Linking process rates with modelling data and ecosystem characteristics

This report is related to the BONUS project “Nutrient Cocktails in COAstal zones of the Baltic Sea” alias COCOA. The aim of BONUS COCOA is to investigate physical, biogeochemical and biological processes in a combined and coordinated fashion to improve the understanding of the interaction of these processes on the removal of nutrients along the land-sea interface. The report is especially related to BONUS COCOA WP 6 in which the main objective is extrapolation of results from the BONUS COCOA learning sites to coastal sites around the Baltic Sea in general. Specific objectives of this deliverable (D6.4) were to connect observed process rates with modelling data and ecosystem characteristics. In the report we made statistical analyses of observations from BONUS COCOA study sites together with results from the Swedish Coastal zone Model (SCM). Eight structural variables (water depth, temperature, salinity, bottom water concentrations of oxygen, ammonium, nitrate and phosphate, as well as nitrogen content in sediment) were found common to both the experimentally determined and the model data sets. The observed process rate evaluated in this report was denitrification. In addition regressions were tested between observed denitrification rates and several structural variables (latitude, longitude, depth, light, temperature, salinity, grain class, porosity, loss of ignition, sediment organic carbon, total nitrogen content in the sediment,  sediment carbon/nitrogen-ratio, sediment chlorphyll-a as well as bottom water concentrations of oxygen, ammonium, nitrate, and dissolved inorganic  phosphorus and silicate) for pooled data from all learning sites. The statistical results showed that experimentally determined multivariate data set from the shallow, illuminated stations was mainly found to be similar to the multivariate data set produced by the SCM model. Generally, no strong correlations of simple relations between observed denitrification and available structural variables were found for data collected from all the learning sites. We found some non-significant correlation between denitrification rates and bottom water dissolved inorganic phosphorous and dissolved silica but the reason behind the correlations is not clear. We also developed and evaluated a theory to relate process rates to monitoring data and nutrient retention. The theoretical analysis included nutrient retention due to denitrification as well as burial of phosphorus and nitrogen. The theory of nutrient retention showed good correlations with model results. It was found that area-specific nitrogen and phosphorus retention capacity in a sub-basin depend much on mean water depth, water residence time, basin area and the mean nutrient concentrations in the active sediment layer and in the water column

Linking process rates with modellingdata and ecosystem characteristics

This report is related to the BONUS project “Nutrient Cocktails in COAstal zones of the Baltic Sea” alias COCOA. The aim of BONUS COCOA is to investigate physical, biogeochemical and biological processes in a combined and coordinated fashion to improve the understanding of the interaction of these processes on the removal of nutrients along the land-sea interface. The report is especially related to BONUS COCOA WP 6 in which the main objective is extrapolation of results from the BONUS COCOA learning sites to coastal sites around the Baltic Sea in general. Specific objectives of this deliverable (D6.4) were to connect observed process rates with modelling data and ecosystem characteristics. In the report we made statistical analyses of observations from BONUS COCOA study sites together with results from the Swedish Coastal zone Model (SCM). Eight structural variables (water depth, temperature, salinity, bottom water concentrations of oxygen, ammonium, nitrate and phosphate, as well as nitrogen content in sediment) were found common to both the experimentally determined and the model data sets. The observed process rate evaluated in this report was denitrification. In addition regressions were tested between observed denitrification rates and several structural variables (latitude, longitude, depth, light, temperature, salinity, grain class, porosity, loss of ignition, sediment organic carbon, total nitrogen content in the sediment,  sediment carbon/nitrogen-ratio, sediment chlorphyll-a as well as bottom water concentrations of oxygen, ammonium, nitrate, and dissolved inorganic  phosphorus and silicate) for pooled data from all learning sites. The statistical results showed that experimentally determined multivariate data set from the shallow, illuminated stations was mainly found to be similar to the multivariate data set produced by the SCM model. Generally, no strong correlations of simple relations between observed denitrification and available structural variables were found for data collected from all the learning sites. We found some non-significant correlation between denitrification rates and bottom water dissolved inorganic phosphorous and dissolved silica but the reason behind the correlations is not clear. We also developed and evaluated a theory to relate process rates to monitoring data and nutrient retention. The theoretical analysis included nutrient retention due to denitrification as well as burial of phosphorus and nitrogen. The theory of nutrient retention showed good correlations with model results. It was found that area-specific nitrogen and phosphorus retention capacity in a sub-basin depend much on mean water depth, water residence time, basin area and the mean nutrient concentrations in the active sediment layer and in the water column