Sound Speed in the Shallow Waters: Direct and Indirect Water Parameters Impact

Sound Speed in the Shallow Waters: Direct and Indirect Water Parameters Impact | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sound Speed in the Shallow Waters: Direct and Indirect Water Parameters Impact Amron Amron, Rizqi Rizaldi Hidayat, Iqbal Ali Husni, Agung Tri Nugroho, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653523/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Ocean Dynamics → Version 1 posted 9 You are reading this latest preprint version Abstract The sound speed is a crucial parameter in various environmental processes occurring in shallow waters. Therefore, this study aimed to investigate how water parameters influence sound speed by collecting data from four shallow water sites namely Cilacap Fishing Port, Pangandaran, the Experimental Pond of the Faculty of Fisheries and Marine Sciences, and Brebes Waters. The results showed that salinity had a stronger linear influence on sound speed compared to temperature, with Total Dissolved Solids (TDS) serving as a salinity indicator. Tidal dynamics and freshwater inflows also contributed to variations in salinity, impacting sound speed. Although the prediction model was generally reliable, the accuracy varied based on the specific conditions of each water location. In conclusion, this study underscores the necessity of understanding the interactions among different water parameters to enhance predictions of sound speed and the implications for managing aquatic resources and conserving ecosystems. sound speed shallow water water parameters salinity temperature Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The speed of sound in shallow waters is a crucial parameter that affects numerous environmental processes, including underwater communication, sonar operations, and marine ecology. Accurate sound speed is essential for effective signal transmission in underwater communication, which is crucial for ship navigation and scientific exploration. In sonar applications, sound speed plays a significant role in determining the depth and distance of detected objects, thereby influencing fishing strategies and marine surveys. Moreover, in marine ecology, sound speed can impact the behavior of organisms, such as fish migration and interspecies interactions, ultimately influencing ecosystem health. Understanding the factors that affect sound speed is essential for precise modeling of acoustic propagation in these dynamic environments. This speed is influenced not only by the physical properties of water but also by various environmental factors, including weather conditions and human activities. Key variables impacting sound speed include temperature, salinity, density, and presure (Del Grosso, 1974 ; Medwin, 1975 ; Chen and Millero, 1977 ; Coppens, 1981 ; Mackenzie, 1981 ; UNESCO, 1983 ; Li et al., 2021 ; Makar, 2022 ). Temperature and salinity are key factors affecting sound speed in water. As temperature increases, sound speed generally rises, attributed to the enhanced kinetic energy of water molecules (Bulut and Ergin, 2021 ; Possenti et al., 2024 ). This relationship is particularly important in shallow waters, where rapid temperature fluctuations potentially occur, especially in coastal areas influenced by solar heating and varying weather conditions (Exley et al., 2021 )(González Ávila et al., 2021 ). Understanding how temperature variations correlate with sound speed is crucial for accurately predicting acoustic behavior in these environments. Salinity also plays a significant role in influencing sound speed, with changes in salinity affecting seawater density, which in turn impacts sound propagation. Numerous studies have documented a strong positive correlation between salinity and sound speed (Del Grosso, 1974 ; Medwin, 1975 ; Chen and Millero, 1977 ; Coppens, 1981 ; Mackenzie, 1981 ; UNESCO, 1983 ) Grekov, Grekov and Sychov, 2021 ; Affatati, Scaini and Salon, 2022 ; Gu et al., 2022 ). In shallow waters where salinity can vary greatly due to freshwater influx from rivers and rainfall, it is essential to comprehend these fluctuations for precise sound speed predictions. Density, influenced by temperature and salinity, is another crucial factor in determining the sound speed. As water density increases, sound travels faster through it. The interplay between density, temperature, and salinity creates a complex environment in which changes in one parameter potentially trigger a chain reaction in others (Bryan et al., 2022 ). Understanding these interactions is essential for marine scientists and engineers working in the field of underwater acoustics. Additionally, conductivity, which is closely associated with salinity, significantly affects sound speed. Conductivity measures the ability of water to conduct electricity, influenced by the presence of ions, particularly salts. As conductivity increases, the sound speed also rises, adding further complexity to the shallow water environment (Duarte et al., 2021 ; Le Menn and Naïr, 2022 ; Wu et al., 2024 ). This relationship underscores the importance of monitoring conductivity alongside other parameters to gain a comprehensive understanding of sound speed dynamics. Aside from direct influences, indirect factors such as tidal dynamics and freshwater influx can significantly affect sound speed. Tidal movements, driven by the gravitational pull of the moon and sun, cause fluctuations in water levels that modify the composition and physical properties of water in shallow regions. These alterations may lead to variations in temperature, salinity, and density, complicating the acoustic environment (Makar, 2022 ; Harris, Lin and Andres, 2025 ). For example, when sea levels rise, freshwater from colder rivers may mix with seawater, resulting in substantial changes in salinity and temperature. These modifications not only impact sound speed but also influence marine life and biogeochemical processes within the ecosystem. By investigating these indirect effects, a more comprehensive understanding of sound propagation in shallow waters can be achieved. Additionally, the interplay between tidal dynamics and freshwater inflow affects the distribution of nutrients and oxygen in the water, subsequently influencing marine biological activity. When freshwater enters the ocean, it often carries various nutrients that stimulate phytoplankton growth. This increase in phytoplankton alters ocean acoustics, as the concentration of particles in the water affects sound transmission (Possenti et al., 2024 ; Prosnier, 2024 ). Studies on sound speed in shallow waters are highly relevant for various applications, including fisheries management, marine navigation, and environmental monitoring. Developing accurate sound speed models can enhance the effectiveness of sonar systems and enrich our understanding of marine ecosystems (González Ávila et al., 2021 ). Therefore, ongoing studies on the relationship between sound speed and different water parameters are crucial for advancing knowledge in oceanography. Investigating both the direct and indirect effects of these parameters can lead to the creation of more precise predictive models, enhancing current comprehension of underwater acoustics. This knowledge is essential for numerous marine applications, ultimately aiding in the better management and conservation of marine resources. Material and methods Data Acquisition Sound speed and water parameters including salinity, density, conductivity, and temperature were collected at four shallow water locations using a CTD SVP Midas SVX2. The same parameters, along with additional water quality metrics comprising TDS, pH, and dissolved oxygen/DO, were measured with a water quality checker (HI 98194 Multiparameter). Tidal data were obtained from Meteorology, Climatology, and Geophysical Agency, Indonesia ( www.stamet-kotim.bmkg.go.id ). The CTD instrument was positioned at a depth of one meter below the water surface and deployed using a mooring buoy system to maintain a fixed recording position during observations. Water quality measurements with the water quality checker were conducted hourly throughout the daytime period of the CTD observations. The shallow water locations selected for this study included Cilacap Fishing Port, Pangandaran Waters, Brebes Waters, and the Experimental Pond of the Fisheries and Marine Science Faculty (Purwokerto) (Fig. 1 ). Observations of sound speed, temperature, and salinity were conducted at all stations (Cilacap: January 20–30, 2022; Brebes: March 5–6, 2022; Pangandaran: June 8–9, 2022; and Purwokerto: June 17–18, 2022), while additional measurements of other water quality parameters were only taken at Cilacap Fishing Port Waters. Each station had distinct water characteristics, resulting in varying levels of temperature and salinity, which in turn affected sound speed. At Cilacap Fishing Port Waters, the water characteristics were influenced by the river flowing into the port. In contrast, Pangandaran Waters were significantly affected by the dynamics of the Indian Ocean. Similarly, the Brebes Waters were influenced by the dynamics of the Java Sea. At Purwokerto Station, the experimental pond was isolated from water flow, meaning its characteristics were primarily affected by weather conditions, such as solar heating and rainfall. Data analysis The sound speed recorded from all observation stations using the CTD SVP was compared to the equations established by Del Grosso ( 1974 ); Medwin ( 1975 ); Chen and Millero ( 1977 ); Coppens ( 1981 ); Mackenzie ( 1981 ), and UNESCO ( 1983 ) through a simple linear regression. The predicted sound speed was calculated based on the temperature and salinity measurements obtained from CTD. Pressure in shallow water was assumed to be consistent (1 atm) across all stations, implying that sound speed was influenced solely by variations in temperature and salinity. The accuracy of each equation in predicting sound speed under different water conditions was assessed using the regression coefficient. The variability in temperature and salinity at the study stations will lead to different recommendations regarding the accuracy of each developed equation. The patterns of changes in water parameters namely tidal level, TDS, pH, DO, salinity, density, conductivity, and temperature that directly or indirectly influence sound speed were analyzed using interpolation. The variability of these parameters was then examined in stages through a simple linear regression model to establish the linear relationships between each parameter and the effects on sound speed. Tidal conditions were identified as the primary factor affecting water variability (pH, salinity, and DO), hence, the relationships among these parameters were analyzed first. Following this, salinity, an important factor in determining sound speed, was evaluated for the impact on conductivity, density, and sound speed. Additionally, the relationships between conductivity and density concerning sound speed were analyzed to confirm the connections. The influence of temperature on sound speed was also assessed using a simple linear regression model to verify the linear effect. Results and discussion Sound speed The predicted sound speed using various equations was quite accurate for several shallow water conditions (Fig. 2 ). Although generally close to the measurement results, there were minor differences in accuracy levels for each type of water. In the inlet waters of Cilacap Fishing Port, nearly all equations showed slight overestimation for lower sound speed categories and greater precision for higher sound speed categories (Fig. 2 A). As sound speed increased due to rising temperature and salinity, the predictions from all models also became more accurate. Conversely, when temperature and salinity were lower, resulting in reduced sound speed, the prediction models tended to be higher than the measured results. In the coastal waters of Brebes, the prediction model was more accurate for lower sound speeds compared to higher ones (Fig. 2 B). All prediction models demonstrated overestimation at higher sound speeds. As sound speed increased due to higher temperature and salinity, the accuracy of predictions decreased further. In other words, overestimation was more pronounced at higher sound speeds across all models. In contrast to the waters of Cilacap and Brebes, the prediction model for all equations indicated that nearly all overestimations occurred in Pangandaran Waters (Fig. 2 C). For both lower and higher sound speeds, almost all models showed a similar pattern, though there were slight differences in accuracy levels. The interaction of temperature and salinity variability affecting sound speed in the waters had measurement results slightly below the predictions for nearly all equations used. The prediction model was significantly different in freshwaters (Experimental Pond of Fisheries and Marine Science Faculty – Jenderal Soedirman University, Purwokerto), where clear distinctions appeared for each equation (Fig. 2 D). At higher sound speeds resulting from increased temperature, the prediction model tended to be more accurate. Conversely, when lower temperatures were associated with lower sound speeds, the prediction model diverged further from accuracy. Some equations predicted lower than the measured results, while others predicted higher. The graph illustrates that predictions become more contrasting for all models as sound speed decreases, while predictions get closer to accuracy as sound speed increases. The differences in accuracy of the prediction models for various types of water are attributed to the distinct water conditions. The variability in both temperature and salinity was significantly different across the four stations (Table 1 ). The water conditions at Cilacap Fishing Port, which serves as an entry point for fishing vessels, are influenced by upstream river runoff, particularly during low tide. This leads to a wider range of variability in temperature and especially salinity compared to other waters, affecting the regression coefficient values to approach 1 for all prediction models. In Pangandaran waters, salinity variability was also more pronounced due to the interaction between land and sea during high and low tides, leading to more accurate regression coefficient values. Meanwhile, Brebes Waters, located farther from land, experienced a narrower salinity range, which negatively impacted the accuracy of the prediction model. This is further supported by the prediction model for sound speed in freshwaters (Purwokerto), where variability is limited to temperature alone. The results are in line with earlier studies indicating that factors such as temperature and salinity significantly influence the sound speed in shallow waters. Del Grosso ( 1974 ); Medwin ( 1975 ); Chen and Millero ( 1977 ); Coppens ( 1981 ); Mackenzie ( 1981 ), and UNESCO ( 1983 ) demonstrated that the sound speed increases with rising temperature and salinity, also impacting the accuracy of prediction models. Furthermore, Chen et al. ( 2022 ) showed that more complex models could yield more accurate results under specific conditions, particularly when various environmental variables are considered. The differing levels of temperature and salinity variability across the study sites contributed to the variations in the accuracy of the prediction models. In Pangandaran Waters, the interaction between freshwater and seawater leads to greater salinity fluctuations, affecting the sound speed. Zhang et al. ( 2021 ); Makar ( 2022 ), and; Liu et al. ( 2024 ) also observed that changes in salinity could influence the accuracy of sound speed predictions. This study indicates that the accuracy of the prediction model generally improves as the sound speed increases, suggesting the model may be more effective in environments with higher temperature and salinity, resulting in elevated sound speeds. This observation supports Chen et al. ( 2022 ) emphasis on the necessity of considering environmental conditions when employing prediction models. In freshwater settings, such as the Experimental Pond at the Faculty of Fisheries and Marine Sciences, Jenderal Soedirman University, distinct differences in the accuracy of the prediction model are evident. The model tends to be more accurate at higher sound speeds due to increased temperatures, while lower temperatures result in greater deviations from measured values. This result is consistent with Chen and Millero ( 1977 ) who reported that temperature significantly affected sound speed in freshwater. Water parameters Parameters of shallow water characteristics play a crucial role in sound speed, both directly and indirectly. Figure 3 shows the daily variability of various shipping characteristics in the Cilacap Fishing Port channel during the study. Overall, the variation in water characteristics is heavily influenced by physical processes occurring in the water, such as waves, currents, and tides. In this area, tidal movements are the primary physical process affecting water characteristics. The tidal conditions in Cilacap are generally classified as semi-diurnal, meaning high and low tides occur twice daily, although there are variations in the peak tide levels (Fig. 3 A). The highest tide typically occurs in the evening, followed by another high tide in the morning (as seen on January 29–30, 2022). The lowest tides happen at noon and midnight. These tidal events lead to the movement of water masses, with seawater flowing into the area during high tide and river water dominating during low tide Table 1 Summary of temperature and salinity ranges with corresponding sound speed prediction models at various stations Station Temperature range ( o C) Salinity range (PSU) Model Equation R 2 Cilacap 30.006–31.342 26.450–32.296 Del Grosso ( 1974 ) y = 0.9970x + 4.5942 0.9980 Medwin ( 1975 ) y = 0.9846x + 23.968 0.9973 Chen and Millero ( 1977 ) y = 0.9940x + 9.3442 0.9979 Mackenzie ( 1981 ) y = 0.9863x + 21.131 0.9979 Coppens ( 1981 ) y = 0.9920x + 12.335 0.9979 UNESCO ( 1983 ) y = 0.9795x + 31.924 0.9977 Brebes 29.095–29.501 31.356–32.153 Del Grosso ( 1974 ) y = 1.0433x − 66.6280 0.9497 Medwin ( 1975 ) y = 1.0680x − 104.6200 0.9510 Chen and Millero ( 1977 ) y = 1.0512x − 78.5900 0.9491 Mackenzie ( 1981 ) y = 1.0433x − 66.6360 0.9494 Coppens ( 1981 ) y = 1.0400x − 61.6950 0.9529 UNESCO ( 1983 ) y = 1.0520x − 79.7500 0.9478 Pangandaran 29.278–30.421 31.015–33.334 Del Grosso ( 1974 ) y = 1.0016x − 2.2820 0.9994 Medwin ( 1975 ) y = 1.0143x − 21.9550 0.9994 Chen and Millero ( 1977 ) y = 1.0048x − 7.0496 0.9994 Mackenzie ( 1981 ) y = 0.9978x + 3.4724 0.9994 Coppens ( 1981 ) y = 1.0013x − 1.9537 0.9994 UNESCO ( 1983 ) y = 0.9973x + 4.5310 0.9994 Purwokerto 25–697–26.897 0.083–0.094 Del Grosso ( 1974 ) y = 1.0102x − 15.0280 0.9995 Medwin ( 1975 ) y = 1.0786x − 118.3200 0.9995 Chen and Millero ( 1977 ) y = 1.0093x − 13.9450 0.9995 Mackenzie ( 1981 ) y = 1.0399x − 60.0430 0.9995 Coppens ( 1981 ) y = 1.0120x − 18.0840 0.9995 UNESCO ( 1983 ) y = 1.0181x − 26.9190 0.9995 The movement of water masses caused by tides leads to daily fluctuations in the concentration of TDS (Fig. 3 B). The concentrations typically begin to rise as high tide starts and continues to increase throughout the high tide period. Conversely, values decrease when the tide shifts to low tide and continue to drop. This pattern repeats in the subsequent tidal phases, with smaller changes in TDS concentration due to lower tidal variations. These changes are closely linked to alterations in water surface elevation, which reflects current speed during both high and low tides. Currents affect the movement of water masses with specific TDS concentrations, where faster currents tend to result in higher concentrations, as fewer particles settle. The TDS concentration in these waters reflects the pH and salinity levels due to tidal processes (Figs. 3 C and 3 E). During high tide, the TDS concentration increases due to the influx of seawater, indicated by a rise in salinity. Conversely, during low tide, land runoff dominates, leading to significant changes in the pH value. The TDS levels have an indirect correlation with dissolved oxygen levels, particularly at low tide due to land runoff (Fig. 3 D). In this scenario, complete entry of atmospheric oxygen into the waters becomes challenging because it is obstructed by land-derived particles. The results are in line with those of Azeez et al. ( 2021 ), and Zhang et al. ( 2021 ), who demonstrated that tidal fluctuations can significantly alter salinity and temperature profiles. These two are crucial factors influencing sound speed in shallow waters. The interaction between freshwater and seawater during tidal cycles can lead to complex changes in water density and acoustic properties, as reported by Nascimento et al. ( 2021 ), and Nguyen, Kawanisi and Sawaf (2021). The movement of water masses driven by tides affects not only sound speed but also other physical and chemical parameters, such as nutrient distribution and dissolved oxygen levels, which are essential for marine ecosystems (Coogan et al., 2021 ; Chakraborty, 2022 ). Tidal changes can lead to significant variations in TDS concentrations, contributing to shifts in water quality in coastal areas (Panseriya et al., 2023 ). Furthermore, Klubi et al. ( 2022 ) indicated that stronger currents during high tides can enhance the uniformity of TDS within the water column, decreasing particle accumulation at the bottom. Other studies have emphasized the importance of the relationship between TDS, pH, and salinity in understanding coastal water quality. For instance, Srilert and Van ( 2022 ) found that tidal salinity fluctuations can influence TDS and pH levels, impacting the health of aquatic ecosystems. Increased salinity during high tides may lead to higher nutrient concentrations, affecting primary productivity in these waters. Additionally, Acharyya et al. ( 2021 ) showed that land runoff during ebb and flow may introduce particles and contaminants capable of compromising water quality, including dissolved oxygen levels. Density and conductivity profiles in the waters are significantly affected by salinity and temperature (Figs. 3 F and 3 G). Variations in salinity due to tidal processes lead to corresponding changes in density and conductivity. High salinity, marked by an increase in salt ion concentration, results in higher density and conductivity. Additionally, temperature changes from solar radiation penetrating the water also directly influence density and conductivity. Higher water temperatures during the day and lower temperatures at night cause significant increases and decreases in density and conductivity during these times. Aside from impacting density and conductivity, salinity and temperature are key factors in shaping the sound speed profile in shallow waters. The aggregate sound speed fluctuates with changes in temperature and salinity (Fig. 3 I). Table 2 Mathematical models along with their coefficients of determination (R²) for different water parameters. Data obtained from the CTD instrument are indicated by black markers, while data from the water quality checker are represented by blue markers. Water parameters Model R 2 Total dissolved solids y = 704841x 5 − 2×10 11 x 4 + 1×10 16 x 3 − 6×10 20 x 2 + 1×10 25 x − 1×10 29 0.9677 pH y = 0.9224x 5 − 2×10 5 x 4 + 2×10 10 x 3 − 8×10 14 x 2 + 2×10 19 x − 2×10 23 0.9308 Dissolved oxygen y = 102.56x 5 − 2×10 7 x 4 + 2×10 12 x 3 − 9×10 16 x 2 + 2×10 21 x − 2×10 25 0.8608 Salinity y = 65.756x 5 − 1×10 7 x 4 + 1×10 12 x 3 − 6×10 16 x 2 + 1×10 21 x − 1×10 25 0.7369 y = 262.83x 5 − 6×10 7 x 4 + 5×10 12 x 3 − 2×10 17 x 2 + 5×10 21 x − 5×10 25 0.9647 Density y = 38.058x 5 − 8×10 6 x 4 + 8×10 11 x 3 − 3×10 16 x 2 + 8×10 20 x − 7×10 24 0.7677 Conductivity y = 130.98x 5 − 3×10 7 x 4 + 3×10 12 x 3 − 1×10 17 x 2 + 3×10 21 x − 2×10 25 0.7978 y = 427.89x 5 − 1×10 8 x 4 + 9×10 12 x 3 − 4×10 17 x 2 + 8×10 21 x − 8×10 25 0.9503 Temperature y = -32.195x 5 + 7×10 6 x 4 − 6×10 11 x 3 + 3×10 16 x 2 − 6×10 20 x + 6×10 24 0.9707 y = -13.674x 5 + 3×10 6 x 4 − 3×10 11 x 3 + 1×10 16 x 2 − 3×10 20 x + 2×10 24 0.9753 Sound speed y = 55.108x 5 − 1×10 7 x 4 + 1×10 12 x 3 − 5×10 16 x 2 + 1×10 21 x − 1×10 25 0.7545 The characteristic profile of Cilacap waters is shown in Table 2 . All water parameters follow a 5th-order exponential model, with R² values ranging from 0.7369 to 0.9707. This model shows the relationships between parameters, whether direct or indirect. The relatively high R² values suggest that the exponential model effectively captures the variability of these water characteristics. The varying values and notations (+ or -) indicate distinct profiles for each variable, with differing relationships (positive or negative). The tidal process is essential for analyzing water parameter profiles, except for temperature, which is influenced by solar intensity. Matsoukis et al. ( 2023 ) demonstrated that the exponential model effectively captures the relationships between salinity, temperature, and other parameters, aligning with the findings of this study. Furthermore, Cheng et al. ( 2022 ) emphasized the significance of comprehending the interactions among different water parameters in relation to climate change and human activities, as these factors can influence overall water quality. Direct and indirect impact of water parameters to sound speed The sound speed in shallow water is primarily affected by temperature and salinity, with several other parameters having an indirect impact (Fig. 4 ). Temperature, as an independent parameter (not influenced by other variables), shows a weak linear relationship with sound speed, reflected in a low coefficient of determination (R² = 0.1907). This indicates that the relationship can be better represented by polynomial equations or other interpolation methods. In contrast, salinity shows a more linear influence on sound speed, indicated by a higher coefficient of determination (R² = 0.6908). This relationship can also be more accurately explained using alternative interpolation models. Given that temperature and salinity vary significantly over time, examining the interaction is a suitable approach for predicting sound speed in shallow water. Aside from examining the effects of temperature and salinity, the sound speed can be analyzed through the parameters of density and conductivity, which have coefficients of determination of 0.5895 and 0.9726, respectively. These two variables have a linear relationship with sound speed, hence, as the values rise, sound speed in water increases and vice versa. This relationship is closely tied to salinity, which significantly influences the values of conductivity and density, though other factors such as temperature also contribute. Therefore, salinity is crucial for understanding the variability in conductivity, density, and sound speed. Given the significant role of salinity in the variability of sound speed in shallow water, studying the factors that influence this parameter is crucial for understanding the indirect effects of various water parameters. Figure 4 shows how salinity variability in shallow waters is formed, with TDS serving as a clear indicator of salinity. This parameter shows a strong positive correlation, although it does not solely represent salinity. Theoretically, TDS reflects both salt ions, which contribute to salinity, and organic ions, indicated by pH levels. The presence of TDS in the water, whether derived from salt or organic ions, affects the levels of dissolved oxygen. Further insights into the TDS phenomenon can be gleaned from tidal processes in coastal areas where river water flows into the sea. As previously mentioned, seawater predominates during high tide, while land runoff leads to variations in TDS at low tide. This demonstrates that salinity, a key factor in the sound speed profile, is largely influenced by the tidal processes in the area. The results are consistent with those of Azeez et al. ( 2021 ), and Zhang et al. ( 2021 ), who observed that increased salinity leads to higher sound speed in shallow waters. The interplay between temperature and salinity is significant, particularly as both parameters can fluctuate greatly over time due to environmental and climate change (Whitfield, 2021 ). Salinity is crucial for understanding the variability in conductivity and density, which subsequently influences sound speed (Le Menn and Naïr, 2022 ). Gu et al. ( 2022 ) mentioned that a deeper understanding of the interactions among salinity, density, and conductivity enhances predictions of sound speed in aquatic environments. TDS acts as a clear indicator of salinity in a strong positive correlation relationship, although TDS does not completely represent salinity alone. This parameter can impact dissolved oxygen levels, which are essential for aquatic ecosystems (Thomas, 2021 ). Moreover, tidal processes in coastal regions offer additional insights into the TDS phenomenon, particularly as river water flows into the sea. During high tides, seawater predominates, while land runoff leads to variations in TDS at low tides. This indicates that salinity, a key factor in the sound speed profile, is significantly influenced by tidal dynamics in the area. Variations in salinity due to tidal processes affect the acoustic quality of water, which is essential for applications in underwater acoustics (Nguyen et al., 2021 ; Makar, 2022 ). Conclusion In conclusion, sound speed in shallow waters is significantly influenced by temperature and salinity, with salinity demonstrating a stronger and more linear correlation than temperature. This study also emphasizes the necessity of understanding the interactions among various water parameters, such as density and conductivity, that contribute to variations in sound speed. Although the results are consistent with previous studies, there are limitations in the accuracy of the sound speed prediction model, particularly in areas with complex and variable water conditions, such as those affected by tides. Relying on simple linear regression models may also not adequately reflect the intricate interactions among environmental variables. Therefore, further studies are essential, using more complex models and examining a wider range of locations to enhance the understanding of sound speed dynamics in shallow waters Declarations Ethical Approval and Consent to participate Ethical Approval and Consent to participate is not applicable Human Ethics Human Ethics is not applicable Consent for publication Consent for publication is not applicable Funding This work was supported by Kementerian Riset, Teknologi dan Pendidikan Tinggi, 3.34/UN23.35.5/PT.01/VII/2023 Authors' contributions Conceptualization, AA, RRH, IAH and HH; methodology, AA and RRH; validation, AA and HH; formal analysis, AA and RRH; investigation, AA, RRH, IAH, ATN, RJS and HH; resources, AA RRH, IAH and HH; data curation, AA, RRH, IAH, ATN, RJS and HH; writing original draft preparation, AA, RRH, IAH, ATN, RJS and HH; writing review and editing, AA and RH; supervision, AA and HH; project administration, RJS; funding acquisition, AA, RRH, IAH and HH. 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(2021) ‘Estimating quality of indirect measurements of sea water sound velocity by CTD data’, Measurement , 175, p. 109073. https://doi.org/10.1016/j.measurement.2021.109073 Del Grosso, V. A. (1974) ‘New equation for the speed of sound in natural waters (with comparisons to other equations)’, The Journal of the Acoustical Society of America , 56(4), pp. 1084–1091. https://doi.org/10.1121/1.1903388 Gu, L. et al. (2022) ‘Advances in the technologies for marine salinity measurement’, Journal of Marine Science and Engineering , 10(12), p. 2024. https://doi.org/10.3390/jmse10122024 Harris, W. R., Lin, Y.-T. and Andres, M. (2025) ‘Interannual changes in sound propagation across the Gulf Stream’, The Journal of the Acoustical Society of America , 157(2), pp. https://doi.org/1004–1018. 10.1121/10.0035815 Klubi, E. et al. (2022) ‘Water Quality Status Within The Anchorage Space of Tema Harbour, Ghana’, West African Journal of Applied Ecology , 30(1), pp. 82–96. Li, G. et al. (2021) ‘Relationships between the sound speed ratio and physical properties of surface sediments in the South Yellow Sea’, Acta Oceanologica Sinica , 40(4), pp. 65–73. https://doi.org/10.1007/s13131-021-1764-8 Liu, Y. et al. (2024) ‘A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning’, Journal of Marine Science and Engineering , 12(11), p. 1943. https://doi.org/10.3390/jmse12111943 Mackenzie, K. V (1981) ‘Nine‐term equation for sound speed in the oceans’, The Journal of the Acoustical Society of America , 70(3), pp. 807–812. https://doi.org/10.1121/1.386920 Makar, A. (2022) ‘Simplified method of determination of the sound speed in water on the basis of temperature measurements and salinity prediction for shallow water bathymetry’, Remote Sensing , 14(3), p. 636. https://doi.org/10.3390/rs14030636 Matsoukis, C. et al. (2023) ‘Numerical investigation of river discharge and tidal variation impact on salinity intrusion in a generic river delta through idealized modelling’, Estuaries and Coasts , 46(1), pp. 57–83. https://doi.org/10.1007/s12237-022-01109-2 Medwin, H. (1975) ‘Speed of sound in water: A simple equation for realistic parameters’. The Journal of the Acoustical Society of America, 58 (6). pp. 1318-1319. http://dx.doi.org/10.1121/1.380790 Le Menn, M. and Naïr, R. (2022) ‘Review of acoustical and optical techniques to measure absolute salinity of seawater’, Frontiers in Marine Science , 9, p. 1031824. https://doi.org/10.3389/fmars.2022.1031824 Nascimento, Â. et al. (2021) ‘Tidal variability of water quality parameters in a mesotidal estuary (Sado Estuary, Portugal)’, Scientific reports , 11(1), p. 23112. https://doi.org/10.1038/s41598-021-02603-6 Nguyen, H. T., Kawanisi, K. and Sawaf, M. B. Al (2021) ‘Acoustic Monitoring of Tidal Flow and Salinity in a Tidal Channel’, Journal of Marine Science and Engineering , 9(11), p. 1180. https://doi.org/10.3390/jmse9111180 Panseriya, H. Z. et al. (2023) ‘Assessment of surface water quality during different tides and an anthropogenic impact on coastal water at Gulf of Kachchh, West Coast of India’, Environmental Science and Pollution Research , 30(10), pp. 28053–28065. https://doi.org/10.1007/s11356-022-24205-z Possenti, L. et al. (2024) ‘The present and future contribution of ships to the underwater soundscape’, Frontiers in Marine Science , 11, p. 1252901. https://doi.org/10.3389/fmars.2024.1252901 Prosnier, L. (2024) ‘Zooplankton as a model to study the effects of anthropogenic sounds on aquatic ecosystems’, Science of the Total Environment , p. 172489. https://doi.org/10.1016/j.scitotenv.2024.172489 Srilert, C. and Van, T. P. (2022) ‘Spatial and temporal variabilities of surface water and sediment pollution at the main tidal-influenced river in Ca Mau Peninsular, Vietnamese Mekong Delta’, Journal of Hydrology: Regional Studies , 41, p. 101082. https://doi.org/10.1016/j.ejrh.2022.101082 Thomas, E. O. (2021) ‘Effect of temperature on DO and TDS: A measure of ground and surface water Interaction’, Water Science , 35(1), pp. 11–21. https://doi.org/10.1080/11104929.2020.1860276 UNESCO (1983) ‘Algorithms for computation of fundamental properties of seawater’, UNESCO. Whitfield, A. K. (2021) ‘Estuaries–how challenging are these constantly changing aquatic environments for associated fish species?’, Environmental Biology of Fishes , 104(4), pp. https://doi.org/517–528. 10.1007/s10641-021-01085-9 Wu, P. et al. (2024) ‘Real-time estimation of underwater sound speed profiles with a data fusion convolutional neural network model’, Applied Ocean Research , 150, p. 104088. https://doi.org/10.1016/j.apor.2024.104088 Zhang, J. et al. (2021) ‘Improving the Estimation of Temperature and Salinity by Assimilation of Observed Sound Speed Profiles’, Journal of Atmospheric and Oceanic Technology , 38(7), pp. 1277–1289.https://doi.org/10.1175/JTECH-D-20-0121.1. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6653523","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483094690,"identity":"1d33a9dc-f7cf-4a3c-a10f-9ce3ceb61a2d","order_by":0,"name":"Amron Amron","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYNCCCgglASLYGBKI0XKGZC2MbUhaGAhp4Z929uHjwnmH8+Tbmw/eYKixY+BjJ6BF4na6sfHMbYeLDc4cS7ZgOJbMwMbzgICjbqexSfNuO5y4QSLHTIKB7QADmwQBW+TBWuYcTpw/I/+bBMM/IrQYgLU0HE5suJHDJsHYRoQWw9tpzMY8x9ITN5w5ZmyR2JfMQ9AvcrfTGB/z1Fgnzm9vfnjjwzc7Ofl2AragAqBiHlLUj4JRMApGwSjAAQCyND1XCKrSgAAAAABJRU5ErkJggg==","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":true,"prefix":"","firstName":"Amron","middleName":"","lastName":"Amron","suffix":""},{"id":483094691,"identity":"2cb90a43-b864-4321-aac0-6870d66673fe","order_by":1,"name":"Rizqi Rizaldi Hidayat","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Rizqi","middleName":"Rizaldi","lastName":"Hidayat","suffix":""},{"id":483094692,"identity":"cff82e2c-ce72-4cc9-9db6-a168e5373bae","order_by":2,"name":"Iqbal Ali Husni","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Iqbal","middleName":"Ali","lastName":"Husni","suffix":""},{"id":483094693,"identity":"bb8bf5fe-f013-49c4-8e94-f9f59c21751c","order_by":3,"name":"Agung Tri Nugroho","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Agung","middleName":"Tri","lastName":"Nugroho","suffix":""},{"id":483094694,"identity":"7efa40c3-6b82-4bd2-9049-59307887fcc1","order_by":4,"name":"Ratna Juita Sari","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Ratna","middleName":"Juita","lastName":"Sari","suffix":""},{"id":483094695,"identity":"cf48e187-9849-4d91-be4d-895e4ff2ff72","order_by":5,"name":"Hartoyo Hartoyo","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Hartoyo","middleName":"","lastName":"Hartoyo","suffix":""}],"badges":[],"createdAt":"2025-05-13 08:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6653523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6653523/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10236-025-01742-y","type":"published","date":"2025-10-27T15:58:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86425370,"identity":"cddf7897-2b33-4bfe-90f5-fab48e46923a","added_by":"auto","created_at":"2025-07-10 13:21:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206957,"visible":true,"origin":"","legend":"\u003cp\u003eResearch sites\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6653523/v1/9d2d328546290b19106cae3d.png"},{"id":86425371,"identity":"a0b9639b-92f2-4f59-9575-4c8aaebf63d6","added_by":"auto","created_at":"2025-07-10 13:21:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":399088,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of predicted vs. measured sound speed in shallow water across different locations. (A) Cilacap Fishing Port Waters; (B) Brebes Waters; (C) Pangandaran Waters; (D) Experimental Pond of the Faculty of Fisheries and Marine Sciences – Jenderal Soedirman University, Purwokerto.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6653523/v1/2f56ae8be8a76211f9443ba3.png"},{"id":86425372,"identity":"572b4e03-cf9c-4d0f-b7d7-790d8d039921","added_by":"auto","created_at":"2025-07-10 13:21:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283320,"visible":true,"origin":"","legend":"\u003cp\u003eThe daily fluctuations of various water parameters in the Cilacap Fishing Port Channel over time. Panels (A) to (I) show tidal height, total dissolved solids (TDS), dissolved oxygen, pH, salinity, density, conductivity, temperature, and sound speed, respectively. The green, blue, and red dots and lines indicate data collected from BMKG, the CTD instrument, and the water quality checker, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6653523/v1/2b801235fb02b16081abc74f.png"},{"id":86426227,"identity":"da215aaf-d577-4d9f-93e3-b3fd3dc6eace","added_by":"auto","created_at":"2025-07-10 13:29:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":293141,"visible":true,"origin":"","legend":"\u003cp\u003eLinear relationships among water parameters and their impact on sound speed\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6653523/v1/1e973ecf60f1f4d6014e704b.png"},{"id":95040564,"identity":"85a71f85-e0f7-4517-8370-46e1a93ad4ff","added_by":"auto","created_at":"2025-11-03 16:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1670160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6653523/v1/442ce193-e940-4440-95b4-216233758747.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sound Speed in the Shallow Waters: Direct and Indirect Water Parameters Impact","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe speed of sound in shallow waters is a crucial parameter that affects numerous environmental processes, including underwater communication, sonar operations, and marine ecology. Accurate sound speed is essential for effective signal transmission in underwater communication, which is crucial for ship navigation and scientific exploration. In sonar applications, sound speed plays a significant role in determining the depth and distance of detected objects, thereby influencing fishing strategies and marine surveys. Moreover, in marine ecology, sound speed can impact the behavior of organisms, such as fish migration and interspecies interactions, ultimately influencing ecosystem health. Understanding the factors that affect sound speed is essential for precise modeling of acoustic propagation in these dynamic environments. This speed is influenced not only by the physical properties of water but also by various environmental factors, including weather conditions and human activities. Key variables impacting sound speed include temperature, salinity, density, and presure (Del Grosso, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Medwin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Chen and Millero, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Coppens, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Mackenzie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Makar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemperature and salinity are key factors affecting sound speed in water. As temperature increases, sound speed generally rises, attributed to the enhanced kinetic energy of water molecules (Bulut and Ergin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Possenti et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This relationship is particularly important in shallow waters, where rapid temperature fluctuations potentially occur, especially in coastal areas influenced by solar heating and varying weather conditions (Exley et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)(Gonz\u0026aacute;lez \u0026Aacute;vila et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding how temperature variations correlate with sound speed is crucial for accurately predicting acoustic behavior in these environments. Salinity also plays a significant role in influencing sound speed, with changes in salinity affecting seawater density, which in turn impacts sound propagation. Numerous studies have documented a strong positive correlation between salinity and sound speed (Del Grosso, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Medwin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Chen and Millero, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Coppens, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Mackenzie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) Grekov, Grekov and Sychov, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Affatati, Scaini and Salon, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In shallow waters where salinity can vary greatly due to freshwater influx from rivers and rainfall, it is essential to comprehend these fluctuations for precise sound speed predictions.\u003c/p\u003e\u003cp\u003eDensity, influenced by temperature and salinity, is another crucial factor in determining the sound speed. As water density increases, sound travels faster through it. The interplay between density, temperature, and salinity creates a complex environment in which changes in one parameter potentially trigger a chain reaction in others (Bryan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding these interactions is essential for marine scientists and engineers working in the field of underwater acoustics. Additionally, conductivity, which is closely associated with salinity, significantly affects sound speed. Conductivity measures the ability of water to conduct electricity, influenced by the presence of ions, particularly salts. As conductivity increases, the sound speed also rises, adding further complexity to the shallow water environment (Duarte et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Le Menn and Na\u0026iuml;r, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This relationship underscores the importance of monitoring conductivity alongside other parameters to gain a comprehensive understanding of sound speed dynamics.\u003c/p\u003e\u003cp\u003eAside from direct influences, indirect factors such as tidal dynamics and freshwater influx can significantly affect sound speed. Tidal movements, driven by the gravitational pull of the moon and sun, cause fluctuations in water levels that modify the composition and physical properties of water in shallow regions. These alterations may lead to variations in temperature, salinity, and density, complicating the acoustic environment (Makar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Harris, Lin and Andres, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, when sea levels rise, freshwater from colder rivers may mix with seawater, resulting in substantial changes in salinity and temperature. These modifications not only impact sound speed but also influence marine life and biogeochemical processes within the ecosystem. By investigating these indirect effects, a more comprehensive understanding of sound propagation in shallow waters can be achieved. Additionally, the interplay between tidal dynamics and freshwater inflow affects the distribution of nutrients and oxygen in the water, subsequently influencing marine biological activity. When freshwater enters the ocean, it often carries various nutrients that stimulate phytoplankton growth. This increase in phytoplankton alters ocean acoustics, as the concentration of particles in the water affects sound transmission (Possenti et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Prosnier, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies on sound speed in shallow waters are highly relevant for various applications, including fisheries management, marine navigation, and environmental monitoring. Developing accurate sound speed models can enhance the effectiveness of sonar systems and enrich our understanding of marine ecosystems (Gonz\u0026aacute;lez \u0026Aacute;vila et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, ongoing studies on the relationship between sound speed and different water parameters are crucial for advancing knowledge in oceanography. Investigating both the direct and indirect effects of these parameters can lead to the creation of more precise predictive models, enhancing current comprehension of underwater acoustics. This knowledge is essential for numerous marine applications, ultimately aiding in the better management and conservation of marine resources.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData Acquisition\u003c/h2\u003e\n \u003cp\u003eSound speed and water parameters including salinity, density, conductivity, and temperature were collected at four shallow water locations using a CTD SVP Midas SVX2. The same parameters, along with additional water quality metrics comprising TDS, pH, and dissolved oxygen/DO, were measured with a water quality checker (HI 98194 Multiparameter). Tidal data were obtained from Meteorology, Climatology, and Geophysical Agency, Indonesia (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.stamet-kotim.bmkg.go.id\u003c/span\u003e\u003c/span\u003e). The CTD instrument was positioned at a depth of one meter below the water surface and deployed using a mooring buoy system to maintain a fixed recording position during observations. Water quality measurements with the water quality checker were conducted hourly throughout the daytime period of the CTD observations.\u003c/p\u003e\n \u003cp\u003eThe shallow water locations selected for this study included Cilacap Fishing Port, Pangandaran Waters, Brebes Waters, and the Experimental Pond of the Fisheries and Marine Science Faculty (Purwokerto) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Observations of sound speed, temperature, and salinity were conducted at all stations (Cilacap: January 20\u0026ndash;30, 2022; Brebes: March 5\u0026ndash;6, 2022; Pangandaran: June 8\u0026ndash;9, 2022; and Purwokerto: June 17\u0026ndash;18, 2022), while additional measurements of other water quality parameters were only taken at Cilacap Fishing Port Waters. Each station had distinct water characteristics, resulting in varying levels of temperature and salinity, which in turn affected sound speed. At Cilacap Fishing Port Waters, the water characteristics were influenced by the river flowing into the port. In contrast, Pangandaran Waters were significantly affected by the dynamics of the Indian Ocean. Similarly, the Brebes Waters were influenced by the dynamics of the Java Sea. At Purwokerto Station, the experimental pond was isolated from water flow, meaning its characteristics were primarily affected by weather conditions, such as solar heating and rainfall.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eThe sound speed recorded from all observation stations using the CTD SVP was compared to the equations established by Del Grosso (\u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e); Medwin (\u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e); Chen and Millero (\u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e); Coppens (\u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e); Mackenzie (\u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e), and UNESCO (\u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e) through a simple linear regression. The predicted sound speed was calculated based on the temperature and salinity measurements obtained from CTD. Pressure in shallow water was assumed to be consistent (1 atm) across all stations, implying that sound speed was influenced solely by variations in temperature and salinity. The accuracy of each equation in predicting sound speed under different water conditions was assessed using the regression coefficient. The variability in temperature and salinity at the study stations will lead to different recommendations regarding the accuracy of each developed equation.\u003c/p\u003e\n \u003cp\u003eThe patterns of changes in water parameters namely tidal level, TDS, pH, DO, salinity, density, conductivity, and temperature that directly or indirectly influence sound speed were analyzed using interpolation. The variability of these parameters was then examined in stages through a simple linear regression model to establish the linear relationships between each parameter and the effects on sound speed. Tidal conditions were identified as the primary factor affecting water variability (pH, salinity, and DO), hence, the relationships among these parameters were analyzed first. Following this, salinity, an important factor in determining sound speed, was evaluated for the impact on conductivity, density, and sound speed. Additionally, the relationships between conductivity and density concerning sound speed were analyzed to confirm the connections. The influence of temperature on sound speed was also assessed using a simple linear regression model to verify the linear effect.\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSound speed\u003c/h2\u003e\u003cp\u003eThe predicted sound speed using various equations was quite accurate for several shallow water conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although generally close to the measurement results, there were minor differences in accuracy levels for each type of water. In the inlet waters of Cilacap Fishing Port, nearly all equations showed slight overestimation for lower sound speed categories and greater precision for higher sound speed categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). As sound speed increased due to rising temperature and salinity, the predictions from all models also became more accurate. Conversely, when temperature and salinity were lower, resulting in reduced sound speed, the prediction models tended to be higher than the measured results. In the coastal waters of Brebes, the prediction model was more accurate for lower sound speeds compared to higher ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). All prediction models demonstrated overestimation at higher sound speeds. As sound speed increased due to higher temperature and salinity, the accuracy of predictions decreased further. In other words, overestimation was more pronounced at higher sound speeds across all models.\u003c/p\u003e\u003cp\u003eIn contrast to the waters of Cilacap and Brebes, the prediction model for all equations indicated that nearly all overestimations occurred in Pangandaran Waters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). For both lower and higher sound speeds, almost all models showed a similar pattern, though there were slight differences in accuracy levels. The interaction of temperature and salinity variability affecting sound speed in the waters had measurement results slightly below the predictions for nearly all equations used. The prediction model was significantly different in freshwaters (Experimental Pond of Fisheries and Marine Science Faculty \u0026ndash; Jenderal Soedirman University, Purwokerto), where clear distinctions appeared for each equation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). At higher sound speeds resulting from increased temperature, the prediction model tended to be more accurate. Conversely, when lower temperatures were associated with lower sound speeds, the prediction model diverged further from accuracy. Some equations predicted lower than the measured results, while others predicted higher. The graph illustrates that predictions become more contrasting for all models as sound speed decreases, while predictions get closer to accuracy as sound speed increases.\u003c/p\u003e\u003cp\u003eThe differences in accuracy of the prediction models for various types of water are attributed to the distinct water conditions. The variability in both temperature and salinity was significantly different across the four stations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The water conditions at Cilacap Fishing Port, which serves as an entry point for fishing vessels, are influenced by upstream river runoff, particularly during low tide. This leads to a wider range of variability in temperature and especially salinity compared to other waters, affecting the regression coefficient values to approach 1 for all prediction models. In Pangandaran waters, salinity variability was also more pronounced due to the interaction between land and sea during high and low tides, leading to more accurate regression coefficient values. Meanwhile, Brebes Waters, located farther from land, experienced a narrower salinity range, which negatively impacted the accuracy of the prediction model. This is further supported by the prediction model for sound speed in freshwaters (Purwokerto), where variability is limited to temperature alone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results are in line with earlier studies indicating that factors such as temperature and salinity significantly influence the sound speed in shallow waters. Del Grosso (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e); Medwin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e); Chen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e); Coppens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e); Mackenzie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), and UNESCO (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) demonstrated that the sound speed increases with rising temperature and salinity, also impacting the accuracy of prediction models. Furthermore, Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that more complex models could yield more accurate results under specific conditions, particularly when various environmental variables are considered. The differing levels of temperature and salinity variability across the study sites contributed to the variations in the accuracy of the prediction models. In Pangandaran Waters, the interaction between freshwater and seawater leads to greater salinity fluctuations, affecting the sound speed. Zhang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Makar (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and; Liu et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also observed that changes in salinity could influence the accuracy of sound speed predictions. This study indicates that the accuracy of the prediction model generally improves as the sound speed increases, suggesting the model may be more effective in environments with higher temperature and salinity, resulting in elevated sound speeds. This observation supports Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasis on the necessity of considering environmental conditions when employing prediction models. In freshwater settings, such as the Experimental Pond at the Faculty of Fisheries and Marine Sciences, Jenderal Soedirman University, distinct differences in the accuracy of the prediction model are evident. The model tends to be more accurate at higher sound speeds due to increased temperatures, while lower temperatures result in greater deviations from measured values. This result is consistent with Chen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) who reported that temperature significantly affected sound speed in freshwater.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWater parameters\u003c/h3\u003e\n\u003cp\u003eParameters of shallow water characteristics play a crucial role in sound speed, both directly and indirectly. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the daily variability of various shipping characteristics in the Cilacap Fishing Port channel during the study. Overall, the variation in water characteristics is heavily influenced by physical processes occurring in the water, such as waves, currents, and tides. In this area, tidal movements are the primary physical process affecting water characteristics. The tidal conditions in Cilacap are generally classified as semi-diurnal, meaning high and low tides occur twice daily, although there are variations in the peak tide levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The highest tide typically occurs in the evening, followed by another high tide in the morning (as seen on January 29\u0026ndash;30, 2022). The lowest tides happen at noon and midnight. These tidal events lead to the movement of water masses, with seawater flowing into the area during high tide and river water dominating during low tide\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of temperature and salinity ranges with corresponding sound speed prediction models at various stations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003cp\u003erange (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSalinity\u003c/p\u003e\u003cp\u003erange (PSU)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEquation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eCilacap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e30.006\u0026ndash;31.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e26.450\u0026ndash;32.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDel Grosso (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9970x\u0026thinsp;+\u0026thinsp;4.5942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedwin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9846x\u0026thinsp;+\u0026thinsp;23.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9940x\u0026thinsp;+\u0026thinsp;9.3442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMackenzie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9863x\u0026thinsp;+\u0026thinsp;21.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoppens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9920x\u0026thinsp;+\u0026thinsp;12.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNESCO (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9795x\u0026thinsp;+\u0026thinsp;31.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eBrebes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e29.095\u0026ndash;29.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e31.356\u0026ndash;32.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDel Grosso (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0433x \u0026minus;\u0026thinsp;66.6280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedwin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0680x \u0026minus;\u0026thinsp;104.6200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0512x \u0026minus;\u0026thinsp;78.5900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMackenzie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0433x \u0026minus;\u0026thinsp;66.6360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoppens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0400x \u0026minus;\u0026thinsp;61.6950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNESCO (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0520x \u0026minus;\u0026thinsp;79.7500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9478\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ePangandaran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e29.278\u0026ndash;30.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e31.015\u0026ndash;33.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDel Grosso (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0016x \u0026minus;\u0026thinsp;2.2820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedwin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0143x \u0026minus;\u0026thinsp;21.9550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0048x \u0026minus;\u0026thinsp;7.0496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMackenzie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9978x\u0026thinsp;+\u0026thinsp;3.4724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoppens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0013x \u0026minus;\u0026thinsp;1.9537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNESCO (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9973x\u0026thinsp;+\u0026thinsp;4.5310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9994\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003ePurwokerto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e25\u0026ndash;697\u0026ndash;26.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.083\u0026ndash;0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDel Grosso (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0102x \u0026minus;\u0026thinsp;15.0280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedwin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0786x \u0026minus;\u0026thinsp;118.3200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChen and Millero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0093x \u0026minus;\u0026thinsp;13.9450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMackenzie (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0399x \u0026minus;\u0026thinsp;60.0430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoppens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0120x \u0026minus;\u0026thinsp;18.0840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNESCO (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.0181x \u0026minus;\u0026thinsp;26.9190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe movement of water masses caused by tides leads to daily fluctuations in the concentration of TDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The concentrations typically begin to rise as high tide starts and continues to increase throughout the high tide period. Conversely, values decrease when the tide shifts to low tide and continue to drop. This pattern repeats in the subsequent tidal phases, with smaller changes in TDS concentration due to lower tidal variations. These changes are closely linked to alterations in water surface elevation, which reflects current speed during both high and low tides. Currents affect the movement of water masses with specific TDS concentrations, where faster currents tend to result in higher concentrations, as fewer particles settle.\u003c/p\u003e\u003cp\u003eThe TDS concentration in these waters reflects the pH and salinity levels due to tidal processes (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). During high tide, the TDS concentration increases due to the influx of seawater, indicated by a rise in salinity. Conversely, during low tide, land runoff dominates, leading to significant changes in the pH value. The TDS levels have an indirect correlation with dissolved oxygen levels, particularly at low tide due to land runoff (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In this scenario, complete entry of atmospheric oxygen into the waters becomes challenging because it is obstructed by land-derived particles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results are in line with those of Azeez et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Zhang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who demonstrated that tidal fluctuations can significantly alter salinity and temperature profiles. These two are crucial factors influencing sound speed in shallow waters. The interaction between freshwater and seawater during tidal cycles can lead to complex changes in water density and acoustic properties, as reported by Nascimento et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Nguyen, Kawanisi and Sawaf (2021). The movement of water masses driven by tides affects not only sound speed but also other physical and chemical parameters, such as nutrient distribution and dissolved oxygen levels, which are essential for marine ecosystems (Coogan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chakraborty, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Tidal changes can lead to significant variations in TDS concentrations, contributing to shifts in water quality in coastal areas (Panseriya et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Klubi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated that stronger currents during high tides can enhance the uniformity of TDS within the water column, decreasing particle accumulation at the bottom. Other studies have emphasized the importance of the relationship between TDS, pH, and salinity in understanding coastal water quality. For instance, Srilert and Van (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that tidal salinity fluctuations can influence TDS and pH levels, impacting the health of aquatic ecosystems. Increased salinity during high tides may lead to higher nutrient concentrations, affecting primary productivity in these waters. Additionally, Acharyya et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that land runoff during ebb and flow may introduce particles and contaminants capable of compromising water quality, including dissolved oxygen levels.\u003c/p\u003e\u003cp\u003eDensity and conductivity profiles in the waters are significantly affected by salinity and temperature (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Variations in salinity due to tidal processes lead to corresponding changes in density and conductivity. High salinity, marked by an increase in salt ion concentration, results in higher density and conductivity. Additionally, temperature changes from solar radiation penetrating the water also directly influence density and conductivity. Higher water temperatures during the day and lower temperatures at night cause significant increases and decreases in density and conductivity during these times. Aside from impacting density and conductivity, salinity and temperature are key factors in shaping the sound speed profile in shallow waters. The aggregate sound speed fluctuates with changes in temperature and salinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMathematical models along with their coefficients of determination (R\u0026sup2;) for different water parameters. Data obtained from the CTD instrument are indicated by black markers, while data from the water quality checker are represented by blue markers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal dissolved solids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;704841x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e11\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;6\u0026times;10\u003csup\u003e20\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e25\u003c/sup\u003ex \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.9224x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e5\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 2\u0026times;10\u003csup\u003e10\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;8\u0026times;10\u003csup\u003e14\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 2\u0026times;10\u003csup\u003e19\u003c/sup\u003ex \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDissolved oxygen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;102.56x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e7\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 2\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;9\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 2\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSalinity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;65.756x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e7\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;6\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;262.83x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;6\u0026times;10\u003csup\u003e7\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 5\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e17\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 5\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;5\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;38.058x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;8\u0026times;10\u003csup\u003e6\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 8\u0026times;10\u003csup\u003e11\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;3\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 8\u0026times;10\u003csup\u003e20\u003c/sup\u003ex \u0026minus;\u0026thinsp;7\u0026times;10\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eConductivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;130.98x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;3\u0026times;10\u003csup\u003e7\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 3\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e17\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 3\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;2\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;427.89x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e8\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 9\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;4\u0026times;10\u003csup\u003e17\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 8\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;8\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey = -32.195x\u003csup\u003e5\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;7\u0026times;10\u003csup\u003e6\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e \u0026minus;\u0026thinsp;6\u0026times;10\u003csup\u003e11\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e + 3\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;6\u0026times;10\u003csup\u003e20\u003c/sup\u003ex + 6\u0026times;10\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey = -13.674x\u003csup\u003e5\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;3\u0026times;10\u003csup\u003e6\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e \u0026minus;\u0026thinsp;3\u0026times;10\u003csup\u003e11\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;3\u0026times;10\u003csup\u003e20\u003c/sup\u003ex + 2\u0026times;10\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSound speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;55.108x\u003csup\u003e5\u003c/sup\u003e \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e7\u003c/sup\u003ex\u003csup\u003e4\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e12\u003c/sup\u003ex\u003csup\u003e3\u003c/sup\u003e \u0026minus;\u0026thinsp;5\u0026times;10\u003csup\u003e16\u003c/sup\u003ex\u003csup\u003e2\u003c/sup\u003e + 1\u0026times;10\u003csup\u003e21\u003c/sup\u003ex \u0026minus;\u0026thinsp;1\u0026times;10\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe characteristic profile of Cilacap waters is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All water parameters follow a 5th-order exponential model, with R\u0026sup2; values ranging from 0.7369 to 0.9707. This model shows the relationships between parameters, whether direct or indirect. The relatively high R\u0026sup2; values suggest that the exponential model effectively captures the variability of these water characteristics. The varying values and notations (+\u0026thinsp;or -) indicate distinct profiles for each variable, with differing relationships (positive or negative). The tidal process is essential for analyzing water parameter profiles, except for temperature, which is influenced by solar intensity. Matsoukis et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that the exponential model effectively captures the relationships between salinity, temperature, and other parameters, aligning with the findings of this study. Furthermore, Cheng et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasized the significance of comprehending the interactions among different water parameters in relation to climate change and human activities, as these factors can influence overall water quality.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDirect and indirect impact of water parameters to sound speed\u003c/h2\u003e\u003cp\u003eThe sound speed in shallow water is primarily affected by temperature and salinity, with several other parameters having an indirect impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Temperature, as an independent parameter (not influenced by other variables), shows a weak linear relationship with sound speed, reflected in a low coefficient of determination (R\u0026sup2; = 0.1907). This indicates that the relationship can be better represented by polynomial equations or other interpolation methods. In contrast, salinity shows a more linear influence on sound speed, indicated by a higher coefficient of determination (R\u0026sup2; = 0.6908). This relationship can also be more accurately explained using alternative interpolation models. Given that temperature and salinity vary significantly over time, examining the interaction is a suitable approach for predicting sound speed in shallow water.\u003c/p\u003e\u003cp\u003eAside from examining the effects of temperature and salinity, the sound speed can be analyzed through the parameters of density and conductivity, which have coefficients of determination of 0.5895 and 0.9726, respectively. These two variables have a linear relationship with sound speed, hence, as the values rise, sound speed in water increases and vice versa. This relationship is closely tied to salinity, which significantly influences the values of conductivity and density, though other factors such as temperature also contribute. Therefore, salinity is crucial for understanding the variability in conductivity, density, and sound speed.\u003c/p\u003e\u003cp\u003eGiven the significant role of salinity in the variability of sound speed in shallow water, studying the factors that influence this parameter is crucial for understanding the indirect effects of various water parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows how salinity variability in shallow waters is formed, with TDS serving as a clear indicator of salinity. This parameter shows a strong positive correlation, although it does not solely represent salinity. Theoretically, TDS reflects both salt ions, which contribute to salinity, and organic ions, indicated by pH levels. The presence of TDS in the water, whether derived from salt or organic ions, affects the levels of dissolved oxygen. Further insights into the TDS phenomenon can be gleaned from tidal processes in coastal areas where river water flows into the sea. As previously mentioned, seawater predominates during high tide, while land runoff leads to variations in TDS at low tide. This demonstrates that salinity, a key factor in the sound speed profile, is largely influenced by the tidal processes in the area.\u003c/p\u003e\u003cp\u003eThe results are consistent with those of Azeez et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Zhang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who observed that increased salinity leads to higher sound speed in shallow waters. The interplay between temperature and salinity is significant, particularly as both parameters can fluctuate greatly over time due to environmental and climate change (Whitfield, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Salinity is crucial for understanding the variability in conductivity and density, which subsequently influences sound speed (Le Menn and Na\u0026iuml;r, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Gu et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) mentioned that a deeper understanding of the interactions among salinity, density, and conductivity enhances predictions of sound speed in aquatic environments. TDS acts as a clear indicator of salinity in a strong positive correlation relationship, although TDS does not completely represent salinity alone. This parameter can impact dissolved oxygen levels, which are essential for aquatic ecosystems (Thomas, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, tidal processes in coastal regions offer additional insights into the TDS phenomenon, particularly as river water flows into the sea. During high tides, seawater predominates, while land runoff leads to variations in TDS at low tides. This indicates that salinity, a key factor in the sound speed profile, is significantly influenced by tidal dynamics in the area. Variations in salinity due to tidal processes affect the acoustic quality of water, which is essential for applications in underwater acoustics (Nguyen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Makar, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, sound speed in shallow waters is significantly influenced by temperature and salinity, with salinity demonstrating a stronger and more linear correlation than temperature. This study also emphasizes the necessity of understanding the interactions among various water parameters, such as density and conductivity, that contribute to variations in sound speed. Although the results are consistent with previous studies, there are limitations in the accuracy of the sound speed prediction model, particularly in areas with complex and variable water conditions, such as those affected by tides. Relying on simple linear regression models may also not adequately reflect the intricate interactions among environmental variables. Therefore, further studies are essential, using more complex models and examining a wider range of locations to enhance the understanding of sound speed dynamics in shallow waters\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical Approval and Consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval and Consent to participate is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Ethics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHuman Ethics is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Kementerian Riset, Teknologi dan Pendidikan Tinggi, 3.34/UN23.35.5/PT.01/VII/2023\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, AA, RRH, IAH and HH; methodology, AA and RRH; validation, AA and HH; formal analysis, AA and RRH; investigation, AA, RRH, IAH, ATN, RJS and HH; resources, AA RRH, IAH and HH; data curation, AA, RRH, IAH, ATN, RJS and HH; writing original draft preparation, AA, RRH, IAH, ATN, RJS and HH; writing review and editing, AA and RH; supervision, AA and HH; project administration, RJS; funding acquisition, AA, RRH, IAH and HH. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interest\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of supporting data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData included in article/supplementary material/referenced article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharyya, T. \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026lsquo;A systematic review of biogeochemistry of Mahanadi river estuary: Insights and future research direction\u0026rsquo;, \u003cem\u003eEstuarine biogeochemical dynamics of the East coast of India\u003c/em\u003e, pp. 57\u0026ndash;80. https://doi.org/10.1007/978-3-030-68980-3_5\u003c/li\u003e\n\u003cli\u003eAffatati, A., Scaini, C. and Salon, S. 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(2021) \u0026lsquo;Estuaries\u0026ndash;how challenging are these constantly changing aquatic environments for associated fish species?\u0026rsquo;, \u003cem\u003eEnvironmental Biology of Fishes\u003c/em\u003e, 104(4), pp. https://doi.org/517\u0026ndash;528. 10.1007/s10641-021-01085-9\u003c/li\u003e\n\u003cli\u003eWu, P. \u003cem\u003eet al.\u003c/em\u003e (2024) \u0026lsquo;Real-time estimation of underwater sound speed profiles with a data fusion convolutional neural network model\u0026rsquo;, \u003cem\u003eApplied Ocean Research\u003c/em\u003e, 150, p. 104088. https://doi.org/10.1016/j.apor.2024.104088\u003c/li\u003e\n\u003cli\u003eZhang, J. \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026lsquo;Improving the Estimation of Temperature and Salinity by Assimilation of Observed Sound Speed Profiles\u0026rsquo;, \u003cem\u003eJournal of Atmospheric and Oceanic Technology\u003c/em\u003e, 38(7), pp. 1277\u0026ndash;1289.https://doi.org/10.1175/JTECH-D-20-0121.1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ocean-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"odyn","sideBox":"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)","snPcode":"10236","submissionUrl":"https://submission.springernature.com/new-submission/10236/3","title":"Ocean Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sound speed, shallow water, water parameters, salinity, temperature","lastPublishedDoi":"10.21203/rs.3.rs-6653523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6653523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe sound speed is a crucial parameter in various environmental processes occurring in shallow waters. Therefore, this study aimed to investigate how water parameters influence sound speed by collecting data from four shallow water sites namely Cilacap Fishing Port, Pangandaran, the Experimental Pond of the Faculty of Fisheries and Marine Sciences, and Brebes Waters. The results showed that salinity had a stronger linear influence on sound speed compared to temperature, with Total Dissolved Solids (TDS) serving as a salinity indicator. Tidal dynamics and freshwater inflows also contributed to variations in salinity, impacting sound speed. Although the prediction model was generally reliable, the accuracy varied based on the specific conditions of each water location. In conclusion, this study underscores the necessity of understanding the interactions among different water parameters to enhance predictions of sound speed and the implications for managing aquatic resources and conserving ecosystems.\u003c/p\u003e","manuscriptTitle":"Sound Speed in the Shallow Waters: Direct and Indirect Water Parameters Impact","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 13:21:11","doi":"10.21203/rs.3.rs-6653523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T16:35:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T18:36:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156007431279358031503770452635754304716","date":"2025-08-06T12:55:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T09:12:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87304389393545684733687286162737324868","date":"2025-07-09T16:51:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T23:09:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T12:02:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T11:57:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ocean Dynamics","date":"2025-05-13T08:51:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ocean-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"odyn","sideBox":"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)","snPcode":"10236","submissionUrl":"https://submission.springernature.com/new-submission/10236/3","title":"Ocean Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c170f9fd-1391-46b4-a6f1-e8489b1cd188","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:06:33+00:00","versionOfRecord":{"articleIdentity":"rs-6653523","link":"https://doi.org/10.1007/s10236-025-01742-y","journal":{"identity":"ocean-dynamics","isVorOnly":false,"title":"Ocean Dynamics"},"publishedOn":"2025-10-27 15:58:49","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-07-10 13:21:11","video":"","vorDoi":"10.1007/s10236-025-01742-y","vorDoiUrl":"https://doi.org/10.1007/s10236-025-01742-y","workflowStages":[]},"version":"v1","identity":"rs-6653523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6653523","identity":"rs-6653523","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}