A framework to identify critical dynamics of water quality for diagnosing river basin ecosystem resilience and management

This study proposed a novel framework to identify critical water quality dynamics as early warning signals for diagnosing changes in the resilience of river basin ecosystems. We established empirical linkages between the theoretical background of three resilience capacities (robustness, adaptability and transformability) and water quality dynamics. Then, the processes of resilience degradation and their risk transfer or accumulation have been identified based on the shifts among different states. The methods of time-domain analysis and frequency-domain analysis were integrated into this framework, aiming to identify gradual and transient responses of water quality and its periodic fluctuation characteristics at multiple temporal scales. The time-domain analysis methods obtained the trend, cumulative periodic fluctuation of water quality by extracting the key characteristic parameters from the time-series data. The wavelet transform methods were introduced into the frequency-domain analysis to reveal the water quality fluctuation patterns at specific temporal scales. We tested the proposed framework in a typical agriculture-intensive watershed in eastern China. The results showed that this framework can be effectively used to identify three resilience states of river basin ecosystems. The degraded resilience regions were mainly distributed in the downstream area, which was influenced by their specific land use/cover and different agricultural soil health conditions. Particularly, urban sewage discharge was the main cause of periodic fluctuation in water quality time series at multiple high-frequency scales. The theoretical background of resilience capacities was elaborated in non-equilibrium dynamics before resilience degradation. Thus, this novel framework could reveal the pollution processes and driving mechanisms in different river reaches, and it also can provide adaptive management suggestions according to resilience dynamics traits.