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

Floodplains and polder-type agricultural catchments, are
specific land formations which management is highly demanding from
several aspects. Water pumping system affects and modifies natural
hydrology, influencing thus surface and groundwater quality. The close
contact with the coastal sea may affect the quality of adjacent marine
environment as well. The 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. Sea water intrusion and nutrients entry from polders' drainage
system into the adjacent water system of the study area exhibit
pronounced variability in space and time. Therefore, the methodology
combined big spatial-temporal data from the long-term water quality
monitoring program and specific (geo)statistical methods to provide a
global model by effectively fusing spatial and temporal information which
could be used for site-specific environmental management. The objective
was to assess the impact of natural processes and human activities on
water quality. 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 four classes of water bodies in each: river streams, lateral
canals, pumping stations, drainage canals and groundwater. The method of
data analysis used is based on LME theory with correlated spatial and
temporal residuals, which takes also into account the heterogeneity of
the variance associated with each type of water quality monitoring
station. The results show that the main source of variation are sea water
intrusion and agricultural activities and variations are significantly
depending on the seasons. LME model could be effectively used for sitespecific
environmental management.