BACKGROUND AND OBJECTIVES: Mangroves are unique plants distributed in tropical regions, such as Indonesia. Mangrove areas and various mangrove ecosystems have been lost in the past decades. The purpose of this study is to investigate community participation using structural equations modeling to enhance involvement in mangrove forest rehabilitation. The study was conducted in two sub-districts in East Lampung Regency, Lampung Province.METHODS: The study employed a survey method with quantitative descriptive analysis and Structural Equation Models analysis. The sampling method used was simple random sampling. The community under investigation is a part of the mangrove forest management group in Labuhan Maringgai and Pasir districts, Lampung, Indonesia. The total number of individuals who are members of the mangrove forest management group is 292, distributed with 140 in Margasari and 152 in Pasir Sakti District. The sample size in Margasari District was 81 respondents, and in Pasir Sakti, it was 87 respondents, totaling 168 respondents. The sample size determination was based on the Slovin formula, considering a precision of 5 percent when estimating the proportion of the population.FINDINGS: This study’s results show that the level of community participation in mangrove forest rehabilitation still falls within the low category, particularly in planning and evaluation, while implementation is categorized as medium. community leaders often possess influence and authority that is recognized by community members. When these leaders actively support mangrove conservation, they can influence the opinions and actions of the entire community. In this capacity, they serve as role models, inspiring others to participate in mangrove conservation activities. community participation includes planning, implementation, evaluation, and utilization of results. Participation influences welfare: the more active the community, the more space there will be to utilize mangrove products and interact with other community members, so that income, needs for food, and adequate housing can be met. Local working groups or initiatives that focus on mangrove conservation coordinate field activities and organize necessary resources and manpower.CONCLUSION: The role of community leaders, farmer groups, government support, and non-government organizations plays a key role in increasing community participation in mangrove forest rehabilitation. Additionally, the increase in non-formal education (training and mentoring), type of work, number of family dependents, length of residence, and distance to the mangrove location are crucial factors in enhancing community involvement.

BACKGROUND AND OBJECTIVES: Studying the monthly variations in the surface features of the Bay of Bengal is a complex task that involves numerous large-scale ocean-atmosphere dynamics. This study identified the bay’, s changing circulation patterns over recent decades as a crucial study area requiring in-depth research. Understanding the changes in circulation patterns provides valuable insights into the Bay dynamics. It helps identify the potential impacts of climate change, ocean currents, and other factors on the bay’, s ecosystem. This study aims to understand the seasonal variability of the Bay of Bengal’, s surface circulation features using a high-resolution numerical Coastal and Regional Ocean community simulations model. METHODS: To conduct the study in the Bay of Bengal, the Coastal and Regional Ocean community model, a numerical ocean model, was utilized. The high-resolution numerical model for ocean circulation is three-dimensional and uses hydrostatic primitive equations in generalized curvilinear coordinates. Simulations were conducted over 8 years using a grid comprising 256 x 249 horizontal surface points to model a range of ocean-atmospheric parameters. This grid provided an approximate resolution of 10 kilometers. FINDINGS: The findings are based on the model’, s enhanced performance compared to previous study results. It was observed that the sea surface temperature remains above 28 degrees Celsius throughout the bay except in winter. During the monsoon season, surface salinity was observed to be reduced in the Bay of Bengal’, s northern region and western and eastern boundaries. Surface eddies along the western bay extend to deep waters before the onset of monsoon. The net heat flux in the bay has been determined as positive before monsoon, negative post-monsoon, and mixed during the monsoon season. CONCLUSION: This analysis focuses on the ocean surface layer with more prominent dynamics. Various surface parameters were calculated, and discussions on surface temperature, salinity, D20, D26, and net heat flux across seasons have been presented.

BACKGROUND AND OBJECTIVES: The innovativeness of this study lies in achieving a comprehensive understanding of the seasonal variations and oceanic characteristics of the Bay of Bengal by addressing the complex interplay of large-scale ocean-atmosphere dynamics. The study aimed to understand the upper ocean characteristics of the Bay of Bengal by analyzing the surface variables such as salinity and temperature using a high-resolution model simulation. To accomplish this, advanced high-resolution numerical simulations were employed, utilizing the Coastal and regional ocean community model. This model was crucial for investigating and analyzing the circulation features throughout the entire Bay of Bengal, contributing knowledge and insights about the Coastal and regional oceanographic community.METHODS: To investigate the temporal variability of the upper ocean in the Bay of Bengal, climatological simulations were performed over eight years using the Coastal and regional ocean community model. Including a three-year spin-up phase facilitated the adjustment of the model to initial conditions and the attainment of equilibrium, ensuring its fidelity to real-world conditions. The follow-up analyses and comparisons were performed five years after the spin-up phase. The primary objective of this study was to examine the temporal evolution of the kinetic energy throughout the eight-year simulation. The volume-averaged kinetic energy was computed, revealing a gradual increase throughout the simulation, with particularly pronounced enhancements observed during the monsoon period. A Taylor diagram was used for predicting the model with the other data sets.FINDINGS: The analysis is performed above the surface and sub-surface oceanic layers with prominent dynamics. The temperature and salinity for the surface and sub-surface layers were validated and analyzed for their seasonal variations. The simulations were validated against the existing satellite, reanalysis, and in situ data.CONCLUSIONS: The innovativeness of this study lies in its successful demonstration of the seasonal variability of temperature and salinity in the Bay of Bengal. Through extensive validations, it establishes the model to accurately simulate the climatological surface features of the Bay of Bengal. This study highlights the effectiveness of numerical models when combined with observations, and the data were reanalyzed, showcasing their utility as valuable tools for studying oceanic conditions. The utilization of a Taylor diagram further supports the validation and excellent performance of the model compared to other available datasets. During the simulation, there is a high correlation (0.96) between the evolution of the salinity and temperature values obtained from the model and the corresponding data from the World Ocean Atlas. This indicates a strong agreement between the model-based simulations and the assimilated data, as supported by the notable correlation values of 0.96 for salinity and temperature. These findings reinforce the existing knowledge regarding the influential role of monsoon winds in shaping the circulation patterns within the Bay of Bengal. Overall, this study contributes to advancing our understanding of the ocean dynamics of the region and underscores the importance of considering seasonal variations for comprehensive oceanographic research, Coastal management, climate modeling, and future studies in the Bay of Bengal.