villages is same as shown in previous columns.b House damage is assumed proportional to wind velocity to the power ‘r’ and we calibrate expected damage for two different values of r, i.e. 1.5 and 2.

Table 3House damage in villages of storm outer eye area lying within different band width from landfall point of cyclone and from the coastline.

Radial distancefrom thelandfall (km)

Expected house damage (%) Average mangrovewidth (km)

Actual average house damage per village (FC/Adult males) (%)

r ¼ 1.5 r ¼ 2 Villages within10 km from coast

Villages within10e20 km from coast



15.1e25.5 >65 >56.4 0 110a 95.5 81 No village25.5e28 59.9e65 50.5e56.3 0 65 51.5 No village No village28e34.5 49.5e59.8 39.1e50.4 0 88.1 41.6 55.6 No village

1.36 71 46.8 37.7 3134.5e41 42.5e49.4 32e39 0 No village No village No village No village

2.61 48.7 43.2 39 No village41e50 35.5e42.4 25.1e31.9 0 33.9 21 16.8 No village

0.48 37.5 30 21 No village50e59 30.4e35.4 20.5e25 0 17.9 13 9 13

0.44 7.3 7.4 12.7 15.359e82 23.1e30.3 14.1e20.4 0 18.6 No village No village No village

1.86 8.7 7.1 5.6 7.7

Note.a More than 100% house damage could be possible if there are female headed households in villages, in which case male population above 15 years of age underestimates

the number of families present in a village.



same categories as shown in Table 2 and used distance d (see pointsA, B, etc in Fig. 2) to categorise the villages (column 1). Each mea-sure of distance d corresponds to a measure of potential wind ve-locity and a measure of expected damage (from model simulation)for each category (columns 2 (for r¼ 1.5) and 3 (for r¼ 2)). Column4 shows the averagemangrove forest width for each radial distancefrom landfall. We grouped villages by their distance from the coastinto 10-km band width classes (10e20 km, 20e30 km etc.). Foreach category, we characterised the villages with mangroves andwithout mangroves and noted the average actual percentage of FChouses for each subcategory. These are shown in the last four col-umns in Table 3. Similar calculations for SA þ FC houses did notdisplay significant differences (data available on demand from theauthors).

As Table 3 illustrates, villages within 15e25.5 km from landfallhad no mangroves in their coastline (third row). For this category,all houses within 10 km from the coast were fully collapsed, whilethose within 10e20 km suffered 95.5% FC and those within 20e30 km, 81% FC. Villages within 25.5e28 km band width fromlandfall, which had no mangroves in the coastline either, suffered65-51% FC. There were villages with and without mangroveswithin 28e34.5 km from landfall (within B and C in Fig. 2) andthe house damage for these were reported separately. For villageswithout mangroves, the proportion of FC houses varied from 88%within 10 km from the coast to 55.6% within 20e30 km from thecoast. For villages with mangroves, the corresponding valueswere 71 and 37.7%. The average mangrove forest width for thesevillages was 1.36 km. Table 3 shows the proportion of FC housesfor villages within other band widths from landfall and thecoastline.

The existence of wind protection services by mangroves is alsoillustrated by the results presented in Table 3. Villages with andwithout mangroves occurred in four categories: within 28e34.5 km, 41e50 km, 50e59 km and 59e82 km band width fromlandfall. Except for villages in the 41e50 km band, wheremangroveprotected villages suffered more damage, in all other categories,except few cases, mangrove protected areas were less damaged.The reduction in damage for mangrove-protected villages within10 km from the coast was 17, 10.6 and 9.9% for villages in the 28e34.5, 50e59 km and 59e82 km band category, respectively.

4.3. Wind protection value of mangroves

We calibrated the wind protection value of mangroves usinghouse damage in villages without mangrove as counterfactual formangrove villages (Table 2). We used the difference (di) in per-centage of FC houses between no-mangrove and mangrove villages(columns 8 and 9), the average number of FC houses (AFCi) and thenumber of units (ni) in each ith category to calculate the totalnumber of houses ðP

idi*AFCi*niÞ that would have been fully

collapsed without the mangroves. We performed these estimationsfor the six categories for which we had villages with and withoutmangroves22 and found 1096 such houses23 for the 174 units(villages þ gram panchayats). Using the construction cost of an FChouse in the study area in 1999 prices (INR53800), the windattenuation value of mangroves came to INR969 or US$23 (at 1999exchange rate) per family. Aggregating families for all 240 unitsbehindmangroves, wemeasured this value to come around US$177per hectare of mangroves in 1999 prices.

4.4. Estimating wind attenuation parameters

We used the results in Table 3 to measure wind attenuationparameters l and d. In order to determine l, we:

(1) measured the actual storm impact on villages correspondingto the actual damagewitnessed using the inverse of EquationS(3) 24

(2) subtracted the impact of storm surge on villages followingthe same rule as we used to add it to wind velocity (if surgeheight �0.3 m);

(3) denoted the difference actual wind velocity ðVaW Þ and calcu-

lated its average for the groups of villages in Table 3; and(4) measured the exponential rate of decline in radial wind (l)

per km distance from the coast for each band width of radialdistance.25

We measured l separately for villages without and with man-groves and found the average l value per km distance from thecoast to be l1 ¼ 0.013 for non-mangrove areas and l2 ¼ 0.02 formangrove areas.

We measured d, the exponential rate of decline in wind velocityper km of mangroves, by comparing the actual wind velocity (step(3) above) within 10 km from the coast. Within this distance fromthe coast, villages behind mangroves were farther from the coastthan villages without mangroves, and were thus likely to have areduced cyclone impact. We controlled for distance from the coastwhile calculating d by letting Va

Wm and VaWnm denote the actual wind

velocity for villages within 10 km inland with and without man-groves, respectively. Similarly, we let c1 and c2 denote the averagedistance from the coast of villages within 10 km of the coast in non-mangrove and mangrove areas, respectively, and let m denoteaverage width of mangrove forest for these villages. Using Equation(2) to calculate the ratio of Va

Wm and VaWnm, we produced the

following expression for the rate of wind decrease d due tomangroves:

d ¼ ln�VaWnmVaWm

el1c1�l2c2

1m

(7)

The d value per km mangrove forest width varied from 0.098 to0.260, giving an average value of d¼ 0.179. Using l¼ 0.016 (averageof 0.013 and 0.02) and d ¼ 0.179 we calculated house damage. Theresults matched actual damage accurately for the whole outer eyearea, but underestimated the damage for areas closer to the eye andoverestimated it for areas beyond 59 km radial distance. Thus, oneshould use l and d values lower than the average for the former, andhigher than the average for the later areas.

5. Discussion

For the storm eye area, model-simulated damage matchedactual damage quite accurately in all three categories of stormimpact (Table 1). This confirms the validity of our methodology andparameter calibration. For the outer eye area (Table 2), model-simulated damage matched actual damage for the whole samplefor the higher category of storm impacts, i.e. above 140 km/h.Simulated damage with r ¼ 1.5, as recommended in the literature(Farber, 1987) matched actual average damage most accurately in

22 We ignored the first (>201 km/h) and the last category (112e93 km/h) as therewere no comparison groups.23 1096 ¼ 1137e41. In the 6th category (140e124 km/h), 41 more houses withmangroves were destroyed.

24 We do these calculation using the value of r ¼ 1.5, that best describes the re-sults for the study area.25 For example, for the band width 15.1e25.5, l ¼ ln (actual wind velocity cor-responding to 81 per cent house damage/actual wind velocity corresponding to 110per cent house damage)/20.



villages with no mangroves (columns 5 and 8, Table 2). This againgives credibility to the model. However, there was a model biastoward higher damage for lower categories of storm impact (be-tween 140 and 93 km/h). Including partial damage estimates in theanalysis might have produced a better match between predictedand actual damage for such villages, as partial damage was moresevere there, but we did not have access to sufficient informationon the exact nature of partial damage to houses to do this. Theresults on the two different types of actual damage also showedminor differences (columns 4 and 7, Table 2), which indicates thatwind rather than water caused the maximum damage. Thusignoring wave impacts in the model would only exert a marginalbias on the effect of mangroves.

A comparison of damage for different band widths from thecoast helped validate our first hypothesis (H1) that radial tangentialwind speed declines with distance from the coast (Table 3). Villageswithin 20e30 km from the coast had fewer damaged houses thanvillages within 10e20 km, which in turn had less damage thanvillages within 0e10 km from the coast.26 This happened in eight ofthe ten categories if we consider areas within 30 km inland. Thus, lcannot be zero and radial wind velocity cannot be identical alongevery circle at d km distance from the eye. This finding persistedevenwhen SA and FC houses were accounted for in the comparison(data available from the authors). Of the two cases of high damagein succeeding category, the observation in 3rd radial distancecategory looks to be a random occurrence, probably due to somevillage specific factors as in the same category the villages withmangrove depict declining damage throughout. But the observa-tion in 6th category looks to be systematic. The damage is nearlysame in 0e10 and 10e20 km category for mangrove villages andincreasing after that. Either some village features or being behindthe mangrove edge could be the reasons. Even though the studyarea is very similar, there could be few exceptions in villagefeatures.

The exponential rate of decline in tangential wind wasmeasured from the analysis of house damage data in the supercyclone studied and found as 0.013 and 0.02 per km of distancefrom the coast for areas without and with mangrove respectively.The high rate of decline for mangrove area indicates that mangrovecould accelerate the decline of tangential wind.

In almost all ranges of impact except one (124e140 km/h), vil-lages with mangroves suffered much less damage than villageswithout for both definitions of actual damage (Table 2). These dif-ferences were larger for villages suffering strong impacts from thestorm (176e201 km/h) than for villages suffering lower impacts(<176 km/h). This becomes clearer when the percentage of FChouses is plotted against the mid values of cyclone impact (Fig. 3).Interestingly, the gap between the percentage of FC houses inmangrove and non-mangrove villages was wider than that forFC þ SA houses. This further supports the hypothesis that man-groves provide wind protection, because FC houses were mainlydamaged by wind and not particularly affected by surge.

Comparisons of the damage for villages with and withoutmangrove revealed less damage in mangrove villages everywhereexcept in the 41e50 km band (Table 3), where they suffered moredamage. These villages with mangroves faced impacts of between124 and 140 km/h and suffered 1.8% higher damage as a groupcompared with villages without mangroves (Table 2). Mangroveswithin 41e50 km distance from landfall (between D and E, Fig. 2)occurred in small and fragmented patches whereas in other bandsthey formed a larger forest (Fig. 2). This indicates that mangroves

can provide storm protection when they exist in large, continuouspatches, but that otherwise they can worsen storm damage. Suchobservation is also supported by studies that have found habitatsize and vegetation features to be influencing the surge attenuationservice provided by coastal marshes, mangroves, sea grass bed etc(Shepard et al., 2011; Pinsky et al., 2013).

Other cases where mangrove villages suffered more damagethan no-mangrove villages occurred within 10e20 km in the 3rdcategory and within 20e30 km and 30e40 km in the 6th category(Table 3). The low damage in no-mangrove villages in the 3rdcategory (41.6%) could be due to village specific features as the nextcategory witnessed much higher damage (55.6%) than themangrove protected villages (37.7%). However, in the 50e59 kmband (between E and F, Fig. 2), mangrove protection seemed limitedto villages within 20 km from the coast. Mangroves here werenarrow and at the edge of the northernmangrove block of the studyarea, which could explain such limited protection.

Table 3 confirms the observations from Table 2 of stronger windprotection by mangroves for villages close to landfall or within thehigh impact zone of the storm and weaker protection for villages inthe low impact zone if we consider damage within 10 km fromcoast. Mangrove protection did not seem to be confined to near-coast areas. Villages located as far as 20e30 km away frommangrove forest on the coast showed lower damage than similarlylocated villages without mangroves in most cases if the forest iscontinuously spread along the coast and its average width is morethan 1 km. According to our calculations, the estimated exponentialrate of wind velocity attenuation by mangroves varied between0.098 in high impact zone to 0.260 in low impact zone per km ofmangrove forest width. In spite of a lower attenuation rate, highimpact areas witnessed more saving in house damage (Fig. 3) asstructural damage are nonlinearly related to wind velocity (eitherto square value (Pielke and Pielke, 1997) or to the power of 1.5(Farber, 1987 and present case).

Hence the results supported hypothesis (H2), that mangrovesprovide protection from wind damage by a storm, especially inareas where mangroves are present in large, continuous patches.We found substantial evidence that mangroves attenuate wind andprovide more storm protection than previously established and tostructures much farther away from the forest than generallybelieved. Based on avoided reconstruction costs, the estimatedeconomic value of the wind attenuation services of mangroves wasaround US$23 for each mangrove protected family and US$177 perhectare of mangroves, both at 1999 prices.

Average house damage per village

0

10

20

30

40

50

60

70

80

118* 132* 148* 166* 181* 193.5*

Cyclone Impact

% o

f h

ou

se

s fu

lly

d

am

ag

ed

No_mangrovevillagesMangrovevillages

Fig. 3. Average house damage due to cyclonic winds in mangrove-protected villagesand in villages without mangroves.

26 We are not making any comment beyond 30 km as we do not have observationsin most of the categories beyond this distance.



In the present study we adopted a socio-economic approach,with a combination of model building, model simulations andanalysis of real world data. These data and the model willhopefully help those planning land use changes in coastal areasto better account for the ability of mangroves to mitigate stormdamage, in addition to all other benefits that mangrovesprovide.

However, further scientific research is needed to confirm thisless well recognised ecosystem service of mangroves. Our meth-odology is attractive for use in resource-poor regions where morecomplicated numerical models may not be practical, but it also hasits limitations. Model development could focus on the role of wavesand the combined role of surge, waves and wind on damagingproperties. The interactions between wind speed and wave heightand those betweenwater depth and wave height are all non-linear,and would require a more complex non-linear model. Fluid dy-namic analysis of the interactions between cyclonic winds, coastaltopography and mangrove features could shed more light on howparticular coastal features can provide more or less protection fromcyclonic winds as has been experimented and validated in case ofsurge attenuation bymangroves (Wamsley et al., 2010; Pinsky et al.,2013). Studies combining hydrodynamic and economic analysis,such as that performed by Barbier et al. (2013) for Louisiana wet-lands, would also be useful. Another consideration is that sea levelrise may affect the protection role of mangroves (see Smith et al.(2010) for a study on wetlands).

We based our measures on data for fully damaged houses. Morereliable results could be obtained with better quality data on theexact nature of house damage and house quality before the storm.Furthermore, our empirical results relate only to one storm in oneparticular location and cannot be extrapolated to place a generalvalue on mangroves. However, the model can be used to makesimilar calculations for other storms and in other places. Testing themodel on a number of other suitable regions could lay the foun-dations for a more systematic empirical study of the role of man-groves in protection against wind damage and thus shedmore lighton the particular features of mangroves that provide the best windprotection. This would require choosing sites following some of thesuggestions in Wamsley et al (2010) e.g. sites with homogeneouslandscape feature that do not contain man-made features that mayinfluence storm impacts (levees, canals, etc).

6. Conclusions

This study examined whether mangroves can attenuate windvelocity and provide protection from wind-related damage duringstorms, especially in areas affected by tangential wind. We built atheoretical model which we calibrated to simulate wind damage bya storm that hit the state of Odisha in India in 1999. We then usedempirical data on actual storm damage to test whether the modelpredictions were accurate.

We found that not accounting for the role of mangrovessignificantly overestimated actual wind damages in village behindmangroves, but not in villages without mangroves. Villageswithoutmangroves suffered more wind-related damage than villagesbehind mangroves at the same level of storm impact. We foundevidence that tangential wind declines while moving inland, aswind damage at a coastal point distinct from landfall seemed to bemuch higher than the damage at an inland village at the samedistance from landfall. Accounting for mangroves in the modelprovided relatively accurate damage predictions. Hence we provideevidence that wind barriers like mangroves reduce tangential windand contribute to substantially reducing wind-caused damage tostructures. While the simplicity of the model makes it very trac-table for use in empirical studies in poor regions, further model

development and better data would shed more light on theparticular mechanisms underlying mangrove protection fromstorms.

Conflict of interest

The authors declare no conflict of interest.

Author contribution

S.D. and A.S.C. designed the study; S.D. performed the research;S.D. analysed the data; S.D. and A.S.C. wrote the paper.

Acknowledgements

This research was conducted within the Mäler Scholarshipprogramme of the Beijer Institute of Ecological Economics. Wegratefully acknowledge funding provided by the Swedish Interna-tional Development Cooperation Agency and the Ebba and SvenSchwartz Foundation. We thank U. C. Mohanty for meteorologicalinput; K-G Mäler, C. Folke, J. R. Vincent, Sara Aniyar, N. Koedam, andmembers of Beijer staff for academic input; M. Pyykönen and E.Andersson for help with GIS and B. Mishra for house damage data.We sincerely thank our two reviewers for their in-depth commentsthat helped improve the quality of the manuscript.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ecss.2013.09.021.

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Mangroves can provide protection against wind damage during storms