Modelling spatial and temporal variability of water quality from different monitoring stations using mixed effects...

Polder-type agricultural catchments within river deltas are specific land formations which management is highly demanding from several aspects. The close contact with the coastal sea may additionally affect the quality of adjacent marine environment. This study uses the case of the Lower Neretva Valley (LNV) to test the efficiency of applying Linear Mixed Effect (LME) theory in modelling spatial and temporal variations of surface and groundwater quality within a polder-type agricultural catchment. The methodology uses linear regressive techniques while taking into account spatial and temporal autocorrelation of residuals. The objective was to assess and model the spatial and temporal variability of the quality of surface- and ground-waters, in order to predict the impact of natural processes and human activities. A dataset of physicochemical properties of surface and groundwater quality of the LNV, recorded monthly in the period 2009-2017, was used to model the spatial and temporal variations of water salinity and nitrate concentrations. The network of water quality monitoring sites covers four polders on five thousand hectares of agricultural land, including the following types of water bodies: river streams, lateral canals, pumping stations, drainage canals and groundwater. The method of data analysis, based on LME theory with correlated spatial and temporal residuals, takes also into account the heteroscedasticity of the variance associated with each type of water quality monitoring station. The two Linear Mixed Effects models proposed for the prediction of electrical conductivity and nitrate concentration in the surface waters and groundwater, proved to be efficient at adequately reproducing the heterogeneity and complexity of the study area. However, the prediction of nitrate concentration in the water was not equally satisfactory of the one of electrical conductivity due to the large variation in nutrient concentrations. To improve spatial prediction, the density of monitoring network should be increased.