Regional hydrologic response of loblolly pine to air temperature and precipitation changes
Large deviations in average annual air temperatures and total annual precipitation were observed across the Southern United States during the last 50 years, and these fluctuations could become even larger during the next century. The authors used PnET-IIS, a monthly time-step forest process model that uses soil, vegetation, and climate inputs to assess the influence of changing climate on Southern U.S. pine forest water use. After model predictions of historic drainage were validated, the potential influences of climate change on loblolly pine forest water use was assessed across the region using historic (1951 to 1984) monthly precipitation and air temperature which were modified by two general circulation models (GCM’s). The GCM’s predicted a 3.2EC to 7.2EC increase in average monthly air temperature, a -24 percent to +31 percent change in monthly precipitation and a -1 percent to +3 percent change in annual precipitation. As a comparison to the GCM’s, a minimum climate change scenario using a constant 2EC increase in monthly air temperature and a 20 percent increase in monthly precipitation was run in conjunction with historic climate data. Predicted changes in forest water drainage were highly dependent on the GCM used. PnET-IIS predicted that along the northern range of loblolly pine, water yield would decrease with increasing leaf area, total evapotranspiration, and soil water stress. However, across most of the Southern U.S., PnET-IIS predicted decreased leaf area, total evapotranspiration, and soil water stress with an associated increase in water yield. Depending on the GCM and geographic location, predicted leaf area decreased to a point which would no longer sustain loblolly pine forests, and thus indicated a decrease in the southern most range of the species within the region. These results should be evaluated in relation to other changing environmental factors (i.e., C02 and 03) which are not present in the current model.