As patterns of precipitation and evapotranspiration change, human water security and aquatic ecosystem health depend on understanding how catchment characteristics interact with climate to control river flow and water budget imbalances. We compiled estimates of precipitation, actual and potential evapotranspiration, temperature, and river discharge for over 1,148 catchments during the 2001-2020 period and used these estimates to calculate water budget imbalances as well as changes in runoff ratio and numerous river flow properties including timing, magnitude, and variability of flow. We found that that the parameter from Fuh’s equation (m) was a powerful predictor of hydrologic sensitivity to climate fluctuations, but not necessarily the magnitude of these changes. Specifically, water budget imbalances were almost entirely explained by two catchment properties: m and aridity. Runoff ratio sensitivity to temporal fluctuations in wetness index were also best explained by m, compared to a host of other catchment characteristics tested. In contrast to its predictive power for sensitivity, m was a poor predictor of total changes in runoff ratio. A subsequent correlational analysis between changes in runoff ratio and 66 geographic, climatic, land use, and human impact metrics, found that fluctuations in climate were a far more powerful predictor of changes in runoff ratio (and a suite of other flow properties) than m, indicating that at the global scale, the magnitude of changes in climate dominate the idiosyncratic catchment-level responsiveness to changes in climate, emphasizing the paramount importance of addressing climate change in protecting freshwater resources.

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.