International Conference of Scour and Erosion Long-term prediction of sediment budgets with dynamic cross-shore migration of sand spits Yoshihiro Hamada Yoshimitsu Tajima Bandula Wickramaarachchi Shinichi Sobue Tomoyuki Nukui Coastal Engineering Lab., The University of Tokyo Coastal Conservation Department, Sri Lanka JAXA
Background and Target area /9 1/1 Long-term predictions of sediment budgets and resulting coastal morphology changes are essential for the near-shore development. Serious Erosion on the south west coast of Sri Lanka Influence on Kalpitiya, north west coast of Sri Lanka? 7 km Sri Lanka 1 km Kalpitiya
Coastal Erosion in Sri Lanka /1 4m change Shoreline retreated around the sand spits Goal To investigate long-term sediment budgets and dynamic migration of sand spits through the analysis of satellite data and numerical analysis 1 5 1 11 google earth 1km
Outline 3/9 3/1 Analysis of Satellite images To capture the overall shoreline changes Wave Estimation(WAM) Wave conditions near Kalpitiya were estimated from wind data Following Funatake et al. (1) Following WAMDI GROUP (1988) Numerical shoreline model Predictions of the observed shoreline changes Influence of sand spits migration
Shoreline changes by coral reefs a)from1956 to 1 (a) 4 6 (b) coral reefs b)from 1956 to 1/9 4/1 1 3km 1956 1988 km -4-6 (c) 1km 4-1 km 1956 1988 7 1-5 5 7 1-4 - Coral reefs position c)from to 1
Shoreline changes by coral reefs a)from1956 to 1 (a) 4 6 (b) coral reefs b)from 1956 to 1/9 4/1 1 3km 1956 1988-1 km 1km 1956 1988 7 1 6 4 (c) -4 - Shoreline changes of local accumulation and 7 1-4 - Coral reefs position erosion km appear to be related to the coral reefs. -5 5 c)from to 1
cross-shore seaward distance from 7114 1-1 7114 731 8117 833 8418 863 8719 9119 936 97 996 917 11 PALSAR images analysis..1 -.1 -. -.3 -.4 -.5 PALSAR image from 7 to 1 3m 731 8719 9119 139 18 19 1 distance along the shoreline cyclone hit the target site during this period overtopping waves were observed by local fisherman large amount of sand was transported behind the spit in 5 in 1 139 PALSAR As -5 image a result of this event, sand spit was largely deteriorated. 5 5/1 Google Earth
5 1 15 15 5 375 5 65 5 75 1 15 15 Shoreline reproduction by the model Wave Height distribution Coral reef were placed on the Etopo bathymetry data. 5 5 75 1 15 15 3 5 15 1 5..5.5.75 1. 1.5 (m) 1.5..5.5.75 1. 1.5 1. Hrms_dat.75.5.5. Hrms_dat Calculation of Wave field Height Period 1.(m) 7.7(s) Direction 43 Coral reefs Energy balance equation Sediment transport rate shoreline change was validly reproduced 8/1
km Shoreline reproduction by the model shoreline reproduction Calculation of Wave field 7/9 9/1 1 1-1 -1 1956(obs.) (obs.) (cal.) -5 5-5 5 Q km Ozasa and Brampton s formula E C Sediment transport rate b b K1 sin b cos b K cos b cot g1 y s g x t s 1 D s Q y q H Validates validates the present model and support our assumption of coral reefs
Change Influence of sand of sand budgets spit by migration influence of sand spit migration on longshore sediment budgets Compare without migration the sediment transport rate and shoreline with migration changes in two different cases Initial condition from 7 to 47 The same initial condition except for the sand spits km 1 3km 1/1 km 1km -1 km The difference of the results of these two cases should only reflect the influences of sand spit Before migration After migration km -5 5 with migration without migration
(m 3 /year) Sediment transport rate comparison the longshore distributions of sediment transport rates a)1 years later Sand Spits with migration without migration (m 3 /year) b)4 years later Sand Spits with migration without migration 11/1 The very little influence of the sandspit migration is observed except the migrated area Alongshore distributions of the cross-shore distance between predicted shorelines with and without migration -.1 -. -.3 Sand Spits 1 3 7(initial) 1 years later 4 years later
(m 3 /year) Sediment transport rate comparison the longshore distributions of sediment transport rates a)1 years later Sand Spits with migration without migration (m 3 /year) b)4 years later Sand Spits with migration without migration 11/1 The very little influence of the sandspit migration is observed except the migrated area Alongshore distributions of the cross-shore distance between predicted shorelines with and without migration -.1 -. -.3 1 3 7(initial) 1 years later 4 years later The recovery of the deteriorated shoreline is very slow Sand Spits
Conclusion 1/1 Long-term process of morphology changes on the west coast of Sri Lanka was investigated through satellite images, wind data and the shoreline model. The observed erosion and accumulation were mainly caused by local unbalance of the sediment transport rates due to coral reefs. Severe stormy event forces landward migration of the sand spit. migration has relatively little impact on the longshore sediment budgets retreated shoreline showed slow recovery processes. It is thus essential to keep the continuous monitoring around Kalpitiya area focusing on sand spit migration and generations of the coral reefs.
Field survey in Sri Lanka on August in11 Thank you for listening
Q&A
Shoreline Extraction from images 1/9 (a) 3km shoreline extraction 1 km coordinate transformation -1 1km km 1956 1988 7 1 comparison -5 5
Wave model (WAM) Wave estimation Wind field Model WAM Wave field 6/9 6/1 WAMDI GROUP in 1988 F t 1 cos cosf Wind Field F F The result Sin Sds Snl Sin = Wind energy Sds = Energy dissipation Snl = Nonlinear interaction Wave Field 14 1 1 8 6 4 Wind speed(m/s) 3.5 1.5 1.5 Wave height(m)
Wave height Observation and Calculation Wave period 7/9 7/1 Fluctuation of wave height is underestimated while Wave direction average wave heights are well represented. Calculation Cal. for 1 years Past research The result for 1 years 1~1 Average height, period and direction Offshore Condtion Height Period 1.(m) 7.7(s) Direction 43
風速場データについて Wind field data Wind field data(final Operational Global Analysis data ) published by NCEP 1m height data (NCEP : National Centers for Environmental Prediction) 1/1/1/: NE monsoon 1/6/1/6: SW monsoon Monsoon Period NE monsoon SW monsoon inter monsoon Nov.~Mar. May~Sep Apr. & Oct. Wind field interpolation Development of Scheme for Predicting Atmospheric Dispersion of Radionuclides during Nuclear Emergency by Using Atmospheric Dynamic Model (Nagai et al.1999) Interpolation mass conservation modification by NCEP data u v G x y 1 I [ 1 ( u u ) ( v v ) ] dv J I GdV
計算値と観測値の比較 Wave direction 6/9 Observed direction Calculated direction Past research
Wave height(m) Wave height(m) 風速場データについて Wave estimation result 1.8 1.6 1.4 1. 1.8.6.4. 1.8 1.6 1.4 1. 1.8.6.4. 1 3 4 5 6 7 8 9 1 11 1 (b) monthly changes of significant wave heights averaged in each year and month. 4 6 8 1 a) Yearly changes of significant wave heights averaged in each year and month. month year 1 3 4 5 6 7 8 9 1 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov Dec.
Space Application 風速場データについて for Environment(SAFE) SAFE JAXA DATA Provider Sri Lanka Coastal Conservation Development Local Government Satellite images Software for images UT,AIT Technical Adviser Satellite images analysis Wave estimation Numerical model Filed survey Local data countermeasure
PALSAR, Landsat, aerial photo Aerial Photo LANDSAT PALSAR Image high resolution low resolution good resolution low frequency Optical interruptions good frequency Optical interruptions high frequency No Optical interruptions