All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non–specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub–canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could
We quantified canopy transpiration ( E C ) using sap flux measurements representing the four major forest types (northern hardwoods, conifer, aspen/fir, and forested wetland) around the WLEF‐TV tall tower in northern Wisconsin. In order to scale individual sap flux measurements to E C , we quantified the amount of sapwood area per unit ground area and the spatial distribution of sap flux within trees. Contrary to our hypothesis that all tree species would have the same positive relationship between tree diameter and sapwood depth, white cedar and speckled alder, both wetland species, showed no relationship. We also hypothesized that the conifer trees would have a lower whole tree hydraulic conductance than deciduous trees. We actually discovered that white cedar had the highest hydraulic conductance. Our third hypothesis, that sapwood area per unit ground area would determine stand E C , was not rejected. The resulting average daily E C values over 53 days (23 June to 16 August 2000) from combining sap flux and sapwood area per unit ground area were 1.4, 0.8, 2.1, and 1.4 mm d −1 for conifer, northern hardwoods, aspen/fir, and forested wetland cover types, respectively. Average daily E C was only explained by an exponential saturation with daily average vapor pressure deficit.
Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPP A ) for a land surface in northern Wisconsin, examine the spatial patterns of NPP A and its components, and correlate NPP A with vegetation cover types and leaf area index. Mean NPP A for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha 1 ·year 1 , respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPP A (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPP A differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPP A was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPP A for the 3 km × 2 km site was 5.8 t dry mass·ha 1 ·year 1 .
Abstract Forest management presents challenges to accurate prediction of water and carbon exchange between the land surface and atmosphere, due to its alteration of forest structure and composition. We examined how forest species types in northern Wisconsin affect landscape scale water fluxes predicted from models driven by remotely sensed forest classification. A site‐specific classification was developed for the study site. Using this information and a digital soils database produced for the site we identified four key forest stand types: red pine, northern hardwoods, aspen, and forested wetland. Within these stand types, 64 trees representing 7 species were continuously monitored with sap flux sensors. Scaled stand‐level transpiration from sap flux was combined with a two‐source soil evaporation model and then applied over a 2.5 km × 3.0 km area around the WLEF AmeriFlux tower (Park Falls, Wisconsin) to estimate evapotranspiration. Water flux data at the tower was used as a check against these estimates. Then, experiments were conducted to determine the effects of aggregating vegetation types to International Geosphere– Biosphere Program (IGBP) level on water flux predictions. Taxonomic aggregation resulting in loss of species level information significantly altered landscape water flux predictions. However, daily water fluxes were not significantly affected by spatial aggregation when forested wetland evaporation was included. The results demonstrate the importance of aspen, which has a higher transpiration rate per unit leaf area than other forest species. However, more significant uncertainty results from not including forested wetland with its high rates of evaporation during wet summers.
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