Syria 2024/2025 Drought Season
This section analyzes the evolution and spatial distribution of the Agricultural Stress Index (ASI) across Syrian governorates during the critical 2024/2025 winter cropping season (November 2024 to April 2025), a period highlighted in FAO’s drought alert as the most severe in 36 years.
We use ASI data obtained through the cumulus R package via FAO GIEWS, filtered by province and dekad to explore stress trends across crop-growing areas.
rp_empirical <- function(x, direction = c("1", "-1"), ties_method = "average") {
direction <- as.numeric(rlang::arg_match(direction))
rank <- rank(x * direction, ties.method = ties_method)
q_rank <- rank / (length(x) + 1)
rp <- 1 / q_rank
return(rp)
}box::use(
cumulus
)
box::use(
lubridate[...],
dplyr[...],
tidyr[...],
readr[...],
ggplot2[...],
glue[...],
forcats[...],
gghdx[...],
sf[...]
)
df_asi <- cumulus$fao_asi_adm1_tabular(iso3 = "syr")
gdf_syr <- cumulus$download_fieldmaps_sf(iso3 = "syr", layer = "syr_adm1")
gghdx()df_filtered <- df_asi |>
mutate(
mo_num = as.numeric(Month),
harvest_year = case_when(
mo_num %in% c(11, 12) ~ Year + 1,
TRUE ~ Year
),
mo_chr = fct_expand(month.abb[mo_num], month.abb),
date = glue("{Year}-{Month}-21")
) |>
filter(
Dekad == 3,
mo_num %in% c(11, 12, 1, 2, 3, 4)
) |>
mutate(
mo_fct = fct_relevel(mo_chr, c("Nov", "Dec", "Jan", "Feb", "Mar", "Apr")),
harvest_year_label = case_when(
harvest_year %in% c(2023:2025) ~ as.character(harvest_year),
.default = "Other"
),
alpha_flag = if_else(harvest_year_label == "Other", "low", "high") # for alpha mapping
)
df_rps <- df_filtered |>
group_by(ADM1_CODE, Province, mo_num, mo_chr) |>
mutate(
rp_empirical = rp_empirical(
Data,
ties_method = "average",
direction = "-1"
)
) |>
arrange(
Province, mo_chr
)
df_rps_2025 <- df_rps |>
filter(
harvest_year == 2025
)The numbers represent the return period of the ASI values for the 2025 harvest year (calculated empirically).
df_filtered |>
mutate(
mo_fct = fct_relevel(mo_chr, c("Nov", "Dec", "Jan", "Feb", "Mar", "Apr")),
harvest_year_label = case_when(
harvest_year %in% c(2023:2025) ~ as.character(harvest_year),
.default = "Other"
),
alpha_flag = if_else(harvest_year_label == "Other", "low", "high") # for alpha mapping
) |>
ggplot(
aes(
x = mo_fct,
y = Data,
group = harvest_year,
color = harvest_year_label,
alpha = alpha_flag
)
) +
geom_text(
data = df_rps_2025,
aes(label = scales::number(rp_empirical, accuracy = 1), vjust = -.5),
show.legend = FALSE
) +
expand_limits(y = max(df_filtered$Data, na.rm = TRUE) * 1.3) +
labs(color = "Harvest Year", y = "ASI") +
scale_alpha_manual(values = c("low" = 0.3, "high" = 1), guide = "none") +
geom_line() +
facet_wrap(~Province) +
theme(
axis.text.x = element_text(angle = 90),
axis.title.x = element_blank()
)
gdf_syr_adm1 <- gdf_syr$syr_adm1 |>
mutate(
adm1_harm = case_when(
ADM1_EN == "Dar'a" ~ "Dara",
ADM1_EN == "Quneitra" ~ "Al_Qunaytirah",
ADM1_EN == "Rural Damascus" ~ "City_Damascus",
ADM1_EN == "As-Sweida" ~ "As_Suweida",
ADM1_EN == "Ar-Raqqa" ~ "Raqqa",
ADM1_EN == "Deir-ez-Zor" ~ "Dayr_Az_Zor",
ADM1_EN == "Al-Hasakeh" ~ "Hassakeh",
.default = ADM1_EN
)
)
gdf_syr_adm1_asi <- gdf_syr_adm1 |>
left_join(
df_rps_2025 |>
filter(mo_chr == "Mar"),
by = c("adm1_harm" = "Province")
)
gdf_syr_adm1_asi <- gdf_syr_adm1_asi |>
mutate(
rp_bin = cut(
rp_empirical,
breaks = c(-Inf, 2, 5, 10, 20, Inf),
labels = c("<2", "2-5", "5-10", "10-20", "≥20"),
right = FALSE
)
)
ggplot() +
geom_sf(
data = gdf_syr_adm1_asi,
aes(fill = rp_bin), color = "black"
) +
scale_fill_manual(
values = c(
"<2" = "#ffffff",
"2-5" = "#ffffcc",
"5-10" = "#ffeda0",
"10-20" = "#feb24c",
"≥20" = "#f03b20"
),
name = "Return Period"
)+
ggfx::with_outer_glow(
geom_sf_text(data= gdf_syr_adm1_asi,
aes(label = ADM1_EN)),
colour = 'white',
sigma = 1,
expand = 3
)+
# theme_void()+
labs(
title = "March 2025: ASI Return Periods"
)+
theme(
axis.text = element_blank(),
axis.title = element_blank()
)