利用R提取海拔、温度和降水等信息

全球范围内的温度降水等信息， 常用于物种潜在分布区预测研究中。与此同时，在生态学研究中，常需要某些地点的温度降水等信息。 较早公开的较高精度的气候数据，包括Worldclim（http://www.worldclim.org/）。 2017年，另一套高精度数据CHELSA （Climatologies at high resolution for the earth’s land surface areas， http://chelsa-climate.org/） 公开发布。 CHELSA数据整合了局域尺度的降雨等信息， 部分气象因子比Worldclim更准确。 同一年，Worldclimate也发布了2.0版本。

已知某些地点的经纬度，要提取这些地点的温度降水等信息，用DIVA-GIS(http://www.diva-gis.org/ ) 和Worldclim数据很容易做到， 具体方法参见 http://blog.sciencenet.cn/blog-255662-679159.html

不过由于DIVA-GIS是依靠鼠标操作，很难批量化处理。 本文给出提取气候数据的R代码。相关操作主要基于Robert Hijmans的raster程序包， 更多详细信息请参考 （http://rspatial.org/index.html）

海拔数据下载
http://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/Grid_ZipFiles/mn30_grd.zip

CHELSA提供的19个Bioclimate 数据下载
https://www.wsl.ch/lud/chelsa/data/bioclim/integer/

各环境变量
Bio1 = Annual Mean Temperature 年均温

Bio2 = Mean Diurnal Range，即 Mean of monthly (max temp - min temp) 平均月温差

Bio3 = Isothermality 等温性

Bio4 = Temperature Seasonality 温度的季节性

Bio5 = Max Temperature of Warmest Month 最暖月最高温

Bio6 = Min Temperature of Coldest Month 最冷月最低温

Bio7 = Temperature Annual Range 气温年较差

Bio8 = Mean Temperature of Wettest Quarter 最湿季均温

Bio9 = Mean Temperature of Driest Quarter 最干季均温

Bio10 = Mean Temperature of Warmest Quarter 最暖月均温

Bio11 = Mean Temperature of Coldest Quarter 最冷月均温

Bio12 = Annual Precipitation 年降水

Bio13 = Precipitation of Wettest Month 最湿润月降水

Bio14 = Precipitation of Driest Month 最干旱月降水

Bio15 = Precipitation Seasonality 气温的季节性

Bio16 = Precipitation of Wettest Quarter 最湿润季降水

Bio17 = Precipitation of Driest Quarter 最干旱季降水

Bio18 = Precipitation of Warmest Quarter 最暖季降水

Bio19 = Precipitation of Coldest Quarter 最冷季降水

CHELSA气候图层， 选择整数还是浮点数图层？
可选择整数图层 integer ，这是因为整数图层占用空间较小， 同时也有相对较高的精度， 如温度可精确到0.1度。 注意，这里的温度是*10的值， 也就说，某地年均温为15.2度， 则提取出的数值为152。

30s数据能达到什么精度？
edge.length <- 63782pi1000/(36060*60)*30
edge.length
表明最大栅格边长约为928米。

计算对角线长度
edge.length *1.414

[1] 1311.686

所以如果两个地点相距不足1312m， 那么两个点有可能落在同一个栅格内， 那么提取的气候要素也就没有任何区别。

图层用的是什么投影？
所有图层都使用WGS1984投影， 用于提取数据的坐标， 也必须是WGS1984坐标

解压缩
下载的7z文件用 7-Zip（https://www.7-zip.org/ ）

R代码

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setwd("C:/Users/jlzhang/Desktop/CHELSA")
library(raster)
## Loading required package: sp
library(openxlsx)
#### locations <- read.csv("https://simplemaps.com/static/data/world-cities/basic/simplemaps-worldcities-basic.csv", header = TRUE)
#### write.csv(locations, "locations.csv")
locations <- read.csv("locations.csv")
locations30 <- locations[sample(1:nrow(locations), 30),]
colnames(locations30)
##  [1] "X"          "city"       "city_ascii" "lat"        "lng"       
##  [6] "pop"        "country"    "iso2"       "iso3"       "province"
locations30
##         X            city      city_ascii        lat           lng
## 3081 3081       Bhagalpur       Bhagalpur  25.229996   86.98000321
## 6838 6838           Homer           Homer  59.642934 -151.54827970
## 7257 7257           Lahij           Lahij  13.058181   44.88376135
## 1214 1214            Dori            Dori  14.033997   -0.02799852
## 6716 6716         Douglas         Douglas  31.507778  -82.85068994
## 4313 4313      Bluefields      Bluefields  11.999977  -83.76494938
## 1743 1743          Fuyang          Fuyang  32.900407  115.82000000
## 6831 6831          Ambler          Ambler  67.086485 -157.85140910
## 6249 6249     Southampton     Southampton  50.900031   -1.39997685
## 4920 4920     Vladikavkaz     Vladikavkaz  43.050381   44.66997595
## 5423 5423     Al Mubarraz     Al Mubarraz  25.429054   49.56590450
## 5784 5784    Schaffhausen    Schaffhausen  47.706003    8.63299852
## 1304 1304         Creston         Creston  49.099960 -116.51669700
## 7110 7110        Guliston        Guliston  40.495731   68.79072587
## 5309 5309     Pervouralsk     Pervouralsk  56.910026   59.95503780
## 6658 6658        Woodward        Woodward  36.433421  -99.39769027
## 1405 1405        Cochrane        Cochrane  49.067017  -81.01656417
## 7264 7264          Sayhut          Sayhut  15.210504   51.24544023
## 187   187   Troll Station   Troll Station -72.016290    2.53332312
## 7034 7034      Cincinnati      Cincinnati  39.161885  -84.45692265
## 4395 4395          Sokoto          Sokoto  13.060015    5.24003129
## 3078 3078         Lucknow         Lucknow  26.855039   80.91499874
## 3877 3877       Tidjikdja       Tidjikdja  18.550016  -11.41660059
## 4130 4130           Rabat           Rabat  34.025299   -6.83613082
## 7000 7000      Montgomery      Montgomery  32.361602  -86.27918868
## 1990 1990            Bose            Bose  23.899716  106.61332680
## 6893 6893       Kalispell       Kalispell  48.197767 -114.31597860
## 3587 3587           Rudny           Rudny  52.952697   63.13003780
## 1030 1030        Mineiros        Mineiros -17.569536  -52.55998071
## 3114 3114 Padangsidempuan Padangsidempuan   1.388738   99.27336137
##            pop                  country iso2 iso3
## 3081  361548.0                    India   IN  IND
## 6838    5021.5 United States of America   US  USA
## 7257   44831.5                    Yemen   YE  YEM
## 1214   37806.0             Burkina Faso   BF  BFA
## 6716   12159.0 United States of America   US  USA
## 4313   40033.0                Nicaragua   NI  NIC
## 1743  170023.0                    China   CN  CHN
## 6831     258.0 United States of America   US  USA
## 6249  384417.0           United Kingdom   GB  GBR
## 4920  341000.0                   Russia   RU  RUS
## 5423  294682.0             Saudi Arabia   SA  SAU
## 5784   33863.0              Switzerland   CH  CHE
## 1304    4816.0                   Canada   CA  CAN
## 7110   74446.5               Uzbekistan   UZ  UZB
## 5309  127236.0                   Russia   RU  RUS
## 6658   12339.5 United States of America   US  USA
## 1405    4441.0                   Canada   CA  CAN
## 7264     189.0                    Yemen   YE  YEM
## 187       25.5               Antarctica   AQ  ATA
## 7034  971191.0 United States of America   US  USA
## 4395  648019.5                  Nigeria   NG  NGA
## 3078 2583505.5                    India   IN  IND
## 3877   19981.0               Mauritania   MR  MRT
## 4130 1680376.5                  Morocco   MA  MAR
## 7000  194491.5 United States of America   US  USA
## 1990  132942.5                    China   CN  CHN
## 6893   25040.0 United States of America   US  USA
## 3587  104235.5               Kazakhstan   KZ  KAZ
## 1030   28247.0                   Brazil   BR  BRA
## 3114  183721.5                Indonesia   ID  IDN
##                            province
## 3081                          Bihar
## 6838                         Alaska
## 7257                          Lahij
## 1214                          Séno
## 6716                        Georgia
## 4313                 Atlántico Sur
## 1743                          Anhui
## 6831                         Alaska
## 6249                    Southampton
## 4920                  North Ossetia
## 5423                  Ash Sharqiyah
## 5784                   Schaffhausen
## 1304               British Columbia
## 7110                       Sirdaryo
## 5309                     Sverdlovsk
## 6658                       Oklahoma
## 1405                        Ontario
## 7264                      Al Mahrah
## 187                                
## 7034                           Ohio
## 4395                         Sokoto
## 3078                  Uttar Pradesh
## 3877                         Tagant
## 4130 Rabat - Salé - Zemmour - Zaer
## 7000                        Alabama
## 1990                        Guangxi
## 6893                        Montana
## 3587                       Qostanay
## 1030                         Goiás
## 3114                 Sumatera Utara
coordinates(locations30) = c("lng","lat") # specify column names
### 年均温
bio1 <- raster("CHELSA_bio10_1.tif")
plot(bio1, main="Annual Mean Temperature", 
xlab="Longitude", ylab="Latitude", col=rev(heat.colors(100)))
img

res_bio1 <- extract(x = bio1, y = locations30)


### 海拔
elevation <- raster("mn30_grd")
plot(elevation, main="Altitude", 
xlab="Longitude", ylab="Latitude",col= terrain.colors(100))
img

res_elevation <- extract(x = elevation, y = locations30)

res <- data.frame(locations30, res_bio1, res_elevation)
res
##         X            city      city_ascii        lat           lng
## 3081 3081       Bhagalpur       Bhagalpur  25.229996   86.98000321
## 6838 6838           Homer           Homer  59.642934 -151.54827970
## 7257 7257           Lahij           Lahij  13.058181   44.88376135
## 1214 1214            Dori            Dori  14.033997   -0.02799852
## 6716 6716         Douglas         Douglas  31.507778  -82.85068994
## 4313 4313      Bluefields      Bluefields  11.999977  -83.76494938
## 1743 1743          Fuyang          Fuyang  32.900407  115.82000000
## 6831 6831          Ambler          Ambler  67.086485 -157.85140910
## 6249 6249     Southampton     Southampton  50.900031   -1.39997685
## 4920 4920     Vladikavkaz     Vladikavkaz  43.050381   44.66997595
## 5423 5423     Al Mubarraz     Al Mubarraz  25.429054   49.56590450
## 5784 5784    Schaffhausen    Schaffhausen  47.706003    8.63299852
## 1304 1304         Creston         Creston  49.099960 -116.51669700
## 7110 7110        Guliston        Guliston  40.495731   68.79072587
## 5309 5309     Pervouralsk     Pervouralsk  56.910026   59.95503780
## 6658 6658        Woodward        Woodward  36.433421  -99.39769027
## 1405 1405        Cochrane        Cochrane  49.067017  -81.01656417
## 7264 7264          Sayhut          Sayhut  15.210504   51.24544023
## 187   187   Troll Station   Troll Station -72.016290    2.53332312
## 7034 7034      Cincinnati      Cincinnati  39.161885  -84.45692265
## 4395 4395          Sokoto          Sokoto  13.060015    5.24003129
## 3078 3078         Lucknow         Lucknow  26.855039   80.91499874
## 3877 3877       Tidjikdja       Tidjikdja  18.550016  -11.41660059
## 4130 4130           Rabat           Rabat  34.025299   -6.83613082
## 7000 7000      Montgomery      Montgomery  32.361602  -86.27918868
## 1990 1990            Bose            Bose  23.899716  106.61332680
## 6893 6893       Kalispell       Kalispell  48.197767 -114.31597860
## 3587 3587           Rudny           Rudny  52.952697   63.13003780
## 1030 1030        Mineiros        Mineiros -17.569536  -52.55998071
## 3114 3114 Padangsidempuan Padangsidempuan   1.388738   99.27336137
##            pop                  country iso2 iso3
## 3081  361548.0                    India   IN  IND
## 6838    5021.5 United States of America   US  USA
## 7257   44831.5                    Yemen   YE  YEM
## 1214   37806.0             Burkina Faso   BF  BFA
## 6716   12159.0 United States of America   US  USA
## 4313   40033.0                Nicaragua   NI  NIC
## 1743  170023.0                    China   CN  CHN
## 6831     258.0 United States of America   US  USA
## 6249  384417.0           United Kingdom   GB  GBR
## 4920  341000.0                   Russia   RU  RUS
## 5423  294682.0             Saudi Arabia   SA  SAU
## 5784   33863.0              Switzerland   CH  CHE
## 1304    4816.0                   Canada   CA  CAN
## 7110   74446.5               Uzbekistan   UZ  UZB
## 5309  127236.0                   Russia   RU  RUS
## 6658   12339.5 United States of America   US  USA
## 1405    4441.0                   Canada   CA  CAN
## 7264     189.0                    Yemen   YE  YEM
## 187       25.5               Antarctica   AQ  ATA
## 7034  971191.0 United States of America   US  USA
## 4395  648019.5                  Nigeria   NG  NGA
## 3078 2583505.5                    India   IN  IND
## 3877   19981.0               Mauritania   MR  MRT
## 4130 1680376.5                  Morocco   MA  MAR
## 7000  194491.5 United States of America   US  USA
## 1990  132942.5                    China   CN  CHN
## 6893   25040.0 United States of America   US  USA
## 3587  104235.5               Kazakhstan   KZ  KAZ
## 1030   28247.0                   Brazil   BR  BRA
## 3114  183721.5                Indonesia   ID  IDN
##                            province optional res_bio1 res_elevation
## 3081                          Bihar     TRUE      252            44
## 6838                         Alaska     TRUE       46            56
## 7257                          Lahij     TRUE      294           128
## 1214                          Séno     TRUE      290           281
## 6716                        Georgia     TRUE      185            81
## 4313                 Atlántico Sur     TRUE      266             5
## 1743                          Anhui     TRUE      158            34
## 6831                         Alaska     TRUE      -46            34
## 6249                    Southampton     TRUE      109             6
## 4920                  North Ossetia     TRUE      103           663
## 5423                  Ash Sharqiyah     TRUE      273           155
## 5784                   Schaffhausen     TRUE      100           446
## 1304               British Columbia     TRUE       88           622
## 7110                       Sirdaryo     TRUE      163           273
## 5309                     Sverdlovsk     TRUE       20           329
## 6658                       Oklahoma     TRUE      146           592
## 1405                        Ontario     TRUE       16           276
## 7264                      Al Mahrah     TRUE      283            10
## 187                                     TRUE     -212          1242
## 7034                           Ohio     TRUE      126           193
## 4395                         Sokoto     TRUE      285           291
## 3078                  Uttar Pradesh     TRUE      251           128
## 3877                         Tagant     TRUE      283           409
## 4130 Rabat - Salé - Zemmour - Zaer     TRUE      193            22
## 7000                        Alabama     TRUE      183            88
## 1990                        Guangxi     TRUE      223           137
## 6893                        Montana     TRUE       78           903
## 3587                       Qostanay     TRUE       39           145
## 1030                         Goiás     TRUE      229           769
## 3114                 Sumatera Utara     TRUE      250           325
write.xlsx(res, "extracted.xlsx")