Modeling Climate Change Uncertainty and Its Impact on the Nemunas River Watershed and Curonian Lagoon Ecosystem

• Main Authors: Čerkasova, Natalja, Mėžinė, Jovita, Idzelytė, Rasa, Lesutienė, Jūratė, Erturk, Ali, Umgiesser, Georg
• Format: Text
• Language: English
• Published: 2024
• Subjects: Burbot; Lota lota; lota
• Online Access: https://doi.org/10.5194/egusphere-2024-890; https://egusphere.copernicus.org/preprints/2024/egusphere-2024-890/

This study advances the understanding of climate projection variabilities in the Nemunas River, Curonian Lagoon, and southeastern Baltic Sea continuum by analyzing the output of a coupled ocean and drainage basin modeling system forced by a subset of climate models. A dataset from a downscaled high-resolution regional atmospheric climate model driven by four different global climate models was bias-corrected and used to set up the hydrological (Soil and Water Assessment Tool, SWAT) and hydrodynamic (Shallow water HYdrodynamic Finite Element Model, SHYFEM) modeling system. This study investigates the variability and trends in environmental parameters such as water fluxes, timing, nutrient load, water temperature, ice cover, and saltwater intrusions under Representative Concentration Pathway 4.5 and 8.5 scenarios. The analysis highlights the differences among model results underscoring the inherent uncertainties in projecting climatic impacts, hence highlighting the necessity of using multi-model ensembles to improve the accuracy of climate change impact assessments. Modeling results were used to evaluate the possible environmental impact due to climate change through the analysis of the cold-water fish species reproduction season. We analyze the duration of cold periods ( <1.5 °C) as a thermal window for burbot ( Lota lota L.) spawning, calculated assuming different climate forcing scenarios and models. The analysis indicated coherent shrinking of the cold period and presence of changepoints during historical and different periods in the future; however, not all trends reach statistical significance, and due to high variability within the projections, they are less reliable. This means there is a considerable amount of uncertainty in these projections, highlighting the difficulty of making reliable climate change impact assessments.