『Abstract
For the evaluation of policy action programs to improve groundwater
quality, research institutes and governments intensively monitor
nitrate concentrations in shallow or near surface groundwater.
However, trend detection is often hampered by the large seasonal
and multi-annual temporal variability in nitrate concentrations,
especially in shallow groundwater within 0-5 m below the surface
in relatively humid regions. This variability is mainly caused
by variations in precipitation excess (precipitation minus evapotranspiration)
that results in strong variability in groundwater recharge. The
objective of this study was to understand and quantify this weather-induced
variability in shallow groundwater nitrate concentrations.
We present an example of measured weather related variations
in shallow groundwater nitrate concentrations from De Marke, an
intensively monitored experimental farm in The Netherlands. For
the quantification of the weather-induced variability, concentration-indices
were calculated using a 1D model for water and solute transport.
The results indicate that nitrate concentrations in the upper
meter of groundwater at De Marke vary between 55％ and 153％ of
the average concentration due to meteorological variability. The
concentration-index quantification method was successfully used
to distinguish weather related variability from human-induced
trends in the nitrate concentration monitoring data from De Marke.
Our model simulations also shows that sampling from fixed monitoring
wells produces less short term variability than measuring from
open boreholes. In addition, using larger screen depths and longer
screens filters out short term temporal variability at the cost
of a more delayed detection of trends in groundwater quality.
Keywords: Groundwater quality; Weather-induced temporal variability;
Concentration-index; Monitoring; Nitrate』
Introduction
Measurements from De Marke
Methods
Concentration-indices
General Hydrus-1D model setup
Well types and sampling depths
Simplified model
Real world model
Evaluation of sampling well types
Results and discussion
Simplified model
Real world model
Evaluation of sampling well types
Conclusions
Acknowledgements
References