feat: Mae Ngat dam features — built, evaluated, defaulted OFF

src/ml/dam.py loads rid_reservoir_daily into a leakage-safe hourly frame
(daily row visible from 07:00 its own date, ffill capped at 48 h) and is
plumbed through features/train/predict/evaluate exactly like rain, gated
to the six mainstem stations below the Mae Ngat confluence.

The experiment concludes as a documented NEGATIVE result: on the 2024
record-flood backtest every dam-feature subset costs 1-3 h of first-alert
lead (13h -> 10-12h) for <=3 cm of peak-error gain, because the daily RID
report lags up to 31 h and describes yesterday's benign absorbing
reservoir during fast onset. Features therefore default OFF (--dam
opt-in on the training and backtest CLIs; rise_rain_dam/rise_dam harness
variants, excluded from the default variant set). The ablation also
isolated the HII gap-fill as lead-neutral: the acceptance gate holds at
13 h with fill enabled, and docs/img charts are regenerated with the
shipping configuration. Full table in docs/FLOOD_FORECASTING.md §5.

Review-swarm fixes: evaluate.py skips variants whose feature family is
absent instead of crashing the run; --dam forwards --db-url and warns
loudly when no dam history loads; an empty DB result can no longer wipe
a good dam cache; run-level metrics version claims v4 only when a dam
station is actually in the set.

tests/test_flood_forecast.py

@@ -197,7 +197,7 @@ def test_train_smoke_and_roundtrip(tmp_path):
     df = make_synth(n, data_stations, seed=7, pulses=pulses)
 
     metrics = train.train_all(
-        df, target_stations, models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 20}, use_rain=False
+        df, target_stations, models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 20}, use_rain=False, use_dam=False
     )
     assert metrics["stations"]["P.1"]["status"] == "trained"
     assert metrics["stations"]["P.20"]["status"] == "trained"
@@ -237,7 +237,7 @@ def test_feature_name_stability(tmp_path):
     upstream = [code for code, _lead in features.UPSTREAM_LEADS["P.1"]]
     data_stations = ["P.1"] + upstream
     df = make_synth(300, data_stations, seed=11, pulses={"P.1": [(100, 20, 2.0)]})
-    train.train_all(df, ["P.1"], models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 10}, use_rain=False)
+    train.train_all(df, ["P.1"], models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 10}, use_rain=False, use_dam=False)
     # Safe: loading the bundle this same test just wrote to tmp_path, not an external file.
     bundle = joblib.load(tmp_path / "flood_P.1.joblib")