Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States.

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere-ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2 effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.

The effects of climate downscaling technique and observational data set on modeled ecological responses.

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change.

Predicting acidification recovery at the Hubbard Brook Experimental Forest, New Hampshire: evaluation of four models.

The performance and prediction uncertainty (owing to parameter and structural uncertainties) of four dynamic watershed acidification models (MAGIC, PnET-BGC, SAFE, and VSD) were assessed by systematically applying them to data from the Hubbard Brook Experimental Forest (HBEF), New Hampshire, where long-term records of precipitation and stream chemistry were available. In order to facilitate systematic evaluation, Monte Carlo simulation was used to randomly generate common model input data sets (n = 10,000) from parameter distributions; input data were subsequently translated among models to retain consistency. The model simulations were objectively calibrated against observed data (streamwater: 1963-2004, soil: 1983). The ensemble of calibrated models was used to assess future response of soil and stream chemistry to reduced sulfur deposition at the HBEF. Although both hindcast (1850-1962) and forecast (2005-2100) predictions were qualitatively similar across the four models, the temporal pattern of key indicators of acidification recovery (stream acid neutralizing capacity and soil base saturation) differed substantially. The range in predictions resulted from differences in model structure and their associated posterior parameter distributions. These differences can be accommodated by employing multiple models (ensemble analysis) but have implications for individual model applications.

Projections of water, carbon, and nitrogen dynamics under future climate change in an old-growth Douglas-fir forest in the western Cascade Range using a biogeochemical model.

Statistically downscaled climate change scenarios from four General Circulation Models for two Representative Concentration Pathways (RCP) were applied as inputs to a biogeochemical model, PnET-BGC, to examine potential future dynamics of water, carbon, and nitrogen in an old-growth Douglas-fir forest in the western Cascade Range. Projections show 56% to 77% increases in stomatal conductance throughout the year from 1986-2010 to 2076-2100, and 65% to 104% increases in leaf carbon assimilation between October and June over the same period. However, future dynamics of water and carbon under the RCP scenarios are affected by a 49% to 86% reduction in foliar biomass resulting from severe air temperature and humidity stress to the forest in summer. Important implications of future decreases in foliar biomass include 1) 20% to 71% decreases in annual transpiration which increase soil moisture by 7% to 15% in summer and fall; 2) decreases in photosynthesis by 77% and soil organic matter by 62% under the high radiative forcing scenario; and 3) altered foliar and soil carbon to nitrogen stoichiometry. Potential carbon dioxide fertilization effects on vegetation are projected to 1) amplify decreases in transpiration by 4% to 9% and increases in soil moisture in summer and fall by 1% to 2%; and 2) alleviate decreases in photosynthesis by 4%; while 3) having negligible effects on the dynamics of nitrogen. Our projections suggest that future decrease in transpiration and moderate water holding capacity may mitigate soil moisture stress to the old-growth Douglas-fir forest. Future increases in nitrogen concentration in soil organic matter are projected to alleviate the decrease in net nitrogen mineralization despite a reduction in decomposition of soil organic matter by the end of the century.

Projections of water, carbon, and nitrogen dynamics under future climate change in an alpine tundra ecosystem in the southern Rocky Mountains using a biogeochemical model.

Using statistically downscaled future climate scenarios and a version of the biogeochemical model (PnET-BGC) that was modified for use in the alpine tundra, we investigated changes in water, carbon, and nitrogen dynamics under the Representative Concentration Pathways at Niwot Ridge in Colorado, USA. Our simulations indicate that future hydrology will become more water-limited over the short-term due to the temperature-induced increases in leaf conductance, but remains energy-limited over the longer term because of anticipated future decreases in leaf area and increases in annual precipitation. The seasonal distribution of the water supply will become decoupled from energy inputs due to advanced snowmelt, causing soil moisture stress to plants during the growing season. Decreases in summer soil moisture are projected to not only affect leaf production, but also reduce decomposition of soil organic matter in summer despite increasing temperature. Advanced future snowmelt in spring and increasing rain to snow ratio in fall are projected to increase soil moisture and decomposition of soil organic matter. The extended growing season is projected to increase carbon sequestration by 2% under the high radiative forcing scenario, despite a 31% reduction in leaf display due to the soil moisture stress. Our analyses demonstrate that future nitrogen uptake by alpine plants is regulated by nitrogen supply from mineralization, but plant nitrogen demand may also affect plant uptake under the warmer scenario. PnET-BGC simulations also suggest that potential CO2 effects on alpine plants are projected to cause larger increases in plant carbon storage than leaf and root production.