NewGraphEnvironment
diff --git a/‎DESCRIPTION‎
Lines changed: 1 addition & 0 deletions b/‎DESCRIPTION‎
Lines changed: 1 addition & 0 deletions
diff --git a/‎R/cd_anomaly.R‎
Lines changed: 9 additions & 4 deletions b/‎R/cd_anomaly.R‎
Lines changed: 9 additions & 4 deletions
diff --git a/‎R/cd_stac_catalog.R‎
Lines changed: 19 additions & 8 deletions b/‎R/cd_stac_catalog.R‎
Lines changed: 19 additions & 8 deletions
diff --git a/‎R/cd_variables.R‎
Lines changed: 33 additions & 11 deletions b/‎R/cd_variables.R‎
Lines changed: 33 additions & 11 deletions
diff --git a/‎README.md‎
Lines changed: 5 additions & 2 deletions b/‎README.md‎
Lines changed: 5 additions & 2 deletions
diff --git a/‎data-raw/peace_fwcp_vignette_data.R‎
Lines changed: 6 additions & 2 deletions b/‎data-raw/peace_fwcp_vignette_data.R‎
Lines changed: 6 additions & 2 deletions
@@ -23,6 +23,7 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.3
 Suggests:
     aws.s3,
+    bcsnowdata,
     bookdown,
     ecmwfr,
     ggplot2,
 
@@ -2,15 +2,20 @@
 #'
 #' Calculates departure from a baseline for each year. Uses
 #' [cd_variables()] to determine the anomaly type: absolute deviation
-#' for temperature, VPD, and RH; percent of normal for precipitation
-#' and soil moisture.
+#' for temperature, VPD, RH, and the annual snow scalars; percent of
+#' normal for precipitation, soil moisture, and the monthly snow vars
+#' (`swe`, `snowfall`, `snowmelt`); percentage-point difference for
+#' variables that are already fractions/percentages (`snow_cover`,
+#' `snowfall_fraction`).
 #'
 #' @param x A tibble from [cd_extract()] with columns `variable`,
 #'   `period`, `year`, `value`.
 #' @param baseline A tibble from [cd_baseline()] with columns
 #'   `variable`, `period`, `baseline_mean`.
 #' @param cap_pct Numeric. Cap for percent-of-normal anomalies.
-#'   Values beyond +/- `cap_pct` are clamped. Default `200`.
+#'   Values beyond +/- `cap_pct` are clamped. Default `200`. Only
+#'   applies to `pct_normal` variables; `absolute` and
+#'   `pct_point_diff` anomalies are not capped.
 #'
 #' @return A tibble with columns `variable`, `period`, `year`,
 #'   `anomaly`, `anomaly_type`, `unit`.
@@ -43,7 +48,7 @@ cd_anomaly <- function(x, baseline, cap_pct = 200) {
       anomaly_type = lookup[.data$variable],
       unit = units[.data$variable],
       anomaly = dplyr::case_when(
-        .data$anomaly_type == "absolute" ~
+        .data$anomaly_type %in% c("absolute", "pct_point_diff") ~
           .data$value - .data$baseline_mean,
         .data$anomaly_type == "pct_normal" ~
           pmin(pmax((.data$value / .data$baseline_mean) * 100 - 100, -cap_pct), cap_pct)
 
@@ -74,17 +74,28 @@ cd_stac_item <- function(cog_path, base_url) {
   fname <- basename(cog_path)
   name_parts <- tools::file_path_sans_ext(fname)
 
-  # Parse variable and period from filename
-  # Expected patterns: "tmean_annual.tif", "vpd_2024.tif",
-  # "example_climate.tif", etc.
+  # Parse variable and period from filename. Expected pattern:
+  # "{variable}_{period}.tif". Variable names may themselves contain
+  # underscores (e.g. "swe_max", "snowmelt_doy_50") so substring matching
+  # is unsafe — "swe" substring-matches "swe_max_annual" and would
+  # mis-route the file. Use exact "{var}_{period}" matching against the
+  # registries; fall back to filename-as-variable / "unknown" period if
+  # nothing matches.
   known_vars <- cd_variables()$variable
   known_periods <- cd_periods(include_monthly = TRUE)
 
-  var_match <- known_vars[vapply(known_vars, function(v) grepl(v, name_parts), logical(1))]
-  period_match <- known_periods[vapply(known_periods, function(p) grepl(p, name_parts), logical(1))]
-
-  variable <- if (length(var_match) > 0) var_match[1] else name_parts
-  period <- if (length(period_match) > 0) period_match[1] else "unknown"
+  variable <- name_parts
+  period <- "unknown"
+  for (v in known_vars) {
+    for (p in known_periods) {
+      if (identical(name_parts, paste0(v, "_", p))) {
+        variable <- v
+        period <- p
+        break
+      }
+    }
+    if (period != "unknown") break
+  }
 
   # Extract spatial metadata
   r <- terra::rast(cog_path)
 
@@ -1,16 +1,23 @@
 #' Climate variable metadata
 #'
-#' Returns a tibble of metadata for the seven climate variables supported
-#' by the cd package. Used internally for unit labels, anomaly type routing,
-#' and ERA5-Land API variable names.
+#' Returns a tibble of metadata for the climate variables supported by the
+#' cd package. Used internally for unit labels, anomaly type routing, and
+#' ERA5-Land API variable names. Covers the seven core climate variables
+#' plus eight snow-related variables (four monthly natives, four annual
+#' derived) added in v0.2.0.
 #'
 #' @return A tibble with columns:
 #'   \describe{
 #'     \item{variable}{Short name used throughout the package.}
 #'     \item{long_name}{Human-readable label for plots and tables.}
-#'     \item{unit}{Measurement unit (degree C, percent, Pa).}
-#'     \item{anomaly_type}{"absolute" for direct departures, "pct_normal"
-#'       for percent-of-normal anomalies.}
+#'     \item{unit}{Measurement unit (degree C, percent, Pa, mm, day, mm/wk).}
+#'     \item{anomaly_type}{"absolute" for direct departures (value - baseline,
+#'       reported in the variable's native unit), "pct_normal" for
+#'       percent-of-normal anomalies (100 * value / baseline - 100, capped),
+#'       "pct_point_diff" for departures in percentage points (used for
+#'       variables that are already fractions/percentages, e.g. snow cover,
+#'       snowfall fraction, where pct-of-normal is meaningless and the
+#'       natural delta is value - baseline interpreted as percentage points).}
 #'     \item{era5_name}{ERA5-Land variable name for CDS API requests,
 #'       or NA for derived variables.}
 #'   }
@@ -22,24 +29,39 @@
 #' @export
 cd_variables <- function() {
   tibble::tibble(
-    variable = c("tmean", "tmax", "tmin", "prcp", "vpd", "rh", "soil_moisture"),
+    variable = c(
+      "tmean", "tmax", "tmin", "prcp", "vpd", "rh", "soil_moisture",
+      "swe", "snowfall", "snowmelt", "snow_cover",
+      "swe_max", "snowfall_fraction", "snowmelt_doy_50", "snowmelt_rate_peak"
+    ),
     long_name = c(
       "Mean temperature", "Maximum temperature", "Minimum temperature",
       "Precipitation", "Vapour pressure deficit", "Relative humidity",
-      "Soil moisture"
+      "Soil moisture",
+      "Snow water equivalent", "Snowfall", "Snowmelt", "Snow cover",
+      "Annual peak snow water equivalent", "Snowfall fraction",
+      "Day of 50% melt", "Peak weekly melt rate"
+    ),
+    unit = c(
+      "\u00b0C", "\u00b0C", "\u00b0C", "%", "Pa", "%", "%",
+      "%", "%", "%", "%",
+      "mm", "%", "day", "mm/wk"
     ),
-    unit = c("\u00b0C", "\u00b0C", "\u00b0C", "%", "Pa", "%", "%"),
     anomaly_type = c(
       "absolute", "absolute", "absolute",
       "pct_normal",
       "absolute", "absolute",
-      "pct_normal"
+      "pct_normal",
+      "pct_normal", "pct_normal", "pct_normal", "pct_point_diff",
+      "absolute", "pct_point_diff", "absolute", "absolute"
     ),
     era5_name = c(
       "2m_temperature", NA_character_, NA_character_,
       "total_precipitation",
       NA_character_, NA_character_,
-      NA_character_
+      NA_character_,
+      NA_character_, NA_character_, NA_character_, NA_character_,
+      NA_character_, NA_character_, NA_character_, NA_character_
     )
   )
 }
@@ -52,8 +52,11 @@ Catalog (STAC) in a public S3 bucket.
 
 - Catalog (JSON): <https://stac-era5-land.s3.us-west-2.amazonaws.com/catalog.json>
 - Region: BC (~48–60° N, 114–140° W), 1950–2025, ~9 km native grid
-- Variables: tmean, tmax, tmin, prcp, vpd, rh, soil_moisture
-- Periods: monthly, seasonal (DJF/MAM/JJA/SON), annual
+- Variables (15):
+  - **Core climate** (7): tmean, tmax, tmin, prcp, vpd, rh, soil_moisture
+  - **Snow monthly natives** (4): swe, snowfall, snowmelt, snow_cover
+  - **Snow annual derived** (4): swe_max, snowfall_fraction, snowmelt_doy_50, snowmelt_rate_peak
+- Periods: seasonal (DJF/MAM/JJA/SON) and annual for monthly-native vars; annual only for snow_max / snowfall_fraction / snowmelt_doy_50 / snowmelt_rate_peak
 
 The catalog is consumable directly outside R — for example, in QGIS via
 the STAC plugin, in `gdalcubes`, or with any STAC-aware client. The
 
@@ -45,8 +45,12 @@ regional_trn <- cd_trend(regional_ano, trend_start = c(1951, 1981))
 regional_cmp <- cd_compare(regional_ts,
                            window_a = 2015:2025, window_b = 1951:1980,
                            method = "mean_diff")
+# pct_change for vars with pct_normal anomaly type (#48 added swe / snowfall /
+# snowmelt to this list; snow_cover and snowfall_fraction are pct_point_diff so
+# mean_diff is the right comparison method for those — already in regional_cmp).
+pct_normal_vars <- c("prcp", "soil_moisture", "swe", "snowfall", "snowmelt")
 regional_cmp_pct <- cd_compare(
-  regional_ts[regional_ts$variable %in% c("prcp", "soil_moisture"), ],
+  regional_ts[regional_ts$variable %in% pct_normal_vars, ],
   window_a = 2015:2025, window_b = 1951:1980, method = "pct_change"
 )
 
@@ -58,7 +62,7 @@ ecoregion_results <- lapply(seq_len(nrow(ecoregions)), function(i) {
   ano <- cd_anomaly(ts, cd_baseline(ts, baseline_years = 1951:1980))
   trn <- cd_trend(ano, trend_start = c(1951, 1981))
   cmp_pct <- cd_compare(
-    ts[ts$variable %in% c("prcp", "soil_moisture"), ],
+    ts[ts$variable %in% pct_normal_vars, ],
     window_a = 2015:2025, window_b = 1951:1980, method = "pct_change"
   )
   list(ts = ts, ano = ano, trn = trn, cmp_pct = cmp_pct)