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Ramakrishna R. Nemani and Steven W. Running

Abstract

Infrared surface temperatures from satellite sensors have been used to infer evaporation and soil moisture distribution over large areas. However, surface energy partitioning to latent versus sensible heat changes with surface vegetation cover and water availability. We tested a hypothesis that the relationship between surface temperature and canopy density is sensitive to seasonal changes in canopy resistance of conifer forests. Surface temperature (Ts) and canopy density were computed for a 20 × 25 km forested region in Montana, from the NOAA/AVHRR for 8 days during the summer of 1985. A forest ecosystem model, FOREST-BGC, simulated canopy resistance (Rc) for the same period.

For all eight days. surface temperatures had high association with canopy density, measured as Normalized Difference Vegetation Index (NDVI) (R 2 = 0.73 − 0.91), implying that latent heat exchange is the major cause of spatial variations in surface radiant temperatures. The slope of Ts and NDVI, σ, was sensitive to changes in canopy resistance on two contrasting days of canopy activity. The trajectory of σ followed seasonal changes in canopy resistance simulated by the model. The relationship found between σ and Rc (R 2 = 0.92), was nonlinear, expected because Rc values beyond 20 s cm−1 do not influence energy partitioning significantly. The slope of Ts and NDVI, σ, could provide a useful parameterization of surface resistance in regional evapotranspiration research.

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David J. Mildrexler, Maosheng Zhao, and Steven W. Running

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Jared W. Oyler, Solomon Z. Dobrowski, Zachary A. Holden, and Steven W. Running

Abstract

Remotely sensed land skin temperature (LST) is increasingly being used to improve gridded interpolations of near-surface air temperature. The appeal of LST as a spatial predictor of air temperature rests in the fact that it is an observation available at spatial resolutions fine enough to capture topoclimatic and biophysical variations. However, it remains unclear if LST improves air temperature interpolations over what can already be obtained with simpler terrain-based predictor variables. Here, the relationship between LST and air temperature is evaluated across the conterminous United States (CONUS). It is found that there are significant differences in the ability of daytime and nighttime observations of LST to improve air temperature interpolations. Daytime LST mainly indicates finescale biophysical variation and is generally a poorer predictor of maximum air temperature than simple linear models based on elevation, longitude, and latitude. Moderate improvements to maximum air temperature interpolations are thus limited to specific mountainous areas in winter, to coastal areas, and to semiarid and arid regions where daytime LST likely captures variations in evaporative cooling and aridity. In contrast, nighttime LST represents important topoclimatic variation throughout the mountainous western CONUS and significantly improves nighttime minimum air temperature interpolations. In regions of more homogenous terrain, nighttime LST also captures biophysical patterns related to land cover. Both daytime and nighttime LST display large spatial and seasonal variability in their ability to improve air temperature interpolations beyond simpler approaches.

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Michael A. White, Peter E. Thornton, Steven W. Running, and Ramakrishna R. Nemani

Abstract

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed.

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Joseph D. White, Neal A. Scott, Adam I. Hirsch, and Steven W. Running

Abstract

Potential forest growth predicted by the Physiological Principles in Predicting Growth (3-PG) model was compared for forest and deforested areas in the Legal Amazon to assess potential differing regeneration associated with climate. Historical deforestation and regeneration have occurred in environmentally marginal areas that influence regional carbon sequestration estimates. Effects of El Niño–induced drought further reduce simulated production by decreasing soil water availability in areas with shallow soils and high transpiration potential. The model was calibrated through comparison of literature biomass and with satellite-based estimates. Net primary productivity (NPP) for mature Amazonian forests from the 3-PG model was positively correlated (r2 = 0.77) with a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived algorithm, though with some bias. Annual total NPP for the study area using a 1961–90 average climatology was 4.6 Pg C yr−1, which decreased to 4.2 Pg C yr−1 when simulated with climate from the severe 1997/98 El Niño event. From a regional analysis, results showed that biomass accumulation is almost entirely controlled by the availability of soil water. Also, areas currently forested in the eastern Amazon are more sensitive to extreme El Niño–induced drought than southern areas with the greatest deforestation extent.

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Qiaozhen Mu, Maosheng Zhao, John S. Kimball, Nathan G. McDowell, and Steven W. Running

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000–11) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (correlation coefficient r = 0.43) with the precipitation-based Palmer drought severity index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite-based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely sensed global terrestrial DSI enhances capabilities for nearreal-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods.

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