ReseaRch PaPeR Journal of Agricultural and Marine Sciences 2022, 27(2): 19–27 DOI: 10.53541/jams.vol27iss2pp19–27 Received 31 October 2021 Accepted 16 February 2022 Analysis of Shoreline Change along the Coast of the Wadi Al Ma’awil Watershed, Oman, Using the Digital Shoreline Analysis System Amna Al Ruheili* Amna Al Ruheili*( ) alruheli@squ.edu.om, Department of Plant Sci- ences, College of Agriculture and Marine Science, Sultan Qaboos Uni- versity, Muscat, Oman. Introduction The coastal zones provide both environmental and economic assets for a country. Coastal regions are dynamic systems that undergo key changes because of natural and anthropogenic factors. To main- tain a coastal zone, regular sediment input for the deltas is necessary because the sediment maintains the deltas’ surface elevation, thereby contributing to reduced sa- linization, erosion, and flooding. Failure to transport sediment to the coastal plains and deltas increases the vulnerabilities that arise from sediment starvation and the land sinking (Wang et al., 2018). More than 95% of ocean sediments come from wa- ter streams (Syvitski, 2003). The sediments carried by the water streams lead to the formation of deltas and development of coastal zones (Rao et al., 2010). However, deltas can be threatened by the changes in land use in upstream catchments and reservoir con- struction. These developments can impact the flu- vial sediment inputs downstream and result in sed- iment starvation (Dunn et al., 2018; Kondolf, Rubin and Minear, 2014). Bird (1985) reported that most of the beaches in the world (about 70%) experience ero- sion because construction of reservoirs and dams in rivers prevent 20% of the global sediment from reach- ing the coast (Syvitski et al., 2005; Li et al., 2018). A study conducted by Syvitski et al. (2009) showed that in recent decades, construction of reservoirs has prevented 20 to 100% of the global sediment from reach- ing the deltas. Moreover, there has also been a huge fall in river sediment loads in about 50% of the world’s rivers and dams. This is probably an important factor that de- termines sediment fluctuations between land and ocean حتليل تغري اخلط الساحلي على طول ساحل مستجمعات مياه وادي املعاول،عمان، ابستخدام نظام حتليل اخلط الساحلي الرقمي أمنة الرحيلية* Abstract. In an arid climate, lack of water constitutes a challenge. One solution can be to use storage dams as a tool to facilitate groundwater recharge and provide water for various uses. However, dams cannot be constructed without affecting the environment of the coastal shoreline and its ecological habitats. This study investigated the structural changes (i.e. accretion or erosion) of the coastline along the Wadi Ma’awil watershed. The Wadi Ma’awil watershed was dammed in 1991, providing a 10M m3 dam capacity. Satellite images were obtained for 1972, 1984, 1994, 2008, 2014, and 2018, and coastlines were digitized. For this research, we employed the Digital Shoreline Analysis System (DSAS) to calculate the rate of coastline movement and the changes arising from dam construction or anthropogenic changes to the beach. The results showed that from 1972 to 2018, the shoreline experienced erosion of up to -0.70 m/yr for 56% of the watershed coast. This loss could result in remarkable coastal change. This study can be used by urban planners as support for the importance of preserving natural resources and ecological habitats. Keywords: Coastal erosion, Ma’ awil watershed, Dam, Accretion. املستخلص:يشــكل نقــص امليــاه يف مناطــق املنــاخ اجلــاف يشــكل حتــدًي كبــرا. وتعتــر ســدود التخزيــن مــن أحداحللــول املســتخدمة لتســهيل إعــادة تغذيــة امليــاه اجلوفيــة وتوفــر امليــاه لالســتخدامات املختلفــة. ومــع ذلــك ، ال ميكــن بنــاء الســدود دون وجــود أتثــر علــى بيئــة الشــواطئ الســاحلية ومواردهــا البيئيــة. اســتقصت هــذه الدراســة التغــرات الــي تتمثــل يف عمليــات الرتاكــم أو التعريــة للخــط الســاحلي علــى طــول مســتجمعات امليــاه يف وادي معــاول. حيــث مت بنــاء ســد مســتجمعات املياهاجلوفيــة يف وادي معــاول يف عــام 1991 ، ممــا وفــر 10 مليــون مــرت مكعــب مــن امليــاه كمخــزون يف الســد. اســتخدمت هــذه الدراسة عددا من صور األقمار الصناعية لألعوام 1972 و 1984 و 1994 و 2008 و 2014 و 2018 ، ومت رقمنة اخلطوط الساحلية ملنطقة الدراســة. واســتخدم هــذا البحــث حتليــل اخلــط الســاحلي الرقمــي )DSAS( حلســاب معــدل حركــة اخلــط الســاحلي والتغــرات الناشــئة عــن بنــاء الســدود أو التغيــرات البشــرية علــى الشــاطئ. أظهــرت النتائــج أنــه مــن عــام 1972 إىل عــام 2018 ، شــهد اخلــط الســاحلي آتكاًل يصــل إىل -0.70 م / ســنة لـــ 56 ٪ مــن ســاحل مســتجمعات املياه.حيــث ميكــن أن تــؤدي هــذه اخلســارة الــي نتجــت مــن حجــب وصــول الرواســب اىل اتمناطــق الســاحلية إىل تغيــر يف اخلــط الســاحلي بشــكل ملحــوظ.و ميكــن اســتخدام نتائــج هــذه الدراســة مــن قبــل املخططــن احلضريــن كأداة تدعــم التخطيــط العمــراين كصديــق للبيئــة و تعزيــز االهتمــام و احلفــاظ علــى املــوارد الطبيعيــة واملــوارد البيئيــة. الكلمات املفتاحية: آتكل الساحل , مستجمع وادي املعاول , السد , الرتاكم. 20 SQU Journal of Agricultural and Marine Sciences, 2022, Volume 27, Issue 2 Analysis of Shoreline Change along the Coast of the Wadi Al Ma’awil Watershed, Oman, Using the Digital Shoreline Analysis System (Li et al., 2018; Syvitski and Milliman, 2007). Sediment load plays an important role in assessing the quality of the environment, and it allows researchers to evaluate the level of potential impact on the ecosystem (Khan- choul et al., 2012). Sediment is known to play a very important role in maintaining and developing coastal habitats and their ecosystems, which include wetlands, lagoons, coral reefs, mangrove swamps, dunes, and sand barriers (Kotti et al., 2018). The construction of large dams has increased schol- ars’ debate about the environmental impact of such structures because such dams decrease the rates of sed- iment delivery downstream, indirectly enhancing coast- al erosion and decreasing habitat heterogeneity (Chang and Chuang, 2018; Rao et al., 2010; Syvitski, 2003; Tealdi et al., 2011). According to a study by Vorosmarty et al. (1997), about 30 to 40% of the sediment that would have been taken to the areas along the coast through the riv- er systems was retained by man-made structures. The changes downstream were obvious and clearly arose from the shift in sedimentation deposits, which affected the morphology of the fluvial system (Ma et al., 2012). In another example, the Mekong River delta shoreline showed acceleration in erosion that was related to hu- man-induced modifications such as dam constructions (Besset et al., 2016; Li et al., 2017; Anthony et al., 2015; Van Manh et al., 2015). Since the construction of the Aswan Dam on the Nile, moreover, the coastal area lost 98% of its sediment input and showed an increased ero- sion that impacted its coastal ecosystem (Giosan et al., 2014; Kim and Sultan, 2002; Syvitski, 2003; Syvitski et al., 2009). Moreover, the construction of the Three Gorges Dam led to 65% of the sediment load in the Yangtze Riv- er being lost (Yang et al., 2014). In Tunisia, inspection carried out after the Mejerda Dam was built showed an alarming narrowing of waterbeds downstream from the dam (Zahar et al., 2008). Furthermore, the Amazon River basin showed environmental and ecological distress re- sulting from the dam construction upstream. This con- struction resulted in a lack of sedimentation input that changed the downstream area (Latrubesse et al., 2017). Satellite images and GIS data are important be- cause they give us early estimates of shoreline change. Both these sources give us a good database for dig- itizing shoreline positional information which helps researchers to calculate the rates of historical change at the selected sites. The statistics estimated the speed of shoreline change gave us a cumulative summary of the processes that had affected the coast (Dolan et al., 1991). Varying sets of data can be used to assess coastal changes. The importance of this data depends on the way in which it was obtained (Dolanetal., 1991; Polk and Eulie, 2018). This study used End Point Rates (EPR) and Net Shoreline Movement (NSM) to calcu- late the speed of change of the coastline (Thieler et al., 2012). The study objectives were: (i) to find the defi- nitions of the accretion and erosion areas, and (ii) to evaluate the speed at which shoreline was changing. Materials and Methods Study Site The research area is situated in Northern Oman in the Al Batinah region along the Oman Sea. The coastal plain is narrow in shape at the northwestern and southeastern ends, and widens in the middle to a breath of around 50 km (Hayes and Baird, 1993). The geology of Al Batinah coastal region is composed of a tectonically emplaced late Paleozoic and Mesozoic continental margin and Tethys deep sea sediments known as Samail Ophiolites (Robertson et al, 1990). The local winds rarely exceed 10 knots at 10 m height with upwelling appearing as short irregular events (Vic et al., 2015). The coast of Al Batinah is mesotidal and tide-dominated. It has low wave ener- gy and a limited littoral drift of less than 100,000 m3/ year (Kwarteng et al., 2016). The coastal plain has alluvial fans that have undergone the process of sedimentation that varies from gravel, coarse sands to fine sands and silt near the coast (Al Hatrushi et al., 2014). As a result, the plains along the coast are known for their high fertil- ity, and that has resulted in heavy urbanization including agriculture and fishing activities (Al-Hatrushi, 2013). The coastal shore of the Wadi Ma’awil watershed is located south of the coastal town of Barka. This area has small periodic (tidal) currents and a stronger flow (more relevant for sediment transport) occurs during a-peri- odic events that last several days (Bruss et al, 2018). The watershed has many wadis that flowed directly into the coastal area before they were dammed. The mean tidal range is 3.65 meters (m) (ONHO, 2018). The amount of yearly discharge of the catchment is estimated at 21.1 Mm3/yr (Wilson, n.d.). The Ma’awil dam was construct- ed in 1991 as a ground water recharge dam with a capaci- ty of 10 Mm3 (MRMWR, 2012) as shown in Figure 1. The study area coastline stretches for about 25 km and has a gentle foreshore slope that ranges between 0.6 and 0.7%. Study Approach This study went through three phases. First, various satellite images from different years were obtained (see Table 1). Second, the band combination shortwave in- frared: Near Infrared: Red was used to emphasize the difference between land and water, after which the coastline was digitized, followed by determination of shoreline rates of change. Historical Shoreline Analysis The information on changing shoreline was based on sat- ellite images (1972, 1984, 1994, 2008, 2014, 2018) span- ning a period of 46 years (see Table 1). The images were obtained from the USGS Global Visualization Viewer (http://glovis.usgs.gov) with their multicolored bands with a resolution of 30m×30m for pixel size, except for 21Research Paper Al Ruheili Sentinel at 15×15m pixel size. All the image data used were geometrically corrected based on the Universal Transverse Mercator (UTM) projection system—zone 40N using ArcGIS software 10.5.1 to attain less than 20 m accuracy of absolute planimetric (Fossi-Fotsi et al., 2019). There are numerous possible errors involved in deriving shoreline data and such errors can affect the accuracy of the computed rates in the modeling. Many studies have come up with estimates of the typical mea- surement errors that can happen during using mapping methods and shoreline digitization (Anders and Byrnes, 1991; Moore, 2000; Mortonetal., 2004). Shoreline ex- traction requires geo-referencing maps and subsequent- ly interpreting and digitizing a shoreline position. This study Implemented Fossi-Fotsi et al. (2019) approach in measuring the uncertainty in coastal extraction with some modifications to calculate the annualized error (Table 2). The band combination of Infrared: Near In- frared: Red is used to best display the contrast between land and water boundaries to identify the shoreline in satellite images. The band combinations were done in ArcGIS 10.5.1. The purpose of using the band combi- nation was to optimize the difference between land and water to facilitate digitization. The shorelines were man- ually and visually checked and reviewed to reduce errors. Digital Shoreline Analysis System (DSAS) For the purpose of this study, we used the Digital Shore- line Analysis System (DSAS) Version 4.4, developed by the United States Geological Survey (USGS), to evaluate the speed of shoreline changes. The DSAS can be add- ed as extension to ArcGIS 10.5 software. The DSAS can compute the rate of change statistically from multiple historical shoreline positions. The DSAS multiple sta- tistical approaches, including the End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regres- sion Rate (LRR), were all used to calculate the shore- line change (Thieler et al., 2012). The LRR was used to measure rate of shoreline change because it is widely be- lieved that it is the most statistically robust quantitative method while dealing with a limited number of shore- lines (Addo et al., 2008). After the completing the digitization of the coastline of the Wadi Al Ma’awil watershed, the DSAS was used to calculate the speed of coastline change and erosion. The analytical process involved four steps: (i) shoreline preparation represented by vectors data extracted from satellite images; (ii) baseline creation, onshore or off- shore; (iii) transect generation; and (iv) computation of the speed of shoreline change (Thieler et al., 2012). The software gave us transects along the shoreline which were cast perpendicular to the baseline with spe- Table 1. List of Used Satellite Images Satellite Year Path/Row Landsat 1-5 MSS October 22, 1972 170/44 Landsat 4-5 TM October 8, 1984 158/44 Landsat 4-5 TM October 20, 1994 158/44 Landsat 5 TM November 11, 2008 158/44 Landsat 8 OLI TIRS October 11, 2014 158/44 Sentinel-2A October 23, 2018 Tile #: T40QEM Figure 1. Study site, (a) Oman boundary, (b) Wadi channels of Al Ma’awil Watershed. 22 SQU Journal of Agricultural and Marine Sciences, 2022, Volume 27, Issue 2 Analysis of Shoreline Change along the Coast of the Wadi Al Ma’awil Watershed, Oman, Using the Digital Shoreline Analysis System cific spacing measurements that are chosen by the user. After that, the points where the transect shoreline inter- sected with the baseline were used to calculate the speed of change statistics. During the course of this research we found that the DSAS program generated 3,816 tran- sects with a 25 m spacing and 900 m length. These were perpendicular to the baseline located offshore at an 870 m length along the coast Figure 2. Rate of Shoreline Change The End Point Rate (EPR), the Least Median of Squares (LMS), and the Net Shoreline Movement (NSM) were utilized to calculate the shoreline changes. The EPR measured through the distance division of the moved shoreline between the earliest and latest measurements at each transect by the elapsed time. The LMS was cal- culated by using the median value of the squared resid- uals instead of the mean in the LRR model to determine the best-fit equation for the line to all shoreline points for a specific transect. The NSM was associated with the dates of only two shorelines and it reported the distance between the oldest and youngest shorelines for each transect. The LRR was determined by fitting a least squares regression line to all the comparable shore points of different periods for a particular transect, us- ing a confidence interval of 95.0% (Thieler et al., 2012). Positive values of EPR and LRR stand for sediment ac- cretion through shoreline movement towards the sea, while negative values indicate erosion and shoreline movement towards the land. Results and Discussion In coastal areas, the shoreline is very dynamic as it un- dergoes rapid changes. Waves and currents are the pri- mary causes that lead to sediment re-suspension (Rajaee et al., 2009). The coastal areas provide a wide range of ecosystems that are critical to coastal resiliency and to economic development (Polk and Eulie, 2018). Both nat- ural coastal processes and human interference are the main factors affecting shoreline change (Sheik, 2011). Frequent monitoring to detect shoreline changes is therefore important if we want to understand the pro- cesses and dynamic features of coastal areas. The speed of change along the shoreline was calculat- ed using the DSAS software and three different statisti- cal techniques: End Point Rate (EPR), the Least Median of Squares (LMS), and the Linear Regression Rate-of- Change (LLR). The most significant changes were ob- served between 1972 and 1984, and between 2014 and 2018, when the shoreline was retreating at a rate of 1.6 m/year. The change between the period 1972 and 1984 could be attributed to the starting of development in Oman, and the country movement toward urbanization and modernity. While the period between 2014 and 2018 were subjected to various cyclones and storms events that could contributed to higher erosion. A summary of the statistics for the rate of change are given in Table 3. About 600 transects were used to evaluate the rate of shoreline change along the coastline of the Wadi Al Ma’awil watershed for a short distance of 25 km. This has been shown in Figure 2. The EPR, LMS and LLR mea- sured the rate of shoreline change based on differences between shoreline positions across time. The reported rates were expressed as meters of change along transects per year. The values of EPR and LLR for the research area from1972 to 2018 are presented in Figure 3. The DSAS rate-of-change models for 1972–1984, 1994– 2008 and 2014–2018 are represented in Figure 4. The Table 2. Shorelines estimated and annualized error Variables 1972 1984,1994 2008,2014,2018 Digitizing error 15.3 6.3 Planimetric Error (EP) 35 25 20 Total error (Et) 40.3 29.3 21 Table 3. Statistics for the Rate of Change Year Variables 1972–1984 1994–2008 2014–2018 EPR Mean mobility shoreline change (m/year) -1.60 0.60 - 0.66 Erosion trend (m/yr) -2.59 -1.77 -0.72 & % 73 % 28 % 93 % Accretion trend (m/yr) 1.24 1.52 0.20 & % 27 % 72 % 7 % Total transects that record accretion 183 478 68 Total transects that record erosion 490 86 594 B. dist. from coastline (m) 230 237 400 23Research Paper Al Ruheili EPR-Model and the LRR-Model show the shoreline point data that has the most confidence, with transects running perpendicular to the shoreline. It is clear that the positive and negative rates of change in Figure 4 show that both accretion and erosion are taking place on the coast. Table 2 gives us a summary of the speed of shoreline change as averages of all the changes, includ- ing both erosion (shown by the negative numbers) and accretion (shown by the positive numbers), and as av- erages of only the erosion values and only the accretion values. It would appear that some coastal area is being lost. This could be due to natural changes in the coastal system, or caused by human activities such as agricul- ture, irrigation and the building of dams. For example, dam construction could trap sediments in the upstream area in which obstruct the natural movement of sed- iments to the downstream area resulting in less nour- ishment for the coastal area (Al-Ismaily et al., 2013). Figure 2. Shoreline of the study area. Figure 3. Variations in the rates of shoreline change (in meters) calculated using the DSAS program, alternating between erosion and accretion 24 SQU Journal of Agricultural and Marine Sciences, 2022, Volume 27, Issue 2 Analysis of Shoreline Change along the Coast of the Wadi Al Ma’awil Watershed, Oman, Using the Digital Shoreline Analysis System The coast of the Wadi Al Ma’awil watershed was studied at various intervals in the years 1972-1984, 1994-2008, and 2014-2018. In 1972 through 1984, the net speed of erosion averaged over 490 transects was -2.59 m/yr. The accretion trend was around 1.24 m/yr for 183 tran- sects. In the period 1994–2008, 86 transects accounted for an1.77m/yr erosion rate, and a 1.52 m/yr accretion rate was present for 478 transects. For 2014-2018, the coastal erosion rate was found to be -0.72 m/yr and the speed at which accretion was taking place was 0.20 m/ yr. These smaller numbers could be related to the shorter period span. The movement of building recharge dams in Oman have contributed to shoreline changes. As stat- ed by Al-Ismaily et al. (2013), dam construction in Al Khoud had limited coastal plains enrichment with silt and sediments that the wadies carry downstream to the valley. Other study carried by Graf (2006) indicated dam effect on river discharge, and sediment load that result- ed in a quasi-equilibrium state of the river, altering the channel form. Therefore, the transposition of sediment and its redistribution play a major role in determining the geology, biology, and chemistry of fluvial ecosystems and coastal area. Overall, this research clearly showed that erosion and accretion have led to structural changes at the coastal shoreline of the Wadi Al Ma’awil water- shed. The results also indicated that during 1972–1984 and 2014–2018, shoreline erosion increased. The highs and lows recorded are given in Table 3. The shoreline experienced the greatest shoreline changes, with an NSM of 429 m, during the period 1994–2008. The sudden and rapid rate of shoreline change during this period may be due to beach nourish- ment activities and building of the port at Barka. More- over, in 2007, a major cyclone hit the area and contribut- ed to coastal accretion. In fact, all along Al Batinah coast the most severe erosion and sediment deposition occur during major storms events (Kwarteng et al., 2016). Shoreline changes for the period between 1972 and 1984 were more variable, with most of the coastline be- ing affected by erosion and only a few areas being stable. It was observed that between 1994 and 2008 the shore- line accreted in comparison with the period between 1972 and 1984, and also the period between 2014 and 2018 and this could be attributed to coastal nourishment activities. However, beach nourishment along Louisiana beach, USA, showed a temporal ability in serving as a beach barrier, but due to, wave and current action the shoreline retreated and the coastal vulnerability has increased (Cohen et al., 2021). The research area had an average EPR of -1.60 m/yr and an NSM average of -18.6 m between 1972 and 1984. However, the peri- od of 2014–2018 also showed that erosion was taking place, with an EPR of -0.66 m/yr and an NSM average of -44 m, the coastal erosion could be attributed to cy- clone Phet in 2010. For example, there are some stud- ies indicated the impact of natural hazards and cyclone on beach erosion (Al Ruheili and Boluwade, 2021). This only happened for four years, and it is anticipated that the average EPR can increase with longer spans. It should be noted that, during the coastal nour- ishment that related to coastal development, huge amounts of sediments were discharged along coast- Figure 4. DSAS rate of change models in meters (A, B, C) EPR-Model; (D, E, F) LMS-Model for 1972–1984, 1994–2008 and 2014–2018, respectively. 25Research Paper Al Ruheili al areas. The fast changes or the variations in shore- line change rate show that the dynamics of shore- line are being shaped by both natural processes and man-made activities along the Wadi Mawil shoreline. Conclusion Many researchers have studied the consequences that the natural- and human-induced activities cause on shoreline change. Change can be the result of natural processes. Waves, currents, geology, variations in sea level, and storms can all contribute to shoreline chang- es (Zhu et al., 2018). In addition, human interference can also contribute to coastal erosion through by con- structing dams, or urbanizing the beaches and thus changing the hydrological cycles (Bheeroo et al., 2016). Coastal geology also has a very important role in chang- ing shorelines. The Wadi Mawil coastal area is marked by various coastal landforms, such as bays, beaches, and sand dunes. This study shows that the coastal area of Wadi Mawil is vulnerable to coastal erosion due to low-lying sandy beaches and dunes. The sediment trans- port depended on natural process and climatic factors that influence the nature of waves. The strength of these waves contributed to the amount of sediment trans- ported, to erosion, and to accretion, all of which result in shoreline changes (Manjulavani et al, 2017). The sed- imentation brought to the coastal area by wadis, rivers, tides, and winds through natural processes can be the most important factor in determining the shape of the coastline (Van Rijn, 1993). The amount of the sediment deposited on the coast determines its appearance, cre- ating sand dunes and beaches, mangrove swamps, and mudflats (Storlazzi and Field, 2000). Most studies, to date, have indicated that the growth of the coastal region is associated with sedimentary pro- cesses which contribute to the coastal geomorphology (Pranzini et al., 2013). For example, islands in the Indi- an Ocean have eroded due to a shortage of sediment on the shoreline (Mujabar and Chandrasekar, 2011). During the period from 1994-2008, north Oman experienced Cyclone Gonu (2007) and that resulted in accretion due to the abnormally high quantity of sediment discharged through the wadi and storm surge. However, now the coastal area is showing signs of erosion, due to a short- age of sediment that could be related to disruption of the natural process. Sediment discharges from wadis have been reduced due to various factors such as dam construction, coastal developmental and encroach- ments. This makes the rest of the sandy beaches in the area more susceptible to erosion. The changing shore- line is very important to efforts to calculate the spatial dynamics of the Wadi Al Ma’awil coastal system. The Al Ma’awil coastline is threatened by erosion that may contribute to ecological and economic loss along its coastal zone. Human factors, such as sand extraction from beaches, reclamation of land for agricultural use, and the damming of wadis, have modified the system flows and have contributed to coastal erosion. Reduced sediment supply caused by damming the Wadi flow has also led to more loss and damage of coastal habitats, in- cluding beaches and mangrove swamps. The sediment supply to the coastal area has been trapped by the dams that have been constructed. This research shows that the Wadi Mawil watershed is being eroded due to this im- pediment of the natural process and as a result of the modification of the hydrological processes that occurred in the study area after the dam was constructed in 1991. The Wadi Mawil coast now faces an additional, urban- ization problem as new port and tourism developmen- tal projects take place along the coast. Building artificial barriers increases erosion along the Wadi Mawil coast. Accurate coastline identification conducted using Landsat and Sentinel satellite images is a useful ap- proach given the moderate spatial resolution and good spectral resolution provided by these tools, which helped in defining the shoreline. Coastal accretion was most significant in the period 2008–2014, possibly be- cause of the ports that were being constructed during that time. Coastal erosion was also dramatically appar- ent in some parts of the study area in 1972–1984 and 2014–2018. Erosion was observed at 73%, 28%, and 93% for 1972–1984, 1994–2008, and 2014–2018, respective- ly. The coastline is deteriorating at an average rate of -2.59 m/year, -1.77 m/year, and -0.72 m/year, respective- ly. The overall average of the EPR for the entire coast is accreting at the rate of 0.50 m/year. The research area has natural as well as economic importance for agricul- ture and drainage, but damming the wadi modified the watershed regime and contributed to coastal change. The DSAS shoreline change analysis showed that ero- sion was taking place in the area being studied. Natural processes as well as human interference have changed the shoreline of the study area through erosion and accretion. The coastal zones of the Wadi Mawil water- shed, have accretion due to sediment deposition during the 2007 Cyclone Gonu storm surge and Wadi flash flooding. However, dams have stopped the transpor- Table 4. Shoreline Highs and Lows at Wadi Al Ma’awil Variables NSM (Max) NSM (Min) NSM (Avg) EPR (Avg) 1972–1984 54.53 -400 -18.6 -1.60 1994–2008 429 -332 8.3 0.60 2014–2018 46.65 -749 -44 -0.66 26 SQU Journal of Agricultural and Marine Sciences, 2022, Volume 27, Issue 2 Analysis of Shoreline Change along the Coast of the Wadi Al Ma’awil Watershed, Oman, Using the Digital Shoreline Analysis System tation of sediments which in turn led to erosion in the coastal areas. This research clearly shows that proper beach filling and nourishment should be made along the coast to protect the coastal area from severe hazards. This can also help restore the area. It further explicates that local managers and decision-makers should take into consideration the impacts of dams on shoreline erosion. It is clear that such construction significantly contributes to changing the ecosystems and the habi- tats of the coastal zone. The study also documents that the DSAS can come up with important data which can help us in determining shoreline erosion and accretion. Conflict of Interest This research has no conflict of interest. References Addo KA, Walkden M, Mills JT. (2008). Detection, measurement and prediction of shoreline recession in Accra, Ghana. ISPRS Journal of Photogrammetry and Remote Sensing 63(5): 543-558. Al-Hatrushi, SM. (2013). Monitoring of the shoreline change using remote sensing and GIS: a case study of Al Hawasnah tidal inlet, Al Batinah coast, Sultan- ate of Oman. Arabian Journal of Geosciences 6(5): 1479-1484. Al Hatrushi S, Kwarteng AY, Sana A, McLachlan A, Hamed K, Al Buloshi A, Illenberger WK (2014). Coastal erosion in Al Batinah. Academic Publication Board, Sultan Qaboos University, Muscat, Oman Al-Ismaily SS, Al-Maktoumi AK, Kacimov AR, Al-Saqri SM, Al-Busaidi HA, Al-Haddabi M H. (2013). Mor- phed block-crack preferential sedimentation in a res- ervoir bed: a smart design and evolution in nature. Hydrological Sciences Journal 58(8): 1779-1788. Anthony EJ, Brunier G, Besset M, Goichot M, Dussou- illez P, Nguyen VL. (2015). Linking rapid erosion of the Mekong River delta to human activities. Scientific Reports 5: 1-12 (Article 14745). Al Ruheili AM, Boluwade A. (2021). Quantifying Coast- al Shoreline Erosion Due to Climatic Extremes Using Remote-Sensed Estimates from Sentinel-2A Data. Environmental Processes 8(3): 1121-1140. Bheeroo RA, Chandrasekar N, Kaliraj S, Magesh NS. (2016). Shoreline change rate and erosion risk assess- ment along the Trou Aux Biches–Mont Choisy beach on the northwest coast of Mauritius using GIS-DSAS technique. Environmental Earth Sciences 75(5): 444. Besset M, Anthony EJ, Brunier G, Dussouillez P. (2016). Shoreline change of the Mekong River delta along the southern part of the South China Sea coast using sat- ellite image analysis (1973-2014). Géomorphologie: Relief, Processus, Environnement 22(2): 137–146. Bird ECF. (1985). Coastline changes. A Global review. Journal of Coastal Research 2(2): 231. Bruss G, Kwarteng A, Baawain M, Sana A, Chitrakar P, Al-Abdali F, Al-Habsi H. (2018). Coastal currents on the Northern Omani shelf. ICOPMAS Conference Proceeding. Chang Y, Chu KW, Chuang LZH. (2018). Sustainable coastal zone planning based on historical coastline changes: A Model from Case Study in Tainan, Tai- wan. Landscape and Urban Planning 174: 24–32. Cohen MCL, de Souza AV, Liu KB, Rodrigues E, Yao Q, Pessenda LCR, Dietz M. (2021). Effects of Beach Nourishment Project on Coastal Geomorphology and Mangrove Dynamics in Southern Louisiana, USA. Remote Sensing 13(14): 1-24 (Article 2688). Dunn FE, Nicholls RJ, Darby SE, Cohen S, Zarfl C, Fekete BM. (2018). Projections of historical and 21st century fluvial sediment delivery to the Ganges-Brahmapu- tra-Meghna, Mahanadi, and Volta deltas. Science of The Total Environment 642: 105–116. Fossi-Fotsi Y, Pouvreau N, Brenon I, Onguene R, Etame J. (2019). Temporal (1948–2012) and dynamic evolu- tion of the Wouri Estuary coastline within the Gulf of Guinea. Journal of Marine Science and Engineering 7(10): 1-23 (Article 343). Giosan L, Syvitski J, Constantinescu S, Day J. (2014). Cli- mate change: protect the world’s deltas. Nature News 516(7529): 31–33. Graf WL. (2006). Downstream Hydrologic and Geomor- phic Effects of Large Dams on American Rivers. Geo- morphology 79(34): 336–360. Hayes MO, Baird WF. (1993). Shoreline erosional/dep- ositional patterns in Oman. Proceedings of Coastal Zone 93, Volume on Coastal Engineering. New Orle- ans, Louisiana, USA: 144–158. Kwarteng AY, Al-Hatrushi SM, Illenberger WK, Mc- Lachlan A, Sana A, Al-Buloushi AS, Hamed KH. (2016). Beach erosion along Al Batinah coast, Sultan- ate of Oman. Arabian Journal of Geosciences 9(2): 1-20 (Article 85). Khanchoul K, Boukhrissa ZEA, Acidi A, Altschul R. (2012). Estimation of suspended sediment transport in the Kebir drainage basin, Algeria. Quaternary In- ternational 262: 25–31. Kim J, Sultan M. (2002). Assessment of the long-term hydrologic impacts of Lake Nasser and related irriga- tion projects in Southwestern Egypt. Journal of Hy- drology 262(1–4): 68–83. Kondolf GM, Rubin ZK, Minear JT. (2014). Dams on the Mekong: cumulative sediment starvation. Water Re- sources Research 50(6): 5158–5169. Kotti F, Dezileau L, Mahé G, Habaieb H, Benabdallah S, Bentkaya M, Dieulin C. (2018). Impact of dams and climate on the evolution of the sediment loads to the sea by the Mejerda River (Golf of Tunis) using a pa- leo-hydrological approach. Journal of African Earth Sciences 142: 226–233. Latrubesse EM, Arima EY, Dunne T, Park E, Baker VR, d’Horta FM, Ribas CC. (2017). Damming the rivers 27Research Paper Al Ruheili of the Amazon basin. Nature 546(7658): 363–369. Li T, Wang S, Liu Y, Fu B, Zhao W. (2018). Driving forces and their contribution to the recent decrease in sed- iment flux to ocean of major rivers in China. Science of The Total Environment 634: 534–541. Li X, Liu JP, Saito Y, Nguyen VL. (2017). Recent evolu- tion of the Mekong Delta and the Impacts of Dams. Earth-Science Reviews 175: 1–17. Li X, Bellerby R, Craft C, Widney SE. (2018). Coastal wetland loss, consequences, and challenges for resto- ration. Anthropocene Coasts 1(1): 1–15. Ma Y, Huang HQ, Nanson GC, Li Y, Yao W. (2012). Channel adjustments in response to the operation of large dams: The upper reach of the lower Yellow Riv- er. Geomorphology 147: 35–48. Manjulavani K, Supriya VM, Suhrullekh M, Harish B. (2017) (September). Detection of shoreline change using geo-spatial techniques along the coast between Kanyakumari and Tuticorin. In 2017 IEEE Interna- tional Conference on Power, Control, Signals and In- strumentation Engineering, ICPCSI : 2822-2825. MRMWR [Ministry of Regional Municipalities & Water Resources]. (2012). Dams in the Sultanate of Oman. Mujabar PS, Chandrasekar N. (2011). A shoreline change analysis along the coast between Kanyakumari and Tuticorin, India, using digital shoreline analysis sys- tem. Geo-spatial Information Science 14(4): 282-293. Oman National Hydrographic Office (ONHO), Royal Navy of Oman. (2018). Oman Maritime Book. Oman. Polk MA, Eulie DO. (2018). Effectiveness of living shore- lines as an Erosion Control Method in North Caroli- na. Estuaries and Coasts: 1–11. Pranzini E, Rosas V, Jackson NL, Nordstrom KF. (2013). Beach changes from sediment delivered by streams to pocket beaches during a major flood. Geomor- phology 199: 36-47. Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nou- rani V. (2009). Daily suspended sediment concentra- tion simulation using ANN and neuro-fuzzy models. Science of the Total Environment 407(17): 4916-4927. Rao KN, Subraelu P, Kumar KCVN, Demudu G, Malini BH, Rajawat AS. (2010). Impacts of sediment retention by dams on delta shoreline recession: evidences from the Krishna and Godavari deltas, India. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group 35(7): 817–827. Robertson AH, Searle MP, Ries AC (Eds.). (1990). The geology and tectonics of the Oman region 49(1): 3-25. London: Geological Society. Sheik M. (2011). A shoreline change analysis along the coast between Kanyakumari and Tuticorin, India, using digital shoreline analysis system. Geo-Spatial Information Science 14(4): 282-293. Storlazzi CD, Field ME. (2000). Sediment distribution and transport along a rocky, embayed coast: Mon- terey Peninsula and Carmel Bay, California. Marine Geology 170(3-4): 289-316. Syvitski JP. (2003). Supply and flux of sediment along hydrological pathways: Research for the 21st Century. Global and Planetary Change 39(1–2): 1–11. Syvitski JP, Vörösmarty CJ, Kettner AJ, Green P. (2005). Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308(5720): 376–380. Syvitski JP, Milliman JD. (2007). Geology, geography, and humans battle for dominance over the delivery of fluvial sediment to the coastal ocean. Journal of Ge- ology 115(1): 1–19. Syvitski JP, Kettner AJ, Overeem I, Hutton EW, Hannon MT, Brakenridge GR, Nicholls RJ. (2009). Sinking deltas due to human activities. Nature Geoscience 2(10): 681–686. Tealdi S, Camporeale C, Ridolfi L. (2011). Modeling the impact of river damming on riparian vegetation. Journal of Hydrology 396(3–4): 302–312. Thieler ER, Himmelstoss EA, Zichichi JL, Ayhan E. (2009). Digital Shoreline Analysis System (DSAS) version 4.2 – An ArcGIS Extension forCalculating Shoreline Change. Thieler ER, Himmelstoss EA, Zichichi JL, Ergul, Ayhan, (2012). Digital Shoreline Analysis System (DSAS) version 4.0—An ArcGIS extension for cal- culating shoreline change (ver. 4.3, April 2012): U.S. Geological Survey Open-File Report 2008-1278. Van Manh N, Dung NV, Hung NN, Kummu M, Merz B, Apel H. (2015). Future sediment dynamics in the Mekong Delta floodplains: Impacts of hydro- power development, climate change and sea lev- el rise. Global and Planetary Change 127: 22-33. Van Rijn LC. (1993). Principles of sediment transport in rivers, estuaries and coastal seas, Amsterdam: Aqua Publications 1006: 11-3. Vic C, Roullet G, Capet X, Carton X, Molemaker MJ, Gula J. (2015). Eddy‐topography interactions and the fate of the P ersian G ulf O utflow. Journal of Geo- physical Research: Oceans 120(10): 6700-6717. Wang Z, Ryves DB, Lei S, Nian X, Lv Y, Tang L, Chen J. (2018). Middle Holocene marine flooding and human response in the south Yangtze coastal plain, East Chi- na. Quaternary Science Reviews 187: 80–93. Wilson S. (n.d.). GIST, a Geographic Information System Toolkit for Water Resource and Engineer- ing Applications. Yang SL, Milliman JD, Xu KH, Deng B, Zhang XY, Luo XX. (2014). Downstream sedimentary and geo- morphic impacts of the Three Gorges Dam on the Yangtze River. Earth-Science Reviews 138: 469–486. https://www.isprs .org/proceedings/XXVII/con- gress/part4/497_XXVII-part4.pdf Zhu C, Liu X, Shan H, Zhang H, Shen Z, Zhang B, Jia Y. (2018). Properties of suspended sediment concentra- tions in the Yellow River delta based on observation. Ma- rine Georesources & Geotechnology 36(1): 139-149.