Forested watersheds, an important provider of ecosystems services related to water supply, can have their structure, function, and resulting streamflow substantially altered by land use and land cover. Using a retrospective analysis and synthesis of long-term climate and streamfiow data (75 years) from six watersheds differing in management histories we explored whether streamflow responded differently to variation in annual temperature and extreme precipitation than unmanaged watersheds. We show significant increases in temperature and the frequency of extreme wet and dry years since the 1980s. Response models explained almost all streamflow variability (adjusted R2 > 0.99). In all cases, changing land use altered streamflow. Observed watershed responses differed significantly in wet and dry extreme years in all but a stand managed as a coppice forest. Converting deciduous stands to pine altered the streamflow response to extreme annual precipitation the most; the apparent frequency of observed extreme wet years decreased on average by sevenfold. This increased soil water storage may reduce flood risk in wet years, but create conditions that could exacerbate drought. Forest management can potentially mitigate extreme annual precipitation associated with climate change; however, offsetting effects suggest the need for spatially explicit analyses of risk and vulnerability.

Coweeta Hydrologie Laboratory, located in western North Carolina, USA, is a 2,185 ha basin wherein forest ciimate monitoring and watershed experimentation began in the early 1930s. An extensive ciimate and hydrologie network has facilitated research for over 75 years. Our objectives in this paper were to describe the monitoring network, present long-term air temperature and precipitation data, and analyze the temporal variation in the long-term temperature and precipitation record. We found that over the period of record: (1) air temperatures have been increasing significantly since the late 1970s, (2) drought severity and frequency have increased with time, and (3) the precipitation distribution has become more extreme over time, we discuss the implications of these trends within the context of regional and global climate change and forest health.

Climate change will affect tree species growth and distribution; however, under the same climatic conditions species may differ in their response according to site conditions. We evaluated the climate-driven patterns of growth for six dominant deciduous tree species in the southern Appalachians. We categorized species into two functional groups based on their stomatal regulation and xylem architecture: isohydric, diffuse porous and anisohydric, ring porous. We hypothesized that within the same climatic regime: (i) species-specific differences in growth will be conditional on topographically mediated soil moisture availability; (ii) in extreme drought years, functional groups will have markedly different growth responses; and (iii) multiple hydroclimate variables will have direct and indirect effects on growth for each functional group. We used standardized tree-ring chronologies to examine growth of diffuse-porous (Acer, Liriodendron, and Betula) and ring-porous (Quercus) species vs. on-site climatic data from 1935 to 2003. Quercus species growing on upslope sites had higher basal area increment (BAI) than Quercus species growing on mesic, cove sites; whereas, Acer and Liriodendron had lower BAI on upslope compared to cove sites. Diffuse-porous species were more sensitive to climate than ring porous, especially during extreme drought years. Across functional groups, radial growth was more sensitive to precipitation distribution, such as small storms and dry spell length (DSL), rather than the total amount of precipitation. Based on structural equation modeling, diffuse-porous species on upslope sites were the most sensitive to multiple hydroclimate variables (r(2)  = 0.46), while ring-porous species on upslope sites were the least sensitive (r(2)  = 0.32). Spring precipitation, vapor pressure deficit, and summer storms had direct effects on summer AET/P, and summer AET/P, growing season small storms and DSL partially explained growth. Decreasing numbers of small storms and extending the days between rainfall events will result in significant growth reduction, even in regions with relatively high total annual rainfall.

A 69-station, densely spaced rain gauge network was maintained over the period [1951][1952][1953][1954][1955][1956][1957][1958] in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA. This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10-m elevation grid filtered to an approximately 7-km effective wavelength explained the most variance in precipitation (R 2 = 0.82-0.95). A 'dump zone' of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation-elevation relationship.These data and maps provided a rare 'ground-truth' for estimating uncertainty in the national-scale Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation grids for this location and time period. Differences between PRISM and ground-truth were compared with uncertainty estimates produced by the PRISM model and cross-validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations.The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12-20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high-density data exist to gain a more comprehensive picture of the uncertainties in national-level datasets, and can be used in network optimization exercises.