Alan V. Di Vittorio

The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape

Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling. Results demonstrate that a steady state of patches of varying successional age occurs over a relatively large spatial scale, with important implications for detecting temporal trends on plots that sample a small fraction of the landscape. Long highly significant stochastic runs averaging 1.0 Mg biomass⋅ha−1⋅y−1 were often punctuated by episodic disturbance events, resulting in a sawtooth time series of hectare-scale tree biomass. To maximize the detection of temporal trends for this Central Amazon site (e.g., driven by CO2 fertilization), plots larger than 10 ha would provide the greatest sensitivity. A model-based analysis of fractional mortality across all gap sizes demonstrated that 9.1–16.9% of tree mortality was missing from plot-based approaches, underscoring the need to combine plot and remote-sensing methods for estimating net landscape carbon balance. Old-growth tropical forests can exhibit complex large-scale structure driven by disturbance and recovery cycles, with ecosystem and community attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition.

Evaluation of hydrologic components of community land model 4 and bias identification

Abstract Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. We have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

Tropical forest carbon balance: effects of field- and satellite-based mortality regimes on the dynamics and the spatial structure of Central Amazon forest biomass

Debate continues over the adequacy of existing field plots to sufficiently capture Amazon forest dynamics to estimate regional forest carbon balance. Tree mortality dynamics are particularly uncertain due to the difficulty of observing large, infrequent disturbances. A?recent paper (Chambers et al 2013 Proc. Natl Acad. Sci.?110 3949?54) reported that Central Amazon plots missed 9?17% of tree mortality, and here we address ?why? by elucidating two distinct mortality components: (1)?variation in annual landscape-scale average mortality and (2)?the frequency distribution of the size of clustered mortality events. Using a stochastic-empirical tree growth model we show that a power law distribution of event size (based on merged plot and satellite data) is required to generate spatial clustering of mortality that is consistent with forest gap observations. We conclude that existing plots do not sufficiently capture losses because their placement, size, and longevity assume spatially random mortality, while mortality is actually distributed among differently sized events (clusters of dead trees) that determine the spatial structure of forest canopies.

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961-1990) climate. We reconstruct GCAMs land regions using projected (2071-2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified. Land resource inputs and projections are sensitive to land use region boundaries.Developed a system to generate land data for given Agro-Ecological Zones (AEZs).AEZs based on projected climate differ considerably from historical AEZs.Climate within historical AEZs becomes spatially heterogeneous with climate change.Non-climatic land use region boundaries may reduce model error, compared to AEZs.

Pigment-based Identification of Ozone-Damaged Pine Needles as a Basis for Spectral Segregation of Needle Conditions

Air pollution affects large areas of forest, and field assessment of these effects is a costly, site-specific process. This paper establishes a biochemical basis for identifying ozone-damaged pine trees to facilitate efficient remote sensing assessment of air pollution damage. Several thousand live needles were collected from ponderosa pine (Pinus ponderosa) and Jeffrey pine (P. jeffreyi) trees at three sites in Plumas National Forest and Sequoia-Kings Canyon National Park. These needles were assembled into 504 samples (based on the abaxial surface) and grouped according to five dominant needle conditions (green, winter fleck, sucking insect damage, scale insect damage, and ozone damage) and a random mixture of needles. Pigment concentrations per unit needle area of chlorophyll a, chlorophyll b, and total carotenoids were measured. The following pigment concentration ratios were calculated for all samples: chlorophyll a/total carotenoids, chlorophyll b/total carotenoids, total chlorophyll/carotenoids, chlorophyll a/chlorophyll b. The group of ozone-damaged needles had significantly lower mean pigment concentrations (family-wise p < 0.01) and significantly lower mean chlorophyll a/total carotenoid and total chlorophyll/total carotenoid ratios (family-wise p < 0.01) than all other groups of needles. Ozone-damaged needles had a significantly lower mean chlorophyll a/chlorophyll b ratio than all other groups except one (family-wise p < 0.01). Linear discriminant analysis with three factors (chlorophyll a concentration, the chlorophyll a/carotenoid ratio, and the chlorophyll a/chlorophyll b ratio) and subsequent maximum likelihood classification of damaged and non-damaged needles gave an overall cross-validated accuracy of 96%. These ozone-damaged needles are biochemically unique in relation to other needle conditions in this study, and further research is needed to generalize these results.

GCAM v5.1: Representing the linkages between energy, water, land, climate, and economic systems

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model.