<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McNulty, S.G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scaling predicted pine forest hydrology and productivity across the southern United States.</style></title><secondary-title><style face="normal" font="default" size="100%">D</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CWT</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://cwt33.ecology.uga.edu/publications/2177.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The objective of this chapter is to examine how the aggregation of input data influences the predictive capabilities of forest process models across a changing spatial or temporal resolution. We will examine the influence of data aggregation on multiple-scale forest process model development, use, and validation, using PnET-IIS, a physiologically-based model for predicting forest hydrology and productivity at the stand, ecosystem, and regional scales. Overall model structure and data requirements are outlined and model use and limitations at three spatial scales are discussed.</style></abstract><accession-num><style face="normal" font="default" size="100%">LTER.1997-80599</style></accession-num></record></records></xml>