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Vietnam's coastal regions are highly vulnerable to natural hazards and human-induced changes, posing significant challenges to their ecological and socio-economic systems. The country's mangrove vegetation spans its entire coastline and has been depleted for decades in many regions. Notably, Vietnam's proactive stance on climate change mitigation received significant recognition during the 26th Conference of the Parties (COP26) to the United Nations Framework Convention on Climate Change. This study investigated five critical coastal environmental features (shoreline dynamics, drought conditions, soil salinity trends, mangrove deforestation, and reforestation, as well as spatiotemporal variations in aquaculture and salt farming areas) using satellite data and geospatial analysis. Findings revealed a 58% decline in mangrove areas between 1989 and 2023, with a sharp decline between 1989 and 2001, followed by a gradual recovery. Furthermore, soil salinity along the Ninh Thuan coast indicated a continuous increase, except during the strong La Niña period in 2001. Additionally, aquaculture and salt marshes have expanded significantly, changing land use patterns. These findings highlight the urgent need for integrated coastal zone management to mitigate environmental degradation and enhance ecosystem resilience. Future studies should investigate the socio-economic implications of these changes and evaluate restoration strategies for sustainable coastal development. [ABSTRACT FROM AUTHOR]

Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing T max , T min , RH mean , R s , and U 2 , with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R 2 ), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R 2 values (0.95-0.97) and low RMSE (0.32-0.35 mm/day) and RRSE (8.14-10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015-0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)