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ERROR: type should be string, got "https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Coupled hydrological and hydrodynamic modelling application for \nclimate change impact assessment in the Nemunas River watershed–\nCuronian Lagoon–south-eastern Baltic Sea continuum \nRasa Idzelytė \n1, Natalja Čerkasova \n1,2, Jovita Mėžinė \n1, Toma Dabulevičienė \n1, Artūras \nRazinkovas-Baziukas 1, Ali Ertürk 1,3, Georg Umgiesser 1,4 \n5 \n1Marine Research Institute, Klaipėda University, Klaipėda, 92294, Lithuania \n2Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA \n3Department of Inland Water Resources and Management, Istanbul University, Istanbul, 34134, Turkey \n4CNR–National Research Council of Italy, ISMAR–Institute of Marine Sciences, Venice, 30122, Italy Correspondence to: Georg Umgiesser (georg.umgiesser@ismar.cnr.it) Correspondence to: Georg Umgiesser (georg.umgiesser@ismar.cnr.it) \n10 Abstract. We analyse the cumulative impacts of climate change in a complex basin-lagoon-sea system continuum, which \ncovers the Nemunas River basin, Curonian Lagoon, and the south-eastern part of the Baltic Sea. A unique state-of-the-art \ncoupled modelling system, consisting of hydrological and hydrodynamic models, has been developed and used for this \npurpose. Results of four regional downscaled models from the Rossby Centre high-resolution regional atmospheric climate Abstract. We analyse the cumulative impacts of climate change in a complex basin-lagoon-sea system continuum, which \ncovers the Nemunas River basin, Curonian Lagoon, and the south-eastern part of the Baltic Sea. A unique state-of-the-art \ncoupled modelling system, consisting of hydrological and hydrodynamic models, has been developed and used for this \npurpose. Results of four regional downscaled models from the Rossby Centre high-resolution regional atmospheric climate model have been bias-corrected using in situ measurements, and were used as forcing to assess the changes that the continuum \n15 \nwill undergo until the end of this century. Results show that the Curonian Lagoon will be subjected to higher river discharges that in turn increase the outgoing fluxes \ninto the Baltic Sea. Through these higher fluxes, both the water residence time and saltwater intrusion event frequency will \ndecrease. Most of these changes will be more pronounced in the northern part of the lagoon, which is more likely to be 15 influenced by the variations in the Nemunas River discharge. The southern part of the lagoon will experience lesser changes. 20 \nWater temperatures in the entire lagoon and the south-eastern Baltic Sea will steadily increase, and salinity values will \ndecrease. However, the foreseen changes in physical characteristics are not of the scale suggesting significant shifts in the \necosystem functioning, but are expected to manifest in some quantitative alterations in the nutrient retention capacity. However, some ecosystem services such as ice fishing are expected to vanish completely due to the loss of ice cover. Moreover, the Curonian Lagoon and the south-\neastern Baltic Sea are covered by a finite element grid that resolves its hydrodynamic and physical parameters. A seamless \nintegration of two models represents the watershed–lagoon–sea continuum for the simulations of the present state and future \nscenarios based on the ensemble of meteorological datasets produced by climate models. In this article, we present a framework of coupled hydrological and hydrodynamic models that are able to simulate the \n50 \ncontribution of the entire Nemunas River watershed to the Curonian Lagoon. Moreover, the Curonian Lagoon and the south-\neastern Baltic Sea are covered by a finite element grid that resolves its hydrodynamic and physical parameters. A seamless \nintegration of two models represents the watershed–lagoon–sea continuum for the simulations of the present state and future \nscenarios based on the ensemble of meteorological datasets produced by climate models. One of the new key findings of the 2nd climate change assessment \n40 \n(BACC II Author Team, 2015) was that due to the large bias in the water balance projections the projected changes of the \nBaltic Sea salinity remained uncertain, i.e., it is unclear if the Baltic Sea becomes more or less saline (von Storch et al., 2015). Nevertheless, climate change is consequently going to affect marine ecosystem and may reduce its resilience. The magnitude and severity of climate change impacts will vary considerably within the Baltic Sea region (Graham, 2004) salinity values will decrease (BACC Author Team, 2008). One of the new key findings of the 2nd climate change assessment \n40 \n(BACC II Author Team, 2015) was that due to the large bias in the water balance projections the projected changes of the \nBaltic Sea salinity remained uncertain, i.e., it is unclear if the Baltic Sea becomes more or less saline (von Storch et al., 2015). Nevertheless, climate change is consequently going to affect marine ecosystem and may reduce its resilience. The magnitude and severity of climate change impacts will vary considerably within the Baltic Sea region (Graham, 2004) g\ny\ng\np\ny\ny\ng\n(\n,\n)\nmaking basin-specific studies of high importance for understanding climate change induced impacts on a local scale. Likewise, \n45 \nwith increasing computational capacity, the multidisciplinary modelling studies were progressively evolving during the past \nfew decades (Rodrigues et al., 2015). Thus, the need for a combination of different aquatic processes became essential. The \ncoupling of different numerical models allows researchers to replicate the environmental processes for better assessment of \nthe physical parameters’ response to changing climate. making basin-specific studies of high importance for understanding climate change induced impacts on a local scale. Likewise, \n45 \nwith increasing computational capacity, the multidisciplinary modelling studies were progressively evolving during the past \nfew decades (Rodrigues et al., 2015). Thus, the need for a combination of different aquatic processes became essential. The \ncoupling of different numerical models allows researchers to replicate the environmental processes for better assessment of \nthe physical parameters’ response to changing climate. In this article, we present a framework of coupled hydrological and hydrodynamic models that are able to simulate the \n50 \ncontribution of the entire Nemunas River watershed to the Curonian Lagoon. 1 Introduction \n25 Climate change and increasing anthropogenic pressures are expected to cause drastic changes in the global environment in the \nnear future, and thus may affect the discharge and hydrological regime of rivers (Middelkoop et al., 2001), lagoons \n(Jakimavičius and Kriaučiūnienė, 2020; Anthony et al., 2009) and regional seas (Holt et al., 2016). The Baltic Sea, together \nwith its largest lagoon, the Curonian Lagoon, and the Nemunas River watershed are no exceptions to this trend. The impact of \nclimate change is already evident here through the changes in ice conditions in the sea (Merkouriadi and Leppäranta, 2014) \n30 Climate change and increasing anthropogenic pressures are expected to cause drastic changes in the global environment in the \nnear future, and thus may affect the discharge and hydrological regime of rivers (Middelkoop et al., 2001), lagoons \n(Jakimavičius and Kriaučiūnienė, 2020; Anthony et al., 2009) and regional seas (Holt et al., 2016). The Baltic Sea, together \nwith its largest lagoon, the Curonian Lagoon, and the Nemunas River watershed are no exceptions to this trend. The impact of climate change is already evident here through the changes in ice conditions in the sea (Merkouriadi and Leppäranta, 2014) \n30 1 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. and the lagoon (Idzelytė et al., 2019), increase in sea surface temperature (Kniebusch et al., 2019; Belkin, 2009) or \nredistribution of river runoff over the year (Meier et al., 2022a). and the lagoon (Idzelytė et al., 2019), increase in sea surface temperature (Kniebusch et al., 2019; Belkin, 2009) or \nredistribution of river runoff over the year (Meier et al., 2022a). increasingly important for understanding and projecting climate change impacts, also for supporting the formulation of \n35 \nmanagement measures (Vohland et al., 2014). The first climate change scenario simulations for the Baltic Sea were carried out more than 20 years ago (Meier, 2002a, b; \nOmstedt et al., 2000). ln 2008, the first assessment of the climate change in the Baltic Sea region was prepared with the main \nconclusions that by the end of 2100 the annual mean sea surface temperature will be 2–4 °C higher while the ice cover and increasingly important for understanding and projecting climate change impacts, also for supporting the formulation of \n35 \nmanagement measures (Vohland et al., 2014). The first climate change scenario simulations for the Baltic Sea were carried out more than 20 years ago (Meier, 2002a, b; \nOmstedt et al., 2000). ln 2008, the first assessment of the climate change in the Baltic Sea region was prepared with the main \nconclusions that by the end of 2100 the annual mean sea surface temperature will be 2–4 °C higher while the ice cover and increasingly important for understanding and projecting climate change impacts, also for supporting the formulation of \n35 \nmanagement measures (Vohland et al., 2014). The first climate change scenario simulations for the Baltic Sea were carried out more than 20 years ago (Meier, 2002a, b; \nOmstedt et al., 2000). ln 2008, the first assessment of the climate change in the Baltic Sea region was prepared with the main \nconclusions that by the end of 2100 the annual mean sea surface temperature will be 2–4 °C higher while the ice cover and 35 salinity values will decrease (BACC Author Team, 2008). The study site covers part of the south-eastern Baltic Sea region together with the Curonian Lagoon and the Nemunas River \nwatershed (Fig. 1). The drainage basin is shared by five countries: Belarus (48%), Lithuania (46%) and the other 6% is shared \nby Kaliningrad region of Russian Federation, Poland and Latvia (Gailiušis et al., 2001). The river enters the Curonian Lagoon \nat its central-eastern part and divides the system into two sub-basins; its southern part belongs to the Russian Federation \n60 \n(Kaliningrad region, 76%) and its northern part to Lithuania (24%). 2.1 Study area The Curonian Lagoon is a shallow water body with an average depth of 3.8 m (Gasiūnaitė et al., 2008) located on the south- The Nemunas River is the fourth largest river draining into the Baltic Sea and the largest tributary entering the Curonian \n65 \nLagoon, accounting for ~96% of the total freshwater input into it (Jakimavičius and Kovalenkovienė, 2010). The annual \naverage discharge is 16.4 km3 yr−1 (518.3 m3 s−1), calculated for the period 2012–2016 (Vybernaite-Lubiene et al., 2018), and \nthe long-term average of 21.847 km3 yr−1 (692 m3 s−1) for the period 1960–2007 (Jakimavičius and Kovalenkovienė, 2010). The Curonian Lagoon is a shallow water body with an average depth of 3.8 m (Gasiūnaitė et al., 2008) located on the south- eastern coast of the Baltic Sea. With a total area of ~1584 km2 (Žaromskis, 1996), it is considered the largest lagoon in Europe. 70 \nIt is a transnational lagoon whose northern part is influenced by the Nemunas River flow and brackish water intrusions from \nthe Baltic Sea, while the southern part is more stagnant, where hydrodynamics are influenced by the wind (Vybernaite-Lubiene \net al., 2022; Ferrarin et al., 2008). The intrusions of the sea water through the narrow (0.4–1.1 km) Klaipėda Strait occur in the \nnorthern part of the lagoon and reach up to 20 km southward from the strait (Zemlys et al., 2013). eastern coast of the Baltic Sea. With a total area of ~1584 km2 (Žaromskis, 1996), it is considered the largest lagoon in Europe. 70 \nIt is a transnational lagoon whose northern part is influenced by the Nemunas River flow and brackish water intrusions from \nthe Baltic Sea, while the southern part is more stagnant, where hydrodynamics are influenced by the wind (Vybernaite-Lubiene \net al., 2022; Ferrarin et al., 2008). The intrusions of the sea water through the narrow (0.4–1.1 km) Klaipėda Strait occur in the \nnorthern part of the lagoon and reach up to 20 km southward from the strait (Zemlys et al., 2013). The coastal area of the south-eastern Baltic Sea is relatively shallow, up to 20 m depth. The lowest water temperatures here \n75 \nare observed in winter months (in Jan and Feb the average water temperature is ~2 °C), while in summer months, e.g., Jul and \nAug, the average water temperature is ~18–19 °C (Kozlov et al., 2014). 2.1 Study area The study site covers part of the south-eastern Baltic Sea region together with the Curonian Lagoon and the Nemunas River \nwatershed (Fig. 1). The drainage basin is shared by five countries: Belarus (48%), Lithuania (46%) and the other 6% is shared \nby Kaliningrad region of Russian Federation, Poland and Latvia (Gailiušis et al., 2001). The river enters the Curonian Lagoon \nat its central-eastern part and divides the system into two sub-basins; its southern part belongs to the Russian Federation \n60 \n(Kaliningrad region, 76%) and its northern part to Lithuania (24%). The study site covers part of the south-eastern Baltic Sea region together with the Curonian Lagoon and the Nemunas River \nwatershed (Fig. 1). The drainage basin is shared by five countries: Belarus (48%), Lithuania (46%) and the other 6% is shared \nby Kaliningrad region of Russian Federation, Poland and Latvia (Gailiušis et al., 2001). The river enters the Curonian Lagoon \nat its central-eastern part and divides the system into two sub-basins; its southern part belongs to the Russian Federation \n60 \n(Kaliningrad region, 76%) and its northern part to Lithuania (24%). 60 at its central-eastern part and divides the system into two sub-basins; its southern part belongs to the Russian Federation \n60 \n(Kaliningrad region, 76%) and its northern part to Lithuania (24%). 2 2 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 1. The Nemunas River watershed (coloured polygons in the mainland are major sub-basins), Curonian Lagoon, and south-\neastern Baltic Sea with respect to the entire sea area. Basemap source: ESRI. Figure 1. The Nemunas River watershed (coloured polygons in the mainland are major sub-basins), Curonian Lagoon, and south-\neastern Baltic Sea with respect to the entire sea area. Basemap source: ESRI. The Nemunas River is the fourth largest river draining into the Baltic Sea and the largest tributary entering the Curonian \n65 \nLagoon, accounting for ~96% of the total freshwater input into it (Jakimavičius and Kovalenkovienė, 2010). The annual \naverage discharge is 16.4 km3 yr−1 (518.3 m3 s−1), calculated for the period 2012–2016 (Vybernaite-Lubiene et al., 2018), and \nthe long-term average of 21.847 km3 yr−1 (692 m3 s−1) for the period 1960–2007 (Jakimavičius and Kovalenkovienė, 2010). 2.2.1 Climate projection data was constructed according to the methodology presented in the “Renewal of a River Basin Districts Management Plans and \n100 \nProgrammes of Measures” project report (PAIC, 2015) and were split in three groups corresponding to the closest \nmeteorological stations in Lithuania of which measured data were used for bias correction. The bias correction of climate input \ndata for the hydrodynamic model was performed from the averaged data of three meteorological stations closest to the northern \npart of the lagoon that describe the coastal climate conditions. was constructed according to the methodology presented in the “Renewal of a River Basin Districts Management Plans and \n100 \nProgrammes of Measures” project report (PAIC, 2015) and were split in three groups corresponding to the closest \nmeteorological stations in Lithuania of which measured data were used for bias correction. The bias correction of climate input \ndata for the hydrodynamic model was performed from the averaged data of three meteorological stations closest to the northern \npart of the lagoon that describe the coastal climate conditions. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2.2 Data \n80 \n2.2.1 Climate projection data \nTo feed our developed modelling system we used meteorological forcing data acquired from CORDEX (Coordinated Regional \nDownscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model \n(RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models (specified in Table \n1). The datasets consist of cloud cover, solar radiation, precipitation, surface air pressure, relative humidity, air temperature, \n85 \nand wind speed and direction, and covers a historical period of 1970–2005 and projection period of 2006–2100. The projections \nare derived according to two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5. Abbreviation \nModel \nInstitution \nICHEC \nEC-Earth \nIrish Centre for High-End Computing \nIPSL \nIPSL-CM 5A-MR \nThe Institut Pierre-Simon Laplace \nMOHC \nHadGEM2-ES \nMet Office Hadley Centre \nMPI \nMPI-ESM-LR \nMax Planck Institute for Meteorology \nTable 1. Summary of the global climate model data sources. 2.2 Data \n80 \n2.2.1 Climate projection data \nTo feed our developed modelling system we used meteorological forcing data acquired from CORDEX (Coordinated Regional \nDownscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model \n(RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models (specified in Table \n1). The datasets consist of cloud cover, solar radiation, precipitation, surface air pressure, relative humidity, air temperature, \n85 \nand wind speed and direction, and covers a historical period of 1970–2005 and projection period of 2006–2100. The projections \nare derived according to two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5. Abbreviation \nModel \nInstitution \nICHEC \nEC-Earth \nIrish Centre for High-End Computing \nIPSL \nIPSL-CM 5A-MR \nThe Institut Pierre-Simon Laplace \nMOHC \nHadGEM2-ES \nMet Office Hadley Centre \nMPI \nMPI-ESM-LR \nMax Planck Institute for Meteorology \nTable 1. Summary of the global climate model data sources. 2.1 Study area The salinity in the coastal zone is varying between 6–\n7 g kg-1, although in the plume affected waters from the Curonian Lagoon it can drop drastically to nearly fresh water (0–3 g \nkg-1) (Olenin and Daunys, 2004). The coastal area of the south-eastern Baltic Sea is relatively shallow, up to 20 m depth. The lowest water temperatures here \n75 \nare observed in winter months (in Jan and Feb the average water temperature is ~2 °C), while in summer months, e.g., Jul and \nAug, the average water temperature is ~18–19 °C (Kozlov et al., 2014). The salinity in the coastal zone is varying between 6–\n7 g kg-1, although in the plume affected waters from the Curonian Lagoon it can drop drastically to nearly fresh water (0–3 g \nkg-1) (Olenin and Daunys, 2004). 3 3 2.2 Data \n80 2.2 Data \n80 \n2.2.1 Climate projection data \nTo feed our developed modelling system we used meteorological forcing data acquired from CORDEX (Coordinated Regional \nDownscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model \n(RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models (specified in Table \n1). The datasets consist of cloud cover, solar radiation, precipitation, surface air pressure, relative humidity, air temperature, \n85 \nand wind speed and direction, and covers a historical period of 1970–2005 and projection period of 2006–2100. The projections \nare derived according to two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5. Abbreviation \nModel \nInstitution \nICHEC \nEC-Earth \nIrish Centre for High-End Computing \nIPSL \nIPSL-CM 5A-MR \nThe Institut Pierre-Simon Laplace \nMOHC \nHadGEM2-ES \nMet Office Hadley Centre \nMPI \nMPI-ESM-LR \nMax Planck Institute for Meteorology \nTable 1. Summary of the global climate model data sources. 2.2.1 Climate projection data The applied modelling system uses standard Gregorian calendar, however, MOHC uses 360-day calendar and IPSL 365-day \ncalendar. Therefore, correction was applied to harmonize the input data. MOHC calendar was adjusted by deleting 30 Feb and \n90 \n29 Feb (if not leap year), and interpolating the 31st of respective months. IPSL was corrected by interpolating the 29 Feb \nduring the leap years. The bias correction of air temperature and precipitation datasets were done separately for the hydrological and hydrodynamic The applied modelling system uses standard Gregorian calendar, however, MOHC uses 360-day calendar and IPSL 365-day \ncalendar. Therefore, correction was applied to harmonize the input data. MOHC calendar was adjusted by deleting 30 Feb and \n90 \n29 Feb (if not leap year), and interpolating the 31st of respective months. IPSL was corrected by interpolating the 29 Feb \nduring the leap years. The bias correction of air temperature and precipitation datasets were done separately for the hydrological and hydrodynamic \nmodels by applying the climate data bias correction tool (Gupta et al., 2019), which uses the quantile mapping approach by \nfitting the daily values to normal distribution function for the air temperature and gamma distribution function for the \n95 \nprecipitation. Due to the availability of measurement data, provided by Lithuanian Hydrometeorological Service, the correction \nperiod was from 1993 to 2005. The data from 18 meteorological stations, which are scattered throughout the Republic of Lithuania, were used for the bias \ncorrection of the climate data and used as inputs for the hydrological model. For the Belarus region the meteorological grid correction of the climate data and used as inputs for the hydrological model. For the Belarus region the meteorological grid \nwas constructed according to the methodology presented in the “Renewal of a River Basin Districts Management Plans and \n100 \nProgrammes of Measures” project report (PAIC, 2015) and were split in three groups corresponding to the closest \nmeteorological stations in Lithuania of which measured data were used for bias correction. The bias correction of climate input \ndata for the hydrodynamic model was performed from the averaged data of three meteorological stations closest to the northern \npart of the lagoon that describe the coastal climate conditions. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. developed by the Rossby Centre and the Oceanographic research group at Swedish Meteorological and Hydrological Institute \n(SMHI). This model was run by the same before-mentioned global climate projections (Table 1). developed by the Rossby Centre and the Oceanographic research group at Swedish Meteorological and Hydrological Institute \n(SMHI). This model was run by the same before-mentioned global climate projections (Table 1). The bias correction for the data of RCA4-NEMO model was done by using Copernicus Marine Environment Monitoring \n110 \nService (CMEMS) Baltic Sea Physical Reanalysis product data for the period of 1993–2005. The correction was done by \nsimply adding the difference between the average values of CMEMS, 𝐶𝑂, and RCA4–NEMO data, 𝐶𝑚, (Lenderink et al., \n2007): The bias correction for the data of RCA4-NEMO model was done by using Copernicus Marine Environment Monitoring \n110 \nService (CMEMS) Baltic Sea Physical Reanalysis product data for the period of 1993–2005. The correction was done by \nsimply adding the difference between the average values of CMEMS, 𝐶𝑂, and RCA4–NEMO data, 𝐶𝑚, (Lenderink et al., \n2007): \n𝐶𝐵𝐶(𝑡) = 𝐶𝑚(𝑡) + (𝐶𝑂−𝐶𝑚) , \n \n \n \n \n \n \n \n \n \n(1) 110 𝐶𝐵𝐶(𝑡) = 𝐶𝑚(𝑡) + (𝐶𝑂−𝐶𝑚) , (1) Bathymetry data of a high-resolution spherical grid topography of the Baltic Sea were used as a bottom boundary (Seifert et \n115 \nal., 2001). Data of ice thickness from ice thermodynamic model ESIM2, run using the meteorological data specified in Table \n1, were used as a top boundary during the ice cover season (Idzelytė and Umgiesser, 2021; Tedesco et al., 2009). Bathymetry data of a high-resolution spherical grid topography of the Baltic Sea were used as a bottom boundary (Seifert et \n115 \nal., 2001). Data of ice thickness from ice thermodynamic model ESIM2, run using the meteorological data specified in Table \n1, were used as a top boundary during the ice cover season (Idzelytė and Umgiesser, 2021; Tedesco et al., 2009). 2.2.3 Watershed-scale data Some data had to be manually digitized, i.e., the stream network of the Nemunas watershed outside \nof the territory of Lithuania. Unfortunately, due to political reasons, we could not acquire any observational data from the \nRepublic of Belarus and the Kaliningrad Region. We resolved the issue by calibrating the model against observed data at the \nnearest border locations within the territory of Lithuania, provided by abovementioned governmental institutions. 130 2.2.2 Boundary data \n105 Water level, temperature, and salinity data for the sea boundary of the hydrodynamic model were acquired from a high \nresolution regional coupled ocean–sea ice–atmosphere model RCA4–NEMO (Gröger et al., 2019; Wang et al., 2015) 4 2.2.3 Watershed-scale data For the development of the hydrological model many basin-scale datasets are required, i.e., the digital elevation model (DEM), For the development of the hydrological model many basin-scale datasets are required, i.e., the For the development of the hydrological model many basin-scale datasets are required, i.e., the digital elevation model (DEM), \nland use and management data, hydrologic grid, soil maps, etc. We obtained the data from several governmental sources in \n120 \ndifferent countries as well as public open access databases. For a full list of datasets and their sources, we refer the reader to \nČerkasova et al. (2021), where the acquired datasets are described in depth. Observed discharge data with varying time step \n(daily, weekly, by-weekly, and monthly) for the majority of the Nemunas River tributaries as well as the main branch were \nobtained from the Lithuanian Hydrometeorological Service. Observed nutrient (TN and TP) concentration values for the \noverlapping periods were provided by the Lithuanian Environmental Protection Agency. 125 land use and management data, hydrologic grid, soil maps, etc. We obtained the data from several governmental sources in \n120 \ndifferent countries as well as public open access databases. For a full list of datasets and their sources, we refer the reader to \nČerkasova et al. (2021), where the acquired datasets are described in depth. Observed discharge data with varying time step \n(daily, weekly, by-weekly, and monthly) for the majority of the Nemunas River tributaries as well as the main branch were \nobtained from the Lithuanian Hydrometeorological Service. Observed nutrient (TN and TP) concentration values for the overlapping periods were provided by the Lithuanian Environmental Protection Agency. 125 \nLarge parts of the watershed are outside of Lithuania, hence open-access data for those regions were identified and used \n(Čerkasova et al., 2021). Some data had to be manually digitized, i.e., the stream network of the Nemunas watershed outside \nof the territory of Lithuania. Unfortunately, due to political reasons, we could not acquire any observational data from the \nRepublic of Belarus and the Kaliningrad Region. We resolved the issue by calibrating the model against observed data at the \nnearest border locations within the territory of Lithuania, provided by abovementioned governmental institutions. 130 pp g p\np\ny\ng\ny\nLarge parts of the watershed are outside of Lithuania, hence open-access data for those regions were identified and used \n(Čerkasova et al., 2021). 2.3.1 Hydrological model High-resolution basin-scale model has been developed for the entire Nemunas watershed (Fig. 1) and implemented using Soil \nand Water Assessment Tool (SWAT) using a set of custom tools and scripts. To set up a comprehensive model, we incorporated \nthe topographic information of the area, land use and soil properties, information on water bodies, land management, water \n135 \nuse, crop growth, livestock production, administrative units, etc. The model was calibrated, validated, and proved to reliably \nrepresent the water balance components as well as nutrient and sediment load estimates, and was used in previous studies \n(Čerkasova et al., 2021, 2019, 2018). High-resolution basin-scale model has been developed for the entire Nemunas watershed (Fig. 1) and implemented using Soil \nand Water Assessment Tool (SWAT) using a set of custom tools and scripts. To set up a comprehensive model, we incorporated the topographic information of the area, land use and soil properties, information on water bodies, land management, water \n135 \nuse, crop growth, livestock production, administrative units, etc. The model was calibrated, validated, and proved to reliably \nrepresent the water balance components as well as nutrient and sediment load estimates, and was used in previous studies \n(Čerkasova et al., 2021, 2019, 2018). the topographic information of the area, land use and soil properties, information on water bodies, land management, water \n135 \nuse, crop growth, livestock production, administrative units, etc. The model was calibrated, validated, and proved to reliably \nrepresent the water balance components as well as nutrient and sediment load estimates, and was used in previous studies \n(Čerkasova et al., 2021, 2019, 2018). 5 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The basin-scale hydrological model consists of 11 sub-models, each representing one of the tributaries of the Nemunas River, \nconnected to the main branch (Fig. 2). In total, the model consists of 9012 sub-basins, and 148 212 Hydrologic Response Units. 140 \nThese sub-models are linked from upstream to downstream, where the outputs of two downstream models are directly used as \nriver boundaries for the hydrodynamic model. Discharge and temperature outputs were extracted from the list of variables, \nprovided by the Nemunas drainage basin modelling framework. 140 Figure 2. SWAT models for the Nemunas River watershed and their linkage with SHYFEM through river boundaries. 145 Figure 2. SWAT models for the Nemunas River watershed and their linkage with SHYFEM through river boundaries. 145 Figure 2. SWAT models for the Nemunas River watershed and their linkage with SHYFEM th\n145 Figure 2. SWAT models for the Nemunas River watershed and their linkage with SHYFEM through river boundaries. 145 \n2.3.2 Hydrodynamic model 2.3.2 Hydrodynamic model Hydrodynamics of the Curonian Lagoon and the south-eastern Baltic Sea were modelled with an open-source shallow water \nhydrodynamic finite element model SHYFEM (http://www.ismar.cnr.it/shyfem, Umgiesser et al., 2004), which consists of a \nfinite element 3-D hydrodynamic model, a transport and diffusion model, and a radiation transfer model of heat at the water surface. The model resolves the 3-D primitive equations, vertically integrated over each layer, in their formulations with water \n150 \nlevels and transports. Horizontal spatial discretization of the model is based on an unstructured triangular grid and carried out \nusing a finite element method, which makes it suitable for applications to coastal systems with complicated geometry and \nbathymetry. This model has already been successfully applied to many coastal environments (Umgiesser et al., 2014; De Pascalis et al., surface. The model resolves the 3-D primitive equations, vertically integrated over each layer, in their formulations with water \n150 \nlevels and transports. Horizontal spatial discretization of the model is based on an unstructured triangular grid and carried out \nusing a finite element method, which makes it suitable for applications to coastal systems with complicated geometry and \nbathymetry. This model has already been successfully applied to many coastal environments (Umgiesser et al., 2014; De Pascalis et al., 2011; Ferrarin et al., 2013, 2010; Bellafiore and Umgiesser, 2010; Ferrarin and Umgiesser, 2005), as well as validated for the \n155 \nCuronian Lagoon case study in previous works (Mėžinė et al., 2019; Umgiesser et al., 2016; Zemlys et al., 2013; Ferrarin et \nal., 2008). For this study, SHYFEM was applied in its 2-D version, which is sufficient considering the shallow nature of this \nlagoon. The computational grid consists of 3292 triangular elements with 1986 nodes having a much finer resolution in the \nKlaipėda Strait area (Fig. 3). Model produced output data of hydrodynamic properties every 6 hours. 2011; Ferrarin et al., 2013, 2010; Bellafiore and Umgiesser, 2010; Ferrarin and Umgiesser, 2005), as well as validated for the \n155 \nCuronian Lagoon case study in previous works (Mėžinė et al., 2019; Umgiesser et al., 2016; Zemlys et al., 2013; Ferrarin et \nal., 2008). For this study, SHYFEM was applied in its 2-D version, which is sufficient considering the shallow nature of this \nlagoon. The computational grid consists of 3292 triangular elements with 1986 nodes having a much finer resolution in the \nKlaipėda Strait area (Fig. 3). Model produced output data of hydrodynamic properties every 6 hours. 160 Figure 3. Computational grid of the hydrodynamic model SHYFEM. Dark blue arrows denote the location of the river boundaries \n(modelling system connectivity points), where the hydrological model outputs (water temperature and discharge) are used as inputs \nto the hydrodynamic model. 2.3.3 Coupling of models A better representation of hydrodynamic conditions in the Nemunas Delta region is achieved with a hydrodynamic model, thus \n165 \nthe overlapping parts of SHYFEM and SWAT models were modelled entirely by SHYFEM. The daily water temperature and \ndischarge data from the SWAT model were directly used as Minija and Nemunas river boundaries (Fig. 3). However, other \ntwo main rivers discharging in the southern part of the lagoon (Matrosovka, branch of the Nemunas, and Deima) are not part \nof the Nemunas River watershed model, thus the SWAT output data of the Nemunas River were scaled by the ratio between \nMatrosovka-Nemunas and Deima-Šešupė calculated from the data presented in Jakimavičius (2012)\n170 Matrosovka-Nemunas and Deima-Šešupė calculated from the data presented in Jakimavičius (2012). 170 2.3.2 Hydrodynamic model 6 0 \nFigure 3. Computational grid of the hydrodynamic model SHYFEM. Dark blue arrows denote the location of the river boundaries \n(modelling system connectivity points), where the hydrological model outputs (water temperature and discharge) are used as inputs \nto the hydrodynamic model. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. divided into historical (1975–2005), short-term (2020–2050) and long-term (2070–2100), and the results were compared by \naveraging over these timespans. The period of 1970–1975 was discarded from analysis as it was used for the model spin-up. 175 \nThe main air temperature and precipitation patterns were determined. The parameters of the hydrological model (water \ndischarge) and parameters of the hydrodynamic model (water temperature, salinity, level, residence time, and fluxes through \npredefined cross sections), as well as ice thickness, were assessed. The analysis was done using standard statistical methods, \nand computing the percentage of change from the historical period. divided into historical (1975–2005), short-term (2020–2050) and long-term (2070–2100), and the results were compared by \naveraging over these timespans. The period of 1970–1975 was discarded from analysis as it was used for the model spin-up. 175 \nThe main air temperature and precipitation patterns were determined. The parameters of the hydrological model (water \ndischarge) and parameters of the hydrodynamic model (water temperature, salinity, level, residence time, and fluxes through \npredefined cross sections), as well as ice thickness, were assessed. The analysis was done using standard statistical methods, \nand computing the percentage of change from the historical period. 175 3.1 Bias correction Even though the meteorological data from the global climate models were already downscaled by the regional climate model, \nthere was still some bias remaining in comparison with the local measurement data – underestimation of air temperature and \noverestimation of precipitation. Therefore, an additional correction was applied (Fig. 4). The correlation between the modelled \nand measured air temperature datasets was strong (~0.8), however after bias correction there was an evident improvement of \n5 \nthe standard deviation. Similar change is also equivalent to the monthly precipitation, although here we observe that the climate \nmodels have their own internal dynamics, thus the correlation with measurements is weak (~0.25). Even though the meteorological data from the global climate models were already downscaled by the regional climate model, \nthere was still some bias remaining in comparison with the local measurement data – underestimation of air temperature and \noverestimation of precipitation. Therefore, an additional correction was applied (Fig. 4). The correlation between the modelled 185 and measured air temperature datasets was strong (~0.8), however after bias correction there was an evident improvement of \n85 \nthe standard deviation. Similar change is also equivalent to the monthly precipitation, although here we observe that the climate \nmodels have their own internal dynamics, thus the correlation with measurements is weak (~0.25). and measured air temperature datasets was strong (~0.8), however after bias correction there was an evident improvement of \n185 \nthe standard deviation. Similar change is also equivalent to the monthly precipitation, although here we observe that the climate \nmodels have their own internal dynamics, thus the correlation with measurements is weak (~0.25). Figure 4. Taylor diagram showing a statistical comparison of air temperature (daily mean) and precipitation (monthly sum) before \nand after bias correction (BC) with respect to the reference measurement data. Statistics were computed for the period of 1990-\n0 \n2005. Figure 4. Taylor diagram showing a statistical comparison of air temperature (daily mean) and precipitation (monthly sum) before \nand after bias correction (BC) with respect to the reference measurement data. Statistics were computed for the period of 1990-\n \n2005. 190 2.4 Methods of data analysis Model results based on four global climate model forcings (Table 1) were averaged, therefore the final analysis was done for \nthree aggregated outputs referring to the historical and projection (RCP4.5 and RCP8.5) scenarios. The analysis periods were 7 Therefore, annual\n205 Considering the differences between marine and terrestrial parts of the study area, there is a clear pattern of lower air \ntemperatures (on average by ~1–1.5 °C) over the Baltic Sea and the Curonian Lagoon during March through June, while during \nthe rest of the year it is warmer (on average by ~2.2 °C), compared with that of the Nemunas River watershed. During February \nthrough August and December months, less precipitation over the marine part (on average by 10 mm month-1) is observed, \nwhile during the rest of the months it is higher by the same amount compared with the terrestrial area. Therefore, annual \n205 \nprecipitation is similar to both areas. while during the rest of the months it is higher by the same amount compared with the terrestrial area. Therefore, annual \n205 \nprecipitation is similar to both areas. 3.2 Meteorological changes The bias-corrected statistics of both RCP scenarios show a distinct increase in the projected monthly and annual average \nprecipitation and air temperature (Table 2). The differences between the scenarios in the short-term are smaller than in the \nlong-term by an average of 49% under RCP4.5 and ~123% under RCP8.5. Considering precipitation, the monthly \n195 8 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. wetness/dryness pattern is similar between the analysis periods and scenarios. The average air temperature in the long-term \nhas the highest increase, especially in the months from November to April. The highest changes of these parameters are \nprojected under RCP8.5 scenario. Air temperature (oC) \n \n \nJan \nFeb \nMar \nApr \nMay \nJun \nJul \nAug \nSep \nOct \nNov \nDec \nYear \nHistorical (1975-2005) \n-2.6 \n-2.6 \n0.3 \n5.3 \n10.7 \n14.6 \n17.9 \n17.5 \n13.2 \n8.1 \n1.8 \n-2.3 \n6.8 \nShort-term \n(2020-2050) \nRCP4.5 \n-0.5 \n0.4 \n2.4 \n7.4 \n12.2 \n15.9 \n19.2 \n18.7 \n14.6 \n9.7 \n3.8 \n0.0 \n8.6 \nRCP8.5 \n-0.6 \n0.5 \n2.8 \n7.5 \n12.4 \n16.1 \n19.2 \n18.8 \n14.6 \n10.0 \n4.2 \n-0.2 \n8.8 \nLong-term \n(2070-2100) \nRCP4.5 \n0.9 \n1.4 \n3.4 \n8.4 \n13.3 \n16.9 \n20.1 \n19.7 \n15.5 \n10.9 \n4.7 \n1.0 \n9.7 \nRCP8.5 \n2.9 \n3.4 \n5.0 \n10.0 \n14.7 \n18.1 \n21.8 \n21.5 \n17.6 \n12.4 \n6.7 \n3.3 \n11.5 \n \n \nPrecipitation (mm month-1) \n \n \nJan \nFeb \nMar \nApr \nMay \nJun \nJul \nAug \nSep \nOct \nNov \nDec \nYear \nHistorical (1975-2005) \n42.8 \n36.5 \n43.1 \n36.9 \n55.9 \n87.1 \n57.5 \n57.9 \n48.7 \n63.7 \n39.0 \n48.7 \n617.7 \nShort-term \n(2020-2050) \nRCP4.5 \n54.4 \n47.6 \n53.7 \n41.1 \n53.1 \n83.4 \n69.9 \n64.7 \n55.6 \n72.7 \n49.9 \n56.4 \n702.5 \nRCP8.5 \n53.1 \n46.9 \n50.1 \n43.1 \n61.0 \n83.1 \n75.4 \n69.1 \n54.2 \n71.2 \n50.5 \n58.4 \n716.2 \nLong-term \n(2070-2100) \nRCP4.5 \n56.9 \n46.5 \n53.7 \n44.8 \n58.8 \n83.3 \n69.2 \n70.0 \n54.0 \n78.2 \n53.6 \n59.3 \n727.3 \nRCP8.5 \n72.3 \n52.2 \n65.4 \n49.5 \n67.7 \n93.2 \n73.6 \n77.9 \n60.6 \n85.3 \n62.7 \n70.3 \n830.8 \nTable 2. Monthly and yearly mean of air temperature and precipitation over the study region derived by averaging four bias-\ncorrected climate models’ datasets. Table 2. Monthly and yearly mean of air temperature and precipitation over the study region derived by averaging four bias-\ncorrected climate models’ datasets. 200 corrected climate models’ datasets. 200 \nConsidering the differences between marine and terrestrial parts of the study area, there is a clear pattern of lower air \ntemperatures (on average by ~1–1.5 °C) over the Baltic Sea and the Curonian Lagoon during March through June, while during \nthe rest of the year it is warmer (on average by ~2.2 °C), compared with that of the Nemunas River watershed. During February \nthrough August and December months, less precipitation over the marine part (on average by 10 mm month-1) is observed, \nwhile during the rest of the months it is higher by the same amount compared with the terrestrial area. 3.3 Interactions between domains The historical discharges of the Nemunas River watershed to the Curonian Lagoon were compared to the projected averaged \ndischarges under RCP4.5 and RCP8.5 climate change scenarios for all seasons (Fig. 5). The most noticeable change is the \nprojected drastic increase in runoff during winter, which will almost double in the long-term period under both RCPs. This \n210 \nresult is a combination of increased projected average precipitation and temperatures, which will likely result in warmer winters \nwith reduced snow cover throughout the basin. The increased liquid precipitation in winter will likely lead to an increased soil \nerosion and nutrient wash-off, when the soil is bare and exposed to the elements. 9 9 Figure 5. The percentage of change of seasonal average discharge from the Nemunas River for the RCP4.5 and RCP8.5 scenario \n215 \nruns during the short-term (2020–2050) and long-term periods (2070–2100), compared with the historical period (1975–2005). https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 5. The percentage of change of seasonal average discharge from the Nemunas River for the RCP4.5 and RCP8.5 scenario\nhttps://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 5. The percentage of change of seasonal average discharge from the Nemunas River for the RCP4.5 and RCP8.5 scenario \n215 \nruns during the short-term (2020–2050) and long-term periods (2070–2100), compared with the historical period (1975–2005). Figure 5. The percentage of change of seasonal average discharge from the Nemunas River for the RCP4.5 and RCP8.5 scenario \n215 \nruns during the short-term (2020–2050) and long-term periods (2070–2100), compared with the historical period (1975–2005). In contrast, a decrease in average summer outflow is projected under both RCP scenarios, despite an increase in precipitation \nacross the region. The increased average temperatures in summer months will impact the evapotranspiration (ET) over the \nbasin, where an increase in both potential ET and ET is projected, which will lead to lower discharges at the stream gages during this period. Stable and mostly unchanged Nemunas River discharge will continue to supply the lagoon over the rest of \n220 \nthe season, in spring and autumn. Noteworthy are the differences between the RCP scenarios. 3.3 Interactions between domains We see a stronger shift in the increased winter discharges in the \nRCP8.5 scenario in the short-term period, and a stronger shift towards lesser summer flows in the RCP4.5 scenario (Fig. 5). The RCP8.5 in the short-term can be considered as “wetter” scenario, whereas RCP4.5 is “dryer”. In the long-term, however, both RCPs simulate similar outcomes with slightly different magnitudes. 225 \nThe interactions between the study domains can be defined by the water fluxes from the main discharging rivers into the \nCuronian Lagoon, eventually into the Baltic Sea (water outflow), and vice versa (water inflow). The volume of inflowing water \nis much lower than that of the outflowing (Fig. 6), especially in the northern part of the Curonian Lagoon, meaning that the \noutflow is already dominating and this study shows that it will prevail and even increase in the analysed continuum system. 10 230 \nFigure 6. The percentage of the average outflowing and inflowing water volume of the absolute flux of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. The locations of \ncross-section are shown in Fig. 7. The bottom graph is for reference, indicating the average water flux in m3 s-1 during the historical \nperiod (1975–2005). https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Table 3. Seasonal water flux in m3 s-1 averaged over historical (1975–2005), short-term (2020–2050), and long-term (2070–2100) \n255 \nperiods under RCP4.5 and RCP8.5 scenarios. Percentage in the brackets shows the change compared with the historical period. The \nvalues are averaged over four cross-sections in the Curonian Lagoon: Klaipėda Strait, North of Nemunas, Nemunas Delta, and \nLithuanian Russian border (see map insert in Fig. 7). 230 Figure 6. The percentage of the average outflowing and inflowing water volume of the absolute flux of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. The locations of \ncross-section are shown in Fig. 7. The bottom graph is for reference, indicating the average water flux in m3 s-1 during the historical \nperiod (1975–2005). Figure 6. The percentage of the average outflowing and inflowing water volume of the absolute flux of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. The locations of \ncross-section are shown in Fig. 7. The bottom graph is for reference, indicating the average water flux in m3 s-1 during the historical \nperiod (1975–2005). Comparing the simulation results of the scenario runs (short- and long-term) with the historical period, it is evident that the \n235 \nwater inflow in the northern part of the lagoon (cross-sections 1 and 2 in Fig. 7) is projected to decrease. The change of water \ninflow in the centre of the lagoon (cross-sections 3 and 4 in Fig. 7) is very small and can be considered negligible. The \noutflowing water flux is projected to increase and is related to the growing outflow from the Nemunas River and will increase \nalong the pathway towards the Baltic Sea (cross-sections 1, 2, and 3 in Fig. 7). In the southern part of the lagoon, near the Lithuanian–Russian border, the changes in water fluxes are less evident, due to the fact that water circulation in this area is \n240 \nmostly determined by the wind induced currents. 11 11 Figure 7. The percentage of change of projected (under RCP4.5 and RCP8.5 scenarios) inflowing and outflowing water flux from \nthe historical period (1975–2005). The map insert shows the locations of the predefined cross-sections: 1 – Klaipėda Strait, 2 – North \nof Nemunas, 3 – Nemunas Delta, 4 – Lithuanian–Russian border. Outflow is from south to north (sections 1,2,4), and from east to \nwest (section 3), inflow - vice versa. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Fi\n7 Th\nf h\nf\nj\nd (\nd\nRCP4 5\nd RCP8 5\ni\n) i fl\ni\nd\nfl\ni\nfl\nf Figure 7. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The changes of the outflow during winter, spring, and autumn are expected to increase (Table 3). In the short-term, the changes \nbetween the scenarios are similar, while in the long-term, the change increases nearly twice under RCP8.5, compared with \n260 \nRCP4.5. During summer the outflow changes are negligible, apart from the long-term scenario under RCP8.5 (-8%). 3.4 Salinity During the historical period 48 days year-1 of salt water intrusions were observed, i.e., the average yearly number of days when \nsalinity exceeds the 2 g kg-1 threshold, approximately 20 km below the lagoon sea connection. In the short-term, this number \n265 \nwill decrease by 60% under RCP4.5 scenario (19 days year-1) and 65% under RCP8.5 scenario (17 days year-1) compared with \nthe historical period. While in the long-term, it will decrease by ~67% under RCP4.5 (16 days year-1) and 83% under RCP8.5 \nscenario (8 days year-1). Seasonally, the highest change of saltwater intrusion days is observed during winter and especially spring, when the outflow Seasonally, the highest change of saltwater intrusion days is observed during winter and especially spring, when the outflow \nof the lagoon is dominating and there are no water intrusions from the Baltic Sea (right side of Fig. 8). Saltwater intrusions \n270 \nduring summer season have a larger decrease under RCP8.5 scenario than RCP4.5, which does not have a distinct pattern in \nthe future. In autumn, the changes in the short-term are similar under both scenarios ~27% decrease compared with the \nhistorical period, and it continues to decline in the long-term as well. of the lagoon is dominating and there are no water intrusions from the Baltic Sea (right side of Fig. 8). Saltwater intrusions \n270 \nduring summer season have a larger decrease under RCP8.5 scenario than RCP4.5, which does not have a distinct pattern in \nthe future. In autumn, the changes in the short-term are similar under both scenarios ~27% decrease compared with the \nhistorical period, and it continues to decline in the long-term as well. Figure 8. The percentage of change of seasonal average salinity of saltwater intrusions (solid pattern – left) and number of days \n275 \nwhen salinity exceeded the 2 g kg-1 threshold (hatched pattern – right) in Juodkrantė, approximately 20 km below the strait \nconnecting the Baltic Sea and the Curonian Lagoon. Values denote the percentage of difference of the scenario runs compared with \nthe historical period (1975–2005). Figure 8. The percentage of change of seasonal average salinity of saltwater intrusions (solid pattern – left) and number of days \n275 \nwhen salinity exceeded the 2 g kg-1 threshold (hatched pattern – right) in Juodkrantė, approximately 20 km below the strait \nconnecting the Baltic Sea and the Curonian Lagoon. The percentage of change of projected (under RCP4.5 and RCP8.5 scenarios) inflowing and outflowing water flux from \nthe historical period (1975–2005). The map insert shows the locations of the predefined cross-sections: 1 – Klaipėda Strait, 2 – North \nof Nemunas, 3 – Nemunas Delta, 4 – Lithuanian–Russian border. Outflow is from south to north (sections 1,2,4), and from east to \n \nwest (section 3), inflow - vice versa. 245 The change of water flow through all cross-sections display a similar pattern in all scenarios, the water flux changes from the \nhistorical are very similar in each analysed cross-section and for each flow type. For both the outflow and inflow the highest \nchange from the historical period is observed in the long-term under RCP8.5 scenario. Looking at the water exchange seasonal dynamics, we can see that the highest inflowing water deviations from the historical \n250 \nperiod are predicted during winter and spring, being lower by 10–28% (Table 3). The water exchange is expected to increase \nin summer by 12–22% while in the autumn the changes are very small and similar throughout the analysis periods and \nscenarios. 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \nOUTFLOW \nWinter \n1017 \n1291 (27%) \n1324 (30%) \n1388 (36%) \n1663 (63%) \nSpring \n1046 \n1210 (15%) \n1213 (15%) \n1214 (16%) \n1356 (29%) \nSummer \n858 \n814 (-5%) \n850 (-1%) \n856 (0.3%) \n923 (8%) \nAutumn \n924 \n1007 (9%) \n1030 (12%) \n1076 (17%) \n1190 (29%) \nINFLOW \nWinter \n452 \n387 (-19%) \n383 (-20%) \n380 (-21%) \n357 (-28%) \nSpring \n246 \n225 (-13%) \n220 (-16%) \n233 (-10%) \n225 (-14%) \nSummer \n277 \n309 (13%) \n308 (12%) \n331 (22%) \n315 (14%) \nAutumn \n439 \n449 (2%) \n450 (2%) \n461 (4%) \n456 (2%) Table 3. Seasonal water flux in m3 s-1 averaged over historical (1975–2005), short-term (2020–2050), and long-term (2070–2100) \n255 \nperiods under RCP4.5 and RCP8.5 scenarios. Percentage in the brackets shows the change compared with the historical period. The \nvalues are averaged over four cross-sections in the Curonian Lagoon: Klaipėda Strait, North of Nemunas, Nemunas Delta, and \nLithuanian Russian border (see map insert in Fig. 7). 12 In the coastal waters the highest change, compared with the historical period, is also projected to \n290 \nbe under RCP8.5 scenario. Notably, the highest decrease is projected northward of Klaipėda Strait (lagoon outlet), while \nsouthward the change is lower and salinity values are similar to that of south-eastern Baltic Sea 3.4 Salinity Values denote the percentage of difference of the scenario runs compared with \nthe historical period (1975–2005). Figure 8. The percentage of change of seasonal average salinity of saltwater intrusions (solid pattern – left) and number of days \n275 \nwhen salinity exceeded the 2 g kg-1 threshold (hatched pattern – right) in Juodkrantė, approximately 20 km below the strait \nconnecting the Baltic Sea and the Curonian Lagoon. Values denote the percentage of difference of the scenario runs compared with \nthe historical period (1975–2005). 13 In the Baltic Sea there is no difference of average salinity throughout the season 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \nCoastal waters \nWinter \n6.6 \n5.9 (-11%) \n5.8 (-13%) \n5.4 (-18%) \n4.8 (-27%) \nSpring \n5.4 \n4.9 (-11%) \n4.8 (-12%) \n4.4 (-18%) \n4.0 (-26%) \nSummer \n5.4 \n5.1 (-4%) \n5.0 (-7%) \n4.8 (-12%) \n4.3 (-20%) \nAutumn \n6.3 \n5.9 (-6%) \n5.8 (-8%) \n5.5 (-13%) \n5.0 (-21%) \nBaltic Sea \nWinter \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n6.9 (-11%) \n6.4 (-18%) \nSpring \n7.7 \n7.4 (-5%) \n7.3 (-6%) \n6.9 (-11%) \n6.3 (-18%) \nSummer \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n7.0 (-11%) \n6.4 (-18%) \nAutumn \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n7.0 (-11%) \n6.4 (-18%) \nTable 4. Average seasonal salinity in the Baltic Sea and along the south-eastern Baltic coast. Percentage in the brackets shows the \nh\nf th\ni\nd\nith th hi t\ni\nl\ni d (1975 2005) Table 4. Average seasonal salinity in the Baltic Sea and along the south-eastern Baltic coast. Percentage in the brackets shows the \nchange of the scenario runs compared with the historical period (1975–2005). Salinity in the coastal area is slightly lower compared to that of the Baltic Sea. However, here the difference of seasons is more \napparent, having a higher decease of salinity values during winter and spring when the discharge of freshwater from the \nCuronian Lagoon increases. In the coastal waters the highest change, compared with the historical period, is also projected to \n90 \nbe under RCP8 5 scenario Notably the highest decrease is projected northward of Klaipėda Strait (lagoon outlet) while Salinity in the coastal area is slightly lower compared to that of the Baltic Sea. However, here the difference of seasons is more \napparent, having a higher decease of salinity values during winter and spring when the discharge of freshwater from the Salinity in the coastal area is slightly lower compared to that of the Baltic Sea. However, here the difference of seasons is more \napparent, having a higher decease of salinity values during winter and spring when the discharge of freshwater from the \nCuronian Lagoon increases. In the coastal waters the highest change, compared with the historical period, is also projected to \n290 \nbe under RCP8.5 scenario. Notably, the highest decrease is projected northward of Klaipėda Strait (lagoon outlet), while \nsouthward the change is lower and salinity values are similar to that of south-eastern Baltic Sea Curonian Lagoon increases. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The severity of the saltwater intrusions is also predicted to decrease (left side of Fig. 8). The pattern is similar to that of the \nnumber of days of saltwater intrusions. Due to the increased outflow from the lagoon during spring, the changes are the highest \n280 \nin the same period. The severity of the saltwater intrusions is also predicted to decrease (left side of Fig. 8). The pattern is similar to that of the \nnumber of days of saltwater intrusions. Due to the increased outflow from the lagoon during spring, the changes are the highest \n280 \nin the same period. Salinity in the south-eastern Baltic Sea show a decreasing pattern throughout the analysis periods. Under RCP4.5 scenario, \nthere is a small salt content decrease projected – 5–6% less in the short-term and 11–18% in the long-term (Table 4), compared \nwith the historical period. Difference between the RCP scenarios are only apparent in the long-term, where the change is \nprojected to be higher under RCP8.5 scenario. In the Baltic Sea there is no difference of average salinity throughout the seasons. 285 \n \n \n1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \nCoastal waters \nWinter \n6.6 \n5.9 (-11%) \n5.8 (-13%) \n5.4 (-18%) \n4.8 (-27%) \nSpring \n5.4 \n4.9 (-11%) \n4.8 (-12%) \n4.4 (-18%) \n4.0 (-26%) \nSummer \n5.4 \n5.1 (-4%) \n5.0 (-7%) \n4.8 (-12%) \n4.3 (-20%) \nAutumn \n6.3 \n5.9 (-6%) \n5.8 (-8%) \n5.5 (-13%) \n5.0 (-21%) \nBaltic Sea \nWinter \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n6.9 (-11%) \n6.4 (-18%) \nSpring \n7.7 \n7.4 (-5%) \n7.3 (-6%) \n6.9 (-11%) \n6.3 (-18%) \nSummer \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n7.0 (-11%) \n6.4 (-18%) \nAutumn \n7.8 \n7.4 (-5%) \n7.3 (-6%) \n7.0 (-11%) \n6.4 (-18%) \nTable 4. Average seasonal salinity in the Baltic Sea and along the south-eastern Baltic coast. Percentage in the brackets shows the \nchange of the scenario runs compared with the historical period (1975–2005). Salinity in the south-eastern Baltic Sea show a decreasing pattern throughout the analysis periods. Under RCP4.5 scenario, \nthere is a small salt content decrease projected – 5–6% less in the short-term and 11–18% in the long-term (Table 4), compared \nwith the historical period. Difference between the RCP scenarios are only apparent in the long-term, where the change is \nprojected to be higher under RCP8.5 scenario. In the Baltic Sea there is no difference of average salinity throughout the seasons. 285 der RCP8.5 scenario. The changes between the analysis periods and RCPs scenarios comparing with the historical \n310 \nperiod are higher than that of the south-eastern Baltic Sea. seasons, on average by 4.9 °C. The changes between the analysis periods and RCPs scenarios comparing with the historical \n310 \nperiod are higher than that of the south-eastern Baltic Sea. 3.5 Water temperature The water temperature in the Curonian Lagoon is relatively homogeneous throughout its area with slightly warmer water (by \non average 0.4 °C) in the northern part of the lagoon. Considering the seasonal dynamics, water temperature is projected to \n295 \nincrease the most during winter, by ~60% in the short-term and more than twice in the long-term (Table 5). During spring and \nautumn, the increase is lower – ~17% in the short-term and 19–27% in the long-term. The lowest increase is projected during \nsummer season – 5–6% in the short-term and 9–13% in the long-term. The more apparent difference between the RCP \nscenarios is observed during the long-term, where RCP8.5 displays a higher temperature increase. 14 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \nLagoon \nWinter \n1.5 \n2.5 (60%) \n2.5 (61%) \n3.1 (102%) \n3.9 (151%) \nSpring \n8.3 \n9.6 (16%) \n9.8 (18%) \n9.9 (19%) \n10.5 (27%) \nSummer \n19.8 \n20.8 (5%) \n20.9 (6%) \n21.6 (9%) \n22.3 (13%) \nAutumn \n10.5 \n11.7 (11%) \n12.0 (14%) \n13.1 (24%) \n13.8 (32%) \nCoastal waters \nWinter \n2.6 \n3.7 (42%) \n3.7 (42%) \n4.4 (72%) \n5.9 (129%) \nSpring \n5.4 \n6.7 (24%) \n6.8 (26%) \n7.0 (30%) \n8.4 (56%) \nSummer \n16.0 \n17.0 (7%) \n17.2 (8%) \n17.7 (11%) \n19.2 (20%) \nAutumn \n12.0 \n13.1 (9%) \n13.4 (11%) \n14.4 (20%) \n15.8 (31%) \nBaltic Sea \nWinter \n4.2 \n5.2 (24%) \n5.3 (26%) \n5.9 (41%) \n7.2 (71%) \nSpring \n3.4 \n4.5 (30%) \n4.6 (33%) \n5.1 (50%) \n6.3 (85%) \nSummer \n7.4 \n8.5 (14%) \n8.6 (16%) \n9.2 (23%) \n10.4 (40%) \nAutumn \n8.3 \n9.3 (12%) \n9.4 (13%) \n10.1 (21%) \n11.3 (35%) \nTable 5. Average seasonal water temperature (in °C) in the Curonian Lagoon and Baltic Sea. Percentage in the brackets shows the \nchange of the scenario runs compared with the historical period (1975–2005). Table 5. Average seasonal water temperature (in °C) in the Curonian Lagoon and Baltic Sea. Percentage in the brackets shows the \nchange of the scenario runs compared with the historical period (1975–2005). Table 5. Average seasonal water temperature (in °C) in the Curonian Lagoon and Baltic Sea. Percent\nchange of the scenario runs compared with the historical period (1975–2005). is under RCP8.5 scenario, compared with the historical period. The smallest change throughout the analysis periods is projected \n305 \nin summer and autumn. There is no apparent difference between the RCP scenarios in the short-term, contrary to the long-\nterm, when the RCP8.5 scenario displays a higher water temperature increase. in summer and autumn. There is no apparent difference between the RCP scenarios in the short-term, contrary to the long-\nterm, when the RCP8.5 scenario displays a higher water temperature increase. The seasonal dynamics in the coastal area (nearshore temperature) is very similar to the whole south-eastern Baltic Sea. Although, the water temperature is slightly lower during the winter season, on average by 1.5 °C, and higher during other The seasonal dynamics in the coastal area (nearshore temperature) is very similar to the whole south-eastern Baltic Sea. Although, the water temperature is slightly lower during the winter season, on average by 1.5 °C, and higher during other \nseasons, on average by 4.9 °C. 3.6 Water level Data were averaged over south-eastern Baltic Sea and five selected points in the Curonian \nLagoon (CL) as shown in the map insert. Table 6. The average water level during the historical (1975–2005) and the short- (2020–2050) and long-term (2070–2100) periods \nunder RCP4.5 and RCP8.5 scenarios. Data were averaged over south-eastern Baltic Sea and five selected points in the Curonian \nLagoon (CL) as shown in the map insert. The water level in the Baltic Sea, as well as the coastal area, is homogeneous and most of the time staying below the sea level \n330 \nduring the historical period. The water level in the sea is expected to rise by 16–19 cm in the short-term and by 32–39 cm in \nthe long -term, compared with the historical period (Table 6). This change is one half of that in the Curonian Lagoon. At the \ndomains’ connective area – Klaipėda Strait, the water level corresponds mostly to the dynamics of it in the sea. 3.6 Water level The water level in the Curonian Lagoon usually varies between -0.2–0 m during the historical period. In the short-term \nprojections, the model estimates a water level increase by 17–20 cm compared with the historical period (Table 6). The highest \nincrease can be estimated in the long-term, when the water level in the lagoon increases by 32 cm under RCP4.5 and 41 cm \n315 \nunder RCP8.5 scenario, compared with the historical period. For the latter, the highest increase is observed in the Nemunas \nDelta and northern part of the lagoon. 315 15 320 \n \n \n \n \n \n325 \n \nTable 6. The average water level during the historical (1975–2005) and the short- (2020–2050) and long-term (2070–2100) periods \nunder RCP4.5 and RCP8.5 scenarios. Data were averaged over south-eastern Baltic Sea and five selected points in the Curonian \nLagoon (CL) as shown in the map insert. 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \n \nBaltic Sea \n-0.15 \n0.01 \n0.03 \n0.17 \n0.23 \n1 \nKlaipėda Strait \n-0.13 \n0.04 \n0.06 \n0.19 \n0.26 \n2 \nNorthern CL \n-0.06 \n0.11 \n0.13 \n0.26 \n0.35 \n3 \nNemunas Delta \n-0.06 \n0.11 \n0.14 \n0.26 \n0.35 \n4 \nCentral CL \n-0.07 \n0.10 \n0.12 \n0.25 \n0.34 \n5 \nSouthern CL \n-0.08 \n0.09 \n0.11 \n0.24 \n0.33 \nhttps://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 320 \n \n \n \n \n \n325 \n \nTable 6. The average water level during the historical (1975–2005) and the short- (2020–2050) and long-term (2070–2100) periods \nunder RCP4.5 and RCP8.5 scenarios. Data were averaged over south-eastern Baltic Sea and five selected points in the Curonian \nLagoon (CL) as shown in the map insert. 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \n \nBaltic Sea \n-0.15 \n0.01 \n0.03 \n0.17 \n0.23 \n1 \nKlaipėda Strait \n-0.13 \n0.04 \n0.06 \n0.19 \n0.26 \n2 \nNorthern CL \n-0.06 \n0.11 \n0.13 \n0.26 \n0.35 \n3 \nNemunas Delta \n-0.06 \n0.11 \n0.14 \n0.26 \n0.35 \n4 \nCentral CL \n-0.07 \n0.10 \n0.12 \n0.25 \n0.34 \n5 \nSouthern CL \n-0.08 \n0.09 \n0.11 \n0.24 \n0.33 Table 6. The average water level during the historical (1975–2005) and the short- (2020–2050) and long-term (2070–2100) periods \nunder RCP4.5 and RCP8.5 scenarios. Data were averaged over south-eastern Baltic Sea and five selected points in the Curonian \nLagoon (CL) as shown in the map insert. Table 6. The average water level during the historical (1975–2005) and the short- (2020–2050) and long-term (2070–2100) periods \nunder RCP4.5 and RCP8.5 scenarios. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Table 7. Seasonal water residence time (in days) in the northern, southern, and total lagoon area under RCP4.5 and RCP8.5 \n340 \nscenarios, averaged over historical (1975–2005), short-term (2020–2050), and long-term (2070–2100) periods. Percentage in the \nbrackets shows the change compared with the historical period. Table 7. Seasonal water residence time (in days) in the northern, southern, and total lagoon area under RCP4.5 and RCP8.5 \n340 \nscenarios, averaged over historical (1975–2005), short-term (2020–2050), and long-term (2070–2100) periods. Percentage in the \nbrackets shows the change compared with the historical period. The highest WRT in the Curonian Lagoon is during summer and autumn when the discharge from the surrounding rivers \ndecreases. Seasonal changes compared with the WRT during the historical period revealed that the highest decrease is observed \nduring winter, followed by spring. During summer WRT slightly increases under both RCP scenarios and both analysis periods, \n345 \nwhile in autumn the changes are negligible, apart from RCP8.5 scenario in the long-term, when the WRT decreases by ~10%, \nd t th hi t\ni\nl\ni d Table 7. Seasonal water residence time (in days) in the northern, southern, and total lagoon area under RCP4.5 and RCP8.5 \n340 \nscenarios, averaged over historical (1975–2005), short-term (2020–2050), and long-term (2070–2100) periods. Percentage in the \nbrackets shows the change compared with the historical period. The highest WRT in the Curonian Lagoon is during summer and autumn when the discharge from the surrounding rivers \ndecreases. Seasonal changes compared with the WRT during the historical period revealed that the highest decrease is observed \nduring winter, followed by spring. During summer WRT slightly increases under both RCP scenarios and both analysis periods, \n345 \nwhile in autumn the changes are negligible, apart from RCP8.5 scenario in the long-term, when the WRT decreases by ~10%, \ncompared to the historical period. 3.8 Ice thickness Ice thickness in the Curonian Lagoon is projected to decrease steadily. The average maximum ice thickness during the short-\nterm period can decrease by 25% compared with the historical period. Additionally, there is no apparent difference between \n350 \nthe RCP scenarios (Fig. 9). Adverse case is with long-term projections, for which the average maximum ice thickness under \nRCP4.5 scenario is estimated to decrease by half and by 70% under RCP8.5 scenario, compared with the historical period. These changes indicate a possible severe modification of ice regime in the lagoon, leading to winter seasons with a more likely \nunstable thin ice cover. 55 \nFigure 9. Average ice thickness in the Curonian Lagoon. Black line indicates the average ice thickness of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. Table shows the \naverage maximum ice thickness and a percentage of change of the scenario runs compared with the historical period. Figure 9. Average ice thickness in the Curonian Lagoon. Black line indicates the average ice thickness of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. Table shows the \naverage maximum ice thickness and a percentage of change of the scenario runs compared with the historical period. Figure 9. Average ice thickness in the Curonian Lagoon. Black line indicates the average ice thickness of each analysis period – \nhistorical (1975–2005), short-term (2020–2050), and long-term (2070–2100) under RCP4.5 and RCP8.5 scenarios. Table shows the \naverage maximum ice thickness and a percentage of change of the scenario runs compared with the historical period. 3.7 Water residence time The water residence time (WRT) generally is likely to have a decreasing tendency, apart from the summer season, when it \n335 \nslightly increases (Table 7). In the northern part of the lagoon WRT is always shorter, while the southern part has higher values, \nbecause the water movement in this area is more driven by wind than by the outflowing rivers (e.g., Nemunas). There is no \napparent difference between the climate scenarios in the short-term. The highest decrease, compared with the historical period, \nis observed in the long-term under RCP8.5 scenario. 1975-2005 \n2020-2050 \n2070-2100 \n \n \nHistorical \nRCP45 \nRCP85 \nRCP45 \nRCP85 \nNorth \nWinter \n61 \n49 (-21%) \n48 (-21%) \n46 (-24%) \n41 (-32%) \nSpring \n44 \n39 (-11%) \n40 (-9%) \n41 (-7%) \n39 (-12%) \nSummer \n59 \n63 (7%) \n63 (6%) \n66 (11%) \n61 (4%) \nAutumn \n67 \n66 (-1%) \n66 (-1%) \n67 (0.03%) \n62 (-7%) \nSouth \nWinter \n174 \n123 (-29%) \n119 (-32%) \n112 (-36%) \n88 (-49%) \nSpring \n122 \n96 (-22%) \n99 (-19%) \n101 (-17%) \n94 (-23%) \nSummer \n179 \n196 (9%) \n199 (11%) \n217 (21%) \n192 (7%) \nAutumn \n180 \n178 (-1%) \n178 (-1%) \n176 (-2%) \n158 (-12%) \nTotal \nWinter \n127 \n95 (-25%) \n93 (-27%) \n88 (-30%) \n73 (-43%) \nSpring \n94 \n76 (-18%) \n79 (-16%) \n80 (-14%) \n75 (-20%) \nSummer \n129 \n140 (8%) \n140 (8%) \n147 (14%) \n134 (4%) \nAutumn \n135 \n133 (-1%) \n133 (-1%) \n133 (-2%) \n120 (-11%) 16 The results above give an indication on the variation of the physical parameters that the Nemunas River watershed, Curonian \n360 \nLagoon, and south-eastern part of the Baltic Sea will be subjected to. All parameters indicate a change, more or less strong, \nuntil the end of this century. These changes will be discussed hereafter. 4.1 Local climate insights The results of this study have been achieved with regional climate models that have been downscaled from global models. The \nquality of the model output was generally good, but a comparison with local observed data showed the need to ultimately \n365 \ncorrect the downscaled data by applying a bias correction. This was needed because some variables (precipitation suffices as \none example) were far from being well produced by the regional climate models (Tapiador et al., 2019). g\ng\ng\ncentury (Meier et al., 2022a). Accordingly, our study results show a considerable increase in the projected air temperatures as \nwell (Table 8). These changes will likely result in warmer conditions during winters, less snow and ice cover as well as higher \n370 \nriver discharge in winter, while the increase of air temperature during other seasons might have a severe impact on the crop \nproduction, increase the occurrence of hydrological and/or agricultural droughts and impact changes in the crop calendar of \nthe region. well (Table 8). These changes will likely result in warmer conditions during winters, less snow and ice cover as well as higher \n370 \nriver discharge in winter, while the increase of air temperature during other seasons might have a severe impact on the crop \nproduction, increase the occurrence of hydrological and/or agricultural droughts and impact changes in the crop calendar of \nthe region. Parameter \nDomain \n2020-2050 \n2070-2100 \nRCP4.5 \nRCP8.5 \nRCP4.5 \nRCP8.5 \nAir temperature \nMarine area \n21% \n23% \n34% \n57% \nTerrestrial area \n33% \n35% \n51% \n81% \nPrecipitation \nMarine area \n14% \n15% \n18% \n34% \nTerrestrial area \n14% \n16% \n18% \n35% \nWater inflow \nNemunas River to Lagoon \n13% \n16% \n19% \n37% \nWater outflow \nLagoon to sea \n15% \n18% \n21% \n40% \nSalinity \nCoastal area \n-8% \n-10% \n-15% \n-24% \nSea \n-5% \n-6% \n-11% \n-18% \nSaltwater intrusions (No \nof days year-1) \nJuodkrantė \n-30% \n-34% \n-34% \n-51% \nWater temperature \nLagoon \n11% \n13% \n19% \n26% \nCoastal area \n13% \n14% \n21% \n37% \nSea \n17% \n19% \n30% \n50% \nWater residence time \nNorther part of the lagoon \n-6% \n-6% \n-5% \n-12% \nSouthern part of the lagoon \n-10% \n-9% \n-8% \n-19% \nTotal lagoon area \n-8% \n-8% \n-8% \n-17% \nTable 8. Summary of the percentage change of projected average annual changes (under RCP4.5 and RCP8.5 scenarios) during \nshort-term (2020–2050) and long-term (2070–2100) periods compared with the historical period (1975–2005). 375 \nWith increasing mean temperatures, a distinct increase in the projected monthly and annual average precipitation is also \nexpected. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4 Discussion The results above give an indication on the variation of the physical parameters that the Nemunas River watershed, Curonian \n360 \nLagoon, and south-eastern part of the Baltic Sea will be subjected to. All parameters indicate a change, more or less strong, \nuntil the end of this century. These changes will be discussed hereafter. 17 8. Summary of the percentage change of projected average annual changes (under RCP4.5 and RCP With increasing mean temperatures, a distinct increase in the projected monthly and annual average precipitation is also \nexpected. Our results show that by the end of the century an increase of the total annual precipitation by 18 to 34% (~100–200 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. mm) according to RCP4.5 and RCP 8.5 scenarios respectively, is projected. In comparison with a neighbour country, this is \nconsiderably higher than the projected precipitation for the Latvian case, where the total annual increase was estimated to be \n13 to 16% (~80–100 mm) under the same scenarios (Avotniece et al., 2017). 80 mm) according to RCP4.5 and RCP 8.5 scenarios respectively, is projected. In comparison with a neighbour country, this is \nconsiderably higher than the projected precipitation for the Latvian case, where the total annual increase was estimated to be \n13 to 16% (~80–100 mm) under the same scenarios (Avotniece et al., 2017). 380 380 4.2 Water flow The driving force of changes in the lagoon are the boundaries – the Nemunas River inputs and the influence of the Baltic Sea \nthrough the Klaipėda Strait. Historically every year on average Nemunas River supplies three times the volume of water to the \nlagoon (Žilinskas et al., 2012), which is subject to change in the future. The projections indicate a 13–16% (in the short-term) \nand 19–37% (in the long-term) increase in water discharge from the river, which coincides with the projected precipitation \n385 \nincrease (Table 8). This projected change alone will strongly influence the hydrodynamic conditions of the lagoon. The driving force of changes in the lagoon are the boundaries – the Nemunas River inputs and the influence of the Baltic Sea \nthrough the Klaipėda Strait. Historically every year on average Nemunas River supplies three times the volume of water to the \nlagoon (Žilinskas et al., 2012), which is subject to change in the future. The projections indicate a 13–16% (in the short-term) \nand 19–37% (in the long-term) increase in water discharge from the river, which coincides with the projected precipitation \n385 \nincrease (Table 8). This projected change alone will strongly influence the hydrodynamic conditions of the lagoon. Due to the enhanced Nemunas discharge, the fluxes from the Curonian Lagoon into the Baltic Sea will also increase. The \noutflow to the sea is already dominating, especially in the northern part of the lagoon and it will remain so in the future. The \nwater flow will increase in all its path towards the Baltic Sea by 13–18% in the short-term and by ~20% under RCP4.5 scenario Due to the enhanced Nemunas discharge, the fluxes from the Curonian Lagoon into the Baltic Sea will also increase. The \noutflow to the sea is already dominating, especially in the northern part of the lagoon and it will remain so in the future. The \nwater flow will increase in all its path towards the Baltic Sea by 13–18% in the short-term and by ~20% under RCP4.5 scenario in the long-term, while the changes under RCP8.5 scenario can be expected to increase by up to 40% by the end of the century, \n390 \ncompared with the historical period (Table 8). The volume of discharging river water into the Baltic Sea is projected to be dependent on the season and the region (Graham, \n2004; Meier et al., 2022a). 4.1 Local climate insights Our results show that by the end of the century an increase of the total annual precipitation by 18 to 34% (~100–200 18 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4.3 Water level dynamics Water levels are the main drivers, together with the Nemunas discharge, of the exchanges through the Klaipėda Strait. In the \n410 \nstrait and particularly in the Curonian Lagoon water levels are higher than in the Baltic Sea, which leads to an increase in \noutflow periods through the strait. Since 1960 until 2008, the average measured water level in the Curonian Lagoon has been \nrising at a rate of approximately 3.0 mm year−1, leading to increase by 18 cm during this period (Čepienė et al., 2022, and \nreferences therein). Our modelling results showed that in the future, water level is projected to increase by 16–20 cm in the short-term and by 32–41 cm in the long-term, compared with the historical period. 415 \nSince the projected water level will be above the mean sea level, it could affect the infrastructure and residents living in the \ncoastal area leading to a significant social and economic damages (Vousdoukas et al., 2020). The low elevation wetland areas \ncovering the southern and eastern part of the lagoon (especially Nemunas Delta area) are more likely of getting flooded. However, the Klaipėda city and the Curonian Spit are located higher and the mean water level rise should not permanently short-term and by 32–41 cm in the long-term, compared with the historical period. 415 \nSince the projected water level will be above the mean sea level, it could affect the infrastructure and residents living in the \ncoastal area leading to a significant social and economic damages (Vousdoukas et al., 2020). The low elevation wetland areas \ncovering the southern and eastern part of the lagoon (especially Nemunas Delta area) are more likely of getting flooded. However, the Klaipėda city and the Curonian Spit are located higher and the mean water level rise should not permanently affect these areas. It is worth mentioning, that the hydrodynamic model has a fixed boundary, thus the increase could be \n420 \noverestimated especially considering the role of coastal wetlands in mediation of global change induced water level changes. affect these areas. It is worth mentioning, that the hydrodynamic model has a fixed boundary, thus the increase could be \n420 \noverestimated especially considering the role of coastal wetlands in mediation of global change induced water level changes. 4.4 Salinity dynamics Our study \nof salinity changes in the south-eastern Baltic Sea demonstrates a decreasing tendency, especially in the coastal area, northward \n430 \nfrom the lagoon outlet, where the sea water is diluted by discharging freshwater from the lagoon (Table 8). of salinity changes in the south-eastern Baltic Sea demonstrates a decreasing tendency, especially in the coastal area, northward \n430 \nfrom the lagoon outlet, where the sea water is diluted by discharging freshwater from the lagoon (Table 8). 4.2 Water flow With this, the increase of riverine nutrient loads is also anticipated (Pihlainen et al., 2020), likewise \nsuggesting that “land based nutrient management will have greater effect on nutrient loads than greenhouse gas emissions” (Climate Change in the Baltic Sea: 2021 Fact Sheet). 395 \nThe seasonal river discharge changes in the Baltic Sea correspond with our study noting that a more likely increase in winter \nand decrease in summer can be expected by the end of this century (Donnelly et al., 2014). The magnitude of changes of \nfreshwater inflow within different subregions of the Baltic Sea has a considerable variation, i.e., an increased outflow in the \nnorthern part of the Baltic Sea and a decreased in the southern part (Graham, 2004). Although the combined uncertainties from the climate models, their bias correction, and applied models make it difficult to draw conclusions about the magnitude of \n400 \nchange (Donnelly et al., 2014). The most alarming are the projected hydrologic changes in the Nemunas River watershed during the winter season in the long-\nterm period. As supported by other studies (Čerkasova et al., 2021; Stonevičius et al., 2017), both RCPs point toward a drastic \nincrease in winter discharges, which might lead to severe flooding of the delta region, which suffers from annual floods of different magnitude (Valiuškevičius et al., 2018). Currently these flood events occur in spring, but will likely occur in winter \n405 \nin the future, and with increased severity, which will pose a serious concern to the local population and stakeholders. The \ncurrently functioning polder system in the Nemunas River delta (Lesutienė et al., 2022) region might not withstand such \nchanges. different magnitude (Valiuškevičius et al., 2018). Currently these flood events occur in spring, but will likely occur in winter \n405 \nin the future, and with increased severity, which will pose a serious concern to the local population and stakeholders. The \ncurrently functioning polder system in the Nemunas River delta (Lesutienė et al., 2022) region might not withstand such \nchanges. 19 4.4 Salinity dynamics When looking at the saltwater intrusion events, it can be clearly seen that the intrusion periods are decreasing and the outflow \nfrom the lagoon becomes dominant, simply because the outflowing water fluxes will stop the salty seawater from entering the \nCuronian Lagoon. This is clearly evident for both climate change scenarios – periods where values higher than the threshold \n425 \nof 2 g kg-1 in Juodkrantė drops by 30–50% compared with the historical period (Table 8). This will lead to some alteration of \nthe ecosystem in the northern part of the Curonian lagoon, which is now considered as oligohaline. When looking at the saltwater intrusion events, it can be clearly seen that the intrusion periods are decreasing and the outflow \nfrom the lagoon becomes dominant, simply because the outflowing water fluxes will stop the salty seawater from entering the As seen and suggested from other Baltic Sea salinity projection studies, the salinity in the Baltic does not have a distinct pattern \nof changing in the future and the projections have a lot of uncertainties (Lehmann et al., 2022; Meier et al., 2022b). Our study \nof salinity changes in the south-eastern Baltic Sea demonstrates a decreasing tendency, especially in the coastal area, northward \n430 \nfrom the lagoon outlet, where the sea water is diluted by discharging freshwater from the lagoon (Table 8). As seen and suggested from other Baltic Sea salinity projection studies, the salinity in the Baltic does not have a distinct pattern \nof changing in the future and the projections have a lot of uncertainties (Lehmann et al., 2022; Meier et al., 2022b). Our study \nof salinity changes in the south-eastern Baltic Sea demonstrates a decreasing tendency, especially in the coastal area, northward \n430 As seen and suggested from other Baltic Sea salinity projection studies, the salinity in the Baltic does not have a distinct pattern \nof changing in the future and the projections have a lot of uncertainties (Lehmann et al., 2022; Meier et al., 2022b). Our study As seen and suggested from other Baltic Sea salinity projection studies, the salinity in the Baltic does not have a distinct pattern \nof changing in the future and the projections have a lot of uncertainties (Lehmann et al., 2022; Meier et al., 2022b). https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. the historical period. Additionally, the coastal average water temperature is lower by 1.5 °C during the winter and higher by \n4.9 °C during the rest of the year than that of the south-eastern Baltic Sea. 440 the historical period. Additionally, the coastal average water temperature is lower by 1.5 °C during the winter and higher by 4.9 °C during the rest of the year than that of the south-eastern Baltic Sea. 440 \nOur study supports the findings of projected increasing water temperature in the Baltic Sea (Meier et al., 2022b). In this article \nwe focused only on the average water temperature of the water column, however, there are many studies revealing the warming \npatterns of the sea surface temperature in the Baltic (Dutheil et al., 2022; Zhu et al., 2022). 4.9 C during the rest of the year than that of the south eastern Baltic Sea. 440 \nOur study supports the findings of projected increasing water temperature in the Baltic Sea (Meier et al., 2022b). In this article \nwe focused only on the average water temperature of the water column, however, there are many studies revealing the warming \npatterns of the sea surface temperature in the Baltic (Dutheil et al., 2022; Zhu et al., 2022). 4.7 Ice thickness in the lagoon Although, the role of ice for \ncontrolling WRT is far less than the increased fluxes from the discharging rivers. modelling studies of future projections (Idzelytė and Umgiesser, 2021; Jakimavičius et al., 2019). The decreasing ice \n460 \nparameters impact on the hydrodynamics of the lagoon suggest a possible increase of water flow and saltwater intrusions, as \nwell as shorter WRT during the ice cover season (Idzelytė et al., 2020; Umgiesser et al., 2016). Although, the role of ice for \ncontrolling WRT is far less than the increased fluxes from the discharging rivers. modelling studies of future projections (Idzelytė and Umgiesser, 2021; Jakimavičius et al., 2019). The decreasing ice \n460 \nparameters impact on the hydrodynamics of the lagoon suggest a possible increase of water flow and saltwater intrusions, as \nwell as shorter WRT during the ice cover season (Idzelytė et al., 2020; Umgiesser et al., 2016). Although, the role of ice for \ncontrolling WRT is far less than the increased fluxes from the discharging rivers. 4.6 Water residence time in the lagoon Water residence time (WRT) is generally decreasing with climate change (Table 8), although the decline is small in the short-\n445 \nterm and in the long-term under RCP4.5 scenario (5–10% less). A general decrease can be found in the whole area of the \nlagoon in the long-term under RCP8.5 (17%). As in the case with saltwater intrusions, this is due to the increased outflow from \nthe lagoon to the sea, where WRT are less varying in the north, whereas it decreased sensibly in the south. When looking at seasonal changes, WRT becomes lower in summer due to decreasing river discharge. Comparing the changes Water residence time (WRT) is generally decreasing with climate change (Table 8), although the decline is small in the short-\n445 \nterm and in the long-term under RCP4.5 scenario (5–10% less). A general decrease can be found in the whole area of the \nlagoon in the long-term under RCP8.5 (17%). As in the case with saltwater intrusions, this is due to the increased outflow from \nthe lagoon to the sea, where WRT are less varying in the north, whereas it decreased sensibly in the south. When looking at seasonal changes, WRT becomes lower in summer due to decreasing river discharge. Comparing the changes When looking at seasonal changes, WRT becomes lower in summer due to decreasing river discharge. Comparing the changes \nfrom the historical period it revealed that the highest decrease is observed during winter and spring, presumably due to the \n450 \nincreased outflowing water fluxes and change of the ice cover regime. When looking at seasonal changes, WRT becomes lower in summer due to decreasing river discharge. Comparing the changes \nfrom the historical period it revealed that the highest decrease is observed during winter and spring, presumably due to the \n450 \nincreased outflowing water fluxes and change of the ice cover regime. from the historical period it revealed that the highest decrease is observed during winter and spring, presumably due to the \n450 \nincreased outflowing water fluxes and change of the ice cover regime. 4.7 Ice thickness in the lagoon Ice thickness projections in the lagoon will become thinner for a longer period during the ice season compared to the historical \nperiod. The projected changes of 25% decreased maximum ice thickness in the short-term and 50–70% in the long-term, could \npresume leading to severe alteration of the ice season regime in the future. This pattern corresponds to the trends in the southern \n455 \nBaltic lagoons (Girjatowicz and Świątek, 2021) and gulf of Riga (Siitam et al., 2017; Kļaviņš et al., 2016;), as well as Baltic \nSea itself (Luomaranta et al 2014) Ice thickness projections in the lagoon will become thinner for a longer period during the ice season compared to the historical \nperiod. The projected changes of 25% decreased maximum ice thickness in the short-term and 50–70% in the long-term, could period. The projected changes of 25% decreased maximum ice thickness in the short term and 50 70% in the long term, could \npresume leading to severe alteration of the ice season regime in the future. This pattern corresponds to the trends in the southern \n455 \nBaltic lagoons (Girjatowicz and Świątek, 2021) and gulf of Riga (Siitam et al., 2017; Kļaviņš et al., 2016;), as well as Baltic \nSea itself (Luomaranta et al., 2014). Ice in the Curonian Lagoon has become a trending topic in the recent years, due to its already evident changes as seen from \nthe historical observations (Jakimavičius et al., 2019), remote sensing (Kozlov et al., 2020; Idzelytė et al., 2019), as well as presume leading to severe alteration of the ice season regime in the future. This pattern corresponds to the trends in the southern \n455 \nBaltic lagoons (Girjatowicz and Świątek, 2021) and gulf of Riga (Siitam et al., 2017; Kļaviņš et al., 2016;), as well as Baltic \nSea itself (Luomaranta et al., 2014). Ice in the Curonian Lagoon has become a trending topic in the recent years, due to its already evident changes as seen from \nthe historical observations (Jakimavičius et al., 2019), remote sensing (Kozlov et al., 2020; Idzelytė et al., 2019), as well as modelling studies of future projections (Idzelytė and Umgiesser, 2021; Jakimavičius et al., 2019). The decreasing ice \n460 \nparameters impact on the hydrodynamics of the lagoon suggest a possible increase of water flow and saltwater intrusions, as \nwell as shorter WRT during the ice cover season (Idzelytė et al., 2020; Umgiesser et al., 2016). 4.5 Water temperature dynamics The mean temperature of the water column in the Klaipėda Strait is less sensitive to changes. In the lagoon water temperature \nis projected to increase under both RCP scenarios compared with the historical period. The highest changes are projected in \nthe long-term under RCP8.5 scenario (Table 8). 435 the long-term under RCP8.5 scenario (Table 8). 435 \nThe increasing temperature patterns are also evident in the south-eastern Baltic Sea, having a higher increase in the long-term \nthan in the short-term. The highest change, compared with the historical period, is mostly occurring during winter and spring. Considering only the coastal area, it represents similar dynamics of the sea, although, having higher changes compared with The increasing temperature patterns are also evident in the south-eastern Baltic Sea, having a higher increase in the long-term \nthan in the short-term. The highest change, compared with the historical period, is mostly occurring during winter and spring. Considering only the coastal area, it represents similar dynamics of the sea, although, having higher changes compared with 20 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. A recent analysis (Ivanauskas et al., 2022) indicated statistical relationship between WRT and salinity and both catches and \npopulations of main commercial fish populations. This is in stark contrast to the predictions for the Baltic Sea fishery, where \n470 \ncatches are expected to be decreasing in quantity, and especially in the quality (Climate Change in the Baltic Sea, 2021). This \ncould be explained by the fact that commercial catches in the Curonian Lagoon already are dominated by eutrophication \ntolerant species like bream and pikeperch, which are also known to prefer higher water temperatures. However, projected ice \ncover decrease will inevitably negatively affect the winter ice fishing practices in the lagoon. The latest studies, revealing the mechanisms of nutrient cycling and eutrophication processes (Bartoli et al., 2018), point \n475 \ntoward the expected reduction of the role of the Curonian Lagoon as a coastal filter. The shortening of the ice cover period \nwill reduce the system denitrification capacity while the decrease of WRT will lead to substantial reduction of nutrient \nretention. So far, the only difference is the more favourable conditions for the cage aquaculture in the lagoon, where the \ndecreased WRT and increased flushing in combination with modern practices of multi trophic aquaculture (e.g. floating \nvegetable gardens) could remove some of the constraints existing at present. 480 The latest studies, revealing the mechanisms of nutrient cycling and eutrophication processes (Bartoli et al., 2018), point \n475 \ntoward the expected reduction of the role of the Curonian Lagoon as a coastal filter. The shortening of the ice cover period \nwill reduce the system denitrification capacity while the decrease of WRT will lead to substantial reduction of nutrient \nretention. So far, the only difference is the more favourable conditions for the cage aquaculture in the lagoon, where the \ndecreased WRT and increased flushing in combination with modern practices of multi trophic aquaculture (e.g. floating \nvegetable gardens) could remove some of the constraints existing at present. 480 4.8 Impacts on the ecosystem structure and functions Despite clear trends for physical indicators, the actual consequences of climate change for the functioning of the Curonian \n465 \nLagoon ecosystem are much more complicated to predict. The foreseen salinity changes in the mostly freshwater ecosystem \nare not of the scale suggesting significant shifts in the ecosystem functioning even in the northern part of the lagoon. However, \nsome decrease in distribution areas of some estuarine species (mysids, barnacles) could be foreseen. Despite clear trends for physical indicators, the actual consequences of climate change for the functioning of the Curonian \n465 \nLagoon ecosystem are much more complicated to predict. The foreseen salinity changes in the mostly freshwater ecosystem \nare not of the scale suggesting significant shifts in the ecosystem functioning even in the northern part of the lagoon. However, \nsome decrease in distribution areas of some estuarine species (mysids, barnacles) could be foreseen. 21 https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Acknowledgements This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-MIP-21-24. This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-MIP-21-24. Data availability All numerical modelling results created during this study are openly available in the Zenodo open data repository \n(https://doi.org/10.5281/zenodo.7500744) as cited in Idzelytė et al. (2023). 5 Conclusions The setup of the complex Nemunas River–Curonian Lagoon–Baltic Sea model has been successfully used in the climate change \nstudies presented in this paper. The high-resolution hydrological model for the Nemunas River and hydrodynamic model for \nthe Curonian Lagoon and the Baltic Sea were used. The finite element approach has allowed us to achieve a good compromise \nof needed resolution and computer efficiency to carry out all needed hydrodynamic simulations. With the help of downscaled \n485 \nclimate models, which have been ultimately bias-corrected with observations, the tendency of changes was inferred. The study results showed that the Curonian Lagoon will be subject to higher Nemunas discharges that will in turn increase the \noutgoing fluxes into the Baltic Sea. Through these higher fluxes both water residence times and saltwater intrusion events will \ndecrease. Most of these changes will however be only noticeable in the northern part of the lagoon, which are more likely to be influenced by the Nemunas discharge. The southern part of the lagoon will experience much less changes. 490 \nThe foreseen changes in physical characteristics are not of the scale suggesting significant shifts in the ecosystem functioning, \nbut expected rather to manifest in some quantitative alterations in the nutrient retention capacity. However, some ecosystem \nservices such as ice fishing are expected to be completely vanishing due to the physical constraints. Whereas tendencies could be identified in our analysis, it was yet not possible to discuss the uncertainty of the results achieved. The foreseen changes in physical characteristics are not of the scale suggesting significant shifts in the ecosystem functioning, \nbut expected rather to manifest in some quantitative alterations in the nutrient retention capacity. However, some ecosystem \nservices such as ice fishing are expected to be completely vanishing due to the physical constraints. This can be done only through extensive sensitivity analysis, and was not part of the scope of this research article. It will \n495 \nhowever be part of another investigation that will be carried out in the near future. This can be done only through extensive sensitivity analysis, and was not part of the scope of this research article. It will \n495 \nhowever be part of another investigation that will be carried out in the near future. 22 Author contribution \n500 GU and NC initiated the conceptualization and funding acquisition of the research project. RI, NC, and JM performed the \nanalysis and drafted the paper. RI curated the visualization of the results. RI, NC, JM, TD, ARB, AE prepared the original \nmanuscript draft with the assistance of GU. All co-authors reviewed the paper and contributed to the scientific interpretation \nand discussion. GU and NC initiated the conceptualization and funding acquisition of the research project. RI, NC, and JM performed the \nanalysis and drafted the paper. RI curated the visualization of the results. RI, NC, JM, TD, ARB, AE prepared the original \nmanuscript draft with the assistance of GU. All co-authors reviewed the paper and contributed to the scientific interpretation \nand discussion. Competing interests. 505 \nThe authors declare that they have no conflict of interest. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-303\nPreprint. Discussion started: 24 February 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Belkin, \nI. 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ERROR: type should be string, got "https://doi.org/10.1007/s00441-020-03329-z\nCell and Tissue Research (2021) 383:149–164 https://doi.org/10.1007/s00441-020-03329-z\nCell and Tissue Research (2021) 383:149–164 REVIEW REVIEW Keywords Antenna . Antennal lobe . Mushroom body . Neuromodulation . Structural synaptic plasticity Keywords Antenna . Antennal lobe . Mushroom body . Neuromodulation . Structural synaptic plasticity Sylvia Anton1\n& Wolfgang Rössler2 Sylvia Anton1\n& Wolfgang Rössler2 Received: 12 August 2020 /Accepted: 27 October 2020\n# The Author(s) 2020\n/ Published online: 4 December 2020 Abstract Olfactory circuits change structurally and physiologically during development and adult life. This allows insects to respond to\nolfactory cues in an appropriate and adaptive way according to their physiological and behavioral state, and to adapt to their\nspecific abiotic and biotic natural environment. We highlight here findings on olfactory plasticity and modulation in various\nmodel and non-model insects with an emphasis on moths and social Hymenoptera. Different categories of plasticity occur in the\nolfactory systems of insects. One type relates to the reproductive or feeding state, as well as to adult age. Another type of plasticity\nis context-dependent and includes influences of the immediate sensory and abiotic environment, but also environmental condi-\ntions during postembryonic development, periods of adult behavioral maturation, and short- and long-term sensory experience. Finally, plasticity in olfactory circuits is linked to associative learning and memory formation. The vast majority of the available\nliterature summarized here deals with plasticity in primary and secondary olfactory brain centers, but also peripheral modulation\nis treated. The described molecular, physiological, and structural neuronal changes occur under the influence of neuromodulators\nsuch as biogenic amines, neuropeptides, and hormones, but the mechanisms through which they act are only beginning to be\nanalyzed. 2\nBehavioral Physiology and Sociobiology (Zoology II), Biozentrum,\nUniversity of Würzburg, Am Hubland, 97074 Würzburg, Germany 1\nIGEPP, INRAE, Institut Agro, Univ Rennes, INRAE,\n49045 Angers, France * Sylvia Anton\nsylvia.anton@inrae.fr\n* Wolfgang Rössler\nroessler@biozentrum.uni-wuerzburg.de Introduction This finally promotes fitness of an insect in a given ecological\nand evolutionary context (Agrawal 2001). Many insect species predominantly rely on olfaction for intra-\nand interspecific communication and searching food. Olfaction is\nof particularly high importance in night- or dim-light active spe-\ncies and for social communication as in social insects. The mul-\ntitude of available olfactory cues in the natural environment com-\nbined with limited size of the nervous system and the resulting\nneuronal processing capacities render neuronal plasticity and\nmodulation as major factors to optimize the use of neural sub-\nstrate (Dukas 2008; Gadenne et al. 2016; Groh and Rössler\n2020). However, the complexity of the nervous system can also\nset limits for behavioral and ecological plasticity (Bernays 2001). Plasticity in insect olfactory systems occurs at multiple levels,\nfor example as a function of physiological state, in response to\nenvironmental factors, social interactions, and experience. Whereas most of the literature on the mechanisms of olfactory\nplasticity and modulation in insects concentrated on the central\nnervous system (CNS), recent work has also shown modulation\nalready at the olfactory receptor level within olfactory sensory\nneurons (OSNs) on the antennae, for example, as a function of\nodor exposure (Tsitoura and Iatrou 2016; Guo et al. 2017; Wicher\n2018). Furthermore, modulation of OSN sensitivity due to expe-\nrience has been described, for example, in male moths (Guerrieri\net al. 2012) and due to feeding and maturity in female mosquitoes\nand blood-feeding bugs (Gadenne et al. 2016 and references there-\nin (Davis 1984; Grant and O’Connell 2007; Siju et al. 2008;\nReisenman 2014)). Within the CNS, several levels of plasticity\nhave been identified. We will summarize here mainly the most\nrecent results from studies on plasticity and modulation within the\nprimary and secondary olfactory centers in the brain—the anten-\nnal lobes (ALs) and the mushroom bodies (MBs). * Sylvia Anton\nsylvia.anton@inrae.fr\n* Wolfgang Rössler\nroessler@biozentrum.uni-wuerzburg.de Similar to other sensory systems, various mechanisms are\ninvolved in olfactory plasticity, and recent methodological\nadvances provide increasing access to study these 150 Cell Tissue Res (2021) 383:149–164 mechanisms. At the molecular level, the expression of genes\nassociated with olfactory reception and genes coding for\nneuromodulators and hormones and their receptors can vary\nin status- or context-dependent manners (Gadenne et al. 2016). Introduction At the cellular level, neuronal elements have been iden-\ntified physiologically and anatomically, and various parame-\nters of their activity were monitored to show modulation (e.g.,\nNeupert et al. 2018). New technology allows to detect the\npresence of neuromodulators within individual neurons or\nsmall populations of neurons, such as biogenic amines, neu-\nropeptides, and hormones (Ly et al. 2019). moth Agrotis ipsilon, behavioral responses to the female-\nemitted sex pheromone are inhibited transiently after mat-\ning. This plasticity seems to originate from a decrease in\nsensitivity of AL neurons to the sex pheromone, probably\nthrough the implication of ecdysteroids, whereas antennal\ndetection of the sex pheromone does not change after\nmating (for review see Gadenne et al. (2016)). More re-\ncently, differences in the occurrence of a few neuropep-\ntides, such as insulin-like peptides, have been found be-\ntween brains and more specifically ALs of mated and\nunmated male A. ipsilon, indicating a potential role in\npost-mating sex pheromone response inhibition (Diesner\net al. 2018). In another noctuid moth, Spodoptera\nlittoralis, behavioral inhibition of male sex pheromone\nresponses after mating rather originates from modulation\nin OSNs (Kromann et al. 2014). In the same species, an\nincrease in antennal sensitivity to host plant volatiles has\nbeen shown in females after mating (Martel et al. 2009;\nSaveer et al. 2012). Mating-dependent plasticity of pe-\nripheral sensitivity to fruit odors and sex pheromones\nhas also been investigated in different fruit fly species. In female Drosophila suzukii, mating causes strong up-\nand downregulation of olfactory genes within the antenna. In parallel, female antennae increased their sensitivity to\nisoamyl acetate significantly after mating, which is coher-\nent with the attractant role of this compound emitted by\nfresh fruit to mated females (Revadi et al. 2015; Crava\net al. 2019). In Ceratitis capitata, sensitivity of antennae\nand palps to pheromone components emitted by sexually\nmature males, which attract both males and females, de-\ncreases after mating in both sexes (Sollai et al. 2018). However, the mechanisms leading to mating-induced\nchanges in antennal sensitivity are unknown so far. In\nthe hymenopteran parasitoid wasp Nasonia vitripennis,\nfemales are attracted to a male-emitted sex pheromone,\nand the mating-induced lack of behavioral pheromone re-\nsponses seems to be mediated by dopamine, as virgin\nfemales injected with dopamine did not respond any more\nto the pheromone and mated females injected with a do-\npamine antagonist continued to respond (Lenschow et al. Introduction 2018). However, appetitive learning leads to recovery of\nsex pheromone attraction in these females (Lenschow\net al. 2018). Concerning changes in olfactory sensitivity\ndepending on the reproductive state in social insects, very\nlittle information is available. In the ant Harpegnathos\nsaltator, a reduction in antennal sensitivity to queen-\nproduced cuticular hydrocarbons, involved in inhibiting\nworkers from reproduction, has been revealed in female\nworkers becoming reproductive substitute queens called\ngamergates (Ghaninia et al. 2017). However, this repre-\nsents a special case within social Hymenoptera that may\nbe limited to ponerine ants in which mature workers retain\nthe potential to mate and reproduce Mechanisms of olfactory plasticity have specifically been\nstudied in Drosophila melanogaster, due to the available ge-\nnetic tools. As literature on D. melanogaster has been recently\nreviewed (e.g., Sayin et al. 2018; Amin and Lin 2019; Boto\net al. 2020), we concentrate here on other experimental insect\nmodels, mainly moths and social Hymenoptera, with a focus\non their specific ecological context, because a vast amount of\nliterature is available in these two classical models for olfac-\ntory plasticity. We do, however, also include occasional ref-\nerences to further insect species such as locusts, blood-feeding\ninsects, and aphids, because we would like to emphasize and\npromote the importance of comparative investigations in this\nfield. In order to illustrate neuronal mechanisms, we will pro-\nvide here an integrative view of olfactory plasticity in a be-\nhavioral and ecological context emphasizing the importance\nof structural neuronal plasticity and neuromodulation in\nolfaction. State-dependent plasticity and modulation Responses to intra- and interspecific volatile olfactory stimuli can\nbe modulated as a function of the physiological state. Depending\non the role of an olfactory cue or signal, the age, reproductive or\nfeeding state, but also the circadian rhythm can influence the\nsensitivity of the olfactory system to certain olfactory stimuli. Such modulation is mostly caused by an interplay between hor-\nmones, neuropeptides, and biogenic amines, acting at the periph-\neral or the central olfactory levels (for review see Gadenne et al. (2016)). As an example, the titer of the biogenic amine serotonin\n(5HT) within the AL varies in a circadian fashion in male moths,\nwhichis correlated withAL neuronand behavioral responsiveness\nto sex pheromone or host plant volatiles (Kloppenburg et al. 1999;\nGatellier et al. 2004). However, circadian modulation of olfactory\nsensitivity seems to be primarily modulated at the peripheral level\nand has been reviewed earlier (Gadenne et al. 2016). Feeding state In the mosquito Aedes aegypti, the abundance of two\npeptides within the ALs, short neuropeptide F2 (sNPF-2) and\nallatostatin-A-5 (AstA-5), increased 24 and 48 h after a blood\nmeal and systemic injection of both neuropeptides mimicked\nthe host-seeking inhibition effect of a blood meal in unfed\nfemales, thus downregulating responses to food odor (Christ\net al. 2017). In the oriental fruit fly, Bactrocera dorsalis, sNPF\nhas been shown to be involved in feeding state-dependent\nantennal sensitivity to a host plant odor, but in this case up-\nmodulating responses to food odor. When sNPF gene expres-\nsion was inhibited via RNAi, the sensitivity to the odor de-\ncreased in starved flies, which normally exhibit a high sensi-\ntivity (Jiang et al. 2017). In D. melanogaster, sNPF also con-\ntributes to starving-induced improved responses to food odor\nat the receptor neuron level and changes odor representation in\nthe AL, resulting in more robust food-search behavior (Root\net al. 2011). A more general nutritional effect on olfactory\nsensitivity has been identified in D. melanogaster: when fed\nwith a high fat diet, antennal sensitivity to various odors de-\ncreased. This was correlated with a decreased expression of\nthe olfactory co-receptor Orco (Jung et al. 2018). A big chal-\nlenge now is to unravel how peripheral and central nervous\nmodulation interact in feeding state-dependent changes of ol-\nfactory sensitivity. Many aphid species change their dispersal capacities by\nproducing winged morphs when population density increases,\nplant quality decreases, or stress factors such as enemy attacks\noccur (Braendle et al. 2006). The formation of wings in par-\nthenogenetic aphid females improves their dispersal capacities\nand allows them to colonize new habitats more easily than\nwingless females. It is known for several aphid species that\nthe sensory equipment of winged individuals is more elabo-\nrate than that of wingless aphids: besides differences in eye\nmorphology (Ishikawa and Miura 2007; Kollmann et al. 2010), they possess longer antennae and more olfactory or-\ngans, so-called rhinaria, on their antennae (Shambaugh et al. 1978; Miyazaki 1987). A recent study has found evidence that\nalso primary sensory centers in the brain, i.e., visual neuropils\nand ALs, are larger in winged females than in wingless indi-\nviduals of the pea aphid, Acyrthosiphon pisum (Gadenne et al. 2019). Feeding state threshold, the insects change from a solitary to a gregarious\nlifestyle (Simpson and Sword 2008). This phase change\ncauses not only behavioral but also morphological and phys-\niological modifications. Among others, the olfactory system is\nstrongly modified: gregarious locusts have less olfactory sen-\nsilla on the antennae than solitary locusts, along with a lower\ndiscrimination ability for food sources (Greenwood and\nChapman 1984; Ochieng et al. 1998). This correlates with a\nsmaller relative size of the ALs in relation to the volume of the\nentire brain and to the midbrain in gregarious compared to\nsolitary locusts, even though the total brain size is much larger\nin gregarious locusts (Ott and Rogers 2010). Whereas the\nanatomy of AL projection neurons did not show any obvious\ndifferences between the two phases, solitary adult females\npossessed a higher proportion of AL neurons responding to\ntwo components of the egg-laying aggregation pheromone\n(Anton et al. 2002). In addition, AL neurons in solitary third\ninstar nymphs responded more frequently to phenylacetonitril,\nthe major component of the adult aggregation pheromone\n(Ignell et al. 1999). Interestingly, opposing attraction\n(gregarious) and repulsion (solitary) behavior of the aggrega-\ntion pheromone are mediated by octopamine and tyramine,\nrespectively (Ma et al. 2015). Phase switch in locusts does\nnot only modify the olfactory system and its sensitivity but\nalso influences associative food odor learning. Gregarious lo-\ncusts do not acquire new olfactory aversions, contrary to sol-\nitary locusts (Simoes et al. 2016). The effect of the feeding state on olfactory sensitivity has\nmainly been investigated in blood-feeding insects. The ex-\npression of olfactory genes in the antennae and responses of\nOSNs are modulated after a blood meal in, e.g., mosquitoes,\ntsetse flies, and triatomine bugs. This leads to reduced behav-\nioral responses to host cues, but increased responses to alter-\nnative signals, such as odors emitted by oviposition sites, or\naggregation pheromones in bugs (e.g., Rinker et al. 2013;\nTaparia et al. 2017) (for review see also Gadenne et al. (2016)). Recently, also a role of neuropeptides in the brain\nhas been revealed to contribute to feeding-dependent modula-\ntion of olfactory sensitivity in blood-feeding and herbivorous\ndiptera. Environmental conditions during development in\nnon-social insects Several insect species change their lifestyle and phenotype\ndepending on environmental conditions during\npostembryonic development and accordingly modify their ol-\nfactory communication skills. Locusts, for example, strongly\nchange their lifestyle as a function of population densities\nduring development. When densities exceed a certain Feeding state The available genome for this aphid species should\nallow in the future to pinpoint neuromodulators and their re-\nceptors involved in the structural (and probably physiological)\nchanges between winged and wingless females. Reproductive state The reproductive state, i.e., either the mating state or the\ncapacity to reproduce, has important effects on the re-\nsponses to pheromones or volatile host cues. In the male Cell Tissue Res (2021) 383:149–164 151 Variations of postembryonic brood care in social\ninsects For example, honeybee queens do not respond to their\nown mandibular pheromone bouquet, and in sterile workers,\nthe response to the queen pheromone is both age- and stage-\ndependent (Vergoz et al. 2009). Interestingly, the effects of\nqueen mandibular pheromone are mediated by a single com-\nponent (homovanillyl alcohol) that has high chemical similar-\nity with dopamine and acts on brain dopamine receptors that\nmodulate aversive olfactory learning (Vergoz et al. 2009). The\npostembryonic pupal development in the two female castes\nshows marked differences. The ALs develop much faster\n(by about 4 days) in queens compared to workers, and the\nsame applies to synaptogenesis in olfactory sub-regions with-\nin secondary olfactory centers in the MBs (Groh and Rössler\n2011). Whereas the number of olfactory glomeruli in the adult\nAL is only slightly smaller in queens, the spatial arrangement\nand sizes of individual glomeruli show marked differences in\nqueens compared to workers. Differences in the AL and MB\nphenotypes are even more pronounced in ants comprising\npermanent worker castes, especially in leaf-cutting ants\n(Kelber et al. 2010; Groh et al. 2014). For example, in Atta\nvollenweideri, the development of trail pheromone–specific\nmacroglomeruli is worker size-dependent, and the overall\nnumber of glomeruli in a specific AL glomerular cluster (T4\ncluster) may differ by more than 50 glomeruli in minor vs. major workers (Kelber et al. 2010). Whether this marked AL\npolyphenism in the female worker castes is a sole effect of\ndifferential feeding still needs to be investigated. Whereas\nminor workers engage as fungus gardeners inside the nest,\nlarge workers leave the nest as foragers and search for profit-\nable food sources using primarily olfactory cues. Consequently, the behavioral responses to trail pheromone\nwere also shown to be worker size-dependent (Kleineidam\net al. 2007). Variations of postembryonic brood care in social\ninsects Cooperative brood care is a hallmark feature of insect socie-\nties, and differential conditions during postembryonic brood 152 Cell Tissue Res (2021) 383:149–164 regions of the MBs. The resulting synaptic changes correlate\nwith inferior olfactory learning and memory capabilities or\nchanges in the timing of foraging (Tautz et al. 2003; Jones\net al. 2005; Becher et al. 2009). Furthermore, bees raised at\nlower temperatures performed less well in associative olfacto-\nry memory tasks, and they differed in dance-communication\nperformance and undertaking behavior compared to bees\nraised at higher temperatures within the range of naturally\noccurring temperatures in the brood area. Similarly, in\nCamponotus ants, workers control the temperature of pupae\nto specific temperature ranges during postembryonic meta-\nmorphic development by brood carrying behavior. Ant nurses\nrespond to changes in the ambient temperature by placing the\nbrood to nest compartments with the appropriate temperatures\nfollowing circadian rhythms (Roces and Núñez 1989;\nFalibene et al. 2016). Also in ants, suboptimal temperature\nregimes affect proper development of olfactory synapses in\nthe MBs (Falibene et al. 2016). Most interestingly, ants that\nhad experienced diverging brood temperature regimes exhibit\ndifferences in their stimulus response thresholds for adult\nbrood carrying behavior, most likely due to changes in senso-\nry thresholds (Weidenmüller et al. 2009). Taken together, dif-\nferential environmental influences caused by variations of\nbrood care conditions during postembryonic metamorphic de-\nvelopment affect olfactory circuits in the brain and have con-\nsequences for a range of adult olfaction-related behaviors. The\nabove forms of plasticity, therefore, represent interesting cases\nof metaplasticity (Abraham 2008), meaning that olfactory\nplasticity induced by brood care conditions affects adult be-\nhavioral plasticity in social insect colonies. However, how\nexactly changes in olfactory circuits are causally linked to\nchanges in complex olfactory behavior (both at the individual\nand colony levels) still requires further investigations. development may affect the adult phenotype (Weaver 1957). For example, in the honeybee, the reproductive status and\ndevelopment of the female castes (queen-worker\npolymorphism) are induced by differential larval feeding and\nmediated via an epigenetic mechanism involving royal jelly\nproduced in the hypopharyngeal glands (Kucharski et al. 2008). Queens develop from fertilized eggs that are genetical-\nly not different from those that develop into workers, but they\ndevelop faster, are larger, live much longer, and differ mark-\nedly in their adult behavior, including olfactory-guided behav-\niors. Variations of postembryonic brood care in social\ninsects For example, honeybee queens do not respond to their\nown mandibular pheromone bouquet, and in sterile workers,\nthe response to the queen pheromone is both age- and stage-\ndependent (Vergoz et al. 2009). Interestingly, the effects of\nqueen mandibular pheromone are mediated by a single com-\nponent (homovanillyl alcohol) that has high chemical similar-\nity with dopamine and acts on brain dopamine receptors that\nmodulate aversive olfactory learning (Vergoz et al. 2009). The\npostembryonic pupal development in the two female castes\nshows marked differences. The ALs develop much faster\n(by about 4 days) in queens compared to workers, and the\nsame applies to synaptogenesis in olfactory sub-regions with-\nin secondary olfactory centers in the MBs (Groh and Rössler\n2011). Whereas the number of olfactory glomeruli in the adult\nAL is only slightly smaller in queens, the spatial arrangement\nand sizes of individual glomeruli show marked differences in\nqueens compared to workers. Differences in the AL and MB\nphenotypes are even more pronounced in ants comprising\npermanent worker castes, especially in leaf-cutting ants\n(Kelber et al. 2010; Groh et al. 2014). For example, in Atta\nvollenweideri, the development of trail pheromone–specific\nmacroglomeruli is worker size-dependent, and the overall\nnumber of glomeruli in a specific AL glomerular cluster (T4\ncluster) may differ by more than 50 glomeruli in minor vs. major workers (Kelber et al. 2010). Whether this marked AL\npolyphenism in the female worker castes is a sole effect of\ndifferential feeding still needs to be investigated. Whereas\nminor workers engage as fungus gardeners inside the nest,\nlarge workers leave the nest as foragers and search for profit-\nable food sources using primarily olfactory cues. Consequently, the behavioral responses to trail pheromone\nwere also shown to be worker size-dependent (Kleineidam\net al. 2007). In addition to controlled feeding of larvae, many social\ni\nt\ni\nid\nt ll d li\nt\nditi\n(\ni\nd development may affect the adult phenotype (Weaver 1957). For example, in the honeybee, the reproductive status and\ndevelopment of the female castes (queen-worker\npolymorphism) are induced by differential larval feeding and\nmediated via an epigenetic mechanism involving royal jelly\nproduced in the hypopharyngeal glands (Kucharski et al. 2008). Queens develop from fertilized eggs that are genetical-\nly not different from those that develop into workers, but they\ndevelop faster, are larger, live much longer, and differ mark-\nedly in their adult behavior, including olfactory-guided behav-\niors. Adult maturation and polyethism Some of the related\nchanges in olfactory circuits were assigned to age, but the\ntemporal flexibility of task-related changes in adult behavior\n(adult polyethism) adds another level of complexity of olfac-\ntory plasticity in social insects that needs to be studied in more\ndetail in the future. In addition to the AL, robust structural changes associated\nwith adult behavioral maturation were observed in olfactory\ninput regions of the MBs, as reported by several studies in\nsocial Hymenoptera (e.g., honeybee (Withers et al. 1993;\nDurst et al. 1994; Fahrbach et al. 1998; Groh et al. 2012;\nScholl et al. 2014; Muenz et al. 2015), ants (Gronenberg\net al. 1996; Kühn-Bühlmann and Wehner 2006; Stieb et al. 2010, 2012)). The cellular processes underlying these volume\nchanges involve massive outgrowth of Kenyon cell (KC) den-\ndrites and, at the same time, pruning of presynaptic boutons\nwithin microglomerular synaptic complexes (Farris et al. 2001; Stieb et al. 2010; Groh et al. 2012; Muenz et al. 2015). Dendritic expansion is the main cause for the volume\nincrease in the MB calyx during the transition from nursing to\nforaging. The overall result of this structural plasticity is an\nincrease in the olfactory projection neuron to KC synaptic\ndivergence of olfactory circuits by about 33% (the number\nof KC dendritic profiles forming synapses with one\nprojection neuron bouton; Groh et al. 2012). Pharmacological stimulation suggests that the underlying pro-\ncesses are promoted by activity in muscarinic cholinergic\ntransmission during foraging experience (Ismail et al. 2006). Sensory exposure was also shown to play an important role in\nthis olfactory plasticity in leaf-cutting ants (Falibene et al. 2015). A combined anatomical and patch-clamp study in\nD. melanogaster confirmed that structural plasticity of olfac-\ntory MB input synapses is induced by sensory activation\n(Kremer et al. 2010). Interestingly, aged honeybee queens\nexhibit an increase in the relation of olfactory versus visual\ninput synapses in the MB calyx (Groh et al. 2006). More\nrecent studies revealed that social experience influences the The rather drastic interior-forager transition in social in-\nsects correlates with changes in diverse neuromodulators and\nhormones (Hamilton et al. 2016). For example, variations\nwere found in biogenic amine levels (reviewed in Kamhi\nand Traniello (2013)), juvenile hormone (Robinson 1987;\nBloch et al. 2002; Dolezal et al. 2012), and vitellogenin\n(e.g., Amdam and Omholt 2003). Adult maturation and polyethism Future cohort experiments using more\ntightly controlled sensory manipulations and high-resolution\nanatomical and behavioral analyses are needed to dissect the\nchanges in olfactory circuits caused by differences in sensory\nor social experience in order to find the mechanisms how they\naffect adult olfactory behavior (Groh and Rössler 2020). Winter bees (the last generation of bees in fall) might be a\nvaluable model for studying adult olfactory plasticity in the\nfuture, as they live much longer than summer bees and start to\nresume foraging in the next spring after staying in the hive\nduring the entire winter. The winter bee model may help to\ndissect more clearly effects of age and sensory experience. The molecular mechanisms underlying structural neuronal\nplasticity of olfactory circuits are still unknown. A gene ex-\npression study (Becker et al. 2016) revealed several genes that\nmight be associated with epigenetic regulation of neuronal\nplasticity during behavioral maturation of the honeybee, and\nGTPase activities were correlated with the nurse-forager tran-\nsition (Dobrin and Fahrbach 2012). However, in both cases, it\nremained unclear how the molecular changes causally link to\nstructural plasticity in olfactory circuits, which opens an im-\nportant field for future studies. Recent studies on changes in\nthe activity of immediate early genes following odorant expo-\nsure are highly promising in this respect (reviewed in\nSommerlandt et al. (2019)). Schachtner 2005; Tomé et al. 2014). These age-dependent\nchanges in olfactory sensitivity, at least in moths, have been\nshown to be independent of experience. Adult behavioral maturation and the associated changes in\nsensory experience affect the olfactory system in social in-\nsects. Various studies revealed substantial effects of sensory\nexperience on the development of the AL particularly mor-\nphological aspects of individual olfactory glomeruli and their\nresponses to odorants in the AL of honeybees (Winnington\net al. 1996; Jernigan et al. 2020). Calcium-imaging experi-\nments suggest that the odor responsiveness of AL glomeruli\nin honeybee workers increases during the first days of adult\nlife (Wang et al. 2005). Studies in D. melanogaster indicate\nthat activity-related volume increases in olfactory glomeruli\nare mainly caused by an increase in synaptic density within\nthe glomeruli, most likely mediated via local interneurons\n(Devaud et al. 2003; Sachse et al. 2007). Adult maturation and polyethism There is ample evidence that early adult development modu-\nlates behavioral, peripheral, and central nervous olfactory sen-\nsitivity to pheromones in various insects, but also to non-\npheromonal odors. During early adult life, increasing antennal\nresponses to pheromones or kairomones have been shown to\ncorrelate, for example, with increased odorant receptor ex-\npression in mosquitoes or increased hormone receptor expres-\nsion in a noctuid moth (Bigot et al. 2012; Bohbot et al. 2013). At the CNS level, the age-dependent modulation of attraction\nbehavior and AL neuron sensitivity to sex or aggregation\npheromones in moths and locusts has been shown to depend,\namong others, on juvenile hormone titers (for review see\nGadenne et al. (2016)). In various insects, morphological\nchanges have been observed in primary and secondary olfac-\ntory centers associated with age-dependent increases in olfac-\ntory sensitivity to specific cues or changes in olfactory learn-\ning and memory performance (e.g., Huetteroth and In addition to controlled feeding of larvae, many social\ninsect species provide controlled climate conditions (reviewed\nby, e.g., Seeley and Heinrich (1981)), which have conse-\nquences for metamorphic development including the forma-\ntion of olfactory centers in the brain. Experimental manipula-\ntions in the honeybee have shown that accurate temperature\ncontrol is required for proper development of olfactory sub-\nregions in the MBs (Groh et al. 2004, 2006). Slight deviations\n(1 °C) from the optimal temperature range (36 °C ± 0.5 °C)\nlead to deficits in synaptic maturation in olfactory input 153 Cell Tissue Res (2021) 383:149–164 number of MB olfactory input synapses in worker bees\n(Cabirol et al. 2017, 2018). However, a major problem with\nmanipulations of the social environment is that too many var-\niables (e.g., pheromonal, tactile, visual) may change at the\nsame time and, in most cases, are difficult to control. This\nproblem, for example, became evident while studying the in-\nfluence of the primer pheromone ethyl oleate on maturation of\nthe olfactory circuits in the honeybee brain (Muenz et al. 2015). Ethyl oleate is present at high concentrations on the\ncuticle of experienced foragers, sensed by OSNs on the anten-\nnae of nurse bees, processed in the AL (Muenz et al. 2012),\nand finally causes a delay in adult behavioral maturation\n(Leoncini et al. 2004). Adult maturation and polyethism However, the causal links\nof these modulators and hormones, especially how they affect\nspecific sensory pathways, including the olfactory pathways,\nand/or individual behavioral modules, are still discussed con-\ntroversially (reviewed in Hamilton et al. (2016)). In recent\nyears, studies on social Hymenoptera began to focus on the\nlarge and diverse group of neuropeptides as potential modu-\nlators of behavioral pattern transitions (Takeuchi et al. 2003;\nBrockmann et al. 2009; Pratavieira et al. 2014; Schmitt et al. 2015, 2017; Han et al. 2015; Gospocic et al. 2017). For Cell Tissue Res (2021) 383:149–164 154 example, in the desert ant Cataglyphis fortis, tachykinin was\nshown to express age- and behavioral state-related changes\nassociated with task transitions (Schmitt et al. 2017). In the\nponerine ant Harpegnathos saltator, corazonin was identified\nas an important driver of behavioral changes (e.g., worker-\nspecific hunting behavior) associated with the transition of\nfemale workers into reproductive substitute queens\n(Gospocic et al. 2017). Neuropeptides have a specifically high\npotential to mediate a variety of specific or highly localized\nmodulatory actions on neuronal circuits associated with dif-\nferent behavioral patterns, as they represent a very large and\ndiverse group of messenger molecules that may act both as\nneurohormones or neuromodulators (e.g., reviewed by\nSchoofs et al. (2017); Nässel and Zandawala (2019)). Future\nlocalization analyses of stage-specific changes in the spatial\ndistribution of individual neuropeptides within primary and\nsecondary olfactory centers combined with functional analy-\nses using manipulation experiments appear highly promising\nin understanding the role of neuropeptides in age- and stage-\nspecific plasticity of olfactory behaviors and circuits. simultaneous application (Dupuy et al. 2017), which corre-\nlates with delayed behavioral responses. In another noctuid\nmoth, Helicoverpa armigera, calcium imaging revealed a re-\nduced increase of intracellular calcium levels when stimulated\nwith a blend of sex pheromone and complex plant odors as\ncompared to individual odor application (Ian et al. 2017). On\nthe other hand, synergistic responses to a mixture of sex pher-\nomone and a volatile originating from the larval host plant,\npear ester, were reported in the AL of the codling moth Cydia\npomonella and well correlated with behavioral responses\n(Trona et al. 2013). In the noctuid moth S. littoralis, host plant\nvolatiles enhance the selectivity for conspecific pheromone\nblends (Borrero-Echeverry et al. 2018), but nothing is known\nso far about the underlying neural mechanisms. Context-dependent plasticity and modulation Behavioral responses to olfactory signals are modulated by\nvarious environmental factors, including different sensory\ncues emitted by conspecifics, for example, social interactions\n(see following paragraphs), or other organisms, as well as\nabiotic factors, such as climate, and pollutants. Such modula-\ntion and plasticity can occur at different levels within the ol-\nfactory system, starting from the periphery in OSNs, and, in\nmany cases, results in changes within the AL, and especially\nthe MBs (Fig. 1). Adult maturation and polyethism Non-host volatiles or herbivore feeding-induced volatiles\nhave been shown in several insects to reduce responses to\npheromones. One example is the response to aggregation\npheromones in bark beetles, which is inhibited by non-host\nvolatiles or volatiles emitted by attacked host trees, originating\nfrom inhibition within the OSNs on the antennae (Zhang et al. 1999; Jactel et al. 2001; Andersson et al. 2010). In several\nmoth species, non-host plant volatiles also modulate male\nsex pheromone responses, but again, interactions have only\nbeen investigated at the antennal level (Party et al. 2009, 2013;\nFaraone et al. 2013; Binyameen et al. 2013; Hatano et al. 2015; Wang et al. 2016). Signals emitted by herbivore-\nattacked plants can also modulate the attractiveness of host\nplants for female moths searching for an oviposition site. Females of the tobacco hawk moth, Manduca sexta, prefer\nundamaged host plants above herbivore-damaged plants, in\nwhich enhanced emission of (−) linalool renders the signal\nless attractive (Reisenman et al. 2013). This correlates with\ninhibitory interactions between two AL glomeruli specific for\nthe two linalool enantiomers in female M. sexta (Reisenman\n2005). How exactly odor responses are modulated in the pres-\nence of other volatiles at the different levels of the olfactory\npathway is still a matter of debate. In the peripheral system,\ndirect chemical interactions, competition for binding sites, and\ninteractions within co-localized neurons might be possible\n(Renou 2014). At the CNS level, additional interactions via\nseparate input channels have to be considered (Renou and\nAnton 2020). Immediate sensory environment The blue pathway depicts influences of ascending and protocerebral neu-\nronal systems mediating associative influences (e.g., octopaminergic, do-\npaminergic systems). G, olfactory glomerulus; KC, Kenyon cell; LN,\nlocal interneuron; OSN, olfactory sensory neurons; MBON, mushroom\nbody output neuron; PN, projection neuron\n155\nCell Tissue Res (2021) 383:149–164 indicate sites that have been shown to express structural plasticity in\nolfactory neuronal circuits (structural synaptic changes, changes in\naxonal/dendritic structure and connectivity, neuropil volume changes). The blue pathway depicts influences of ascending and protocerebral neu-\nronal systems mediating associative influences (e.g., octopaminergic, do-\npaminergic systems). G, olfactory glomerulus; KC, Kenyon cell; LN,\nlocal interneuron; OSN, olfactory sensory neurons; MBON, mushroom\nbody output neuron; PN, projection neuron Fig. 1 Schematic view of the insect olfactory pathway—from the sensory\nstructures on the antenna to primary (antennal lobe, AL) and secondary\nolfactory centers (mushroom body, MB and lateral horn, LH) in the brain,\nindicating factors inducing plasticity and modulation at various process-\ning levels. The blue asterisks indicate sites of action of neuromodulators\n(biogenic amines, neuropeptides) or hormones and sites for associated\nphysiological and molecular changes (spontaneous activity, response\nthreshold, changes in the expression of odorant receptors, changes in\nthe expression of receptors for modulators or hormones). Red asterisks (Skals et al. 2005). Other sensory modalities, such as vision\nand taste, are also known to modulate/modify olfactory-\nguided behavior, but these interactions rather occur within\nsecondary olfactory centers such as the MBs, for example,\nas shown in moths and honeybees (Balkenius and Balkenius\n2016; Strube-Bloss and Rössler 2018). impaired short-term memory, probably due to increased ex-\npression of nicotinic acetylcholine receptor expression and\nincreased neural sensitivity to acetylcholine (Desneux et al. 2007; Wright et al. 2015; Cabirol and Haase 2019). Opposite to decreased olfactory responses, sugar sensitivity\nin honeybees increased after treatments with sublethal doses\nof the neonicotinoid acetamiprid (El Hassani et al. 2008). Inversely, in the noctuid moth A. ipsilon, different sublethal\ndoses of the neonicotinoid insecticide clothianidin were\nshown to up- or downregulate the sensitivity of AL neurons\nto the sex pheromone, depending on the dose, and in parallel\nincreased or decreased the behavioral response probability to\nthe sex pheromone (Rabhi et al. 2014, 2016). In another noc-\ntuid moth, S. littoralis, peripheral and behavioral modulation\nof sex pheromone responses was caused by sublethal doses of Immediate sensory environment The presence of different sensory stimuli in the immediate\nenvironment of an insect can alter responses to a given olfac-\ntory stimulus through interactions of the odorants at the pe-\nripheral and/or central level. Even though this type of interac-\ntion needs not necessarily fall into the category of modulation,\nwe would like to include them here, because they might inter-\nfere or provide the basis for some cases of experience-\ndependent plasticity. A prominent example is the interaction\nbetween sex pheromones and plant-emitted volatiles in male\nmoths. A flower volatile, heptanal, for example, reduces re-\nsponses to the sex pheromone within the macroglomerular\ncomplex of the AL in the noctuid moth A. ipsilon both at the\ninput and output level (Deisig et al. 2012) but also results in an\nimproved temporal resolution of pheromone pulses by AL\noutput neurons (Chaffiol et al. 2014). When the two odors\nare presented with a time shift, the responses of AL neurons\nto the sex pheromone are delayed as compared to a There is also evidence for the modulation of sex phero-\nmone responses within the AL by mechanical stimulation of\nthe antennae in the noctuid moth S. littoralis with a clean air\npuff (Han et al. 2005). This indicates that modulation of ol-\nfactory responses occurs as a function of antennal\nmechanosensory detection, which can result from air move-\nments in the environment or from feedback of flight activity. Auditory and olfactory inputs also interact in the case of sex\npheromone responses in moths when predatory bats emit ul-\ntrasound signals. Behavioral responses to the ultrasound sig-\nnals depend on the quality of the sex pheromone stimulus 155 Cell Tissue Res (2021) 383:149–164 Fig. 1 Schematic view of the insect olfactory pathway—from the sensory\nstructures on the antenna to primary (antennal lobe, AL) and secondary\nolfactory centers (mushroom body, MB and lateral horn, LH) in the brain,\nindicating factors inducing plasticity and modulation at various process-\ning levels. The blue asterisks indicate sites of action of neuromodulators\n(biogenic amines, neuropeptides) or hormones and sites for associated\nphysiological and molecular changes (spontaneous activity, response\nthreshold, changes in the expression of odorant receptors, changes in\nthe expression of receptors for modulators or hormones). Red asterisks\nindicate sites that have been shown to express structural plasticity in\nolfactory neuronal circuits (structural synaptic changes, changes in\naxonal/dendritic structure and connectivity, neuropil volume changes). Abiotic environmental factors A major anthropogenic factor influencing the insect olfactory\nsystem are insecticides remaining in the environment for a\nlong time. Especially sublethal doses of neonicotinoid insec-\nticides were shown to have negative effects on pollinating\ninsects, such as honeybees and bumblebees, including de-\ncreased behavioral responses to attractive olfactory cues and Cell Tissue Res (2021) 383:149–164 156 another insecticide, deltamethrin, a widely used pyrethroid\n(Lalouette et al. 2016). So far, it is, however, not known if\nthe observed modulatory effects of insecticides are caused\ndirectly by receptor-ligand interactions, or if insecticides cause\nmodifications of neuromodulator levels or expression of their\nreceptors (Abrieux et al. 2013, 2014, 2016). In addition, py-\nrethroid insecticides were shown to disturb the wiring of ol-\nfactory glomeruli during postembryonic metamorphic devel-\nopment in M. sexta (Wegerhoff et al. 2001). MB calyces increased in size after brief pre-exposure to these\nsame stimuli (Guerrieri et al. 2012; Anton et al. 2015). Attractive and repellent gustatory stimuli also improved sub-\nsequent behavioral responses to the sex pheromone, but nei-\nther modified AL neuron responses to the sex pheromone nor\nthe volume of MGC glomeruli or the MB calyces, indicating\nthat the behavioral effects might originate from neural modi-\nfications in higher brain centers (Minoli et al. 2012; Anton\net al. 2015). Olfactory plasticity involving learning\nand memory Associative learning is very common in a large variety of\ninsects. As associative learning and memory represent a very\nlarge and rapidly expanding research field, we here mostly\nfocus on plasticity associated with stable olfactory long-term\nmemory (LTM), as it has the potential to affect insect behavior\nover extended time. The wealth of literature in the field of\nassociative learning and memory, especially in\nD. melanogaster, is beyond the scope of this review (for re-\ncent reviews, see, e.g., Kahsai and Zars (2011); Guven-Ozkan\nand Davis (2014); Sugie et al. (2018); Kacsoh et al. (2019);\nBoto et al. (2020)). Even though behavioral and molecular\nstudies of learning in parasitoid wasps are numerous (for re-\nview see Hoedjes et al. (2011); Smid and Vet (2016)), neuro-\nbiological studies besides D. melanogaster have largely fo-\ncused on social insects, which shall be the main topic here. Nevertheless, associative olfactory learning has been evi-\ndenced in various other insects, such as moths, locusts,\ncrickets, and parasitic wasps (e.g., Fan et al. (1997); Hartlieb\net al. (1999); Daly and Smith (2000); Meiners et al. (2003);\nSkiri et al. (2005); Costa et al. (2010); Simoes et al. (2016)),\nbut the underlying neurobiological mechanisms are unex-\nplored except for a few rare cases (Cayre et al. 2007;\nCassenaer and Laurent 2012). Among the social insects, the\nhoneybee has proven a very valuable model for the study of\nplasticity related to long-term memory (> 24 h) (e.g., Menzel\n(1999); Müller (2000); Menzel and Giurfa (2001); Menzel\net al. (2007)). Experience-related changes in the activity of\nglomeruli were described in the AL of the honeybee using\ncalcium-imaging techniques indicating that changes in olfac-\ntory responses persist over extended time periods after asso-\nciative learning at this early sensory processing level (Rath\net al. 2011). The robust and well-studied proboscis extension\nresponse is a favorable behavioral paradigm for classical con-\nditioning to study olfactory LTM in detail. Using sequential\nassociative conditioning, bees can be trained to memorize the\nassociation between a sugar reward and an odor over extended\ntime (> 3 days up to weeks, months, or lifetime). The reward\nor punishment pathways for appetitive and aversive olfactory\nlearning have been linked to ascending and brain\noctopaminergic and dopaminergic modulatory systems—\nmainly via their influences on odor responses at the level of Non-associative experience Multiple-\ntrial conditioning leading to LTM has previously been shown\nto depend on intracellular calcium levels, which indicates a\nrole of calcium in structural plasticity associated with stable\nLTM (Perisse et al. 2009). In the same line (Scholl et al. 2015),\nusing RNAi knockdown and pharmacological manipulation\nin the MBs showed that CaMKII is required for the formation\nof both early and late olfactory LTM, indicating that the\ncalcium-dependent “learning protein” might be involved in\ntriggering structural synaptic plasticity. The above studies\nsuggest that olfactory LTM is associated with structural A similar effect was observed in leaf-cutting ants, in that\ncase after aversive olfactory learning of odors associated with\nunsuitable plant material for cultivating the underground sym-\nbiotic fungus maintained by the ants (Falibene et al. 2015). The formation of an aversive olfactory LTM leads to an in-\ncrease of the synaptic densities in olfactory (not visual) cir-\ncuits of the MBs, whereas pure sensory exposure resulted in\nsynaptic pruning. Whereas the increase of synaptic boutons\nmay also represent a form of Hebbian plasticity, pruning of\nsynapses after pure sensory exposure may lead to adjustments\nin MB circuits resulting in homeostatic regulation to a drasti-\ncally changing olfactory sensory input. Physiological access to plasticity of olfactory circuits in the\nMBs is sparse, except for few calcium-imaging studies sug-\ngesting physiological plasticity at the olfactory projection\nneuron-to-KC synapses and electrophysiological recordings\nrevealing spike-timing-dependent plasticity at mushroom\nbody output neuron synapses (Faber et al. 1999; Szyszka\net al. 2008; Cassenaer and Laurent 2012). Learning-related\nolfactory plasticity was also revealed by intracellular record-\nings and calcium imaging of GABAergic neurons in the hon-\neybee forming recurrent circuits from the MB output to the\ninput (Grünewald 1999; Haenicke et al. 2018). Similarly, re-\ncordings revealed olfactory plasticity in another type of MB\nextrinsic neurons (Haehnel and Menzel 2012). However, as\nintracellular recordings and calcium imaging are limited to\nshort-term recording times, it is difficult or rather impossible\nto monitor changes over extended periods, for example, after\nassociative conditioning. More recent studies employing long-\nterm recordings (over several hours to days) of MB extrinsic\nor MB output neurons via multiple thin wire tetrodes emerged\nas a feasible approach to monitor learning- and memory-\nrelated long-term changes in olfactory circuits. In the honey-\nbee, multi-unit recordings can even be combined with olfac-\ntory conditioning experiments using the proboscis extension\nresponse (Strube-Bloss et al. 2012). Non-associative experience This technique also opens\nup possibilities to look into multimodal (olfactory-visual) in-\nteractions and their role in context-specific influences on ol-\nfactory perception (Strube-Bloss and Rössler 2018). Non-associative experience Experience has long been known to modify behavioral re-\nsponses to olfactory stimuli, and the neuronal and molecular\nmechanisms underlying these modifications have been inves-\ntigated for many years. Here we will review only recent data\non the role of physiological mechanisms and anatomical long-\nterm modifications that occur within the olfactory pathway as\na consequence of different forms of learning in insects. As an\nextreme case, experience can be acquired during early devel-\nopment and influence larval or adult behavior, or, more fre-\nquently, during the adult stage that may lead to long-lasting\nadaptive changes in olfactory behavior. Even though there are\nindications, that larval host plant experience in moths modu-\nlates female oviposition behavior and even male partner\nchoice, so far the neural substrate concerning the transfer of\nmemories from the larval to the adult stage remains largely\nspeculative (Anderson and Anton 2014). Simple forms of non-associative experience modulating\nolfactory-guided behavior, such as sensitization and habitua-\ntion, have been revealed in many insects. Nevertheless, very\nlittle is known about the neural mechanisms underlying these\nforms of learning. Brief exposure to a behaviorally relevant\ndose of the sex pheromone in the male moth S. littoralis, on\nthe other hand, has been shown to modify the expression of an\nodorant-binding protein in the antenna and leads to stronger\nsubsequent OSN responses to the same signal (Guerrieri et al. 2012). However, as physiological and anatomical changes\noccur in the AL, too, upon pheromone exposure, we cannot\nexclude a feedback to the peripheral system causing this form\nof sensitization (Anderson et al. 2007; Guerrieri et al. 2012). In addition, brief exposure to various behaviorally active sen-\nsory signals, including predator sound and different olfactory\nstimuli, improved behavioral responses and increased the sen-\nsitivity of neurons within the AL to the sex pheromone, rather\nthan in the antennae in the same moth species (Anton et al. 2011; Minoli et al. 2012). At the same time, the volume of the\nmacroglomerular complex (MGC) glomerulus, processing in-\nformation on the major sex pheromone component, and of the Cell Tissue Res (2021) 383:149–164 157 to keep in mind that structural changes in olfactory synaptic\ncircuits themselves may be part of a memory trace, but wheth-\ner they are actually required for memory storage and retrieval\nremains to be determined. Non-associative experience the MBs, but also at the levels of the AL and lateral horn (LH)\n(Mauelshagen 1993; Hammer and Menzel 1995; Okada et al. 2007; Tedjakumala et al. 2014; Jarriault et al. 2018). Whereas\nthe majority of modifications due to associative LTM are lo-\ncalized at the CNS level, physiological plasticity associated\nwith LTM has nevertheless also been evidenced at the anten-\nnal level. Expression of olfactory receptors known to bind the\nlearned odor compounds was significantly downregulated af-\nter associative learning, and electroantennogram responses\nwere significantly reduced in honeybees which had formed a\nLTM, compared to control bees (Claudianos et al. 2014). The\nfeedback mechanism towards the CNS, however, remained\nunclear in this case. At the CNS level, the formation of a stable\nolfactory LTM was shown to be transcription-dependent and\nto involve structural synaptic changes in olfactory circuits at\nthe input of the MBs (Hourcade et al. 2010). Only bees that\nhad received paired stimulation of the conditioned (odor\npulse, CS) and unconditioned stimulus (sugar water, US),\nand that were not injected with the transcription inhibitor ac-\ntinomycin D (ActD) after training, had retained a stable LTM\nwhen they were tested with the CS after 3 days. Most inter-\nestingly, stable LTM formation after 3 days was associated\nwith an increase in synaptic complexes within olfactory com-\npartments of the MB calyces. This effect was absent in neigh-\nboring visual input regions. Naïve bees, i.e., bees that had\nreceived unpaired stimulation and paired stimulated bees that\nhad received ActD, were unable of memory retrieval and did\nnot show any changes in synaptic densities. The authors con-\nclude that growth of new synapses may be involved in stable\nLTM in the insect brain, similar to what has been found in the\nmammalian brain (Abraham et al. 2019). Compared with syn-\naptic pruning following sensory exposure as described above,\nassociative olfactory learning and the formation of\ntranscription-dependent stable LTM resulted in a volume-\nindependent increase of synaptic complexes in olfactory com-\npartments of the honey bee MBs (Groh and Rössler 2020). This suggests that the increases in densities of synaptic\nboutons after associative LTM formation may represent a\nform of learning-related (Hebbian) structural plasticity in\nMB-calyx microcircuits. Transcription-independent memo-\nries, such as early long-term memory, did not lead to any\ndetectable structural changes in olfactory circuits. Outlook Pre-adaptations for high levels of olfactory plasticity\nmay allow species more easily to invade new habitats in the course\nof climate change. Olfactory plasticity is also an important feature\nfrom an ecological point of view. We should investigate how\ndifferent lifestyles and interactions within trophic networks as well\nas with the abiotic environment influence plasticity. Studies on the\nmechanistic nature and role of such differences between closely\nand distantly related insect species with similar or different life-\nstyles, habitat preferences, and olfactory behaviors provide a rich\nground for future comparative research on the causes and conse-\nquences of olfactory plasticity. There is still a long way to go until we fully understand the\npowerful mechanisms and influences of olfactory plasticity\nand modulation on insect behavior and their ecological con-\nsequences. Both the experimental accessibility and rich diver-\nsity of insects clearly promise exciting future advances in this\nimportant field of research. Another important perspective is to investigate the role of\nmultimodal interactions, aiming towards understanding multi-\nsensory, context-dependent plasticity influencing olfactory per-\nception. Here the role of the MBs has been highlighted, but the\nfunction of other protocerebral neuropils, like the function and\npotential interactions with lateral horn neurons, is still largely\nunexplored in most insects. Recent advances in high-resolution\ninsect neuronal brain atlases that started in D. melanogaster\n(Dolan et al. 2019) will help to explore plasticity in these brain\nareas. The potential role of the lateral horn in memory formation\nshould be explored in future studies, as another recent study in\nD. melanogaster already showed that specifically context-\ndependent LTM appears to be mediated by lateral horn neurons\nafter only single trial conditioning (Zhao et al. 2019). Funding Open Access funding enabled and organized by Projekt DEAL. The authors have been supported by grants from the French National\nFunding Agency (ANR), the Region “Pays de la Loire” and the French\nInstitute of Agricultural Research (INRAE) to SA, and the German\nResearch Foundation (DFG) grants SPP 1392 (Ro1177/5-2) and SFB\n1047 (B6) and the University of Würzburg to WR. Open Access This article is licensed under a Creative Commons\nAttribution 4.0 International License, which permits use, sharing, adap-\ntation, distribution and reproduction in any medium or format, as long as\nyou give appropriate credit to the original author(s) and the source, pro-\nvide a link to the Creative Commons licence, and indicate if changes were\nmade. Outlook A major conclusion from previous studies is that plasticity and\nmodulation occur at all levels of the insect olfactory pathway. Whereas some drivers of plasticity like internal programs,\nage- and status-/stage-specific causes of plasticity, seem to\nact at both peripheral and central levels, experience-\ndependent plasticity like learning and memory as well as Cell Tissue Res (2021) 383:149–164 158 multimodal interactions preferentially, but not exclusively,\noccur at higher central levels, particularly the MB. The mech-\nanisms by which sensory and modulatory influences target the\ndifferent levels of the olfactory pathway are comparably well\nunderstood for learning and memory in the MBs (especially\nfrom work in D. melanogaster and the honeybee on dopami-\nnergic or octopaminergic modulation). Much less, however, is\nknown for other modes of plasticity including bottom-up and\ntop-down influences of olfactory memory. This clearly needs\nmore intense investigations in the future, for example, efforts\nto understand distributed forms of plasticity and to identify the\nmajor neuromodulators, for example, within the large family\nof neuropeptides. Furthermore, we need to find causal links\nbetween changes in gene expression or epigenetic regulation,\nmessenger molecules, and their action on identified neuronal\ncircuits all the way up to how plasticity in these circuits mod-\nulates behavior. In addition, we need more information on\nlocal modulatory interactions, such as between different olfac-\ntory glomeruli in the AL, recurrent pathways within the MBs,\nor interactions (bottom up and top down) between primary\nand secondary olfactory centers (MB, LH, and AL). In that\nrespect, MB output neurons (MBONs) might play a key role\nin mediating such interactions. Integrative and multidisciplin-\nary approaches at different levels are necessary to fully under-\nstand the mechanisms underlying age-, status-, and state-\nspecific changes in olfactory processing and perception. moths and social Hymenoptera, with rich knowledge on their\nolfactory systems, behaviors, and their plasticity, are specifically\nimportant from an applied point of view, because they include\nbothimportant pest species, but also beneficial (pollinator) species. Understanding olfactory plasticity in these insects will largely\ncontribute to efforts of environmentally acceptable control of pest\ninsects and to improve protection of beneficial species. To study\nnon-model insects, novel tools like CRISPR/Cas9 manipulation of\ngene expression already started to become very helpful. Comparative mechanistic approaches are highly important in fu-\nture research aimed at understanding the role of olfactory plasticity\nin the dynamics of adaptation of insect species under global\nchange. Abraham WC (2008) Metaplasticity: tuning synapses and networks for\nplasticity. Nat Rev Neurosci 9:387–387. https://doi.org/10.1038/\nnrn2356 Outlook The images or other third party material in this article are included\nin the article's Creative Commons licence, unless indicated otherwise in a\ncredit line to the material. If material is not included in the article's\nCreative Commons licence and your intended use is not permitted by\nstatutory regulation or exceeds the permitted use, you will need to obtain\npermission directly from the copyright holder. To view a copy of this\nlicence, visit http://creativecommons.org/licenses/by/4.0/. In evolutionary terms, variations in olfactory plasticity between\ndifferent insect species provide a promising source of knowledge\nto understand their efficient adaptation to the environment. Insects\nrepresent by far the most abundant group of animal species with\nhighly diverse lifestyles. Because of this rich species diversity and\nthe multitude of evolutionary adaptations across insect taxa, it will\nbe most important to promote comparative research on plasticity\nin the olfactory system of diverse insect species. This includes\nclassical model insects like D. melanogaster, using the powerful\ngenetic manipulations available, but equally important, non-model\ninsect species should be investigated to reveal insight into novel\nmodes of plasticity in their olfactory systems and behaviors. 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