Revealing joint evolutions and causal interactions in complex ecohydrological systems by a network-based framework

Abstract

There is evidence that climate change and human activities are changing ecohydrological systems, yet the complex relationships among ecological (normalized difference vegetation index, gross primary productivity, and water use efficiency) and hydrological variables (runoff, soil water storage, groundwater storage, etc.) remain understudied. This study develops a novel framework based on network analysis alongside satellite data and in situ observations to delineate the joint evolutions (phenomena) and causal interactions (mechanisms) in complex systems. The former employs correlations, and the latter uses physically constrained causality analysis to construct network relationships. This framework is applied to the Yellow River basin, a region undergoing profound ecohydrological changes. Results suggest that joint evolutions are controlled by compound drivers and direct causality. Different types of network relationships are found – namely, joint evolution with weak causality, joint evolution with high causality, and asynchronous evolution with high causality. The upstream alpine subregions, for example, where the ecological subsystem is more influenced by temperature, while the hydrological one is more driven by precipitation, show relatively high synchronization but with weak and lagged causality between two subsystems. On the other hand, ecohydrological causality can be masked by intensive human activities (revegetation, water withdrawals, and reservoir regulation), leading to distinct evolution trends. Other mechanisms can also be deduced. Reductions in water use efficiency in the growing season are directly caused by the control of evapotranspiration, and the strength of control decreases with the greening land surface in some subregions. Overall, the proposed framework provides useful insight into the complex interactions within the ecohydrological systems for the Yellow River basin and has applicability to broader geographical contexts.