J. Earth Syst. Sci. (2020) 129:234 Ó Indian Academy of Sciences

https://doi.org/10.1007/s12040-020-01504-y (0123456789().,-volV)(0123456789().,-volV)

Seasonal variation in longshore sediment transport rate and its impact on sediment budget along the wave-dominated coast,

ATEETH SHETTY and K S JAYAPPA* Department of Marine Geology, University, Mangalagangothri, Mangaluru, Karnataka 574 199, India. *Corresponding author. e-mail: [email protected]

MS received 1 May 2020; revised 29 August 2020; accepted 19 September 2020

The Karnataka coast is subjected to high wave activity during the southwest monsoon when most of the sandy beaches undergo erosion. Based on the littoral cell concept, the Karnataka coast is broadly divided into 14 major littoral cells and 26 stations are selected in the present study. WaveWatch III global wave model data at 0.5° interval were used to derive the nearshore wave characteristics from XBeach numerical model. The model results were validated with the measured wave rider buoy data of the Indian National Centre for Ocean Information Services. The beach orientation, nearshore slope, median sediment size, significant wave height, mean wave direction, and the peak wave period were used in the estimation of longshore sediment transport rate. The mean significant wave height along the Karnataka coast was about 0.86 m, wave direction was about 210° and peak wave period was about 13 sec. The wave height during southwest monsoon (June–September) was higher, post-monsoon (October–December) was moderate and pre-monsoon (January–May) was the calmest period. Direction of longshore sediment transport was southwards during pre- and post-monsoons when waves were from the south–southwest. Whereas, northwards during monsoon when the wave approach from west–southwest to west. The annual net longshore sediment transport rate estimated was about 0.659106 m3 towards the south and the sediment budget investigation depicts the loss of 0.0679106 m3 during the study period. Keywords. Littoral cell; wave characteristics; XBeach; sediment transport; sediment budget.

1. Introduction Rao et al. 2009). Using various approaches, long- shore studies have provided the foundation in Knowledge of longshore sediment transport along a understanding the longshore transport directions coast is vital for understanding the region’s coastal (Ashley et al. 1986; Esteves et al. 2009; Ari et al. dynamics. Waves approach the shore obliquely and 2013; Chowdhury and Behera 2017; Duc et al. produce longshore currents that transport sedi- 2019). However, most of the previous studies have ment alongshore. Numerous studies on longshore been local in nature, conducted at site-speciBc sediment transport have been published over the beaches. Only a few of these studies extend beyond past decades (Komar 1998; Bayram et al. 2007; the local scale and fewer still compile previous

Supplementary materials pertaining to this article are available on the Journal of Earth Science Website (http://www.ias.ac.in/ Journals/Journal˙of˙Earth˙System˙Science). 234 Page 2 of 14 J. Earth Syst. Sci. (2020) 129:234 studies to provide a large scale overview. The the fair-weather period; such cells are eroding present study will provide a complete view of the in the long-term. The present investigation is longshore sediment transport (LST) along Kar- undertaken in this context and an attempt has nataka coast from in the north to Talapady been made to understand the impact of natural in the south. This work includes both site-speciBc processes and anthropogenic activities on the and littoral cell-wise studies to provide a complete coastal dynamics of the study area. Along the assessment of longshore sediment transport rate west coast of India, quantitative estimation of (LSTR) and direction. Generally, there are two LSTR is reported at different locations. The fundamental approaches for estimation of LSTR: longshore sediment transport is southerly dur- (1) bulk formulation like the Coastal Engineering ing pre- and post-monsoons and northerly dur- Research Center (CERC 1984) and Walton and ing monsoon along Gujarat, Maharashtra, Goa, Bruno (1989) based on the energy Cux method of Karnataka and north Kerala coasts (Chan- simpliBed physical processes, and (2) the process- dramohan and Nayak 1992; Sajeev et al. 1997). based models like Kamphuis (1991), Kamphuis The gross sediment transport rate is high modiBed (Mil-Homens et al. 2013) and Van Rijn (1.5–2.0 million m3/annum) along the coasts of (2014) derived from extensive laboratory and Beld south Gujarat, north Karnataka and north studies which include the eAects of a large number Kerala (Chandramohan and Nayak 1992). LSTR of complex physical processes (Mil-Homens et al. estimates in the above-mentioned studies are based 2012). on predictive empirical formulations calibrated In sandy beaches, the LSTR is controlled by from Beld measurements or laboratory physi- the waves through wave breaking and wave- cal models (Bayram et al. 2001, 2007). induced currents (Van Rijn 2002) and hence are Other studies on selected coastal segments mainly related to breaking wave parameters. So along the west coast of India revealed that the the bulk formulation requires wave character- LSTR is variable, bi-directional, and season- istics in the breaker zone. Also, due to the dependent (Jayappa 1996;Kumaret al. 2003; difBculty of acquiring extensive data in a com- Kurian et al. 2009; Shanas and Sanil Kumar 2014; plex nearshore region using instruments, a Sanil Kumar et al. 2017). commonly used approach is applied to estimate Neither long-term nor short-term nearshore LSTR through empirical bulk formulation. wave characteristics and LSTR are available for LSTR empirical formulations are largely the study area. Hence, the study is carried out to dependent on Beld measurements and site-spe- identify the nearshore wave characteristics (sig- ciBc. Hence, it is important to test these for- nificant wave height, mean wave direction and mulations in different coastal regions that are peak wave period) obtained from the XBeach subjected to different wave conditions. The numerical model and quantify the LSTR from well- accuracy of estimation depends mainly on the known formulation (Kamphuis 1991). WaveWatch nearshore conditions governing physical pro- III global wave model data at 0.5° interval were cesses and the quality of input data used (Ari used as an input to the numerical model and the Guner et al. 2013). The beach material washed model results were validated with the measured away during a rough weather season is re-de- Indian National Centre for Ocean Information posited during the subsequent fair-weather Services (INCOIS) wave rider buoy data. The season and the beach equilibrium is maintained. sediment budget is a balance of sediments entering Meanwhile, if there is an obstruction (presence (source) and leaving (sink) a littoral cell, and the of natural headlands, shoals, and/or artiBcial resulting erosion or accretion (residual) in the structures) on the path of littoral drift, the deBned cell. Source of materials can be varying equilibrium proBle of the natural beach is dis- from place to place and from time to time. turbed (Komar 1998). Due to complexity of the Important sources identiBed are longshore trans- nearshore coastal processes, accurate estimation port, onshore transport, river sediment load, and of LSTR is a challenging task for coastal engi- beach nourishment (Hume et al. 1999). The neers (MaB et al. 2013). The south-west coast of hypothesis addressed in the study is the variation India is known for severe erosion during the in LSTR during different (monsoon and non-mon- southwest monsoon, and the beaches are re- soon) periods and its impact on sediment bud- built during the fair-weather period. There are get along the wave-dominated tropical beaches of certain littoral cells that are not re-built during the central west coast of India. J. Earth Syst. Sci. (2020) 129:234 Page 3 of 14 234

2. Study area of the year (Sanil Kumar et al. 2012). This seasonal cycle of wind leads to changes in wave character- The study area extends along the wave-dominated istics over the open sea and nearshore areas along Karnataka coastline (*304 km oriented in the the west coast of India (Kumar and Anand 2004; NNW–SSE direction) with 14 littoral cells and 26 Semedo et al. 2011). Deep-water waves approach selected stations (Devbagh in the north to Tala- the coast from the south-west and north-west pady in the south) in the eastern directions and the significant wave heights (Hs) (Bgure 1a). The main geomorphic features along have been assessed [3 m during the SW monsoon the Karnataka coast are rivers, lagoons, bays, and \1.5 m during the non-monsoon period creeks, cliAs, spits, sand dunes, sandy and rocky (Kumar et al. 2006; Sanil Kumar et al. 2012). beaches. This coastline experiences severe erosion Erosion becomes severe during monsoon season during the SW monsoon and accretion during the (June–September) due to high energy waves non-monsoon season. Natural processes and (Jayappa et al. 2003; Deepika and Jayappa 2017). anthropogenic activities are the causes of beach About 180 km of the study area is undergoing morphological changes in the study area (Deepika erosion at various magnitudes, and the areas with and Jayappa 2017). From June to September, human intervention are the most erosion-prone referred as the summer monsoon or SW monsoon (Shetty et al. 2019). The coastline is protected with or monsoon, the wind direction is south-westerly coastal protection structures such as seawalls, with significantly higher wind speed than the rest groins, and breakwaters which intervene in the

Figure 1. Study area showing (a) 14 littoral cells and 26 selected stations with beach orientation and, (b) bathymetric grid used in the XBeach model. 234 Page 4 of 14 J. Earth Syst. Sci. (2020) 129:234 nearshore processes. Hence, the central west coast 2014). The LSTR estimate based on the Kamphuis of India is selected for the study as wave conditions formula, which also includes the wave period, beach of the region vary with the seasons. slope, and sediment grain size are found to be a reliable estimate for the study region (Shanas and Sanil Kumar 2014). 3. Materials and methods 4 2 1:5 0:75 À 0:25 0:6 Q ¼ 6:4 Â 10 HsbTp mb d50 sin ðÞð2ab 1Þ Longshore sediment transport rate is estimated and the sediment budget is carried out using empirical In the above equation, Q = longshore sediment 3 equations. XBeach (eXtreme Beach behaviour transport rate (LSTR) in m per unit time, Hsb = model) developed by Delft University of Technol- significant wave height at breaker line in meters, ogy, Deltares and University of Miami (Roelvink Tp = peak wave period in seconds, mb = slope of et al. 2010) is an open-source coupled 2D non-linear the beach/surf zone, d50 = median of sediment shallow water equations coastal morphodynamics grain size in m, ab = angle between breaking wave model. Time-varying wave action balance including crest and coastline in degree. refraction, shoaling, and wave breaking; Roller Site-speciBc significant wave height, mean wave model including breaker delay, wave amplitude direction, peak wave period, surf zone slope, median eAects on wave celerity; wave–current interaction, grain-size, and shore normal angle are used in the Roelvink (1993) wave dissipation model for non- estimation of LSTR at all the 26 stations. The vari- stationary wave energy balance, and Baldock et al. ation in nearshore slope along the study area is (1998) wave dissipation formulation for stationary derived from the IDW bathymetric raster surface. wave energy balance are considered. XBeach uses a Seasonal foreshore beach sediment samples were coordinate system, where the computational x-axis collected at all the stations, and sieved using is always oriented towards the coast, approximately mechanical sieve shaker with 0.50/ interval ASTM perpendicular to the coastline, and the y-axis is sieves from 4000 to 63 lm mesh sizes to derive the alongshore. The coordinate system is deBned rela- median size (d50) as per Folk and Ward (1957)’s tive to the world coordinates (xw, yw) through the graphical method in GRADISTAT package (http:// origin (468516.7E, 1405968.2N) and the orientation www.kpal.co.uk/gradistat.html). Coastline along alfa (20°), deBned counter-clockwise with respect to the study area is oriented in NNW–SSE direction, the east. The grid size in x- and y-directions are wave breaker angle is calculated as the difference 5009500 m and the bed levels are deBned in cell between wave direction and shore normal. The wave centers. Bathymetry data (point and contour) of angle below shore normal is taken as positive with the study area are extracted from the naval longshore current direction towards the north and hydrographic chart (NHO 2004, 2005) to generate above it is considered to be negative and south- the raster bathymetric grid using inverse distance wards. Longshore transport rates require two weightage (IDW) interpolation method (Bgure 1b). directions, which can be deBned either through WaveWatch III global wave model data (https:// the pair of rates as left (QL)andright(QR) pae-paha.pacioos.hawaii.edu/erddap/griddap/ww3˙ directed or as net (Qnet)andgross(Qgross). The global.html) at 0.5° interval for a period of 4 years net LSTR is deBned as the difference between QR (January 2014–December 2017) were used as an and QL (equation 2) and gross LSTR is deBned as input (oAshore boundary condition) to the numer- the sum of QR and QL over a speciBed time interval ical model, and the model results were validated (equation 3). with the measured wave rider buoy deployed in Karwar (https://www.incois.gov.in/portal/ Qnet ¼ QR À QL ð2Þ datainfo/wrb.jsp) by INCOIS. The nearshore Qgross ¼ QR þ QL ð3Þ wave parameters – significant wave height (Hs), mean wave direction (Tdir) and peak wave period (Tp) along the Karnataka coast were derived using In order to better understand the coastal sys- the XBeach numerical model and LST rate is esti- tem, sediment budget analysis has been carried mated using Kamphuis (1991) formula (equa- out. A sediment budget is a tally of sources and tion 1). The result of Kamphuis (1991) formula is sinks or sediment gain and loss within a speciBed found to be matching with the Beld observations littoral cell, or in a series of connecting cells over than the modiBed Kamphuis formula (Van Rijn time. Sediment budget analysis system (Rosati J. Earth Syst. Sci. (2020) 129:234 Page 5 of 14 234

Figure 2. The plot of model estimated and buoy recorded (a) significant wave height, (b) wave direction, and (c) peak wave period along the Karwar coast. and Kraus 2003) is a graphic-based interface to degree to which the cell is balanced (Rosati and solve the sediment budget for each littoral cell Kraus 2003). Thus, knowledge of the net and gross with sources, sinks, volume changes, placement, transport rates, as well as pathways of sediment and removal volumes into and out of the cell transport is required to accurately represent the (equation 4). sediment budget (Bodge 1993). X X Qsource À Qsink À DV þ P À R ¼ Residual 4. Results and discussion ð4Þ 4.1 Nearshore wave characteristics where Qsource and Qsink are the sources and sinks, DV is the net change in volume within the cell, Based on the results of the XBeach model simula- P and R are the amounts of material placed in and tion from January 2014 to December 2017, wave removed from the cell, and residual represents the characteristics (Hs, Tdir, Tp) were extracted from 234 Page 6 of 14 J. Earth Syst. Sci. (2020) 129:234

Figure 3. Plot showing the wave breaker line characteristics (a) water depth, (b) wave height, (c) wave angle, and (d) wave driven longshore current during pre-monsoon, monsoon and post-monsoon seasons at 26 stations along the Karnataka coast.

January to December months. Model results Karwar, the overall trend display a reasonably showing the wave characteristics during pre-mon- good match (Bgure 2). The correlation coefBcient soon, monsoon, and post-monsoon seasons along of significant wave height between the model esti- the Karnataka coast are presented in supplemen- mated values and the measured values during pre- tary Bgures (S1, S2, and S3). The results of the monsoon, monsoon and post-monsoon were 0.62, model output (Hs, Tdir, Tp) are compared with 0.5 and 0.8, respectively. Wave direction between measured INCOIS wave rider buoy data for the model and the measured values during J. Earth Syst. Sci. (2020) 129:234 Page 7 of 14 234

Table 1. Seasonal variation of median grain size (d50) at selected stations along the Karnataka coast.

Sediment d50 values in mm Jan–May June–Sep Oct–Dec Stations (Pre-monsoon) (Monsoon) (Post-monsoon) Sediment type Devbagh 0.20 0.26 0.17 Fine–medium sand Karwar 0.22 0.17 0.15 Fine sand Om Beach 0.14 0.15 0.14 Very Bne–Bne sand Tadadi 0.16 0.16 0.14 Very Bne–Bne sand Vanalli 0.14 0.15 0.13 Very Bne–Bne sand Apsarakonda 0.23 0.28 0.26 Fine–medium sand Manki 0.15 0.27 0.15 Fine–medium sand Murudeshwara 0.12 0.12 0.14 Very Bne–Bne sand Nesther 0.37 0.55 0.35 Medium–coarse sand 0.39 0.61 0.50 Fine–coarse sand Kota 0.17 0.29 0.15 Fine–medium sand Kodi-Bengre 0.28 0.28 0.27 Fine–medium sand 0.21 0.34 0.16 Fine–medium sand Udyavara 0.42 0.29 0.22 Fine–medium sand Kaup 0.15 0.23 0.15 Fine–medium sand Yermal 0.26 0.30 0.24 Fine–medium sand Hejamady 0.23 0.39 0.19 Fine–medium sand Sasihithlu 0.17 0.28 0.18 Fine–medium sand Mukka 0.29 0.27 0.14 Very Bne–medium sand Surathkal 0.17 0.34 0.15 Fine–medium sand Panambur 0.18 0.26 0.15 Very Bne–medium sand Thannirbhavi 0.17 0.57 0.30 Fine–coarse sand Bengre 0.43 0.43 0.59 Medium–coarse sand Ullal 0.71 0.43 0.44 Medium–coarse sand Someshwara 0.62 0.74 0.55 Medium–very coarse sand Talapady 0.47 0.51 0.39 Medium–coarse sand

pre-monsoon, monsoon and post-monsoon were during SW-monsoon. The mean peak wave period 0.62, 0.5 and 0.8, respectively. Whereas, peak wave (Tp in second) varied from 13 to 14 sec during pre- period between the model and measured values monsoon, low and mostly persists for 10–11 sec during during pre-monsoon, monsoon, and post-monsoon SW-monsoon and 12–13 sec during post- or NE- were 0.62, 0.5 and 0.8, respectively. monsoon season. The higher waves (Hs * 2to3m) Mean variations in significant wave height, wave and a peak wave period of 10–12 sec due to the strong direction, and peak wave period along the Karnataka winds from W to WSW, during thepeak monsoon was coast (supplementary Bgure S4) and station-wise also reported by Shanas and Sanil Kumar (2014)for variations during the study period (supplementary the central-west coast of India. The result was also tables S1, S2 and S3, respectively) are derived. Dur- similar to the observations of Chandramohan and ing the study period of 2014–2017, the mean signifi- Nayak (1992) and Sanil Kumar et al. (2012) along the cant wave height (Hs in m) was low from January to Karnataka coast. May and October to December with a monthly The waves break at 0.76–1.45 m water depth average value ranging 0.73–1.05 and 0.73–0.88 m, during pre-monsoon, 1.30–3.37 m during monsoon, respectively. Whereas, from June to September, it and 0.94–1.44 m during post-monsoon. Similarly, was high (1.37–2.86 m). Mean wave direction (Tdir in significant wave height at breaker line (Hsb) degree) were predominantly from the south–south- increased from pre-monsoon (0.46–0.87 m) to west (208°–212°) during pre- and post-monsoons, and monsoon (0.78–2.02 m), and gradually decreased west-southwest to the west direction (239°–266°) during the post-monsoon season (0.56–0.86 m). 234 Page 8 of 14 J. Earth Syst. Sci. (2020) 129:234

seasons at selected stations along the Karnataka coast are presented in Bgure 3.

4.2 Nearshore sediment characteristics

The sediment characteristics are derived for all the 26 stations. During pre-monsoon, the d50 value ranged from 0.12 mm at Murudeshwara to 0.71 mm at Ullal, with a mean of 0.27 mm. Whereas, during monsoon the median size of 0.12 mm at Murudesh- wara and 0.74 mm at Someshwara was noticed with an overall mean of 0.33 mm. However, during post- monsoon period, the median size of 0.13 mm at Vanalli, 0.59 mm at Bengre with an overall mean of 0.25 mm was deciphered. The sediments are medium to coarse in size during monsoon, attributable to high wave energy, and medium to Bne during pre- and post-monsoon seasons which signiBes low to moderate energy. Seasonal variation of median grain size (d50) at selected stations along the Karnataka coast is given in table 1.

4.3 Nearshore slope

The nearshore slope is an important variable in esti- mation of longshore sediment transport. From the NHO chart (2004, 2005), bathymetric point and con- tour are digitized to generate the raster layer using inverse distance weightage (IDW) interpolation method. Subsequently from the raster surface, the variation in nearshore slope (gradient) is derived (Bgure 4). The shelf along Karnataka coast is gentle Figure 4. Showing the variation in nearshore slope along the (Shetty et al. 2019), and nearshore slope varies from area studied. station to station. It is observed that the beaches on the northern side have Cat to gentle slope (values ranging from 0.05 to 0.42), when compared to those beaches on the southern part of the study area with the moderate The wave breaker angle was calculated as a to high slope (values ranging from 0.35 to 0.82). difference between the shore normal angle and peak wave direction. Breaker angle between –10 and 36° during monsoon resulted in the net 4.4 Longshore sediment transport rate northerly longshore current. Whereas, during pre- and post-monsoons, the breaker angle between –24 The LSTR is estimated for all the 26 stations along and 19° caused the net southerly longshore current. the Karnataka coast based on site speciBc near- The longshore current speed ranged from –0.6 to shore wave data derived from XBeach numerical 0.5 m/s during pre- and post-monsoons, and –0.4 to model simulating the wave transformation from 0.9 m/s during monsoon. Increase in longshore oAshore to the nearshore region. The variation in current speed during monsoon attributed to the monthly net and gross LSTR using the Kamphuis choppier and frequent waves. The wave breaker (1991) formula is examined (table 2). It is found line characteristics (water depth, wave height, that the LSTR was high (429534–1155825 m3) wave angle, and wave-driven longshore current) during the peak monsoon period (June–August) during pre-monsoon, monsoon and post-monsoon and the predominant direction of transport was .ErhSs.Sci. Syst. Earth J.

Table 2. Monthly, net, and gross longshore sediment transport rate (m3) at 26 stations selected in the study.

Longshore sediment transport rate (LSTR) in m3/month Net LSTR Gross LSTR 3 3

Stations Jan Feb Mar April May June July Aug Sept Oct Nov Dec (m /year) (m /year) (2020) 129:234 Devbagh –14293 –12343 –20190 –18638 –30569 –22682 39579 –14930 –11690 –17012 –12261 –20053 –155082 234239 Karwar –15340 5671 –18853 –28014 –39919 –60509 –94855 –53242 –35676 –12087 –23685 –12999 –389508 400850 Om Beach 7536 3328 19226 22226 31467 33858 30956 20912 17301 21534 14837 14984 238166 238166 Tadadi 3861 4474 7433 10655 11881 22456 16105 5636 41629 17615 11312 11689 164746 164746 Vanalli 19083 16665 25062 22965 36231 44029 33801 36289 23758 23857 14318 27041 323099 323099 Apsarakonda –20444 –17866 –22341 –31595 –47384 27891 76575 54291 16603 –13655 –27921 –14707 –20553 371273 Manki –8469 3756 –20628 –23844 –35371 41974 70435 19070 –17856 –23392 –17936 –19057 –31318 301788 Murudeshwara –4684 –4583 –10453 –13942 –13593 –69966 –36330 –19443 –48577 –18921 –13093 –13095 –266679 266679 Nesther –9343 –8489 –11043 –11695 –19172 63846 73519 50885 16358 –9511 –9187 –12879 113289 295927 Maravanthe –12856 –14471 –14883 –20572 –33455 –14184 25899 29870 4734 –9313 –18578 –9902 –87711 208717 Kota –9110 3652 –14844 –15863 –26172 –13111 33124 –7610 –15557 –18917 –13239 –15240 –112888 186439 Kodi-Bengre –2777 –3666 –6004 –8371 –8276 –45160 –25865 –9005 –34153 –12567 –8474 –8663 –172982 172982 Malpe –11047 –10467 –15753 –13113 –21565 –45217 –24735 –37301 –19644 –16581 –9068 –16583 –241073 241073 Udyavara –13526 –14514 –15965 –20588 –33494 26414 64626 51864 15507 –12779 –23985 –13144 10415 306407 Kaup –13310 3645 –17740 –18683 –30640 49294 78414 24597 –13475 –20819 –17321 –16122 7839 304060 Yermal –4113 –4038 –6683 –11126 –11503 34848 102417 34856 –38351 –15976 –10027 –10898 59406 284834 Hejamady –4286 –5794 –6570 –12407 –13282 36460 89125 29593 –36525 –17399 –10511 –11329 37076 273280 Sasihithlu –11812 –7044 –15508 –16230 –25727 28403 54939 –13388 –21572 –19306 –16070 –12848 –76165 242848 Mukka –14598 –16390 –17012 –22764 –35416 27398 70845 51075 11471 –13439 –26543 –15531 –905 322484 Surathkal –15067 –12396 –20807 –17681 –28155 34229 50249 17821 –21692 –20972 –11957 –19856 –66285 270883 Panambur –13080 –12071 –17734 –16394 –24396 30328 70064 9106 –25043 –19295 –12642 –13886 –45045 264041 Thannirbhavi –4870 –6166 –11033 –14790 –15652 28012 68577 24924 –32769 –16816 –9758 –9688 –29 243055 Bengre –10810 –9909 –16387 –15015 –22237 37888 68861 23559 –24605 –14589 –10760 –9921 –3926 264542 Ullal –6167 –7382 –13848 –13624 –21308 51975 75119 14962 –24190 –18488 –15120 –10780 11149 272962

Someshwara –13701 –14015 –16007 –21841 –31165 49799 87826 54775 21137 –12207 –19573 –12481 72547 354527 14 of 9 Page Talapady –12944 –9700 –17802 –15427 –25951 50167 56554 30368 –22583 –19549 –10784 –17338 –14989 289167 Net (m3) –216168 –150114 –296368 –346373 –514823 448438 1155825 429534 –275458 –310586 –308027 –263286 –647406 7099067 Note: (+) northwards; (–) southwards. 234 234 Page 10 of 14 J. Earth Syst. Sci. (2020) 129:234

Figure 5. Monthly longshore sediment transport rate (m3) along the Karnataka coast.

Figure 6. Net longshore sediment transport rate (m3) at 26 stations selected in the study.

northwards. Whereas, during the pre-monsoon and monsoon season cause a large amount of sediment post-monsoon seasons, moderate LSTR was transport in a northerly direction. During the post- observed (150114–514823 m3) with southward monsoon season, shifting of direction (S to SW) transport (Bgure 5). The net LSTR estimated for occurred again and LST was towards south till the each month indicates that the predominant direc- onset of monsoon or pre-monsoon period. Annual tion of LST was from north to south except during net longshore sediment transport rate was high the peak monsoon period. During the pre- and (20553–389508 m3) along the northern stations and post-monsoon seasons, the wave height was low relatively low (29–241073 m3) along the southern (monthly breaker height ranged from 0.73 to 1.05 stations (Bgure 6). m) and with the onset of monsoon, the wave height The computed net longshore sediment trans- increased drastically which had a direct inCuence port rates for a few stations are compared with on the transport rate. The directional shift in the the results of earlier studies. Chandramohan and wave direction from WSW to W and relatively Nayak (1992)reportedthatannualnettrans- high (1.37–2.86 m) waves prevailing in the port is relatively high (0.5 to 1.09106 m3)along J. Earth Syst. Sci. (2020) 129:234 Page 11 of 14 234

Table 3. Shoreline change rate, beach volume change, longshore sediment transport rate, source, sink and residual (m3) of the 14 littoral cells along the area studied.

Littoral Shoreline Beach LSTR QSource QSink Residual cell change (m) volume (m3) (m3) (m3) (m3) (m3) LC-1 –24 35799 –155082 35799 155082 –119283 LC-2 –113 308215 –389508 866209 389508 476701 LC-3 –144 17735 402911 730341 402912 327429 LC-4 –45 116676 323099 116676 323099 –206423 LC-5 –85 –279449 –318550 113239 597999 –484760 LC-6 –58 56978 113289 375527 113239 262288 LC-7 –152 106217 –87711 106217 87711 18506 LC-8 –67 –18572 –112888 87711 131460 –43749 LC-9 –175 –242183 –414055 123303 656237 –532934 LC-10 –63 24701 10415 543077 10415 532662 LC-11 –127 65083 104321 83308 122545 –39237 LC-12 –216 –211630 –188399 0 400029 –400029 LC-13 31 –3503 –3955 276986 12348 264638 LC-14 –138 –28279 68708 7991 131000 –123009 Net –1378 –52213 –647406 3466384 3533584 –67200 (+) Accretion, (+) North (+) Gain (–) Loss (+) Gain (–) Erosion (–) South (–) Loss

the north, and low (0.1 to 0.59106 m3)alongthe covering larger areas have pointed out that net south Karnataka coast. Jayappa (1996) studied littoral drift is towards south, though bi-directional south Karnataka coast over a period of one year littoral transport is noted. The difference in pre- and found that the net sediment transport (0.04 vious studies are mainly due to visually observed to 0.49106 m3) was towards the south. Littoral data, variations in nearshore bathymetry and grain drift was towards south from November to April size parameters are not considered. Comparison of when waves approach from WNW and NW, the estimated LSTR using Kamphuis (1991) for- directions and towards north from May to mula and the in-situ data show better correlation October when waves approach from SW, WSW than that obtained using other formulae (CERC and W directions (Jayappa et al. 2003). Sanil 1984; Walton and Bruno 1989; Komar 1998) Kumar et al. (2017) found that the annual net especially, the locations with annual average Hs of transport along Udyavara station was predomi- 1 m (Shanas and Sanil Kumar 2014). nantly northward with LSTR of 0.329106 m3. Shanas and Sanil Kumar (2014)estimatednet 6 3 LSTR of 0.16910 m towards north along 4.5 Sediment budget Kundapura, which is located *20 km north of Kota (net LSTR of –0.0879106 m3)and*10 km Sediment budget helps in understanding the vol- south of Maravanthe (net LSTR of –0.129106 ume of sediment entering and exiting a littoral cell m3) stations selected in the present study. and the surplus or deBcit remaining. Challenges in Chandramohan and Nayak (1992)hadreported constructing a sediment budget include: deter- annual LSTR towards south along Kundapura mining the appropriate boundaries of the littoral (0.369106 m3)andMalpe(0.259106 m3)sta- cell, sediment transport pathways, and their mag- tions. In the present study, Malpe station nitude. The need for sediment budgets is regularly showed a net LSTR of 0.249106 m3 towards the created in coastal engineering projects to develop south. an understanding of the sediment sources, sinks, It is found that longshore littoral transport is transport pathways, and magnitudes for a selected variable depending upon wave climate, availability region of the coast and within a deBned period of of sediments, etc., and is towards the north as well time. Volume change, removal, and placement of as south depending upon wave approach direction dredged material or beach Bll must be included in and conBguration of the coast. However, studies the sediment budget if pertinent to the time period 234 Page 12 of 14 J. Earth Syst. Sci. (2020) 129:234

Figure 7. Showing sediment budget of the 14 littoral cells along the area studied. being analyzed. Residual sediment budget is esti- sediment accumulation in the littoral cell are not mated for each cell using shoreline change rates, static, but change over seasonal time scales. Some beach volume change, long-shore sediment trans- changes in the sediment budget are the result of port, sources, and sinks (table 3). The annual net Cuctuations in wave directions. Other changes in transport is southwards in the littoral cells – LC-1, pathways and sinks in the sediment budget are the LC-2, LC-5, LC-7, LC-8, LC-9, LC-12, and LC-13. result of human inCuences, such as construction of Whereas, northwards in the littoral cells – LC-3, jetties/breakwaters/groins, or dredging practices. LC-4, LC-6, LC-10, LC-11, and LC-14. It is found This information will be very useful for port and that there is a southerly net littoral drift and any harbor authorities to reduce the cost of mainte- obstruction to this, results in erosion (loss) on the nance dredging. downdrift side (Bgure 7). Out of 14 littoral cells along the Karnataka coast, LC-1, LC-2, LC-4, LC- 5, LC-6, LC-8 are classiBed as low erosion, LC-7, 5. Conclusions LC-10, LC-11 as moderate erosion, LC-3 as high erosion, LC-9, LC-12, LC-14 as very high erosion, The direction of longshore sediment transport and LC-13 as low accretion (Shetty et al. 2019). depends on the direction of incoming waves with Sediment-transport pathways and patterns of respect to the shoreline, which in turn determines J. Earth Syst. Sci. (2020) 129:234 Page 13 of 14 234 the direction of the longshore current. 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