Coupling of PnET-II3SL and SWAT for Modeling the Combined Effects of Forests and Agriculture on Water Availability

Abstract

The earth’s climate is governed by a series of complex interactions between the sun, oceans, atmosphere, land-cover, and all living things. Natural variations in the global climate have occurred irregularly throughout history. Modern climatic models suggest that not only will this phenomenon continue in the future, but larger magnitudes and higher frequencies of variability are likely due to human influences. Furthermore, it has been postulated that the increasing concentration of atmospheric greenhouse gases could result in regional changes of soil moisture, runoff, mean sea level, and the potential for severe climatic events (extreme hurricanes, floods, droughts, etc). The climate change / variability phenomenon, because of its potential to exert significant influence on water availability, the economy, and human society as a whole, has drawn considerable attention from the scientific community and government agencies worldwide. In response to growing concerns, we are attempting to expand our understanding of the relationship between land-use heterogeneities, water availability, and climate change / variability in the southeastern United States. Specifically, this paper describes the coupling and validation of a hydrologic / forest productivity model (PnET-II3SL) with a hydrologic / agricultural productivity model (SWAT) to better represent the hydrologic response characteristics of large spatial areas. The resulting model is capable of generating more scientifically sound predictions of the effects of climate change / variability, with regards to water availability, in the heterogeneous watersheds of the southeastern United States. Validation efforts are described, utilizing long-term historical records, for 15 experimental watersheds. Selected sites represent a diversity of climatic regimes, topographic conditions, soil types, area, and vegetal species. Furthermore, validation efforts focus on evaluations of model robustness to changes in management practices and factors such as biological adaptation.