Abstract
Understanding and predicting catchment responses to a regional disturbance is difficult because catchments are spatially heterogeneous systems that exhibit unique moderating characteristics. Changes in precipitation composition in the Northeastern U.S. is one prominent example, where reduction in wet and dry deposition is hypothesized to have caused increased dissolved organic carbon (DOC) export from many northern hemisphere forested catchments; however, findings from different locations contradict each other. Using shifts in acid deposition as a test case, we illustrate an iterative “process and pattern” approach to investigate the role of catchment characteristics in modulating the steam DOC response. We use a novel dataset that integrates regional and catchment-scale atmospheric deposition data, catchment characteristics and co-located stream Q and stream chemistry data. We use these data to investigate opportunities and limitations of a pattern-to-process approach where we explore regional patterns of reduced acid deposition, catchment characteristics and stream DOC response and specific soil processes at select locations. For pattern investigation, we quantify long-term trends of flow-adjusted DOC concentrations in stream water, along with wet deposition trends in sulfate, for USGS headwater catchments using Seasonal Kendall tests and then compare trend results to catchment attributes. Our investigation of climatic, topographic, and hydrologic catchment attributes vs. directionality of DOC trends suggests soil depth and catchment connectivity as possible modulating factors for DOC concentrations. This informed our process-to-pattern investigation, in which we experimentally simulated increased and decreased acid deposition on soil cores from catchments of contrasting long-term DOC response [Sleepers River Research Watershed (SRRW) for long-term increases in DOC and the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) for long-term decreases in DOC]. SRRW soils generally released more DOC than SSHCZO soils and losses into recovery solutions were higher. Scanning electron microscope imaging indicates a significant DOC contribution from destabilizing soil aggregates mostly from hydrologically disconnected landscape positions. Results from this work illustrate the value of an iterative process and pattern approach to understand catchment-scale response to regional disturbance and suggest opportunities for further investigations.
Highlights
Disturbances such as land-cover transformation, amplification of biogeochemical flows, and climate disruption are triggering transitions in the Earth system that are unprecedented on human timescales (Steffen et al, 2018; Abbott et al, 2019)
In the Northeastern United States, we identified only nine catchments as having sufficient decadal records of dissolved organic carbon (DOC) and instantaneous Q to complete our analyses (Table 1 and Figure 4)
In the time series DOCFA, we detected change points for six of the nine catchments ranging from the late 1990s and early 2000s [Biscuit Brooks (BB), McDonalds Branch (MB) and Neversink River (NR)] to very recent [2015 for Choptank River (CR), Table 1]
Summary
Disturbances such as land-cover transformation, amplification of biogeochemical flows, and climate disruption are triggering transitions in the Earth system that are unprecedented on human timescales (Steffen et al, 2018; Abbott et al, 2019). Attributing catchment response to even a specific driver is challenging because the scale of disturbance often transcends the bounds of individual sites that, themselves, have heterogeneous spatial characteristics. While direct upscaling and downscaling might lead to loss of information and insight, an approach that does not seek direct links across scales but that investigates larger-scale pattern and site-specific process in an iterative fashion might provide some remedy (Sivapalan, 2005). In such an approach, regional patterns might point to a possible process (i.e., pattern-to-process), while process investigation might provide more insights with each iteration (i.e., process-to-pattern)
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