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@@ -187,3 +187,29 @@ datasets/draft/era5_2009.nc filter=lfs diff=lfs merge=lfs -text
 datasets/draft/era5_2010.nc filter=lfs diff=lfs merge=lfs -text
 datasets/draft/era5_2011.nc filter=lfs diff=lfs merge=lfs -text
 datasets/draft/era5_2012.nc filter=lfs diff=lfs merge=lfs -text
+datasets/bdcavites/cavite_localisee.geojson filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/Descriptif[[:space:]]des[[:space:]]cartes[[:space:]]géologiques[[:space:]]à[[:space:]]1_50[[:space:]]000[[:space:]]vecteur.pdf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_L_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_L_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_L_FGEOL_2154.shx filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_P_DIVERS_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_P_DIVERS_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_S_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_S_FGEOL_2154.lyr filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_027/GEO050K_HARM_027_S_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_028/Descriptif[[:space:]]des[[:space:]]cartes[[:space:]]géologiques[[:space:]]à[[:space:]]1_50[[:space:]]000[[:space:]]vecteur.pdf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_028/GEO050K_HARM_028_L_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_028/GEO050K_HARM_028_L_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_028/GEO050K_HARM_028_S_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_028/GEO050K_HARM_028_S_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/Descriptif[[:space:]]des[[:space:]]cartes[[:space:]]géologiques[[:space:]]à[[:space:]]1_50[[:space:]]000[[:space:]]vecteur.pdf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_L_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_L_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_L_FGEOL_2154.shx filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_L_STRUCT_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_L_STRUCT_2154.shp filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_P_DIVERS_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_P_STRUCT_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_S_FGEOL_2154.dbf filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_S_FGEOL_2154.lyr filter=lfs diff=lfs merge=lfs -text
+datasets/bdcharm50/dept_061/GEO050K_HARM_061_S_FGEOL_2154.shp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -13,8 +13,10 @@ pinned: true
 
 A physics-informed graph neural network that predicts streamflow (discharge,
 water level) at gauged and ungauged points along two Normandy rivers, La Eure
-and La Risle, treated as two separate surface-hydrology graphs with
-station-level covariates pulled from half a dozen independent data sources.
+and La Risle — each modeled as a real reach-based network (confluences,
+braided splits/rejoins, ~4,500 nodes per basin including virtual infill
+points), not a single chain of gauges, with covariates pulled from nine
+independent data sources.
 
 ```mermaid
 flowchart LR
@@ -22,29 +24,31 @@ flowchart LR
     ades["ADES<br/>groundwater levels"]
     era5["Copernicus ERA5<br/>climate reanalysis"]
     otd["Open Topo Data<br/>station elevation"]
-    brgm["BRGM<br/>IDPR"]
-    bdtopo["IGN BD TOPO<br/>river centerline geometry"]
-
-    hubeau --> loaders
-    ades --> loaders
-    era5 --> loaders
-    otd --> loaders
-    brgm --> loaders
-    bdtopo --> loaders
-
-    loaders["src/data/loaders/"] --> nf["node_features.py<br/>one row per station<br/>inputs kept separate from targets"]
-    loaders --> bg["build_graph.py<br/>surface edges, ordered by<br/>real river course"]
-
-    nf --> pyg
-    bg --> pyg["build_pyg_graphs_per_basin"]
-
-    pyg --> eure["La Eure graph"]
-    pyg --> risle["La Risle graph"]
-
-    eure --> app["src/app.py<br/>Streamlit explorer"]
-    risle --> app
-    eure --> testsuite["test_build_graph.py<br/>validation"]
-    risle --> testsuite
+    brgm["BRGM<br/>IDPR · BD Charm-50 geology"]
+    bdtopo["IGN BD TOPO<br/>real reach topology + catchment polygons"]
+    bdcav["Géorisques<br/>BDCavités (sinkholes)"]
+    wc["ESA WorldCover<br/>landcover · NDVI"]
+
+    bdtopo --> brg["build_reach_graph.py<br/>real confluences, splits/rejoins,<br/>gauge snapping"]
+    brg --> brgs["build_reach_graphs.py<br/>~4,500 nodes/basin"]
+
+    hubeau --> nf
+    ades --> nf
+    era5 --> nf
+    otd --> nf
+    brgm --> nf
+    bdcav --> nf
+    wc --> nf
+    brgs --> nf["node_features.py /<br/>enrich_reach_graph.py<br/>date-filtered 2013-2026"]
+
+    bdtopo --> cc["compute_cumulative_catchment.py<br/>graph-wide catchment area"]
+    cc --> nf
+
+    nf --> pyg["build_pyg_graph<br/>x_static / x_dynamic split"]
+    pyg --> phys["physics_losses.py<br/>confluence · split-rejoin ·<br/>routing · water balance"]
+
+    pyg --> app["src/app.py<br/>Streamlit explorer +<br/>network validation view"]
+    pyg --> testsuite["test_build_graph.py<br/>validation"]
 ```
 
 ---
@@ -53,20 +57,29 @@ flowchart LR
 
 ```
 PoC_v1/
-├── scripts/                     # one-off download / extraction scripts
+├── scripts/                     # one-off download / extraction / build scripts
 │   ├── download_hubeau.py           # discharge + water level, Hub'Eau API v2
 │   ├── download_elevation.py        # point elevations, Open Topo Data
 │   ├── download_era5_sample.py      # ERA5 sanity-check pull (Jan 2020 only)
 │   ├── download_era5_full.py        # ERA5 1960–2026, split instant/accum vars
 │   ├── extract_era5.py              # unzips CDS API's zipped NetCDF output
-│   ├── download_catchment_area.py   # Hub'Eau referentiel/sites -> surface_bv
+│   ├── download_catchment.py        # Hub'Eau referentiel/sites -> surface_bv
 │   ├── download_bdtopo_hydro.py     # IGN WFS -> tronçons, surfaces, catchments
 │   ├── analyze_bdtopo_hydro.py      # centerline export + karst check
 │   ├── run_bdtopo_checks.py         # karst + catchment cross-check, one shot
-│   └── cross_check_catchments.py    # spatial join: station -> containing polygon
+│   ├── cross_check_catchments.py    # spatial join: station -> containing polygon
+│   ├── build_reach_graphs.py        # real reach-based topology, both basins
+│   ├── enrich_reach_graph.py        # runs node_features.py against the reach graph
+│   ├── compute_cumulative_catchment.py  # graph-wide catchment area from BD TOPO polygons
+│   ├── diagnose_confluences.py      # verify real vs. artifact confluences
+│   ├── download_bdcavites.py        # Géorisques BDCavités (sinkhole/cavity inventory)
+│   ├── download_bdcharm.py          # BRGM BD Charm-50 harmonized geology, per department
+│   ├── fetch_idpr_brgm.py           # (unused — see §3.5) live IDPR re-fetch attempt
+│   ├── fetch_landcover.py           # ESA WorldCover landcover class, real gauges
+│   └── fetch_worldcover_ndvi.py     # ESA WorldCover NDVI percentile composite
 │
 ├── src/
-│   ├── app.py                       # Streamlit river explorer (main UI)
+│   ├── app.py                       # Streamlit river explorer + network validation view
 │   ├── generate_plots.py            # batch plot generation across all loaders
 │   ├── test_build_graph.py          # graph-construction test/validation suite
 │   ├── extract_river_centerline.py  # digitizes a traced map image into a centerline
@@ -76,7 +89,7 @@ PoC_v1/
 │   │   │   ├── base.py                  # BaseDataLoader — shared load()/get_metadata()
 │   │   │   ├── hydrometric.py           # discharge & water level (Hub'Eau)
 │   │   │   ├── ades.py                  # groundwater levels (ADES)
-│   │   │   ├── safran.py                # ERA5 reanalysis, interpolated to stations
+│   │   │   ├── safran.py                # ERA5 reanalysis, vectorized station interpolation
 │   │   │   ├── idpr.py                  # infiltration/runoff tendency (BRGM)
 │   │   │   ├── catchment.py             # per-station catchment area (Hub'Eau)
 │   │   │   ├── bdtopo_hydro.py          # IGN BD TOPO hydrography (GeoJSON)
@@ -88,8 +101,10 @@ PoC_v1/
 │   │   └── river_centerline.py      # real-centerline interpolation + gauge snapping
 │   │
 │   └── graph/
-│       ├── build_graph.py           # surface edges + PyTorch Geometric conversion
-│       └── node_features.py         # pulls every loader into one feature table
+│       ├── build_graph.py           # PyG conversion: x_static/x_dynamic split, structural columns
+│       ├── build_reach_graph.py     # real reach topology: confluences, splits/rejoins, MultiDiGraph
+│       ├── node_features.py         # pulls every loader into one feature table
+│       └── physics_losses.py        # confluence/split-rejoin/routing/water-balance loss terms
 │
 ├── datasets/                    # not checked in; populated by the scripts above
 │   ├── station_list.csv             # raw station roster (X, Y, names, INSEE, etc.)
@@ -100,10 +115,13 @@ PoC_v1/
 │   ├── hydrometric/
 │   ├── safran/
 │   ├── bdtopo_hydro/
-│   └── centerlines/
+│   ├── bdcavites/
+│   ├── bdcharm50/
+│   ├── centerlines/
+│   └── reach_graph/                 # {eure,risle}_{nodes,edges}.csv, _nodes_enriched.csv
 ```
 
-`scripts/` talks to the outside world (APIs, WFS);
+`scripts/` talks to the outside world (APIs, WFS, S3);
 `src/` doesn't — nothing under `src/` makes a network call, and a script
 under `src/` that wants one is a bug. Most of `src/data/loaders/` predates
 the graph work — general-purpose readers/plotters for each dataset, with
@@ -115,82 +133,200 @@ way around.
 ## 2. The graph
 
 This is the part everything else in the repo exists to feed. Two graphs, one
-per river — `H4xx…` stations feed the La Eure graph (196 km of real BD TOPO
-course), `H6xx…` feed La Risle (150 km) — built with no edge between them,
-because there's no surface connection between the two basins to model.
+per river — `H4xx…` stations feed the La Eure graph, `H6xx…` feed La Risle —
+built with no edge between them, because there's no surface connection
+between the two basins to model.
 
-![La Eure and its ADES wells, real BD TOPO course, elevation-colored gauges](docs/images/eure_overview.png)
+The graph is now built from **real reach topology**, not a single ordered
+chain of gauges. `build_reach_graph.py` constructs it directly from BD TOPO's
+own tronçon-to-node linkage (`lien_vers_noeud_hydrographique_ini/fin`) — the
+NEXT_DOWN-equivalent approach — rather than inferring station order from
+position along a digitized line. That means real branching, real confluences,
+and real braided-channel structure fall directly out of the data instead of
+needing to be modeled separately.
 
-![La Risle, same view — note the tight cluster of gauges around the karst bétoire stretch](docs/images/risle_full.png)
+### 2.1 Node types
 
-### 2.1 Nodes
+Four kinds of node, not one:
 
-A node is one hydrometric station. The full feature set, as of the last
-`test_build_graph.py` run against real data, is 16 columns wide across all 27
-stations combined (15 per basin, once `basin_id` is dropped — see §2.3):
-
-| Feature | Source | What it is |
+| Type | What it is | Column |
 |---|---|---|
-| `latitude`, `longitude` | `station_elevations.csv` | station coordinates |
-| `elevation_m` | Open Topo Data (`scripts/download_elevation.py`) | point elevation at the station |
-| `idpr_value` | BRGM IDPR | infiltration-vs-runoff tendency; direct ID join, not interpolated, for this dataset |
-| `avg_groundwater_level_m`, `avg_groundwater_depth_m` | ADES, radius-averaged | nearest-well groundwater condition |
-| `n_nearby_wells` | ADES | how many wells contributed to the average above — worth checking before trusting the groundwater value, since some stations have zero |
-| `climate_temp_C`, `climate_wind_speed_ms`, `climate_solar_Wm2`, `climate_precip_mm`, `climate_evap_mm`, `climate_snow_mm`, `climate_runoff_mm` | ERA5, interpolated to the station point | mean/sum over the record, by variable |
-| `avg_groundwater_level_m__was_missing`, `avg_groundwater_depth_m__was_missing`, (and similarly for any other feature with real gaps) | auto-generated | 1 if that station had no data for the column before mean-filling, 0 otherwise |
-
-`basin_id` is also computed but excluded from each per-basin graph's own
-feature set, since it's constant there and would just be a zero-variance
-column sitting in `x` for no reason.
+| Real gauge | one of the 27 hydrometric stations | `is_gauged` |
+| Real confluence | a genuinely different, independently-sourced river joins | `is_confluence` |
+| Split / rejoin | a channel divides and later recombines (braiding, an anabranch) — same water, no new mass | `is_split_point` / `is_rejoin_point`, paired via `braid_id` |
+| Virtual (infill) | inserted along long confluence-free stretches so "predict at any point" has real spatial resolution | none of the above |
+
+A **confluence** requires more than a shared node with in-degree ≥ 2 — BD
+TOPO's fine tronçon segmentation produces plenty of same-river multi-inflow
+points with no real branching involved (confirmed against real data:
+incoming-edge distances as short as 4.6 m at some falsely-flagged
+"confluences"). The real test (`find_real_confluences` in
+`build_reach_graph.py`) requires (a) more than one distinct *normalized* river
+name among the incoming edges — river-name normalization strips articles,
+parenthetical qualifiers, and "bras de/du/d'" (arm-of) prefixes, since a named
+secondary channel of the same river ("Bras de la Charentonne") isn't a
+different river — and (b) that those branches don't trace back to a common
+upstream **split** within 15 km, which would mean it's a rejoin, not a
+confluence. Splits themselves need no such disambiguation: out-degree ≥ 2 is
+an unambiguous physical definition on its own, since a split by construction
+has exactly one thing flowing in.
+
+Real branching topology also meant the underlying graph had to move from a
+plain `DiGraph` to a `MultiDiGraph` — two distinct tronçons directly
+connecting the same two hydrographic nodes (exactly the shape a short braid
+takes) is real data, not a collision, and a plain `DiGraph` was silently
+**overwriting** the second such edge's data on `add_edge` rather than keeping
+both. Confirmed as a real bug with real impact, not just a synthetic-test
+concern: fixing it recovered dozens of previously-invisible parallel edges
+per basin on the actual data.
+
+### 2.2 Node and edge features
+
+The feature set now spans several independent sources, each merged onto the
+node table by `node_features.py`'s `add_*_features` functions. Every column
+lands in exactly one of four places once `build_pyg_graph` processes it:
 
-**Targets are not features.** `target_discharge_m3s_mean/std/count` and
-`target_waterlevel_mm_mean/std/count` exist on the enriched table but never
-enter `x` — they're pulled out into `data.y` separately. A model that could
-see these as inputs would be reading the answer off the label, not learning
-anything.
+```mermaid
+flowchart TD
+    raw["Enriched node table<br/>(node_features.py)"]
+
+    raw --> struct{"structural /<br/>graph-role column?"}
+    struct -->|"is_gauged, is_confluence,<br/>is_split_point, is_rejoin_point,<br/>braid_id, snap_distance_km"| structout["data.is_gauged, data.is_confluence, ...<br/>own Data attribute — never in x"]
 
-**The missingness flags aren't decorative.** A station with zero nearby wells
-gets its groundwater value silently filled with the basin mean — without the
-flag, that looks identical to a station whose real groundwater level happens
-to be close to average. Given how unevenly some of these datasets cover the
-two basins (the Eure's southern reach has essentially no ADES wells within
-range at all), pretending "filled with the mean" and "actually near the mean"
-are the same thing would be a real information loss, not just tidiness.
+    raw --> tgt{"target_* column?"}
+    tgt -->|"target_discharge_m3s_*<br/>target_waterlevel_mm_*"| y["data.y<br/>never in x — label leakage otherwise"]
 
-### 2.2 Edges
+    raw --> feat{"real model input"}
+    feat -->|"static: elevation_m, idpr_*,<br/>catchment_area_km2, landcover_*,<br/>geology_*, cavites distance/count"| xstatic["data.x_static"]
+    feat -->|"dynamic: climate_*,<br/>avg_groundwater_*, ndvi_*<br/>(period-aggregate, not a real series yet)"| xdynamic["data.x_dynamic"]
 
-An edge is "these two gauges are consecutive along the river," in the
-direction water actually flows — upstream to downstream. Three attributes per
-edge:
+    xstatic --> x["data.x — full combined tensor,<br/>z-scored"]
+    xdynamic --> x
+
+    edges["Edge table<br/>(build_reach_graph_tables)"] --> eattr{"numeric edge<br/>attribute?"}
+    eattr -->|"distance_km,<br/>elevation_drop_m,<br/>verified_continuous"| edgeattr["data.edge_attr<br/>[n_edges, 3]"]
+    eattr -->|"toponym, cleabs<br/>(diagnostic metadata)"| meta["not used by build_pyg_graph —<br/>stays in edges_df only"]
+```
+
+**Node features:**
+
+| Feature | Source | Coverage |
+|---|---|---|
+| `latitude`, `longitude`, `elevation_m` | station coords / real BD TOPO tronçon Z | every node |
+| `idpr_value`, `idpr_nearest_point_distance` | BRGM IDPR | every node (spatial fallback for non-gauge codes) |
+| `catchment_area_km2` | Hub'Eau, cumulative, real gauges only | 27 stations |
+| `cumulative_catchment_area_km2` | BD TOPO incremental polygons, summed upstream via real graph topology | graph-wide (~98% of nodes) |
+| `landcover_*` (one-hot) | ESA WorldCover 10 m classification | real gauges only, for now |
+| `ndvi_p10`, `ndvi_p50`, `ndvi_p90` | ESA WorldCover NDVI percentile composite | real gauges only, for now |
+| `geology_*` (one-hot) | BRGM BD Charm-50, point-in-polygon | real gauges only, for now |
+| `distance_to_nearest_cavity_km`, `n_cavities_within_20km` | Géorisques BDCavités, KD-tree + haversine | real gauges only, for now |
+| `avg_groundwater_level_m`, `avg_groundwater_depth_m`, `n_nearby_wells` | ADES, radius-averaged, KD-tree + exact haversine | every node |
+| `climate_*` (temp/wind/solar/precip/evap/snow/runoff) | ERA5, vectorized station interpolation | every node (needs `safran_path`) |
+| `{col}__was_missing` | auto-generated | any feature column with real gaps |
+
+**Edge features** — three numeric attributes per edge, from
+`build_reach_graph.py`'s `build_reach_graph_tables`:
 
 | Feature | Meaning |
 |---|---|
-| `distance_km` | along-river distance between the two stations |
-| `elevation_drop_m` | elevation difference, upstream minus downstream (should be positive; negative would flag a real problem) |
-| `verified_continuous` | `False` for any edge deliberately flagged via `known_losing_reaches` |
-
-That last one exists because of a specific, real finding: two stations on La
-Risle are named *"[amont bétoire]"* and *"[aval bétoire]"* in Hub'Eau's own
-data — *bétoire* being the Normandy term for a karst swallow-hole, where a
-river can vanish underground and resurface downstream. Three consecutive
-edges spanning that stretch (`H605641101 → H605022010 → H605641401 →
-H605641201`) are flagged `verified_continuous=False` rather than the graph
-silently assuming water flows continuously through a reach that might not
-carry it at the surface at all. An edge existing in this graph means "these
-stations are sequential along the mapped course," not "surface flow between
-them is guaranteed."
-
-Ordering within a basin comes from the station's position along the real
-digitized river course where one's available (both basins now have real BD
-TOPO-derived centerlines — see §3.8), falling back to elevation or latitude
-otherwise. Every edge is built between *consecutive* stations in that order,
-which means each basin is currently a single chain, not a branching network.
-Tributaries aren't represented. That's the single biggest structural
-simplification in the graph as it stands — see §6.
-
-![La Risle, position at 50% along its 150 km real course, reading elevation and estimated groundwater level](docs/images/risle_detail.png)
-
-### 2.3 Two graphs, not one
+| `distance_km` | along-river distance between the two endpoint nodes |
+| `elevation_drop_m` | elevation difference, upstream minus downstream — negated on the reverse edge when `bidirectional=True` |
+| `verified_continuous` | `False` for any edge deliberately flagged via `known_losing_reaches` (the bétoire stretch — §3.7) |
+
+`toponym` and `cleabs` also live on the real edges table (the tronçon's river
+name and unique BD TOPO ID) but are diagnostic metadata, not model input —
+`build_pyg_graph` selects `edge_attr` columns by explicit name, so extra
+columns like these pass through harmlessly rather than needing to be
+stripped out first.
+
+**Structural columns never enter `x`.** `is_gauged`, `is_confluence`,
+`is_split_point`, `is_rejoin_point`, `snap_distance_km`, `braid_id` describe
+node *role*, not a physical covariate — `build_pyg_graph`'s auto-detection
+excludes them explicitly (confirmed as a real, not hypothetical, bug once:
+pandas treats `bool` as a numeric dtype, so without this exclusion these
+columns were being silently z-scored and fed to the model as if they were
+elevation or precipitation). They're still attached to the returned `Data`
+object as their own typed attributes, for masking supervised loss to gauged
+nodes and for the physics-loss index builders.
+
+**Landcover and geology are one-hot, not a raw class code.** Both are
+nominal categories (10 = Tree cover, 50 = Built-up; a geological formation
+code), not an ordered quantity — leaving either as a raw integer would let
+auto-detection z-score it as if one category were numerically "more" than
+another, the same class of error as the structural-column bug, just subtler
+since these *are* meant to be real model input.
+
+**Targets are not features.** `target_discharge_m3s_mean/std/count` and
+`target_waterlevel_mm_mean/std/count` exist on the enriched table but never
+enter `x` — they're pulled out into `data.y` separately, and attach only to
+real gauge rows (verified: gauge codes, BD TOPO hydrographic node IDs, and
+virtual-node marker strings occupy structurally distinct namespaces, so a
+left-merge on `station_code` can never mislabel a confluence or virtual node).
+
+### 2.3 Static vs. dynamic features
+
+`build_pyg_graph` also splits every feature by physical temporal nature:
+
+- **`data.x_static`** / **`data.static_feature_names`** — genuinely
+  time-invariant: elevation, IDPR, catchment area, landcover, coordinates.
+- **`data.x_dynamic`** / **`data.dynamic_feature_names`** — physically
+  time-varying quantities: climate, groundwater level/depth, NDVI.
+
+`data.x` remains the full combined tensor unchanged; the split is additional,
+not a replacement. **Important limitation, stated plainly**: "dynamic" here
+still means *one period-aggregated number per node* (mean/sum over the whole
+date range, or a single well reading), not a real `[n_nodes, T]` time series.
+The split makes the physical distinction explicit and gives a clean seam for
+a future temporal pipeline to slot into, but building that pipeline — genuine
+daily/whatever-resolution series per node, aligned across sources — is
+separate, larger, unbuilt work. `physics_losses.py`'s `routing_consistency_loss`
+specifically needs that real time dimension and has nothing to consume yet.
+
+### 2.4 Date-range filtering
+
+`build_node_features`/`enrich_reach_graph.py` accept a `date_range` applied
+to every time-varying source (groundwater, climate, hydrometric targets)
+together, so all three describe the same period rather than each silently
+aggregating over its own full, differently-shaped history (ADES wells
+reporting from the 1970s to 2026 on wildly different schedules; ERA5 spanning
+1960–2026; hydrometric records with their own per-station ranges entirely).
+
+Default: **2013-01-01 to 2026-12-31** — computed, not guessed, via a
+brute-force interval-overlap check across all 8 discharge-gauged stations'
+real date ranges. This is the window that maximizes simultaneous station
+coverage: 6 of 8 stations, **8,923 real, quality-filtered observations**
+(`code_qualification >= 16`, the same threshold `HydrometricLoader` itself
+applies — a naive raw count that skips this filter gives 13,084, which is
+what an earlier pass at this analysis originally reported before the
+discrepancy was traced and corrected). Two stations (`H403301101`: 1969–1985,
+`H605022010`: 1970–1980) are permanently excluded by any reasonable window —
+a ~35–40 year dead gap separates them from every other station's record, so
+including them would mean spanning six mostly-empty decades, not a genuine
+improvement.
+
+### 2.5 Physics-informed loss terms (`physics_losses.py`)
+
+Four constraints, each tied to real graph structure, not generic:
+
+| Term | Constraint | Applies to |
+|---|---|---|
+| `confluence_mass_balance_loss` | `Q_confluence ≈ sum(Q_upstream_branches)` — new mass genuinely enters | `is_confluence` nodes |
+| `split_rejoin_conservation_loss` | `Q_split ≈ Q_rejoin` — same water, no new mass | paired `braid_id` nodes |
+| `routing_consistency_loss` | `Q_downstream[t] ≈ Q_upstream[t - lag]`, lag from real `distance_km`/slope | every edge (needs `[n_nodes, T]` — see §2.3) |
+| `water_balance_loss` | `P - ET - Q - ΔS ≈ 0` in volume terms | nodes with `cumulative_catchment_area_km2` |
+
+Confluence and split/rejoin are deliberately different constraints, not one
+generic "conserve mass everywhere" rule — a model that only learned "sum the
+inflows" would get a split/rejoin wrong, since a rejoin's two branches
+together should equal the *split's* value, not add something new on top.
+All four apply graph-wide, not just at the 27 labeled gauges — that's the
+actual mechanism by which sparse supervision generalizes to the ~4,500
+ungauged nodes, not an incidental detail. `ΔS` (storage change) defaults to
+zero, a named steady-state approximation — this project has no direct
+basin-wide storage measurement, only sparse well *levels*, which aren't the
+same thing.
+
+### 2.6 Two graphs, not one
 
 `build_pyg_graphs_per_basin()` returns `{0: eure_graph, 1: risle_graph}`,
 each with its own local `0..n-1` node indexing, rather than one merged `Data`
@@ -200,26 +336,15 @@ PyTorch Geometric's own batching (`Batch.from_data_list`) expects a list of
 separate small graphs — building two graphs from the start matches that
 convention directly.
 
-### 2.4 What actually goes into `x`
+### 2.7 What it looks like
 
-`build_pyg_graph()` auto-detects feature columns from whatever table it's
-given, so it works whether that's the plain 4-column base table or the fully
-enriched one. Every column gets z-scored (mean 0, unit variance — verified in
-`test_build_graph.py`, including a check that this used the same `ddof=1`
-convention as the pandas `.std()` call that actually does the standardizing,
-since numpy's default `ddof=0` gives a spuriously "wrong-looking" answer for
-small station counts that isn't actually a bug). Missingness flags, described
-above, get appended after standardization so they stay honest 0/1 values
-rather than being z-scored into something less interpretable.
-
-### 2.5 What it looks like
-
-The Streamlit explorer (`src/app.py`) renders both graphs directly — real
-course geometry, gauges colored by elevation, ADES wells layered underneath
-for context. Clicking anywhere on the line resolves to the nearest gauge and
-pulls up its actual discharge/water-level/rating-curve plots:
-
-![Clicking along La Risle resolves to the nearest gauge (H605022010, 1.7 km away) and shows its real water level record back to 1972](docs/images/station_timeseries.png)
+The Streamlit explorer (`src/app.py`) has two views. "Explore" renders the
+original click-to-read interface over real course geometry. "Network
+validation" renders the full reach graph — confluences as diamonds, gauges as
+elevation-colored circles, every edge as one line trace regardless of edge
+count (verified fast at real scale: 0.29s to build a figure for ~2,900
+edges) — specifically for visually confirming the topology looks like a real
+river network before trusting it as model input.
 
 ---
 
@@ -230,18 +355,20 @@ materially affect what the data means.
 
 ### 3.1 Station roster (`station_list.csv`, `station_elevations.csv`)
 
-27 stations across the two basins, split roughly by Hub'Eau code prefix
-(`H4xx…` for La Eure, `H6xx…` for La Risle — this is a heuristic based on
-observed codes, not a documented rule, so it's worth re-checking if new
-stations show up outside that pattern). Elevation comes from Open Topo Data's
+27 stations across the two basins, spanning three French departments —
+verified directly against the real roster: 12 in Eure (27), 10 in
+Eure-et-Loir (28, the Eure's southern tributaries near Chartres/Dreux — Voise,
+Drouette, and others), 2 in Orne (61). Split roughly by Hub'Eau code prefix
+(`H4xx…` for La Eure, `H6xx…` for La Risle — a heuristic based on observed
+codes, not a documented rule). Elevation comes from Open Topo Data's
 `eudem25m` endpoint (`scripts/download_elevation.py`), queried per station
-coordinate — it's a point lookup, not a raster, so there's no slope or
-catchment information hiding in it.
+coordinate — a point lookup, not a raster, so there's no slope or catchment
+information hiding in it.
 
 The row order in `station_list.csv` does **not** follow the river's course —
 verified directly, it jumps around in both latitude and elevation. Anything
 that needs upstream/downstream ordering has to derive it from elevation,
-latitude, or real centerline position; never from file order.
+latitude, or real centerline/graph position; never from file order.
 
 Not every station in this list is actively gauged. Cross-referencing station
 names against the hydrometric data turned up three categories worth knowing
@@ -255,9 +382,10 @@ about:
 - Everything else with no data is unexplained from the name alone and worth a
   direct check on Hub'Eau's site before assuming it's just a gap.
 
-In total, **14 of the 27 stations have no discharge or water-level records at
-all** in the current Hub'Eau export. That's not a bug in the loader — it's
-confirmed against the raw files, not just the merged table.
+Of the 8 stations with any `QmnJ` discharge data at all, only **6 have real
+observations within the project's 2013–2026 date window** (§2.4) — worth
+knowing before assuming "8 gauged stations" translates directly into training
+examples.
 
 ### 3.2 Hydrometric data (`hydrometric/`, via `scripts/download_hubeau.py`)
 
@@ -269,6 +397,15 @@ has `HIXnJ`/`HIXM` (water-level codes) sitting right alongside `QmnJ`
 down to its intended `grandeur` code explicitly (`QmnJ` from the discharge
 file, `HIXnJ` from the water-level file) rather than trusting the filename.
 
+`HydrometricLoader` also filters on Hub'Eau's own `code_qualification` field,
+keeping only `>= 16` (their "acceptable"/"good" threshold) and dropping lower-
+quality/provisional readings. This is real and meaningful, not a rounding
+detail — traced directly against the raw discharge file for the 2013–2026
+window (§2.4): 13,084 raw `QmnJ` rows in range, of which 4,022 have
+`code_qualification == 12` (below the threshold) and get correctly excluded,
+leaving 8,923. Any manual read of the raw CSVs that skips this filter will
+overcount real usable observations by close to a third.
+
 Only 8 of the 27 stations have any `QmnJ` (daily mean discharge) rows at all.
 Several others report water level only. This isn't evenly distributed and
 matters a lot for anything downstream that assumes "gauged" means "has both
@@ -283,18 +420,28 @@ merges the levels file against the stations file on `code_bss` and renames
 `x`/`y` to `lon`/`lat` — those columns are already in degrees in this dataset,
 not a projected CRS, so no reprojection happens or is needed.
 
-Groundwater is used as a **station-level input covariate** (radius-averaged via
-`aggregate_to_stations`), not as a graph edge. Well proximity alone isn't
-sufficient grounds for a subsurface/karst connectivity edge between the two
-basins — two wells being close together doesn't mean they're hydraulically
-connected. That would need either correlated well hydrographs over time or a
-shared BDLISA aquifer-unit code (the `groundwater_stations.csv` file has a
-`codes_bdlisa` column available for exactly this kind of check).
+`node_features.py`'s `add_groundwater_features` does **not** use
+`ADESLoader.aggregate_to_stations` — that method loops per station and does a
+full haversine `.apply()` over the entire groundwater dataframe for each one.
+At 27 stations against ~272k readings that's slow but tolerable; at the reach
+graph's ~4,500 nodes it's over a billion row-wise Python calls, confirmed as a
+genuine, not hypothetical, multi-hour hang. The fix (reduce to each well's
+latest reading first, then a KD-tree coarse prefilter + exact haversine on the
+small candidate set) turned out to also fix a real accuracy bug: the old
+method required wells to share the *exact same reporting date* before
+averaging, but real wells report on wildly different schedules (18 real
+wells within 20 km of one station spanned 13 different "latest dates," one
+from 1972) — silently discarding most real coverage every time.
+
+Groundwater is used as a **station-level input covariate**, not as a graph
+edge. Well proximity alone isn't sufficient grounds for a subsurface/karst
+connectivity edge — that would need either correlated well hydrographs over
+time or a shared BDLISA aquifer-unit code (`groundwater_stations.csv` has a
+`codes_bdlisa` column available for exactly this kind of check; still unused).
 
 Well coverage is not uniform across the two basins. The Eure's southern reach
 (south of roughly 48.68°N, toward Chartres) has essentially zero wells within
-range in this extract — any groundwater estimate south of there will come back
-empty, and that's a property of the source data, not a bug in the interpolation.
+range in this extract.
 
 ### 3.4 Climate (`safran/`, via `download_era5_sample.py` / `download_era5_full.py`)
 
@@ -305,107 +452,203 @@ instantaneous variables (temperature, wind) from accumulated ones
 (precipitation, evaporation, radiation, snowfall, runoff) at the API level —
 `download_era5_full.py` downloads each set separately per year and merges them,
 because the CDS API rejects mixed requests. The full pull spans 1960–2026 and
-is genuinely slow (a `time.sleep` and per-year retry loop is in there for a
-reason).
+is genuinely slow.
 
-`SAFRANLoader` interpolates the ERA5 grid to each station's coordinate
-(nearest or linear, configurable) rather than requiring stations to sit
-exactly on a grid cell — this is standard practice for reanalysis data at
-0.25° resolution, not an approximation specific to this project.
+`SAFRANLoader` interpolates the ERA5 grid to every station **in one
+vectorized xarray call per file**, not one `.sel()` + `.to_dataframe()` call
+per station — the per-station loop version does real per-call work (an index
+lookup, then a full DataFrame conversion) that's tolerable at 27 stations
+(~1,800 calls across ~67 year-files) but was confirmed to actually hang at the
+reach graph's ~4,500 nodes (~193,000 calls). Vectorized indexing with
+DataArray indexers sharing a `station` dimension does every station in one
+call per file instead.
 
 ### 3.5 IDPR (`idpr.csv`)
 
 BRGM's *Indice de Développement et de Persistance des Réseaux* — an
 infiltration-vs-runoff tendency index, and the closest thing this project has
-to a real soil/drainage covariate. The actual file used here is already
-one row per station (`station_id` matching `station_code` exactly, verified
-1:1 against all 27 stations, no duplicates, no nulls), not a raw spatial point
-cloud. `node_features.py` checks for this and does a direct ID join when
-possible rather than nearest-neighbor search — nearest-neighbor is only the
-fallback path for a generic IDPR export that doesn't come pre-matched to
-stations.
-
-There is no soil texture, permeability, or hydraulic conductivity dataset in
-this project. IDPR is a tendency index, not a texture measurement — worth
-keeping that distinction in mind if it ever gets described as "soil data" in a
-writeup.
-
-### 3.6 Catchment area (`catchment_area.csv`, via `scripts/download_catchment_area.py`)
-
-Hub'Eau publishes catchment (drainage basin) area on the **site** referentiel,
-not the station referentiel — `surface_bv` on `hydrometrie/referentiel/sites`,
-in km². Since one site can have several stations, the download script does two
-passes: station → `code_site`, then `code_site` → `surface_bv`, and joins them
-back. 16 of 27 stations have a value; Hub'Eau simply doesn't publish this field
-for every site, and the missing ones correlate with the observer/SEBV stations
-from §3.1 but aren't identical to that set.
-
+to a real soil/drainage covariate (see §3.9 for why it's standing in for soil
+data specifically, not just conveniently similar). The file used here is
+already one row per station (`station_id` matching `station_code` exactly,
+verified 1:1 against all 27 stations), so `node_features.py` does a direct ID
+join when possible rather than nearest-neighbor search, falling back to
+spatial nearest-neighbor for any station code that isn't an exact match
+(which is every non-gauge reach-graph node, and — a real, minor precision
+trade-off worth knowing — every gauge too, once the table also contains
+non-gauge codes, since the exact-match path requires the *entire* table to
+match IDPR's station list).
+
+A live re-fetch from BRGM's own geoservice (`scripts/fetch_idpr_brgm.py`,
+`geoservices.brgm.fr/geologie`, `GetFeatureInfo` point queries against the
+`IDPR_50M` raster layer) was attempted, to get fresher values and eventually
+cover the full reach graph rather than just the 27 gauges. **It failed** in
+testing and the project is currently using the original, already-uploaded
+`idpr.csv` instead — not resolved further, since the existing file already
+gives real, usable IDPR coverage for every gauge.
+
+### 3.6 Catchment area — two independent sources
+
+**Hub'Eau (`catchment_area.csv`, via `scripts/download_catchment.py`)**:
+published on the **site** referentiel, not the station referentiel —
+`surface_bv` on `hydrometrie/referentiel/sites`, in km². Since one site can
+have several stations, the download script does two passes: station →
+`code_site`, then `code_site` → `surface_bv`. 16 of 27 stations have a value.
 This number is **cumulative** — the total catchment area draining to that
-point, all the way to the source. That distinction matters once you bring BD
-TOPO into the picture (§3.7), because BD TOPO's catchment polygons are the
-opposite: incremental, reach-by-reach areas. Comparing the two directly without
-accounting for that gives area ratios that look like measurement error but
-aren't — a spatial join between the 27 stations and the 135 BD TOPO polygons
-gave ratios from about 0.015 near the river mouth up to 0.67 near the source,
-which is exactly the pattern you'd expect from cumulative-vs-incremental, not
-a data quality problem.
+point, all the way to the source.
+
+**BD TOPO, graph-wide (`cumulative_catchment_area_km2`, via
+`scripts/compute_cumulative_catchment.py`)**: sums BD TOPO's incremental
+catchment polygons upstream of any node, via the real graph topology —
+distinct polygons counted once even when many nodes/edges share the same
+coarse polygon (verified with a hand-computed test case specifically checking
+this). Covers ~98% of nodes graph-wide, not just the 27 gauges — the actual
+fix for the "confluences and virtual nodes have no catchment area at all" gap.
+
+**Cross-checked against Hub'Eau's real values on real gauges — and there's a
+real, identified bias, not a clean match.** Ratio (BD-TOPO-summed ÷ Hub'Eau)
+runs from about 0.75 to 1.25 for smaller catchments (< ~800 km², plausibly
+normal polygon-boundary/digitization precision) but drops to 0.75–0.89 for
+the largest catchments (> ~3,500 km²) — a clean, monotonic pattern, not noise.
+Most likely cause: **bounding-box truncation** — the original BD TOPO pull
+bbox had only a 9.6 km margin on its southern edge (the tightest of all four
+directions, and south is exactly where the Eure's longest upstream
+tributaries run, toward Chartres/Dreux), not a safe margin for real watershed
+extent. The bbox in `download_bdtopo_hydro.py` was widened afterward (from
+`(0.3, 48.3, 1.7, 49.5)` to `(-0.1, 47.7, 2.1, 49.9)`, ~2.9x the area) — the
+full `download_bdtopo_hydro.py → build_reach_graphs.py → enrich_reach_graph.py
+→ compute_cumulative_catchment.py` chain needs re-running against the wider
+box to actually resolve this, which had not yet happened as of the last
+verified run in this project.
 
 ### 3.7 BD TOPO hydrography (`bdtopo_hydro/`, via `scripts/download_bdtopo_hydro.py`)
 
 IGN's BD TOPO / BD TOPAGE hydrographic network, pulled from the Geoplateforme
 WFS (`https://data.geopf.fr/wfs`) rather than downloaded as a national bulk
 file — the download script queries a bounding box around the two basins
-instead. Three layers, all scoped to that bbox:
-
-- `troncon_hydrographique.geojson` — river centerline reaches. 30,045 features
-  in the current pull, which sounds like a lot for two rivers because it
-  includes every stream, ditch, and canal in the box, not just the Eure and
-  Risle.
+instead (see §3.6 for why that box was widened). Three layers, all scoped to
+that bbox:
+
+- `troncon_hydrographique.geojson` — river centerline reaches, now the
+  primary source for graph *topology* too (§2), via `lien_vers_noeud_
+  hydrographique_ini/fin` and `sens_de_l_ecoulement`. Real per-vertex
+  altitude data doubles as a fine-grained elevation profile, denser than
+  anything derivable from the 27 gauge points alone.
 - `surface_hydrographique.geojson` — hydrographic surfaces, including a
   `Nature` attribute that's supposed to flag karst-influenced reaches. IGN
-  documents this attribute as **provisional and incomplete** — filled in with
-  a limited, sometimes-default set of values pending refinement by the water
-  agencies. A negative karst search here is not strong evidence of anything;
-  it may just mean that stretch hasn't been annotated yet.
+  documents this attribute as **provisional and incomplete**.
 - `bassin_versant_topographique.geojson` — catchment polygons, incremental
-  (see §3.6). 135 polygons in the current bbox.
-
-**WFS axis order**: when a `BBOX` parameter's CRS is given via the URN form
-(`urn:ogc:def:crs:EPSG::4326`), the OGC spec requires latitude, longitude axis
-order — the opposite of the lon,lat order most GIS tools use by default.
-Getting this backwards doesn't raise an error; it silently matches zero real
-features, which looks identical to "no data in this area." `download_bdtopo_hydro.py`
-tries lon,lat first and automatically retries with the axes swapped if that
-comes back empty — for this project's bounding box, lat,lon is the order that
-returns real data.
-
-**Z-coordinate in tronçon geometry**: `troncon_hydrographique` geometries
-carry a third coordinate — real altitude at every vertex — not just lon/lat.
-Code handling this data needs to account for 3D coordinates explicitly (e.g.
-slicing to the first two values before building a 2-column DataFrame from
-`geometry.coords`). This altitude data doubles as a genuinely useful
-fine-grained elevation profile along the river, denser than anything
-derivable from the 27 gauge points alone (`BDTopoHydroLoader.get_elevation_profile`).
+  (see §3.6).
+
+**WFS axis order**: when a `BBOX` parameter's CRS is given via the URN form,
+the OGC spec requires latitude, longitude axis order — the opposite of the
+lon,lat order most GIS tools use by default. Getting this backwards doesn't
+raise an error; it silently matches zero real features. `download_bdtopo_hydro.py`
+and `scripts/download_bdcavites.py` both try lon,lat first and automatically
+retry with the axes swapped if that comes back empty.
+
+**Real branching topology fixed a naive assumption.** Filtering 30,045
+tronçons down to a single named river and building a graph from their
+endpoints does **not** give one connected line — for "Risle" alone, 1,195
+name-matched tronçons split into 132 disconnected components. Broadening the
+name filter to include known tributaries (§2.1) initially made this *worse*
+(487/214 components), traced to short/generic tributary names ("Bec", "Avre")
+matching unrelated streams elsewhere within the ~100×130 km bbox — fixed by
+requiring every name-matched tronçon to also fall within a real distance of a
+known gauge (`load_troncons_for_basin`'s `anchor_radius_km`), and by selecting
+the connected component actually containing the most real gauges rather than
+the component with the most raw tronçons (`best_component_for_stations`) —
+proven to matter, not just theoretically: a synthetic adversarial test showed
+the naive "biggest component" approach picking a larger but entirely
+unrelated decoy network over the real one.
 
 **The bétoire finding**: two stations in the roster are explicitly named
 *"[amont bétoire]"* and *"[aval bétoire]"* in Hub'Eau's own site names —
-*bétoire* being the Normandy dialect term for a karst swallow-hole. That's
-independent, deliberate naming from the data provider, not a guess. It's the
-strongest evidence in this project for a real losing reach on La Risle around
-Grosley-sur-Risle/Ajou (`H605641101` → `H605641201`), even though the BD TOPO
-karst layer doesn't currently confirm it (see the provisional-attribute note
-above). `build_surface_edges`'s `known_losing_reaches` parameter exists
-specifically for this — the current recommendation is to flag all three edges
-spanning that stretch as `verified_continuous=False`, not just guess at one.
-
-Also worth knowing: filtering the 30,045 tronçons down to a named river and
-building a graph from their endpoints does **not** give one connected line.
-For "Risle," 1,195 name-matched tronçons split into 132 disconnected
-components; the extraction keeps only the largest. That's a real topology gap
-in the source data (tile boundaries, tributaries sharing a base name without
-literally connecting), not a bug in the graph-walking code.
-
-### 3.8 Centerline generation
+*bétoire* being the Normandy dialect term for a karst swallow-hole. Three
+edges spanning that stretch on La Risle (`H605641101 → H605022010 →
+H605641401 → H605641201`) are flagged `verified_continuous=False`. BD TOPO's
+own karst attribute doesn't currently confirm it (see the provisional-
+attribute note above) — `scripts/download_bdcavites.py` (§3.8) exists
+specifically to get an independent, purpose-built second check on this,
+rather than relying only on naming inference.
+
+### 3.8 BDCavités (`bdcavites/`, via `scripts/download_bdcavites.py`)
+
+BRGM's national underground cavity inventory (sinkholes, quarries, natural
+cavities), via Géorisques' WFS (`georisques.gouv.fr/services`, typeName
+`CAVITE_LOCALISEE`, confirmed live and GeoJSON-capable directly against the
+real service). Built specifically as an independent check on the bétoire
+finding (§3.7) — a purpose-built cavity dataset, not inference from station
+naming or a provisional BD TOPO attribute. One real caveat: departments
+75/78/91/92/93/94/95 (Paris region, unrelated to this project) are excluded
+from BDCavités entirely, and the Eure department's own inventory was among
+the later batches of the national 2001–2013 completion program — worth
+checking coverage density before treating a sparse result as a negative
+finding rather than incomplete data.
+
+### 3.9 Geology (`bdcharm50/`, via `scripts/download_bdcharm.py`)
+
+BRGM's BD Charm-50, harmonized 1:50,000 geological maps — free, open
+(Licence Ouverte), no authentication, direct per-department ZIP download from
+InfoTerre (a genuinely different access pattern than the WFS sources
+elsewhere in this project: fixed URL per department, no bbox query, no axis-
+order ambiguity). Departments **27 (Eure), 28 (Eure-et-Loir), 61 (Orne)** —
+verified directly against the real, complete station roster (§3.1), not
+guessed. A separate, CIGAL-membership-gated distribution of similar data
+exists for at least one other French region; this project only uses the free
+InfoTerre path.
+
+### 3.10 Landcover and NDVI (`scripts/fetch_landcover.py`, `scripts/fetch_worldcover_ndvi.py`)
+
+ESA WorldCover, sampled at real gauge points from the public AWS S3 Cloud-
+Optimized GeoTIFFs — **not** the Terrascope WMS, which a source dated within
+the last month of this project's active development reported actively resets
+connections from non-browser HTTP clients (TLS fingerprinting, confirmed
+across multiple tools and User-Agents, not a coding problem to work around),
+and separately was slated for full phase-out already past by the time this
+was checked.
+
+Landcover classification uses the product's 3°×3° tile grid; every real
+station coordinate falls inside exactly one tile (`N48E000`), verified
+directly against all 27 real coordinates. NDVI uses the *annual composites'*
+1°×1° tile grid instead — genuinely different from the classification grid,
+looked up per-station via VITO's own authoritative tile-index grid file
+(`esa_worldcover_grid_composites.fgb`) rather than a second hand-guessed S3
+key pattern. Both need `AWS_NO_SIGN_REQUEST=YES` for `s3://`-scheme tile URLs
+specifically — a plain HTTPS URL to the same public bucket needs no signing
+at all, which is why the landcover script (HTTPS) worked without this while
+the NDVI grid's returned URLs (`s3://`) initially failed on AWS credential
+errors despite the bucket being fully public.
+
+Both currently cover only the 27 real gauges (exact `station_code` match),
+same limitation as Hub'Eau's `catchment_area_km2` before the cumulative-BD-
+TOPO fix (§3.6) — extending either script to the full reach graph is
+unstarted work, not a design decision.
+
+### 3.11 What was tried and didn't work
+
+**SoilGrids (ISRIC)** — confirmed non-functional directly, not from a stale
+search result: even a bare `lon`/`lat` query to the live REST API returned
+`422` consistently, consistent with ISRIC's own currently-posted "temporarily
+paused" service notice.
+
+**INRAE's national soil survey (RRP/BDGSF)** — not a uniformly-accessible
+source at all. Access is explicitly described as depending on regional/
+departmental "référents" (varies by department, sometimes needs a formal
+agreement with the regional chamber of agriculture), and INRAE's own
+documentation states outright that the more detailed scale "n'est pas encore
+en accès libre." IDPR (§3.5) and BD Charm-50 geology (§3.9) are this
+project's actual substitutes for the hydrologically-relevant part of what
+soil data would otherwise provide — not literal soil texture data, but IDPR
+specifically is an *integrated hydrological behavior* indicator (infiltration
+tendency), arguably more directly useful for a streamflow model than a raw
+soil property map would be on its own.
+
+### 3.12 Centerline generation
+
+**Only relevant to the older single-chain pipeline** (`build_surface_edges`,
+still available for direct comparison/debugging) — the reach graph (§2)
+derives its topology directly from BD TOPO's own node linkage and doesn't use
+these centerline files at all.
 
 `centerlines/eure_centerline.csv` and `centerlines/risle_centerline.csv` — the
 geometry `build_surface_edges` orders stations against — are generated by
@@ -433,80 +676,82 @@ vector source isn't available, not the method used for the current
 
 ## 4. Applications
 
-Click-to-explore UI over the river network (`src/app.py`, Streamlit): pick a
+`src/app.py` (Streamlit) has two views, selected by a radio at the top:
+
+**Explore** — the original click-to-read UI over real course geometry: pick a
 river, click (or slide) along its course, see interpolated elevation,
 estimated groundwater level, and — for whichever real gauge is nearest that
 point — water level, discharge, and rating-curve plots pulled directly from
 `HydrometricLoader`'s own plotting methods rather than reimplemented.
 
 Click support uses Streamlit's native chart-selection
-(`st.plotly_chart(..., on_select="rerun")`, requires `plotly` alongside
-`streamlit`), not a third-party click-handling package. The click handler and
-the position slider share a single source of truth by design: Streamlit only
-honors a slider's `value=` argument the first time that widget is created, and
-on every later rerun returns whatever's stored under that widget's own
-session-state key — so the click handler writes directly into the slider's
-own key before it's instantiated, rather than a separate key, keeping the two
-controls in sync. It also de-duplicates incoming click events, since
+(`st.plotly_chart(..., on_select="rerun")`), not a third-party click-handling
+package. The click handler and the position slider share a single source of
+truth by design: Streamlit only honors a slider's `value=` argument the first
+time that widget is created, and on every later rerun returns whatever's
+stored under that widget's own session-state key — so the click handler
+writes directly into the slider's own key before it's instantiated, rather
+than a separate key. It also de-duplicates incoming click events, since
 Streamlit's chart-selection state persists across reruns caused by *other*
-widgets and would otherwise re-fire on every unrelated interaction, not just
-the click that caused it.
+widgets and would otherwise re-fire on every unrelated interaction.
+
+**Network validation** — renders the full reach graph (§2): every edge as one
+Plotly line trace regardless of edge count (a trace-per-edge approach doesn't
+hold up at ~5,000+ edges; verified fast at real scale — 0.29s to build a
+figure for ~2,900 edges), real confluences as diamond markers, real gauges as
+elevation-colored circles. Metrics card reports node/edge/confluence/gauge
+counts and, when available, IDPR and cumulative-catchment coverage. Virtual
+infill nodes are deliberately not drawn individually — at ~2,400 per basin,
+markers for each would bury the actual validation signal (do confluences sit
+where a tributary visibly joins the line? do gauges sit on the network, not
+offset from it?) rather than help it. Reads directly from `reach_graph/
+{basin}_nodes_enriched.csv`, keyed on file modification time so a re-run of
+`build_reach_graphs.py`/`enrich_reach_graph.py` is picked up automatically —
+`st.cache_data` otherwise keys purely on function arguments, not file
+contents, and this was confirmed to actually cause stale numbers once during
+development, not just a theoretical risk.
 
 ---
 
 ## 5. Testing (`src/test_build_graph.py`)
 
-Not a unit test suite in the pytest sense — a single script that runs the full
-`node_features → build_surface_edges → build_pyg_graph(s)` pipeline against
-real data under `--data-root` and checks the result is actually sane: no
-NaN/Inf in the feature tensor, no accidental cross-basin edges, targets
-genuinely excluded from the model input, edge indices within bounds,
-bidirectional edge count exactly double the directed count, per-basin node
-counts summing to the combined total, standardized features actually landing
-near zero mean / unit variance, and the `known_losing_reaches` flag actually
-taking effect on the edge it's supposed to.
-
-It also prints mean/max `snap_distance_km` per basin against whatever
-centerline is currently in `centerlines/` — this is the number that would
-catch "one basin got a real BD TOPO centerline and the other is still on the
-old digitized approximation" without anyone having to notice by eye. As of the
-last run, that threshold is deliberately loose (2 km) so it won't hard-fail on
-a legitimately-imperfect-but-usable centerline; it's a reported number to
-watch, not yet an enforced standard. Worth tightening once both basins are on
-real geometry.
-
-Exits 0 on a clean pass, 1 otherwise, so it's usable as a pre-commit or CI
-gate if that's ever set up.
-
----
-
-## 6. Known limitations and open questions
-
-- **Each basin is modeled as a single chain, not a branching network.** Tributaries
-  aren't represented. Fixing this means pulling BD TOPO's confluence-node
-  layer and reworking `build_surface_edges`'s core assumption, not a small
-  patch.
-- **The karst losing-reach flag is based on naming evidence, not a confirmed
-  BD TOPO classification.** The provisional state of the `Nature` attribute
-  means this may firm up (or not) as IGN's data matures — it's not something
-  this project can resolve unilaterally.
-- **Catchment area exists in two incompatible forms** (Hub'Eau cumulative vs.
-  BD TOPO incremental) and nothing currently derives a true independent
-  cumulative catchment from BD TOPO alone by summing incremental polygons
-  upstream of each gauge. Doable, not done.
-- **13 of 27 stations have some hydrometric data; 8 have discharge
-  specifically.** Any model trained on discharge as a target has a real,
-  uneven gauge-density problem to contend with, not just a "some nodes are
-  unlabeled" abstraction.
-- **No true soil or permeability dataset.** IDPR is a tendency index, useful
-  but not a substitute.
-- **The groundwater-well BDLISA aquifer-unit field is unused.** If a
-  subsurface connectivity edge is ever justified with real evidence rather
-  than proximity, this is the first place to look.
+Not a unit test suite in the pytest sense — a script with two independent
+sections, both run from `main()`.
+
+**`run_checks`** — the original single-chain pipeline: runs `node_features →
+build_surface_edges → build_pyg_graph(s)` against real data and checks the
+result is sane — no NaN/Inf in the feature tensor, no accidental cross-basin
+edges, targets genuinely excluded from the model input, edge indices within
+bounds, bidirectional edge count exactly double the directed count, per-basin
+node counts summing to the combined total, standardized features actually
+landing near zero mean / unit variance, the `known_losing_reaches` flag
+actually taking effect, and mean/max `snap_distance_km` per basin against
+whatever centerline is currently in `centerlines/`.
+
+**`run_reach_graph_checks`** — the reach graph pipeline, gracefully skipped
+(not a failure) if `reach_graph/` doesn't exist yet. Mostly regression tests
+for three bugs found and fixed during development, kept here specifically so
+they can't silently reintroduce themselves:
+
+- structural columns (`is_gauged`/`is_confluence`/etc.) never leak into
+  `feature_names`, but remain accessible as their own `Data` attributes
+- target values never attach to a non-gauge node, and target coverage never
+  exceeds the real gauge count
+- `edge_attr` stays exactly 3 columns despite extra edge metadata
+  (`toponym`, `cleabs`) sitting on the real edges table
+- `physics_losses.py`'s `build_confluence_index`/`build_braid_index` produce
+  counts matching `is_confluence`/`is_rejoin_point` sums, with every index
+  within node bounds and every confluence having ≥ 2 upstream branches
+- IDPR and `cumulative_catchment_area_km2` presence/coverage are reported
+  explicitly (the latter compared against the Hub'Eau-only baseline it's
+  meant to exceed)
+
+Exits 0 on a clean pass across both sections, 1 otherwise — usable as a
+pre-commit or CI gate if that's ever set up.
 
 ---
 
-## 7. Running things
+## 6. Running things
 
 Data acquisition (from repo root, in roughly dependency order):
 
@@ -515,12 +760,35 @@ python -m scripts.download_hubeau
 python -m scripts.download_elevation
 python -m scripts.download_era5_full        # slow; download_era5_sample.py first if just testing
 python -m scripts.extract_era5
-python -m scripts.download_catchment_area
+python -m scripts.download_catchment
 python -m scripts.download_bdtopo_hydro --check   # verify typeNames before the real pull
 python -m scripts.download_bdtopo_hydro
+python -m scripts.download_bdcavites --check
+python -m scripts.download_bdcavites
+python -m scripts.download_bdcharm
+```
+
+Build and validate the reach graph:
+
+```bash
+python -m scripts.build_reach_graphs --data-root datasets
+python -m scripts.enrich_reach_graph --data-root datasets   # --skip-climate if that step hangs
+python -m scripts.compute_cumulative_catchment --data-root datasets
+python -m scripts.diagnose_confluences --data-root datasets --basin eure
+python -m scripts.diagnose_confluences --data-root datasets --basin risle
+```
+
+Landcover / NDVI, real gauges only (needs `rasterio`, and `geopandas` for NDVI's
+tile lookup):
+
+```bash
+python -m scripts.fetch_landcover --check
+python -m scripts.fetch_landcover
+python -m scripts.fetch_worldcover_ndvi --check
+python -m scripts.fetch_worldcover_ndvi
 ```
 
-Validate the graph pipeline against whatever's actually in `datasets/`:
+Validate everything against whatever's actually in `datasets/`:
 
 ```bash
 python -m src.test_build_graph --data-root datasets

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datasets/bdcharm50/dept_027/GEO050K_HARM_027_P_DIVERS_2154.prj

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