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This data release is provided in support of Arismendi, I., Dunham, J.B., Heck, M.P., Schultz, L.D., Hockman-Wert, D.P., 2017, A statistical method to predict flow permanence in dryland streams from time series of stream temperature: Water, v. 9, no. 12, p. 946, https://doi.org/10.3390/w9120946. This code release contains all of the source code from the "Hidden Markov Model" sections of the associated manuscript. The source code was written using the R programming language (www.r-project.org, version 3.3.1). Running the code requires knowlege of the R programming language. The code snippet requires the folder location containing the data, and the site being processed, to be updated. The code requires certain R packages,...
Abstract: The Land Transformation Model (LTM) is hierarchically coupled with meso-scale drivers to project urban growth across the conterminous USA. Quantity of urban growth at county and place (i.e., city) scales is simulated using population, urban density and nearest neighbor dependent attributes. We compared three meso-scale LTMs to three null models that lack meso-scale drivers. Models were developed using circa 1990–2000 data and validated using change in the 2001 and 2006 National Land Cover Databases (NLCD). LTM and null models were assessed using the mean difference in quantity between simulated and actual growth measured at multiple spatial scales. We found that LTM models performed relatively well at...
Categories: Publication; Types: Citation; Tags: National CASC