Highway runoff has been identified as a significant source of contaminants that impact on the receiving aquatic environment. Several studies have been completed documenting the characteristics of highway runoff and its implication to the receiving water in the UK and elsewhere. However, very little information is available for Ireland. The objective of this study was to determine the quality of highway runoff from major Irish roads under the current road drainage design and maintenance practice. Four sites were selected from the M4 and the M7 motorways outside Dublin. Automatic samplers and continuous monitoring devices were deployed to sample and monitor the runoff quality and quantity. More than 42 storm events were sampled and analysed for the heavy metals Cd, Cu, Pb, and Zn, 16 US EPA specified PAHs, volatile organic compounds including MTBE, and a number of conventional pollutants. All samples were analysed based on the Standard Methods. Significant quantities of solids and heavy metals were detected at all sites. PAHs were not detected very often, but when detected the values were different from quantities observed in UK highways. The heavy metal concentrations were strongly related to the total suspended solids concentrations, which has a useful implication for runoff management strategies. No strong relationship was discovered between pollutant concentrations and event characteristics such as rainfall intensity, antecedent dry days (ADD), or rainfall depth (volume). This study has demonstrated that runoff from Irish motorways was not any cleaner than in the UK although the traffic volume at the monitored sites was relatively smaller. This calls for a site specific investigation of highway runoff quality before adopting a given management strategy.

Changes in streamflow within catchments can have a significant impact on agricultural production, as soil moisture loss, as well as frequent drying and wetting, may have an effect on the nutrient availability of many soils. In order to predict future changes and explore the impact of different scenarios, machine learning techniques have been used recently in the hydrological sector for simulation streamflow. This paper compares the use of four different models, namely artificial neural networks (ANNs), support vector machine regression (SVR), wavelet-ANN, and wavelet-SVR as surrogate models for a geophysical hydrological model to simulate the long-term daily water level and water flow in the River Shannon hydrological system in Ireland. The performance of the models has been tested for multi-lag values and for forecasting both short- and long-term time scales. For simulating the water flow of the catchment hydrological system, the SVR-based surrogate model performs best overall. Regarding modeling the water level on the catchment scale, the hybrid model wavelet-ANN performs the best among all the constructed models. It is shown that the data-driven methods are useful for exploring hydrological changes in a large multi-station catchment, with low computational cost.

Karstified carbonate aquifers may receive significant recharge contributions from losing streams, hence, the knowledge about surface water‐groundwater (SW‐GW) interactions is crucial with regard to water management (e.g., source protection zone delineation). The dynamics of SW‐GW interactions may depend on factors such as the relative water levels between streams and aquifers, resulting in a temporal variation of exchange, which imposes complexity to the understanding of such dynamics. This study highlights the use of high‐resolution time series and multiresolution analysis to help to gain insights into such complex dynamics. Wavelet coherence is applied on hourly time series of rainfall, stream, and spring discharges of a low‐lying karstified spring catchment to yield a correlation in the time‐frequency domain. This analysis provides comprehensive information on the overall impact of the river on the spring, which is supported by the cross‐correlation function, as well as by more detailed information, including time‐variant influences such as a threshold level of influence. Field observations of turbidity sampling at the spring appear to support this interpretation. This innovative approach relies on basic hydrological parameters, water level, or discharge, and is therefore applicable to many other systems with such existing time series.