Equations used in climatrends

Growing degree-days

Growing degree-days (gdd) is an heuristic tool in phenology that measures heat accumulation and is used to predict plant and animal development rates¹. Growing degree-days are calculated by taking the integral of warmth above a base temperature (T₀). The function GDD() applies by default the following equation.

Equation [1]

$$GDD = \frac{T_{max} + T_{min}}{2} - T_{0}$$

where T_max is the maximum temperature in the given day, T_min is the minimum temperature in the given day and T₀ is the minimum temperature for growth (as per the physiology of the focal organism or ecosystem averages).

Additionally, the function GDD() offers three modified equations designed for cold environments and for tropical environments. For cold environments, where T_min may be lower than T₀, there are two modified equations that adjust either T_mean (variant a) or T_min (variant b). The variant a changes T_mean to T₀ if T_mean < T₀ and is expressed as follow.

Equation [2]

$$ GDD = max \left(\frac{T_{max} + T_{min}}{2} - T_{0}, \; 0 \right)$$

The variant b, is calculated using Equation 1, but adjusts T_min or T_max to T₀ if T < T₀, the equation is adjusted as follows.

Equation [3]

T < T₀ → T = T₀

where T may refer to T_min and/or T_max when the condition of being below T₀ applies.

For tropical areas, where the temperature may surpass a maximum threshold (T_0max), resulting in limited development, the minimum temperature is adjusted using Equation 3 and the maximum temperature is adjusted to a maximum base temperature as follow.

Equation [4]

T_max > T_0max → T_max = T_0max

where T_0max is the maximum base temperature for growth, defined in GDD() using the argument tbase_max.

These modified equations are defined as ‘a’, ‘b’ and ‘c’, respectively, and can be selected using the argument equation.

By default, the function returns the degree-days that is accumulated over the time series using Equation 1. 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 ‘acc’, ‘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 ^∘C accumulated gdd to onset the photosynthesis². In that case, GDD() will calculate the growing degree-days (gdd) and sum up the values until it reaches 105 ^∘C and return the number of days required in the given season (GDD_r), as follows.

Equation [5]

 ∥ GDD_r∥ = ggd₁ + ... + gdd_n

where GDD_r is the length of the vector with accumulated degree-days from day 1 to n.

Late-spring frost

Late-spring frost is a freezing event occurring after a substantial accumulation of warmth. Frost damage is a known issue in temperate and boreal regions, it is associated with the formation of extracellular ice crystals that cause damage in the membranes³. Freezing occurring after an advanced phenological stage during spring may harm some plant species, resulting in lost of productivity in crop systems⁴ and important ecological impacts⁵.

The function late_frost() supports the computation of late-spring frost events. The function counts for the number of freezing days with minimum temperature below a certain threshold (argument tfrost). And returns the number of days spanned by frost events (temperature below tfrost), latency (event with no freezing temperature but also no accumulation of growing degree-days) and warming (when growing degree-days are accumulated enabling the development of the target organism). Additionally the function returns the first day of the events. The function calculates the growing degree-days applying the variant b (Eq. 3), which can be adjusted using the argument equation passed to GDD() as explained in the later section. The main inputs are a vector with maximum and minimum temperatures to compute the degree-days, a vector of dates (argument date), and, if needed, the tbase and tfrost, set by default to 4 and -2 ^∘C.

Crop ecology indices

Two functions in climatrends are mainly designed to capture the effects of climate on the development and stress of crop species, crop_sensitive() computes indices that aim to capture the changes in temperature extremes during key phenological stages (e.g. anthesis), and ETo() computes the reference evapotranspiration.

The crop ecology indices available in climatrends are described in Table 3. These indices were previously used in crop models to project the impacts of climate change on crop yield^4,6. Each index has a default temperature threshold(s) which can be adjusted by using the arguments *.threshold. Where the * means the index. For example, to change the defaults for hts_max (high temperature stress), a vector with the temperature thresholds is passed through the argument hts_max.thresholds.

Table 3: Crop sensitive indices computed by climatrends.

The reference evapotranspiration measures the influence of the climate on a given plant’s water need⁷. The function ETo() applies the Blaney-Criddle method, a general theoretical method used when only air-temperature is available locally. It should be noted that this method is not very accurate and aims to provide the order of magnitude of evapotranspitation. The reference evapotranspiration is calculated using the following equation.

Equation [6]

$$ETo = p \times \left(0.46 \times \frac{T_{max} + T_{min}}{2} + 8 \right) \times K_c$$

Where p is the mean daily percentage of annual daytime hours, T_max is the maximum temperature, T_min is the minimum temperature, and K_c is the factor for organism water need.

The percentage of daytime hours (p) is calculated internally by the ‘data.frame’ and ‘sf’ methods in ETo() using the given latitude (taken from the inputted object) and date (taken from the inputted day.one). It matches the latitude and date with a table of daylight percentage derived from Brouwer and Heibloem⁷. The table can be verified using climatrends:::daylight.