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Thermal groundwater is currently being exploited for district-scale heating in many locations worldwide. The chemical compositions of these thermal waters reflect the provenance and circulation patterns of the groundwater, which are controlled by recharge, rock type and geological structure. Exploring the provenance of these waters using multivariate statistical analysis (MSA) techniques increases our understanding of the hydrothermal circulation systems, and provides a reliable tool for assessing these resources. Hydrochemical data from thermal springs situated in the Carboniferous Dublin Basin in eastcentral Ireland were explored using MSA, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), to investigate the source aquifers of the thermal

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20The responses of waterbodies to agricultural programmes of measures are frequently delayed 21 by hydrological time lags through the unsaturated zone and groundwater. Time lag may 22 therefore, impede the achievement of remediation deadlines such as those described in the 23 EU Water Framework Directive (WFD). Omitting time lag from catchment characterisation 24 renders evaluation of management practices impossible. Time lag aside, regulators at national 25 scale can only manage the expectations of policy-makers at larger scales (e.g. European26Union) by demonstrating positive nutrient trajectories in catchments failing to achieve at least 27 'good' status. Presently, a flexible tool for developing spatial and temporal estimates of 28 trends in water quality/nutrient transport and time lags is not available. The objectives of the 29 present study were first to develop such a flexible, parsimonious framework incorporating 30 existing soil maps, meteorological data and a structured modelling approach, and to secondly, 31 to demonstrate its use in a grassland and an arable catchment (~10 km 2 ) in Ireland, assuming 32 full implementation of measures in 2012. Data pertaining to solute transport (meteorology, 33 soil hydraulics, depth of profile and boundary conditions) were collected for both catchments. 34 Low complexity textural data alone gave comparable estimates of nutrient trajectories and 35 time lags but with no spatial or soil series information. Taking a high complexity approach, 36 coupling high resolution soil mapping (1:10,000) with national scale (1:25,000) 37 representative profile datasets to < 5 m depth, indicated trends in nutrient transport of 10-12 38 months and 13-17 months throughout the grassland and arable catchments, respectively. For 39 the same conditions, regulators relying on data from groundwater sampling to test the 40 efficacy of the present measures would be delayed by 61-76 months and 46-79 months, 41 respectively. Variation of meteorological datasets enabled temporal analysis of the trends in 42 nutrient transport and time lag estimates. Such a tool could help catchment scientists to better 43 characterise and manage catchments, determine locations for monitoring or mitigation, assess 44

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