Geology topics

Steve G McNulty

Publications and source records attributed to Steve G McNulty.

2 recordsLinked to original sources

USGS2019

Using δ13C and δ18O to analyze loblolly pine (Pinus taeda L.) response to experimental drought and fertilization

Drought frequency and intensity are projected to increase throughout the southeastern USA, the natural range of loblolly pine (Pinus taeda L.), and are expected to have major ecological and economic implications. We analyzed the carbon and oxygen isotopic compositions in tree ring cellulose of loblolly pine in a factorial drought (~30% throughfall reduction) and fertilization experiment, supplemented with trunk sap flow, allometry and microclimate data. We then simulated leaf temperature and applied a multi-dimensional sensitivity analysis to interpret the changes in the oxygen isotope data. This analysis found that the observed changes in tree ring cellulose could only be accounted for by inferring a change in the isotopic composition of the source water, indicating that the drought treatment increased the uptake of stored moisture from earlier precipitation events. The drought treatment also increased intrinsic water-use efficiency, but had no effect on growth, indicating that photosynthesis remained relatively unaffected despite 19% decrease in canopy conductance. In contrast, fertilization increased growth, but had no effect on the isotopic composition of tree ring cellulose, indicating that the fertilizer gains in biomass were attributable to greater leaf area and not to changes in leaf-level gas exchange. The multi-dimensional sensitivity analysis explored model behavior under different scenarios, highlighting the importance of explicit consideration of leaf temperature in the oxygen isotope discrimination (Δ18Oc) simulation and is expected to expand the inference space of the Δ18Oc models for plant ecophysiological studies.

Tree Physiology↗

USGS2015

A comparison of hydrologic models for ecological flows and water availability

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine-scale models and two regional-scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over-predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional-scale models have comparable performance to the more complex, fine-scale models at a monthly time step.

Ecohydrology↗