River flow drives the structure and function of aquatic systems on sub-daily to decadal timescales, and sculpts landscapes on geological timescales from months to millennia (Fisher et al., 1998;Pinay et al., 2018;Tucker & Hancock, 2010). For people, variability in river flow regulates access to freshwater, with extreme flow events such as floods and droughts imposing immense personal and societal costs (
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.
1. Empirical evidence and theory suggest that climate warming and an increase in the frequency and duration of drying events will alter the metabolic balance of freshwater ecosystems. However, the impacts of climate change on ecosystem metabolism may depend on whether energy inputs are of autochthonous or allochthonous origin. To date, few studies have examined how warming and drying may interact to alter stream metabolism, much less how their impacts may depend on the energy-base of the food web.2. To address this research gap, we conducted a multi-factorial experiment using outdoor mesocosms to investigate the individual and synergistic effects of warming and drought on metabolic processes in stream mesocosms with green (algalbased) vs. mixed (algal-and detritus-based) vs. brown (detritus-based) energy pathways.3. We set up 48 mesocosms with one of three different levels of shade and leaf litter input combinations to create mesocosms with different primary energy channels.In addition, we warmed half of the mesocosms by ~2-3°C. We assessed changes in ecosystem respiration (ER), gross primary production (GPP), net ecosystem production (NEP) and organic matter biomass in warmed and ambient temperature mesocosms before a 24 day drying event and after rewetting. 4. Surprisingly, experimental warming had little effect on metabolic processes.Drying, however, led to decreased rates of ER and GPP and led to an overall reduction in NEP. Although the effects of drying were similar across energy channel treatments, reductions in ER and GPP were primarily driven by decreases in biomass of benthic and filamentous algae.5. Overall, we demonstrate that drying led to lower rates of NEP in mesocosms regardless of energy inputs. While warming showed little effect in our study, our results suggest that an increase in the frequency of stream drying events could greatly alter the metabolic balance of many aquatic ecosystems.
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