-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.rmd
128 lines (93 loc) · 5.36 KB
/
README.rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
---
title: "Concentration of daily precipitation in the contiguous United States 1956–2006"
author: "Dominic Royé"
date: "9 de diciembre de 2018"
output: rmarkdown::github_document
---
## Abstract
The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 (p-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 (p-value < 0.001).
## Figures
- Fig. 8. Concentration Index values (1956–2006).
```{r,dpi=300}
#libraries
library(ggplot2)
library(sf)
library(raster)
library(RColorBrewer)
#import raster
ci_raster <- raster("Data/ci_raster_USA.tif")
#import administrative limits
limit <- st_read("Data/USA_limit/USA_continental.shp")
#raster to data.frame
df <- as.data.frame(ci_raster,xy=TRUE,na.rm=TRUE)
names(df)[3] <- "CI" #rename variable column
col_ci <- brewer.pal(11,"RdBu") #colors
#plotting
ggplot()+
geom_tile(data=df,
mapping=aes(x,y,fill=CI))+
geom_sf(data=limit,fill="transparent")+
scale_fill_gradientn(colours=rev(col_ci),
breaks=round(seq(0.43,0.70,0.03),2))+
guides(fill=guide_colorbar(barwidth=0.5,barheight = 10))+
coord_sf(crs=2163,datum=NA)+
theme_void()
```
After the publication of the research article we calculate the Concentration Index with the [PRISM](http://www.prism.oregonstate.edu/) climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.
```{r,dpi=300}
#import raster
r <- raster("./Data/CI_PRISM_USA.tif")
#raster to data.frame
df <- as.data.frame(r,xy=TRUE,na.rm=TRUE)
names(df)[3] <- "CI" #rename variable column
#plotting
ggplot()+
geom_tile(data=df,
aes(x,y,fill=CI))+
geom_sf(data=limit,
fill="transparent")+
scale_fill_gradientn(colours=rev(col_ci),
breaks=round(seq(0.43,0.70,0.03),2))+
guides(fill=guide_colorbar(barwidth=0.5,barheight = 10))+
labs(fill="CI",title="Concentration Index",
caption="Dominic Royé (@dr_xeo) | Data: PRISM Climate Group, Oregon State University")+
coord_sf(crs=2163,datum=NA)+
theme_void()+
theme(plot.title = element_text(size=16))
```
- Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)
```{r,dpi=300}
#libraries
library(ggplot2)
library(sf)
library(raster)
library(RColorBrewer)
#import raster
regp_raster <- raster("Data/pulvio_regimes_raster_USA.tif")
#import administrative limits
limit <- st_read("Data/USA_limit/USA_continental.shp")
#import clasification for regimes
reg.df <- read.csv("Data/pulvio_regimes_id.csv")
#raster to data.frame
df <- as.data.frame(regp_raster,xy=TRUE,na.rm=TRUE)
names(df)[3] <- "regp" #rename variable column
#add labels to variable
df[,"regp"] <- factor(df[,"regp"],levels=reg.df$id,labels=reg.df$reg)
#color definitions
gre <- rev(c('#edf8e9','#c7e9c0','#a1d99b','#74c476','#31a354','#006d2c'))
bl <- rev(c('#eff3ff','#c6dbef','#9ecae1','#6baed6','#3182bd','#08519c'))
roj <- rev(c("#ffffb2","#fed976","#feb24c","#fd8d3c","#f03b20","#bd0026"))
viol <- rev(c('#feebe2','#fcc5c0','#fa9fb5','#f768a1','#c51b8a','#7a0177'))
#plotting
ggplot()+
geom_tile(data=df,mapping=aes(x,y,fill=regp,group=1))+
geom_sf(data=limit,fill="transparent")+
scale_fill_manual("Pluviometric regimes",values=c(gre,roj,viol,bl))+
coord_sf(crs=2163,datum=NA)+
theme_void()
```
## Map projection
EPSG:2163
proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"
## How to cite
Royé D & Martin-Vide J (2017). Concentration of Daily Precipitation in the Contiguous United States. Atmospheric Research, 196C:237-247, doi: [10.1016/j.atmosres.2017.06.011](https://doi.org/10.1016/j.atmosres.2017.06.011).