DEP stormwater specialist + 3 forward-modelled rainfall scenarios

NYC DEP Stormwater Flood Maps as Esri FileGDBs (current, moderate
2050, extreme 2080-with-SLR). These are forward-modelled — what
DEP's hydrology team thinks 1%/0.2% AEP rain events will pool, given
present and projected sea levels — and they pair with Sandy's
empirical extent to give the briefing both 'what happened' and
'what is modelled to happen'.

.gitattributes routes the eight Esri internal binary types through
LFS.

.gitattributes

@@ -1,6 +1,16 @@
 # Riprap-specific LFS tracking
 *.geojson filter=lfs diff=lfs merge=lfs -text
 
+# Esri FileGDB internal binary files (DEP Stormwater scenario data)
+*.gdbtable filter=lfs diff=lfs merge=lfs -text
+*.gdbtablx filter=lfs diff=lfs merge=lfs -text
+*.gdbindexes filter=lfs diff=lfs merge=lfs -text
+*.atx filter=lfs diff=lfs merge=lfs -text
+*.spx filter=lfs diff=lfs merge=lfs -text
+*.freelist filter=lfs diff=lfs merge=lfs -text
+*.horizon filter=lfs diff=lfs merge=lfs -text
+*.FDO_UUID filter=lfs diff=lfs merge=lfs -text
+
 # Hugging Face's standard LFS rules (kept for forward-compat with model assets)
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text

app/flood_layers/dep_stormwater.py

@@ -0,0 +1,73 @@
+"""NYC DEP Stormwater Flood Maps — pluvial scenarios.
+
+Four scenarios, all in EPSG:2263. Polygons are categorized by depth class:
+    1 = Nuisance Flooding (>4" and ≤1 ft)
+    2 = Deep and Contiguous Flooding (>1 ft and ≤4 ft)
+    3 = Deep Contiguous Flooding (>4 ft)
+"""
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+
+import geopandas as gpd
+
+from app.spatial import DATA, NYC_CRS
+
+ROOT = DATA / "dep"
+
+SCENARIOS = {
+    "dep_extreme_2080": {
+        "gdb": "dep_extreme_2080.gdb",
+        "label": "DEP Extreme Stormwater (3.66 in/hr, 2080 SLR)",
+    },
+    "dep_moderate_2050": {
+        "gdb": "dep_moderate_2050.gdb",
+        "label": "DEP Moderate Stormwater (2.13 in/hr, 2050 SLR)",
+    },
+    "dep_moderate_current": {
+        "gdb": "dep_moderate_current.gdb",
+        "label": "DEP Moderate Stormwater (2.13 in/hr, current SLR)",
+    },
+}
+
+DEPTH_CLASS = {
+    1: "Nuisance (>4 in to 1 ft)",
+    2: "Deep & Contiguous (1-4 ft)",
+    3: "Deep Contiguous (>4 ft)",
+}
+
+
+@lru_cache(maxsize=4)
+def load(scenario: str) -> gpd.GeoDataFrame:
+    s = SCENARIOS[scenario]
+    path = ROOT / s["gdb"]
+    g = gpd.read_file(str(path))
+    if g.crs.to_string() != NYC_CRS:
+        g = g.to_crs(NYC_CRS)
+    return g
+
+
+def join(assets: gpd.GeoDataFrame, scenario: str) -> gpd.GeoDataFrame:
+    """Per-asset depth class, or 0 if outside scenario.
+
+    Returns a frame indexed like assets with columns: depth_class, depth_label.
+    Higher class wins on overlap.
+    """
+    z = load(scenario)
+    a = assets[["geometry"]].copy()
+    a["_aid"] = range(len(a))
+    j = gpd.sjoin(a, z[["Flooding_Category", "geometry"]],
+                  how="left", predicate="intersects")
+    # for each asset, take max category hit (3 dominates 1)
+    cat = (j.groupby("_aid")["Flooding_Category"].max()
+              .reindex(range(len(a)))
+              .fillna(0).astype(int))
+    out = a[["_aid"]].copy()
+    out["depth_class"] = cat.values
+    out["depth_label"] = out["depth_class"].map(lambda c: DEPTH_CLASS.get(c, "outside"))
+    return out[["depth_class", "depth_label"]].reset_index(drop=True)
+
+
+def label(scenario: str) -> str:
+    return SCENARIOS[scenario]["label"]

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