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setwd("D:/TWSA_198101-202006/Tiff_198101_202006")
file_list <- list.files(
full.names = FALSE
)
# reorder by gtools
library(gtools)
file_list <- mixedsort(file_list)
% Get geo referenced
% No longer supported:
% R = georasterref('RasterSize',[180 360],'LatitudeLimits',[-89.5,89.5],'LongitudeLimits',[0.5,359.5]);
latlim = [-90,90];
lonlim = [0,360];
rasterSize = [180 360];
R = georefcells(latlim,lonlim,rasterSize,'ColumnsStartFrom','north');
library(Kendall)
# Annual precipitation entire Great Lakes
# The time series plot with lowess smooth suggests an upward trend
# The autocorrelation in this data does not appear significant.
# The Mann-Kendall trend test confirms the upward trend.
data(PrecipGL)
plot(PrecipGL)
lines(lowess(time(PrecipGL),PrecipGL),lwd=3, col=2)
acf(PrecipGL)
MannKendall(PrecipGL)
setwd("I:/OneDrive - University of Texas at Arlington/Summer Papers/Texas_Water_Study/WRM_journal/CMIP5_downloader/evspsbl_Amon_GFDL-CM3_rcp85_r1i1p1_200601-202012")
file_list <- list.files(
full.names = FALSE
)
# reorder by gtools
library(gtools)
file_list <- mixedsort(file_list)
addr = "I:/OneDrive - University of Texas at Arlington/Summer Papers/Texas_Water_Study/WRM_journal/CMIP5_downloader/evspsbl_Amon_GFDL-CM3_rcp85_r1i1p1_200601-201012"
period <- seq(as.Date("2006/01/01"), by = "month", length.out = 60)
name <- paste0("evspsbl_Amon_GFDL-CM3_rcp85_r1i1p1_", period)
old_files <- list.files(addr,pattern = "*.tif", full.names = TRUE)
# reorder by gtools
library(gtools)
old_files <- mixedsort(old_files)
setwd("I:/OneDrive - University of Texas at Arlington/Summer Papers/Texas_Water_Study/WRM_journal/CMIP5_downloader/evspsbl_Amon_GISS-E2-R_rcp45_r1i1p1_200601-202512")
file_list <- list.files(
full.names = FALSE
)
# reorder by gtools
library(gtools)
file_list <- mixedsort(file_list)
library(ggplot2)
library(ggExtra)
# Define the palette
reverseRainbow <- tail(rev(rainbow(10)), 8)
data<-read.csv(file.choose(), header=TRUE)
data$height <- -data$bathymetry
## Chl
p1= ggplot(data, aes(x=vgpm,
y=eppley,
function []=scatter_plot_sta(x,y)
Array=csvread('dryland_BSk.csv',1,0);
x = Array(:, 2);
y = Array(:, 1);
% n = 2000;
% R = [0.1 4];
% x = rand(n,1)*range(R)+min(R)
# Read Data:
d = read.csv(file.choose(), header = TRUE)
# Date suc has 4/1/2002, use "%m/%d/%y"
d$Date<- as.Date(d$Date, "%m/%d/%y")
# Group by Month
Month <- format(d$Date, "%m")
# Calculating the monthly anoamlies
anom <- d$Values - ave(d$Values, Month, FUN = function(x) mean(x, na.rm = TRUE))
d$anom <- anom
library(raster)
library(rgdal)
library(dplyr)
r1 <- raster("pop_GERMANY.tif")
r1 <-r1[(r1 > 1)]
# boxplot(r1, log="y", outline=TRUE, ylim = c(1, 30000), main='Germany', ylab='Population Density (km^2)' )
library(ggplot2)
library(ggExtra)