Mitigating Climate Change Impacts with Agricultural Practices in a Coastal Lagoon

Aquatic ecosystems are essential for human well-being, yet they are increasingly threatened by climate change and anthropogenic pressures. Effective management of lakes and lagoons depends on understanding the interactions between their drainage basins and water bodies. This study addresses these dynamics in the Mar Menor, one of Europe’s largest saltwater lagoons, located in southeastern Spain. Intensive agricultural activities in its catchment have driven a eutrophication crisis, marked by recurrent algal blooms and anoxic events. To assess the impact of climate change and agricultural practices, we developed an integrated modeling framework by coupling the SWAT+ model for the watershed with the GOTM-WET model for the lagoon. Using bias-corrected climate projections from five global models, we simulated future runoff, sediment transport, and nutrient loading under various management scenarios, along with key lagoon conditions such as oxygen levels and chlorophyll-a concentrations to evaluate the frequency of hypoxia and algal blooms. Results indicate that more intense precipitation events will increase runoff, leading to an 11% rise in sediment transport and a significant increase in phosphorus input to the lagoon, more than doubling current levels. Consequently, the frequency and duration of algal blooms and anoxic conditions are expected to worsen. Among the evaluated management strategies, crop rotation was the most effective for reducing sediment transport (by approximately 50%), while contour farming yielded the greatest reductions in algal bloom days (from 93 to 29) and anoxia days (from 45 to 9). Moreover, combining all proposed practices produced a synergistic effect, enhancing resilience against climate change impacts. These findings underscore the importance of holistic management approaches to safeguard the ecological health of the Mar Menor and similar vulnerable aquatic systems. This study forms part of the AGROALNEXT programme and was supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1).