13-Microclimate_algorithms.Rmd

---
pagetitle: Microclimate Algorithms
---

# Microclimate Algorithms

We demonstrate using NichMapR's microclimate functions micro_era5, micro_usa, micro_global, and micro_ncep that are forced with ERA-5, gridMET, NEW01, and NCEP data, respectively. The functions are designed for point locations.

## How to use NicheMapR microclimate functions

1. Install the NicheMapR package. For micro_era5, install the elevatr, ecmwfr, mcera5, lubridate, dplyr and tidync packages. For micro_ncep, install the RNCEP, elevatr, and microclima packages. For 

2. Save the coordinates of the location of interest in an array. `lonlat <- c(lon, lat) # (longitude, latitude)`

3. Save the start and end date for which you want to obtain data.
```
dstart <- paste0("01/0", month, "/2017") # start date
dfinish <- paste0("31/0", month, "/2017") # end date
```

3. Save 10 different depths below ground as a variable. The values have to start from 0 and must have exactly 10 values regardless of what variables you are looking to obtain. `DEP <- c(0, 3, 5, 10, 15, 20, 30, 50, 100, 200)`

4. Save the desired height at which all variables should be calcualted. `Usrhyt <- .01` for a 1cm height is common.

5. Run the function with the above variables. Variables are stored under different categories depending on what you are looking for. `?micro_ncep` will give you a list of what each category includes and how each variable is named. 

## Code example

***Getting hourly air temperatures in Nunn, CO (-104.73°, 40.87°) for January 1-31 in 2017*** 

### micro_era5()

First, register for ERA5 data [here](https://cds.climate.copernicus.eu/user/register). Download the data using the mcera5 package.

```
# Fill $$$ will credentials from CDS
uid <- "$$$$$$"
cds_api_key <- "$$$$$$$$-$$$$-$$$$-$$$$-$$$$$$$$$$$$"

ecmwfr::wf_set_key(user = uid, key = cds_api_key, service = "cds")

# bounding coordinates (in WGS84 / EPSG:4326)
xmn <- -104
xmx <- -105
ymn <- 40
ymx <- 41

# temporal extent
st_time <- lubridate::ymd("2017:01:01")
en_time <- lubridate::ymd("2017:01:31")

# filename and location for downloaded .nc files
file_prefix <- "era5"
op <- "C:/Spatial_Data/"

# build a request (covering multiple years)
req <- build_era5_request(xmin = xmn, xmax = xmx,
                          ymin = ymn, ymax = ymx,
                          start_time = st_time,
                          end_time = en_time,
                          outfile_name = file_prefix)
str(req)
request_era5(request = req, uid = uid, out_path = op)
```

Once data downloads, run micro_era5()

```
library(NicheMapR)
library(lubridate)
library(dplyr)
library(tidync)

lonlat <- c(-104.73, 40.87) # (longitude, latitude)
dstart <- paste0("01/01/2017") # start date
dfinish <- paste0("31/01/2017") # end date
DEP <- c(0, 3, 5, 10, 15, 20, 30, 50, 100, 200)

micro <- micro_era5(loc = lonlat, dstart = dstart, dfinish = dfinish, DEP = DEP,
                    runmoist = 0, runshade = 0, Usrhyt = 0.01)

variable <- micro$metout[, "TALOC"]
  
variableDOY <- micro$metout[, "DOY"]
variableHOUR <- micro$metout[, "TIME"]
  
vals <- c()
begin <- 1 
end <- begin + 24*31
vals <- variable[begin:end]
valsDOY <- variableDOY[begin:end]
valsHOUR <- variableHOUR[begin:end]
  
days <- c()
for (i in 1:31) {
  days <- c(days, paste0("2017-0", month, "-", i))
}

df <- data.frame("Date" = rep(days, each = 24)[1 : (24 * 31)],
                 "Hour" = rep(0 : 23, 31)[1 : (24 * 31)],
                 "Data" = vals[0 : (24 * 31)])


df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")
```

### micro_usa

```
library(NicheMapR)

micro <- micro_USA(loc = lonlat, dstart = dstart, dfinish = dfinish, DEP = DEP,
                        runmoist = 0, runshade = 0, Usrhyt = 0.01)

variable <- micro$metout[, "TALOC"]
  
variableDOY <- micro$metout[, "DOY"]
variableHOUR <- micro$metout[, "TIME"]
  
vals <- c()
begin <- 1 
end <- begin + 24*31
vals <- variable[begin:end]
valsDOY <- variableDOY[begin:end]
valsHOUR <- variableHOUR[begin:end]
  
days <- c()
for (i in 1:31) {
  days <- c(days, paste0("2017-0", month, "-", i))
}

df <- data.frame("Date" = rep(days, each = 24)[1 : (24 * 31)],
                 "Hour" = rep(0 : 23, 31)[1 : (24 * 31)],
                 "Data" = vals[0 : (24 * 31)])


df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")
```

### micro_global

```
library(NicheMapR)

micro <- micro_global(loc = lonlat, dstart = dstart, dfinish = dfinish, DEP = DEP,
                        runmoist = 0, runshade = 0, Usrhyt = 0.01)

variable <- micro$metout[, "TALOC"]
  
variableDOY <- micro$metout[, "DOY"]
variableHOUR <- micro$metout[, "TIME"]
  
vals <- c()
begin <- 1 
end <- begin + 24*31
vals <- variable[begin:end]
valsDOY <- variableDOY[begin:end]
valsHOUR <- variableHOUR[begin:end]
  
days <- c()
for (i in 1:31) {
  days <- c(days, paste0("2017-0", month, "-", i))
}

df <- data.frame("Date" = rep(days, each = 24)[1 : (24 * 31)],
                 "Hour" = rep(0 : 23, 31)[1 : (24 * 31)],
                 "Data" = vals[0 : (24 * 31)])


df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")
```

### micro_ncep

```
library(NicheMapR)
library(RNCEP)
library(elevatr)
librar(microclima)

micro <- micro_ncep(loc = lonlat, dstart = dstart, dfinish = dfinish, DEP = DEP,
                         runmoist = 0, runshade = 0, Usrhyt = 0.01)

variable <- micro$metout[, "TALOC"]
variableDOY <- micro$metout[, "DOY"]
variableHOUR <- micro$metout[, "TIME"]
  
vals <- c()
begin <- 1 
end <- begin + 24*31
vals <- variable[begin:end]
valsDOY <- variableDOY[begin:end]
valsHOUR <- variableHOUR[begin:end]
  
days <- c()
for (i in 1:31) {
  days <- c(days, paste0("2017-0", month, "-", i))
}

df <- data.frame("Date" = rep(days, each = 24)[1 : (24 * 31)],
                 "Hour" = rep(0 : 23, 31)[1 : (24 * 31)],
                 "Data" = vals[0 : (24 * 31)])


df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")  
```