Hydrological modelling: Insights into hydrological signals and contaminant transport

This study examines how climate change impacts hydrological patterns in a heavily contaminated Scottish catchment, focusing on extreme events like floods and droughts. By analysing historical trends, projecting future scenarios, and modelling contaminant transport, it highlights the challenges of predicting hydrological extremes and their implications for water quality and environmental management.Adapting hydrological models to account for future climate conditions is complex, particularly when predicting extreme events like floods and droughts. Traditional models, calibrated using historical data, often fail to capture hydrological behaviour in a rapidly changing environment. This research addresses these challenges by testing calibration techniques to enhance model performance across various flow conditions and contaminant transport processes.A key difficulty lies in balancing model sensitivity to both high-flow events, which drive rapid contaminant transport, and low-flow conditions, where contaminants persist due to slower water movement. Techniques such as parameter sensitivity analysis and statistical optimization methods—like Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE)—are employed to ensure models accurately represent diverse hydrological conditions. These models are validated under different climate scenarios to predict future extreme events and contaminant behaviours.Multi-objective calibration techniques, which account for high- and low-flow dynamics, prove more effective in predicting future hydrological extremes. Metrics like peak flow rates, baseflow, NSE, and RMSE assess performance, aiding in flood mitigation and water quality risk management. By improving model robustness, this approach provides critical insights into water and contaminant movement under varying flow scenarios, supporting better preparedness for climate-driven challenges.The River Almond catchment (375 km²) is one of Scotland’s most polluted river systems, shaped by industrial shifts, agricultural intensification, and urbanisation. These activities have created pollution hotspots, with pharmaceuticals, pesticides, nutrients, and endocrine disruptors as key contaminants. Their transport is closely tied to water flow dynamics, with hydrological signatures offering critical insights, especially during extreme events.Periods of extreme rainfall or drought significantly influence contaminant behaviour. High-flow events mobilize contaminants like ibuprofen, while endocrine disruptors such as bisphenol A display flow-dependent patterns influenced by location and intensity. Rainfall after prolonged dry periods drives sudden spikes in the movement of pollutants, particularly microplastics, emphasizing the role of rain pulses in dispersion.This study uses hydrograph analysis to assess contaminant responses to water flow fluctuations. Floods are expected to accelerate long-distance pollutant transport, while droughts may concentrate contaminants in stagnant water or sediments, creating latent risks reactivated by subsequent rainfall.As climate change intensifies flow variability, understanding these pathways is essential for improving water quality management, mitigating pollution risks, and safeguarding the River Almond catchment’s resources.