Search

Monitoring solutes in precipitation inputs and stream water exports at small watersheds has greatly advanced our understanding of biogeochemical cycling. Surprisingly, although inputs to and outputs from ecosystems are instrumental to understanding sources and sinks of nutrients and other elements, uncertainty in these fluxes is rarely reported in ecosystem budgets. We illustrate error propagation in input-output budgets by comparing the net hydrologic flux of Ca in a harvested and reference watershed at the Hubbard Brook Experimental Forest, New Hampshire. We identify sources of uncertainty and use a Monte Carlo approach to combine many sources of uncertainty to produce an estimate of overall uncertainty. Sources of uncertainty in precipitation inputs included in this study were: rain gage efficiency (undercatch or overcatch), gaps in measurements of precipitation volume, selection of a model for interpolating among rain gages, unusable precipitation chemistry, and chemical analysis. Sources of uncertainty in stream water outputs were: stage height-discharge relationship, watershed area, gaps in the stream flow record, chemical analysis, and the selection of a method for flux calculation. The annual net hydrologic flux of Ca in the harvested and reference watersheds was calculated from 1973 through 2009. Relative to the reference watershed, the harvested watershed showed a marked increase in Ca flux after it was cut in 1983-1984, and slowly declined toward pretreatment levels thereafter. In 2009, the last year evaluated, the 95% confidence intervals for the annual estimates approach the 95% confidence intervals of the pretreatment regression line, suggesting that the increased net loss of Ca in the harvested watershed may soon be indistinguishable from the reference. Identifying the greatest sources of uncertainty can be used to guide improvements, for example in reducing instances of unusable precipitation chemistry and gaps in stream runoff. Our results highlight the value of estimating uncertainty in watershed studies, including those in which replication is impractical. [ABSTRACT FROM AUTHOR]

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flowunconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i ) fine-scale versus broad-scale processes and (ii ) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks. [ABSTRACT FROM AUTHOR]