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Estimating uncertainties in watershed studies
Author(s) -
Campbell John,
Yanai Ruth,
Green Mark
Publication year - 2011
Publication title -
eos, transactions american geophysical union
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 86
eISSN - 2324-9250
pISSN - 0096-3941
DOI - 10.1029/2011eo260004
Subject(s) - watershed , environmental science , ecosystem , biogeochemical cycle , uncertainty , hydrological modelling , disturbance (geology) , environmental resource management , computer science , scale (ratio) , ecology , geography , statistics , climatology , mathematics , paleontology , cartography , machine learning , biology , geology
Quantifying Uncertainty in Ecosystem Studies (QUEST) Workshop: Uncertainty in Hydrologic Fluxes of Elements at the Small Watershed Scale; Boston, Massachusetts, 14–15 March 2011; Small watersheds have been used widely to quantify chemical fluxes and cycling in terrestrial ecosystems for about the past half century. The small watershed approach has been valuable in characterizing hydrologic and nutrient budgets, for instance, in estimating the net gain or loss of solutes in response to disturbance. However, the uncertainty in these ecosystem budget calculations is generally ignored. Without uncertainty estimates in watershed studies, it is difficult to evaluate the significance of observed differences between watersheds or changes in budgets over time, and erroneous conclusions may be drawn. The historical lack of attention given to uncertainty has been due at least in part to the lack of appropriate analytical tools and approaches. The issue of uncertainty has been confronted more rigorously in other disciplines, yet the advances made have not been comprehensively applied to biogeochemical input‐output budgets. In recent years, there has been growing recognition that estimates of uncertainty are essential for coming to sound scientific conclusions, identifying which budget components most need improvement, and developing more efficient monitoring strategies, thereby maximizing information gained per unit cost.

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