Modeling physical and chemical climate of the northeastern United States for a geographic information system

Ollinger, Scott V.; Aber, John D.; Federer, Anthony C.; Lovett, Gary M.; Ellis, Jennifer M.

doi:10.2737/ne-gtr-191

Cited by 32 publications

(2 citation statements)

References 13 publications

(20 reference statements)

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“…Two early studies used GIS and raster-based predictors in regression models to develop layers of climatic variables and annual S and N deposition rates (Ollinger, 1995). Rasterbased methods were used to predict climatic variables of agronomic significance and STRs for the state of Pennsylvania (Waltman, 1997).…”

Section: Core Ideasmentioning

confidence: 99%

Modeling the thermic soil temperature regime boundary of the eastern United States

D'Avello¹,

Bathgate

Thompson

2021

Soil Science Soc of Amer J

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Within U.S. soil taxonomy,soil temperature regimes (STRs) are recognized and incorporated in the classification and mapping of soils. Although knowledge of STR can inform crop selection, yield estimates, and nutrient management decisions, detailed and current maps of STRs derived from measured data are not available for most of the United States. Soil temperature measurements at the prescribed depth of 50 cm were summarized for 69 Soil Climate Analysis Network (SCAN) stations in the eastern United States. Mean annual soil temperature (MAST) was calculated for the 2005-2015 period from monthly averages calculated from daily soil temperature observations. A prediction equation for MAST was developed using mean annual air temperature (MAAT) from the PRISM (Parameter-elevation Regressions on Independent Slopes Model) group as a predictor: MAST = 2.49 + (0.91 × MAAT). The adjusted r 2 was .96 with a RMSE of 0.74 ˚C and mean absolute error of 0.59 ˚C based on 10-fold cross-validation. Orographic influence is incorporated in PRISM MAAT, yielding a simple model. The MAST surfaces were calculated by applying the prediction equation to MAAT grids for the 1895-2015 period. The yearly MAST surfaces allow estimation of STR over time and for various summary periods including the 1895-2015 record, 30-yr normal, and probabilities based on individual years for the 1895-2015 period. Based on this investigation, the mesic-thermic STR boundary occurs north of the presently recognized location, is dynamic, and is supported by existing native plant communities associated with thermic STR. INTRODUCTIONSoil temperature is a dynamic soil property that influences the physical, chemical, and biological processes in the soil (Soil Survey Staff, 2014). Solar radiation is the primary source of heat entering the soil (Shul'gin, 1965). Local site characteristics including land cover, slope aspect, slope gradient, and the

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Section: Core Ideasmentioning

confidence: 99%

Modeling the thermic soil temperature regime boundary of the eastern United States

D'Avello¹,

Bathgate

Thompson

2021

Soil Science Soc of Amer J

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“…More recent studies have considered more refined topographic factors by using higher resolution digital elevation models (DEM) to predict the physical influence of topographic variables on precipitation patterns. Precipitation models integrating statistical and GIS techniques have become widespread and common [19,24]. Multiple linear [25,26,27,28] and non-linear [16,29] regression models have proved to be rather effective.…”

Section: Introductionmentioning

confidence: 99%

Kriging Interpolation Method for Estimation of Continuous Spatial Distribution of Precipitation in Cyprus

Maris

2014

BJAST

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Aims: Development of a precipitation prediction model for Cyprus. Study Design: Precipitation data collected at 78 stations were used: data from 66 stations for model development and data from 12 stations for additional tests. Four topographic factors -altitude, slope, longitude, and latitude -were taken into account for model development. Place and Duration of Study: All variables were obtained from the observation archives of the Water Development Department of the Ministry of Agriculture, Natural Resources and Environment of Cyprus, between 1961 and 1990. Methodology: Multiple regression analysis, combined with residuals correction, was carried out to develop a precipitation prediction model. Results: The multiple regression model can explain 61.3% of the spatial variability of precipitation over the whole year, 57.5% of variability in the wet season (October-April), and 99.6% of variability in the dry season (May-September). Interpolation-based

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Estimation and Analysis for Geographic and Orographic Influences on Precipitation Distribution in China

Shu

Wang

Xiong

2007

Chinese J of Geophysics

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Different from the framework of multiple linear regression based on least square method, this paper tries to apply a novel second‐generation regression method based on partial least‐squares to precipitation estimation in China for the first time. The 45‐yr precipitation data from 394 meteorological stations in study area are used. Several simple formulae used to estimate the annual mean and seasonal precipitation have been obtained, and the characteristics of the geographic or topographic effects have been presented. The impact factors include longitude, latitude, height, slope, sloping direction and close limit. The results show that the fraction of the variation of response explained by the model is above 70%, and the average correlation coefficients are nearly all above 0.84. The results are satisfied through the test of cross‐validation. Though it is not appropriate to set up multiple linear regression model, the estimated precipitation based on partial least‐squares regression correctly replicates real spatial distribution of precipitation qualitatively and quantitatively.

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Modeling physical and chemical climate of the northeastern United States for a geographic information system

Cited by 32 publications

References 13 publications

Modeling the thermic soil temperature regime boundary of the eastern United States

Modeling the thermic soil temperature regime boundary of the eastern United States

Kriging Interpolation Method for Estimation of Continuous Spatial Distribution of Precipitation in Cyprus

Estimation and Analysis for Geographic and Orographic Influences on Precipitation Distribution in China

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