The peatland pole forests of the Pastaza-Marañón Foreland Basin (PMFB), Peru, are the most carbondense ecosystems known in Amazonia once below ground carbon stores are taken into account. Here we present the first multiproxy palaeoenvironmental record including pollen data from one of these
The first dynamic model of dissolved organic carbon (DOC) export in streams derived directly from high frequency (subhourly) observations sampled at a regular interval through contiguous storms is presented. The optimal model, identified using the recently developed RIVC algorithm, captured the rapid dynamics of DOC load from 15 min monitored rainfall with high simulation efficiencies and constrained uncertainty with a second-order (two-pathway) structure. Most of the DOC export in the four headwater basins studied was associated with the faster hydrometric pathway (also modeled in parallel), and was soon exhausted in the slower pathway. A delay in the DOC mobilization became apparent as the ambient temperatures increased. These features of the component pathways were quantified in the dynamic response characteristics (DRCs) identified by RIVC. The model and associated DRCs are intended as a foundation for a better understanding of storm-related DOC dynamics and predictability, given the increasing availability of subhourly DOC concentration data.
Insufficient temporal monitoring of water quality in streams or engineered drains alters the apparent shape of storm chemographs, resulting in shifted model parameterisations and changed interpretations of solute sources that have produced episodes of poor water quality. This so-called 'aliasing' phenomenon is poorly recognised in water research. Using advances in in-situ sensor technology it is now possible to monitor sufficiently frequently to avoid the onset of aliasing. A systems modelling procedure is presented allowing objective identification of sampling rates needed to avoid aliasing within strongly rainfall-driven chemical dynamics. In this study aliasing of storm chemograph shapes was quantified by changes in the time constant parameter (TC) of transfer functions. As a proportion of the original TC, the onset of aliasing varied between watersheds, ranging from 3.9-7.7 to 54-79 %TC (or 110-160 to 300-600 min). However, a minimum monitoring rate could be identified for all datasets if the modelling results were presented in the form of a new statistic, ΔTC. For the eight H, DOC and NO-N datasets examined from a range of watershed settings, an empirically-derived threshold of 1.3(ΔTC) could be used to quantify minimum monitoring rates within sampling protocols to avoid artefacts in subsequent data analysis.
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