Create risk reports for global countries and provinces

VIGNETTE UNDER CONSTRUCTION

# general tools
library(tidyverse)
library(cli)
library(here)
library(common)

# spatial data
library(terra)
library(sf)
library(rnaturalearth)
library(rnaturalearthhires)
library(rnaturalearthdata)
library(ggspatial)

# aesthetics
library(kableExtra)
library(formattable)
library(webshot)
library(webshot2)
library(ggnewscale) ## MUST use version 0.4.10- 0.5.0 has a bug that breaks this function

# for analysis
library(patchwork)
library(gginnards)

library(scari)

Note: I will be setting the global options of this document so that only certain code chunks are rendered in the final .html file. I will set the eval = FALSE so that none of the code is re-run (preventing files from being overwritten during knitting) and will simply overwrite this in chunks with plots.

Generate Individual country and state/province reports

Alternatively, I will create individualized reports and maps for key viticultural areas of interest. I will group these regions by expected gain or loss in viticultural risk, and by geography. I will start out with the USA and China because they each contain so many SLF records and because I want to create reports for specific provinces within these countries.

China

scari::create_risk_report(
  locality.iso = "chn", 
  locality.name = "china",
  locality.type = "country",
  mypath = file.path(here::here(), "vignette-outputs", "reports", "China"),
  create.dir = FALSE,
  save.report = FALSE,
  buffer.dist = 20000
)

## Reading layer `ne_10m_admin_0_countries' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_countries\ne_10m_admin_0_countries.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 258 features and 168 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

## Reading layer `ne_10m_admin_1_states_provinces' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_states_provinces\ne_10m_admin_1_states_provinces.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 4596 features and 121 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

# this report should have 2 IVR points
scari::create_risk_report(
  locality.iso = "chn",
  locality.name = "shandong",
  locality.type = "state_province",
  mypath = file.path(here::here(), "vignette-outputs", "reports", "China"),
  create.dir = FALSE,
  save.report = FALSE,
  buffer.dist = 20000
)

## Reading layer `ne_10m_admin_0_countries' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_countries\ne_10m_admin_0_countries.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 258 features and 168 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

## Reading layer `ne_10m_admin_1_states_provinces' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_states_provinces\ne_10m_admin_1_states_provinces.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 4596 features and 121 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

# this report should have no IVR records
scari::create_risk_report(
  locality.iso = "chn",
  locality.name = "yunnan",
  locality.type = "state_province",
  mypath = file.path(here::here(), "vignette-outputs", "reports", "China"),
  create.dir = FALSE,
  save.report = FALSE,
  buffer.dist = 20000
)

## Reading layer `ne_10m_admin_0_countries' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_countries\ne_10m_admin_0_countries.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 258 features and 168 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

## Reading layer `ne_10m_admin_1_states_provinces' from data source 
##   `C:\Users\tun83449\OneDrive - Temple University\Shared drives\slfClimate\projects\scari\scari\data-raw\ne_states_provinces\ne_10m_admin_1_states_provinces.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 4596 features and 121 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.6341
## Geodetic CRS:  WGS 84

# it worked still so thats good

# save report for other uses
# readr::write_rds(china_slf_risk_report, file = file.path(here::here(), "vignette-outputs", "reports", "China", "china_slf_risk_report.rds"))

Viticultural shifts due to climate change

Now, I will apply this function for specific countries of future viticultural interest.

Morales-Castilla et al 2020 predicted that the following countries would experience a loss in suitability for viticulture under a 4C warming scenario of climate change:

highest losses: * Italy * Spain

others: * Turkey * South Africa * Portugal * mexico

Concurrently, they predicted that the following areas would experience a gain in suitability under the same scenario:

Pacific Northwestern States (USA)
New Zealand
Great Britain
Ireland
Sweden

Finally, some countries are expected to experience a more balanced ratio of losses and gains in suitability:

France

Another paper, Webb et al 2013 predicted analogous climates for specific Vitis cultivars under climate change. This paper focused more on predicting where specific cultivars might move as growers keep pace with climate change. Based on predicted climate shifts in temperature and precipitation, this study predicts that the following areas will be generally less suitable for Vitis cultivars in the future:

Central valley, California, USA
Hotter parts of Australia

References

Webb, L. B., Watterson, I., Bhend, J., Whetton, P. H., & Barlow, E. W. R. (2013). Global climate analogues for winegrowing regions in future periods: Projections of temperature and precipitation. Australian Journal of Grape and Wine Research, 19(3), 331–341. https://doi.org/10.1111/ajgw.12045
Morales-Castilla, I., García de Cortázar-Atauri, I., Cook, B. I., Lacombe, T., Parker, A., van Leeuwen, C., Nicholas, K. A., & Wolkovich, E. M. (2020). Diversity buffers winegrowing regions from climate change losses. Proceedings of the National Academy of Sciences, 117(6), 2864–2869. https://doi.org/10.1073/pnas.1906731117

Samuel M. Owens¹

2025-08-07

Generate Individual country and state/province reports

China

Viticultural shifts due to climate change

References

Create risk reports for global countries and provinces

Samuel M. Owens1

2025-08-07

Generate Individual country and state/province reports

China

Viticultural shifts due to climate change

References

Samuel M. Owens¹