Understanding Hydrological Flow Paths in Conceptual Catchment Models To Improve Water Quality Modelling

Increasing pressures on water quality due to intensification of agriculture have increased the need for environmental modelling to accurately simulate the movement of diffuse (nonpoint) nutrients in catchments. As hydrological flows drive the movement and attenuation of nutrients, individual hydrological processes in models should be adequately represented for water quality simulations to be meaningful. In particular, the relative contribution of groundwater and surface runoff to rivers is of interest, as, for example, higher nitrate concentrations in surface waters are strongly linked to catchments with proportionately larger groundwater contributions to the river. In this study, uncertainty analysis was used to investigate parameter sensitivities and flow partitioning of three conceptual hydrological models simulating 31 Irish catchments. We compared two established conceptual hydrological models (NAM and SMARG) and a new model (SMART), produced especially for water quality modelling. In addition to the criteria that assess streamflow simulations, a ratio of average groundwater contribution to total streamflow was calculated for all simulations over the 16 year study period. As measured time-series of groundwater contributions to streamflow are not available at catchment scale, the groundwater ratios were evaluated against average annual indices of base flow (from the OPW) and estimates of deep groundwater flow (from the Geological Survey of Ireland) for each catchment. The exploration of sensitivities of internal flow path partitioning was a specific focus to assist in evaluating model performances. Results highlight that model structure has a strong impact on simulated groundwater flow path contributions. Sensitivities to the internal pathways in the models are not reflected in the usual (flood forecasting) performance criteria results. This demonstrates that simulated groundwater contribution should be constrained by independent data to ensure results within realistic bounds if such models are to be used in the broader environmental sustainability decision making context.