Introduction to climatrends
In climatrends: Climate Variability Indices for Ecological Modelling

Summary

Understanding how climate variability influences ecological processes is a key approach to provide recommendations for climate adaptation and biodiversity conservation. climatrends provides methods to compute precipitation and temperature indices that serves as input for crop modelling, ecological modelling and trends in climate change. The climate variables available in climatrends were previously used to identify climate patterns in crop growing cycles[@Kehel2016], regional climate change assessment[@Aguilar2005; @Challinor2016; @Trnka2014; @Zohner2020], crowdsourcing citizen science in agriculture[@vanEtten2019] and trends in climate change compared with farmers' perceptions[@DeSousa2018].

Usage

By default, the functions in climatrends requires a vector with climate data, and optionally a vector of dates that can be linked to the data for time series analysis. The package also provides methods for objects of classes array (or matrix), sf and data.frame, where the last two are designed to be used to request remote data from R packages that uses API clients, such as nasapower[@Sparks2018]. This vignette presents the main functions and the methods available in climatrends. Overall, these applications can be generalized to all functions.

Temperature

Here we compute temperature indices for the first semester of 2019 in the Innlandet county in Norway:

library("climatrends")

data("innlandet", package = "climatrends")

temp1 <- temperature(innlandet$tmax, innlandet$tmin)

temp1

With the argument timeseries = TRUE, temperature() returns the indices for a time series based on intervals. Here we compute the indices for intervals of 30 days:

temp2 <- temperature(innlandet$tmax, innlandet$tmin,
                     dates = innlandet$dates,
                     timeseries = TRUE, 
                     intervals = 30)

temp2

Growing degree-days

Growing degree-days is a heuristic tool in phenology, it is a measure of heat accumulation used to predict plant and animal development rates[@Prentice1992]. With the function GDD() we can compute the growing degree-days over a time series. In this case we apply an adjusted equation designed for cold areas with the argument equation:

gdd <- GDD(innlandet$tmax, innlandet$tmin, tbase = 2, equation = "b")

gdd

Additionally, the function may return the daily values of degree-days or the number of days that a given organism required to reach a certain number of accumulated degree-days. These values are defined by 'daily' or 'ndays' and can be adjusted using the argument return.as. The required accumulated gdd is defined with argument degree.days. For example, the Korean pine (Pinus koraiensis) requires 105 $^\circ C$ accumulated gdd to onset of photosynthesis [@JWu2013]. In that case, the function will calculate the growing degree-days and sum up the values until it reaches the defined gdd (105 $^\circ C$) and return the number of days needed in the given season, as follows.

lonlat <- data.frame(lon = 129.19,
                     lat = 36.39)

GDD(lonlat, 
    day.one = "2019-04-01",
    last.day = "2019-10-01",
    degree.days = 150, 
    return.as = "ndays")

This means that 45 days were required to reach 150 degree-days from April 2019.

Late spring frost

Late frost is a freezing event occurring after a substantial accumulation of warmth. These events can be tracked using the function late_frost(). The function returns a data.frame with the duration and gdd accumulated during the events of frost, latency (where there is no frost event, but also there is no GDD), and warming (where GDD is accumulated).

lf <- late_frost(innlandet$tmax, 
                 innlandet$tmin, 
                 dates = innlandet$date, 
                 base = 2)

lf

Rainfall

Precipitation indices are computed in the same way as the temperature() indices using the function rainfall(). But here, we fetch data from NASA POWER using the nasapower package passed to the data.frame method.

library("nasapower")

lonlat <- data.frame(lon = c(-73.3, -74.5),
                     lat = c(-6.1, - 6.2))

rain <- rainfall(lonlat, 
                 day.one = "2018-11-01",
                 last.day = "2018-12-31")

rain

Crop sensitive indices

These indices are designed to capture variability in sensitive stages of crop development [@Challinor2016; @Trnka2014] and can be computed using the function crop_sensitive(). Here we use the sf method with 5 random points across the Sinop municipality in Brazil.

library("sf")
data("lonlatsf", package = "climatrends")

crop_sensitive(lonlatsf, 
               day.one = "2018-12-01",
               last.day = "2019-01-31", 
               as.sf = FALSE)

Additionally the thresholds for each index can be adjusted using the argument *.threshold (where * is replaced by the index abbreviation).

Reference evapotranspiration

Evapotranspiration can be defined as the sum of evaporation and plant transpiration from the Earth's surface to the atmosphere. This is also an important index to include in models for ecological interactions. In *climatrends this index can be calculate using the function ETo() which computes the evapotranspiration based on the Blaney-Criddle method[@Brouwer1986], an ideal equation when only air-temperature data sets are available for a site. Here we use the array method to compute the reference evapotranspiration. And the duration of the time series in each row is adjusted with the argument span based on the duration of the anthesis period in the latitude 25N, adjusted with argument lat.

data("temp_dat", package = "climatrends")

eto <- ETo(temp_dat, 
           day.one = "2013-10-28",
           span = c(9, 10, 11, 12, 8, 10, 11, 11, 12, 10),
           lat = rep(25, 10),
           Kc = 0.92)

eto