A comprehensive framework for assessing the hydro-climatic impacts on water quality using data-driven methods

Climate change is one of the most critical global challenges causing the disruption of the complex hydro-climatic systems and is significantly affecting the quantity and quality of water resources. For the mitigation of the adverse effects of climate change impact on water resources, it should be measured accurately. To the best of the authors’ knowledge, there are no specific approaches available that can be utilized to effectively detect or evaluate the degree of impact that various hydro-climatic factors have on water quality. Addressing these challenges, the research introduced a comprehensive framework for assessing the impact of various hydro-climatic factors on surface water quality (WQ). In terms of the novelty of the research, the developed framework considered a range of vital hydro-climatic and WQ indicators. While most existing studies focus on specific WQ indicator(s) or hydroclimatic variable(s), this study was the first attempt to develop a tool by combining a set of hydro-climatic variables and WQ indicators in order to determine the impact of hydro-climatic factors on WQ. To achieve this, the study utilized 23 years of historical data (2000-2022) for eight hydro-climatic variables including precipitation, temperature, evapotranspiration, windspeed, surface run-off, total run-off, solar radiation, and relative humidity in County Cork and nine WQ indicators including temperature, total organic nitrogen, dissolved oxygen, pH, salinity, molybdate-reactive phosphorus, biological oxygen demand, and transparency, and dissolved inorganic nitrogen in Cork Harbour (2007-2022). Advanced machine learning (ML) and artificial intelligence (AI) techniques were employed to analyze long-term, high-dimensional hydro-climatic data patterns. To detect the historical data pattern in the dataset(s), the study developed 15 ML/AI models to predict the patterns of eight hydro-climatic variables and the overall WQ trend, using the recently developed and widely utilized Irish Water Quality Index (IEWQI). Moreover, advanced statistical methods were also applied to validate the reliability and trend patterns of the ML/AI results.

The research also explored the relationship between the eight hydro-climatic variables and the overall WQ trend (IEWQI scores) by creating two scenarios- actual trends and simulated trends- to evaluate the impact of these variables on water quality in Cork Harbour. ANN-MLP outperformed the 14 ML/AI algorithms in predicting the trends of different hydro-climatic variables (except evaporation) and IEWQI scores, while for evaporation, the hybrid model (CNN+RNN+DNN) outperformed. The advanced statistical approaches confirmed that both hybrid models were effective for identifying historical trends in high-dimensional hydro-climatic data.

Therefore, the findings suggest that hybrid models can effectively predict trends and data patterns in high-dimensional data, such as hydro-climatic variables, with a high degree of confidence (95%) in understanding the historical data characteristics. Additionally, the research suggests that the scalability and applicability of these hybrid models should be further explored using different datasets. It also encourages additional research to assess the impact of hydro-climatic variables on WQ, considering the spatio-temporal resolution of domains. Moreover, the developed framework could effectively aid policymakers, water resource managers, and researchers in formulating strategies to assess changes in WQ due to various hydro-climatic events and promote sustainable resource management.