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

are used to describe the portion of the plant pools that are either replaced each year or removed through fire or plant death. 2) The allocation of photosynthetically accumulated carbon to leaf, stem, and root pools is controlled by a series of allometric parameters. 3) Carbon to nitrogen ratios define nutrient requirements for new growth, plant respiration rates, photosynthetic capacity, and litter quality. 4) The percentage of lignin, cellulose, and labile material in fine roots, leaves, and

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N. S. Oakley, J. T. Lancaster, B. J. Hatchett, J. Stock, F. M. Ralph, S. Roj, and S. Lukashov

landslide-triggering rains. Here, we create a quality-controlled statewide rainfall intensity dataset for California from hourly gauges in the Remote Automated Weather Station (RAWS) network. These stations are often located in steeplands where landslides occur. We parse these data using published rainfall thresholds based on historic precipitation events that triggered shallow landslides. Rainfall events exceeding these thresholds are hereafter referred to as “overthreshold precipitation events” (OTPEs

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Ana M. B. Nunes and John O. Roads

of 159 waves and 60 levels in the vertical, including a well-resolved boundary layer and stratosphere ( Betts et al. 2003 ). 2.3.2. Station datasets LBA and World Meteorological Organization radiosondes During the Wet Season Atmospheric Mesoscale Campaign/Large-Scale Biosphere–Atmosphere Experiment (WETAMC/LBA) radiosonde sites in Rondonia, Brazil, were subject to a quality control process based on visual inspection, plausibility, and spatial and physical consistency ( Longo et al. 2002

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Scott R. Loarie, David B. Lobell, Gregory P. Asner, and Christopher B. Field

drive the albedo change patterns and magnitudes. We chose the quality-controlled, 9-yr time series for all analyses that follow. 2.3. Other data To develop a better understanding of what land-cover changes drive the patterns of albedo changes, we also compiled 16-day MODIS reflectance data for the study area, which included blue (459–479 nm), red (620–670 nm), near-infrared (NIR; 841–876 nm), and midinfrared (MIR; 2105–2155 nm) reflectance bands at 1-km resolution. Surface water

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Xin-Zhong Liang, Hyun I. Choi, Kenneth E. Kunkel, Yongjiu Dai, Everette Joseph, Julian X. L. Wang, and Praveen Kumar

agreement with field surveys and observational studies and the small interannual variability over areas with minimal anthropogenic impact, the FVC derived from the AVHRR NDVI was believed to be robust. The MODIS is providing quality-controlled data for numerous variables that are necessary for terrestrial modeling, such as developing a new land surface albedo parameterization ( Liang et al. 2004c ). It is thus desirable to have a consistent FVC based on the MODIS data. One appealing approach is to scale

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Ashley E. Van Beusekom, Grizelle González, and Maria M. Rivera

trends as a finding in their paper. First, the stated objectives of Torres-Valcárcel et al. (2015 , p. 1649) include: “In [the] third section, we analyze a century of data with different methods to test hypotheses that, after controlling for potential variability related to ecological life zones, there are significant differences in temperature trends between urban and rural areas, with higher absolute values and warming trends in urban areas” (our emphasis). Second, section 3.3.2 (labeled

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Douglas C. Morton, Ruth S. DeFries, Yosio E. Shimabukuro, Liana O. Anderson, Fernando Del Bon Espírito-Santo, Matthew Hansen, and Mark Carroll

resolution, were also evaluated for their potential to identify new deforestation. Following the technique proposed by Shimabukuro and Smith ( Shimabukuro and Smith 1995 ), blue, red, NIR, and MIR bands at 250-m resolution were used in a linear spectral mixing model to estimate the subpixel fraction of soil, vegetation, and shade ( Anderson et al. 2005a ). Nonideal quality data were excluded from analyses based on information contained in the quality control band. Six data layers from the MOD13 16-day

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Jianjun Ge

approximately 5.6 km in the north–south direction and a much smaller distance in the east–west direction because of its high latitude in the study area ( Figure 2 ). Wan et al. ( Wan et al. 2004 ) validated the daily MODIS LST product at 1-km resolution in 11 clear-sky cases with in situ measurement data; the accuracy was better than 1 K in the range from 263 to 300 K. For this study, the quality of LST data is controlled by using monthly composited data and by examining quality control (QC) flags to

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Andrew J. Elmore, Gregory P. Asner, and R. Flint Hughes

similar to data with sensor errors. Since the rmse is an overall measure of SMA model fit, it is desirable to exclude pixels with a high rmse regardless of the underlying reasons (clouds, data errors, etc.). A threshold rmse of 0.18 (units of reflectance) was found to accurately separate cloud-compromised and usable pixels. This rmse value was selected by comparing rmse values for all pixels with the MODIS quality assurance bit and with raw image reflectance data. This approach resulted in greater

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A. H. M. Siddique-E-Akbor, Faisal Hossain, Safat Sikder, C. K. Shum, Steven Tseng, Yuchan Yi, F. J. Turk, and Ashutosh Limaye

International Centre for Integrated Mountain Development (ICIMOD) located in Nepal. The daily precipitation data for the period of 2002–10 were collected for entire GBM basins. Stations with more than 50% missing data were discarded. The missing data were replaced with precipitation data provided by a newly quality controlled dataset called Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) that is built specifically for the Asian region

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