fruclimadapt (version 0.4.5)

bioclim_hydrotherm: Calculation of hydrothermal viticultural indices (Branas, Dryness Index)

Description

This function calculates the hydrotermic index of Branas, Bernon and Levandoux (BBLI, Branas et al 1946) and the Dryness index (Riou et al 1994).

Usage

bioclim_hydrotherm(climdata, lat, elev)

Value

dataframe with the values of the indices for each season in the climdata file.

Arguments

climdata

a dataframe with daily weather data, including temperature Required columns are Year, Month, Day, Tmax, Tmin and Prec. Optional columns are RHmax, RHmin, Rad and u2med.

lat

the latitude of the site, in decimal degrees. Use positive values for Northern latitudes and negatives for Southern.

elev

the elevation of the site, in meters above sea level.

Author

Carlos Miranda,

Details

The BBLI takes into account the influence of both temperature and precipitation on grape yield and wine quality. This index is the sum of the products of monthly mean temperature (Tmean, in Celsius) and monthly accumulated precipitation amount (Prec,in mm) during the April to September season (Northern Hemisphere) or October to February (Southern Hemisphere).

The Dryness index (DI) is measured based on an adaptation of the potential water balance of the soil index of Riou (Riou et al., 1994), developed specially for vineyard use. It enables the characterization of the water component of the climate in a grape-growing region, taking into account the climatic demand of a standard vineyard, evaporation from bare soil, rainfall without deduction for surface runoff or drainage. It indicates the potential water availability in the soil, related to the level of dryness in a region (Tonietto and Carbonneau, 2004). The index uses potential evapotranspiration calculated here with the Penman Monteith method.

Minimum data requirements to calculate the indices are daily temperatures (maximum and minimum temperatures, Tmax and Tmin) and rainfall (l m-2), whereas relative humidity (RHmax and RHmin, %), solar radiation (Rad, MJ m-2 day-1) and mean wind speed at 2m height (u2med,m s-1) are optional. If missing, the function integrates FAO56 (Allen et al 1998) estimations for solar radiation and vapor pressure (air humidity) from daily temperatures. If there is no information available on wind speed, the function assumes a constant value of 2 m s-1.

References

Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations Branas J, Bernon G, Levadoux L. 1946. Elements de Viticulture Generale. Imp. Dehan, Bordeaux Riou C, Carbonneau A, Becker N, Caló A, Costacurta A, Castro R, Pinto PA, Carneiro LC, Lopes C, Climaco P, Panagiotou MM, Sotes V,Beaumond HC, Burril A, Maes J, Vossen P. 1994. Le determinisme climatique de la maturation du raisin: application au zonage de la teneur em sucre dans la communaute europenne. Office des Publications Officielles des Communautes Europennes: Luxembourg, 322pp.

Tonietto J, Carbonneau A. 2004. A multicriteria climatic classification system for grape-growing regions worldwide. Agricultural and Forest Meteorology, 124:81-97.

Examples

# Select the appropiate columns from a larger dataset with date information
# in Year, Month, Day format, define the values for the parameters latitude 
# and elevation and estimate the hydrotermal indices on each year in the series.
library(magrittr)
library(dplyr)
Weather <- Tudela_DW %>%
   select(Year, Month, Day, Tmax, Tmin, Prec, RHmax, RHmin, Rad, u2med)
elevation <- 314
latitude <- 42.13132
Tudela_BHI <- bioclim_hydrotherm(Weather, latitude, elevation)