Groundwater nitrate vulnerability assessment in alluvial aquifer using process-based models and weights-of-evidence method: Lower Savinja Valley case study (Slovenia)

Abstract

This paper describes the implementation of process-based models reflecting relative groundwater nitrate vulnerability of the shallow alluvial Lower Savinja Valley (LSV) aquifer in Slovenia. A spatially explicit identification of the potentially vulnerable priority areas within groundwater bodies at risk from a chemical point of view is being required for cost-effective measures and monitoring planning. The shallow LSV unconfined aquifer system consists of highpermeable Holocene and middle-to low-permeable Pleistocene gravel and sand, with a maximum thickness of about 30 m, mainly covered by shallow eutric fluvisoils or variously deep eutric cambisoil. The hydrogeological parameters, e.g. the depth to the groundwater, hydrological role of the topographic slope, etc. usually used in different point count schemes are, in the case of the lowland aquifer and shallow groundwater, spatially very uniform with low variability. Furthermore, the parametric point count methods are generally not able to illustrate and analyze important physical processes, and validation of the results is difficult and expensive. Instead of a parametric point count scheme, we experimentally used the Arc-WofE extension for weights-ofevidence (WofE) modelling. All measurement locations with a concentration higher than the value of 20 mg NO 3 -per litre of groundwater have been considered as training points (173), and the three process-based models generalized output layers of groundwater recharge (GROWA), nitrate leached from the soil profile (SWAT) and groundwater flow velocity (FE-FLOW), served as evidential themes. The technique is based on the Bayesian idea of phenomena occurrences probability before (prior probability) and after consideration of any evidential themes (posterior probability), which were measured by positive and negative weights as an indication of the association between a phenomena and a prediction pattern. The response theme values describe the relative probability that a 100 9 100 m spatial unit will have a groundwater nitrate concentration higher than the training points' limit values with regard to prior probability value. The lowest probability of groundwater nitrate occurrence is in the parts of the LSV aquifer, which are known as anoxic condition areas with very likely denitrification processes. The cross-validation of the dissolved oxygen and dissolved nitrate response theme confirmed the accuracy of the groundwater nitrate prediction. The WofE model results very clearly indicate regional groundwater nitrate distribution and enable spatial prediction of the probability for increased groundwater nitrate concentration in order to plan the groundwater nitrate reduction measures and optimize the programme for monitoring the effects of these measures.

Model-based assessment of groundwater recharge in Slovenia

“…In this framework, the Slovenian Environmental Agency prepares annual reports on the groundwater quantity status which include the groundwater recharge rates for all 21 Slovenian groundwater bodies quantified with the distributed GROWA model (Andjelov et al 2006;Uhan et al 2010;Uhan 2012). On the regional level, GROWA model results are used for the sustainable development of transboundary groundwater bodies, e.g.…”

Groundwater vulnerability assessment: from overlay methods to statistical methods in the Lombardy Plain area

“…Among the various statistical methods, the Weights of Evidence (WofE, Bonham-Carter 1994) modelling technique has been chosen for its reliability in performing meaningful and scientific defensible groundwater vulnerability maps, which has been proved by various Authors (e.g., Arthur et Uhan et al 2011). WofE is a cell-based modeling technique, which combines different spatial datasets in a Geographical Information System (GIS) environment to analyze and describe their interactions and generate predictive patterns (Bonham-Carter 1994;Raines et al 2000).…”

“…Moreover, they allow identifying the likely important sources of contamination (Sorichetta et al 2013) and assessing the relative probability of contamination occurrence (i.e., groundwater vulnerability), considering the simultaneous presence, or absence, of these factors (e.g., Nolan 2001). In this regard, various authors (Worrall and Besien 2005;Twarakavi and Kaluarachchi 2006;Uhan et al 2011) showed how the high flexibility of statistical methods allows more reliable groundwater vulnerability assessments over large areas compared to the ones obtained with other methods.…”

Runoff simulation using a modified SWAT model with spatially continuous HRUs

“…Owing to its user-friendly interface, open source code and extensive regional adaptation, the SWAT model is becoming increasingly popular worldwide Yang et al 2010;Ouyang et al 2010;Kingston and Taylor 2010;Smith and Dragovich 2008;Schuol et al 2008;Salvetti et al 2008;Arnold et al 2000;Tobin and Bennett 2009). Additional uses of the calibrated and validated SWAT model include analysis of the effects of climate and land use changes on catchmentwide hydrologic processes (Saha et al 2014) as well as turbidity control and groundwater nitrate vulnerability assessment (Noh et al 2014;Uhan et al 2011).…”