The concepts of resistance, recovery, and resilience are in diverse fields from behavioral psychology to planetary ecology. These “three Rs” describe some of the most important properties allowing complex systems to survive in dynamic environments. However, in many fields—including ecology—our ability to predict resistance, recovery and resilience remains limited. Here, we propose new disturbance terminology and describe a unifying definition of resistance, recovery, and resilience. We distinguish functional disturbances that affect short-term ecosystem processes from structural disturbances that alter the state factors of ecosystem development. We define resilience as the combination of resistance and recovery—i.e., the ability of a system to maintain its state by withstanding disturbance or rapidly recovering from it. In the Anthropocene, humans have become dominant drivers of many ecosystem processes and nearly all the state factors influencing ecosystem development. Consequently, the resilience of an individual ecological parameter is not an inherent attribute but a function of linkages with other biological, chemical, physical, and especially social parameters. Because every ecosystem experiences multiple, overlapping disturbances, a multidimensional resilience approach is needed that considers both ecosystem structure (configuration of linkages) and disturbance regime. We explore these concepts with a few case studies and recommend analytical tools and community-based approaches to strengthen ecosystem resilience. Disregarding cultural and social dimensions of disturbance regimes and ecosystem structures leads to undesirable outcomes, particularly in our current context of intensifying socioecological crises. Consequently, cultivating reciprocal relationships with natural disturbance regimes and ecosystem structures is crucial to Earth stewardship in the Anthropocene.

River flows change on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e., flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of flow dynamics. Here, we used a cross-disciplinary analytical approach to investigate two related questions: 1. Is there a low-dimensional structure that can be used to simplify descriptions of streamflow regime? 2. What catchment characteristics are most associated with that structure? Using a global database of daily river discharge from 1988-2016 for 3,120 stations, we calculated 189 traditional flow metrics, which we compared to the results of a wavelet analysis. Both quantification techniques independently revealed that streamflow data contain substantial low-dimensional structure that correlates closely with a small number of catchment characteristics. This structure provides a framework for understanding fundamental controls of river flow variability across multiple timescales. Climate was the most important variable across all timescales, especially those lasting several weeks, and likely contributes as much as dams in controlling flow regime. Catchment area was critical for timescales lasting several days, as was human impact for timescales lasting several years. In addition, both methods suggested that streamflow data also contain high-dimensional structure that is harder to predict from a small number of catchment characteristics (i.e. is dependent on land use, soil structure, etc.), and which accounts for the difficulty of producing simple hydrological models that generalize well.

River flow changes on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e. flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of overall flow regime. In this study, we used three analytical approaches to investigate three related questions: 1. how interrelated are flow regime metrics, 2. what catchment characteristics are most associated with flow regime at different timescales globally, and 3. what hydrological processes could explain these associations? To answer these questions, we analyzed a new global database of river discharge from 3,685 stations with coverage from 1987 to 2016. We calculated and condensed 189 traditional flow metrics via principal components analysis (PCA). We then used wavelet analysis to perform a frequency decomposition of each time series, allowing comparison with the flow metrics and characterization of variation in flow at different timescales across sites. Finally, we used three machine learning algorithms to relate flow regime to catchment properties, including climate, land-use, and ecosystem characteristics. For both the PCA and wavelet analysis, just a few catchment properties (catchment size, precipitation, and temperature) were sufficient to predict most aspects of flow regime across sites. The wavelet analysis revealed that variability in flow at short timescales was negatively correlated with variability at long timescales. We propose a hydrological framework that integrates these dynamics across daily to decadal timescales, which we call the Budyko-Darcy hypothesis.