--- title: "Height above nearest drainage (HAND) flood mapping in Python: the River Severn" description: "Height above nearest drainage (HAND) in Python: compute HAND from EA LIDAR with pysheds, map flood extents by stage and score them against EA flood zones." url: https://www.technicalgis.com/tutorials/height-above-nearest-drainage-python/ author: TechnicalGIS Team publisher: TechnicalGIS (https://www.technicalgis.com/about/) published: 2026-11-03 updated: 2026-11-03 language: en-GB --- # Height above nearest drainage (HAND) flood mapping in Python: the River Severn > Height above nearest drainage (HAND) in Python: compute HAND from EA LIDAR with pysheds, map flood extents by stage and score them against EA flood zones. - Level: Intermediate - Tested with: Python 3.12 - Source code: [Download scripts](https://www.technicalgis.com/code/height-above-nearest-drainage-python/) ## Key takeaways 1. HAND is the height of each DEM cell above the stream cell it drains to. Once the DEM is conditioned and a drainage network chosen, pysheds computes it in one call: grid.compute\_hand(fdir, dem, streams). 2. Thresholding HAND at a stage height gives an inundation extent. On a 13 km × 10 km reach of the River Severn, HAND ≤ 3.75 m matched Environment Agency Flood Zone 3 with a critical success index of 0.59, a hit rate of 0.92 and a false-alarm ratio of 0.38. 3. The drainage network matters as much as the stage. Drawing channels from 0.5 km² cut the best CSI to 0.39, and from 10 km² raised it to 0.75, because one HAND threshold assumes every small stream rises as far as the Severn. 4. HAND has no hydraulics or flood volume. It overpredicts along small tributaries and on flat ground beside them, and underpredicts where the valley narrows and water stands deeper. ## Before you start - Watershed delineation with pysheds: conditioning a DEM, D8 flow direction and flow accumulation, as covered in the watershed delineation tutorial - Python 3.10+ with pysheds, numba, numpy, scipy, rasterio, geopandas, shapely, pyproj, pandas, matplotlib and requests - An internet connection: the DTM, flood zones and gauge data come from Environment Agency web services and the rivers from the OpenStreetMap Overpass API, with no accounts or keys **Short answer:** height above nearest drainage (HAND) is the height of every DEM cell above the stream cell its water drains to. To compute it in Python, condition a DEM with `pysheds`, compute D8 flow direction and flow accumulation, and define a drainage network. Then call `grid.compute_hand(fdir, dem, streams)`. For a simple flood map, every cell with **HAND ≤ h** is flooded at a stage height of _h_ metres above the river. Always score the result against an independent flood map, for example with the critical success index (CSI). HAND is popular because it is fast and needs nothing but a DEM, which is why it underpins national-scale flood inundation mapping such as the NOAA Office of Water Prediction’s HAND-based system. However, it is a terrain index, not a flood model. In this tutorial we compute it from **Environment Agency LIDAR** for a 13 km reach of the **River Severn** between Shrewsbury and Buildwas in Shropshire. We map flood extents for a range of stage heights, and measure how well they agree with the Environment Agency’s own **Flood Map for Planning**. You will learn how to: 1. download the **EA LIDAR Composite DTM** at 5 m through its WCS; 2. download **Flood Zones 2 and 3** from the EA’s OGC API and rasterise them; 3. **burn** mapped rivers into the DTM and route flow with pysheds; 4. compute **HAND** and relate it to the **Buildwas gauge**; 5. threshold HAND into **inundation extents** for several stage heights; 6. score each extent against the flood zones with the **hit rate, false-alarm ratio and CSI**, and test the **stream threshold**. All outputs below are real results from [`hand_flood_mapping.py`](https://www.technicalgis.com/code/height-above-nearest-drainage-python/), run on 3 November 2026 with Python 3.12, pysheds 0.5, numpy 2.5.4, rasterio 1.5.2 and geopandas 1.2.0. DEM conditioning and flow routing are covered in depth in [watershed delineation in Python](https://www.technicalgis.com/tutorials/watershed-delineation-python/), so here we only adapt them for HAND. ## Step 1: the LIDAR DTM The Environment Agency’s **LIDAR Composite DTM** is a bare-earth model of England at 1 m, with buildings and vegetation removed. That makes it far better than a global surface model for floodplains, where a 2 m hedge or a block of trees changes everything. Its Web Coverage Service (WCS) can resample on the server. We ask for 2 km tiles with `SCALEFACTOR=0.2`, which returns 5 m cells and keeps the grid to a manageable 9 million cells: `hand_flood_mapping.py` ```python RES = 5.0 X0, Y0, X1, Y1 = 350_000, 300_000, 366_000, 314_000 # 16 km x 14 km, British National Grid WCS = ("https://environment.data.gov.uk/spatialdata/lidar-composite-digital-terrain-model-dtm-1m/wcs?" "service=WCS&version=2.0.1&request=GetCoverage" "&CoverageId=13787b9a-26a4-4775-8523-806d13af58fc__Lidar_Composite_Elevation_DTM_1m" "&format=image/tiff&subset=E({},{})&subset=N({},{})&SCALEFACTOR={}") TILE = 2_000 dtm = np.full((H, W), np.nan, dtype="float32") for ty in range(Y0, Y1, TILE): for tx in range(X0, X1, TILE): r = requests.get(WCS.format(tx, tx + TILE, ty, ty + TILE, 1 / RES), timeout=120) with rasterio.MemoryFile(r.content) as mem, mem.open() as src: tile = src.read(1).astype("float32") tile[(tile < -100) | (tile > 2000)] = np.nan # the WCS marks gaps with -3.4e38 row, col = rowcol(TRANSFORM, src.bounds.left + RES / 2, src.bounds.top - RES / 2) dtm[row:row + tile.shape[0], col:col + tile.shape[1]] = tile[:H - row, :W - col] ``` `Output — DTM` ```text DTM box 350000-366000 E, 300000-314000 N (16 km x 14 km) | 3200 x 2800 cells at 5 m | evaluation box 13 km x 10 km elevation 22.5-406.7 m | gaps 0 cells (0.000%) ``` The 56 tiles have no gaps in this area. The script still fills any gaps with the nearest valid value, because one hole in a floodplain breaks flow routing. All scores below use an **evaluation box** 1–2 km inside the DTM. Near the edge of a DEM, flow paths are cut off and HAND is unreliable. ## Step 2: the reference flood zones, rivers and gauge The **Flood Map for Planning** shows where flooding from rivers and the sea could happen if there were no defences. **Flood Zone 3** has a 1% or greater chance of river flooding each year, and **Flood Zone 2** a chance between 0.1% and 1%. The EA serves both through an OGC API Features endpoint that pages its results, so the script follows the `next` links: `hand_flood_mapping.py` ```python FZ_URL = ("https://environment.data.gov.uk/geoservices/datasets/04532375-a198-476e-985e-0579a0a11b47/ogc/features/v1/" f"collections/Flood_Zones_2_3_Rivers_and_Sea/items?f=application/json&bbox={lon0},{lat0},{lon1},{lat1}&limit=200") feats, url = [], FZ_URL while url: page = requests.get(url, timeout=180).json() feats += page["features"] url = next((l["href"] for l in page.get("links", []) if l["rel"] == "next"), None) fz = gpd.GeoDataFrame.from_features(feats, crs=4326).to_crs(27700).explode(index_parts=False) fz = fz[fz.geom_type.isin(["Polygon", "MultiPolygon"])] # a few features arrive as mixed collections fz["geometry"] = fz.geometry.buffer(0).intersection(box(X0, Y0, X1, Y1)) fz3_geom = fz[fz.flood_zone == "FZ3"].geometry.union_all() fz2_geom = fz[fz.flood_zone == "FZ2"].geometry.union_all() FZ3 = features.rasterize([(fz3_geom, 1)], out_shape=(H, W), transform=TRANSFORM, dtype="uint8").astype(bool) FZ2 = features.rasterize([(fz2_geom.union(fz3_geom), 1)], out_shape=(H, W), transform=TRANSFORM, dtype="uint8").astype(bool) # the Flood Zone 2 extent includes zone 3 ``` `Output — flood zones, rivers and gauge` ```text 19411 polygons | by zone and source: FZ2/river 17389, FZ3/river 2022 FZ3 15.17 km2 | FZ2 polygons 3.52 km2, of which 0.00 km2 overlaps FZ3 River Severn 30.8 km, River Tern 10.1 km, River Roden 2.7 km, unnamed 0.5 km Buildwas gauge on the River Severn at 364570 E, 304420 N | gauge datum 36 m AOD | typical high 3.4 m | highest recent 6.025 m (2014-02-11) | maximum on record 7.037 m (2000-11-02) ``` The Flood Zone 2 polygons do not overlap zone 3. They are only the extra fringe, so the full 0.1% extent is zone 2 **plus** zone 3. The script also downloads the rivers from OpenStreetMap and the Buildwas gauge record from the EA’s real-time flood-monitoring API: its datum, its typical high level, and the record levels of February 2014 and November 2000. ![An elevation map of the Severn valley between Shrewsbury and Buildwas, blue on the low floodplain and red on the hills, with the hill of the Wrekin in the east. The River Severn meanders from the north-west to the south-east, and the River Tern joins it from the north-east. A translucent blue band of Flood Zone 3 follows both rivers, widest along the Severn, and thin blue outlines of Flood Zone 2 follow smaller streams. A dashed box marks the evaluation area and a triangle marks the Buildwas gauge in the south-east.](https://www.technicalgis.com/_astro/01-study-area.BtQJRNpv_Zkxxg0.webp) _The EA LIDAR composite DTM at 5 m, with Flood Zones 2 and 3, the OSM rivers, the evaluation box and the Buildwas gauge._ ## Step 3: burn the rivers and route the flow HAND needs two things from flow routing: a **D8 flow direction** grid that says where each cell’s water goes, and a **drainage network** to measure heights against. The steps are the same as in [watershed delineation](https://www.technicalgis.com/tutorials/watershed-delineation-python/): fill pits and depressions, resolve flats, then compute flow direction and accumulation. Two adaptations matter on a lowland floodplain. First, **burn the mapped rivers** into a copy of the DTM. On a floodplain only a few metres of relief separate the river from the fields. Without burning, D8 flow can cut across meander necks and old channels instead of following the river. Second, **round to 1 cm** before conditioning. With sub-millimetre differences on flats, pysheds’ `resolve_flats` can leave cells that drain nowhere, and those cells get no HAND value. The LIDAR is only accurate to about 0.1 m vertically, so nothing is lost: `hand_flood_mapping.py` ```python grid = Grid.from_raster(str(dem_path)) dem = grid.read_raster(str(dem_path)) river_px = features.rasterize([(g, 1) for g in rivers.values()], out_shape=(H, W), transform=TRANSFORM, dtype="uint8").astype(bool) burned = Raster(np.round(np.where(river_px, np.asarray(dem) - 5.0, np.asarray(dem)).astype("float64"), 2), viewfinder=grid.viewfinder) # for routing only pit_filled = grid.fill_pits(burned) flooded = grid.fill_depressions(pit_filled) inflated = grid.resolve_flats(flooded) fdir = grid.flowdir(inflated, dirmap=DIRMAP) acc = grid.accumulation(fdir, dirmap=DIRMAP) ``` `Output — conditioning` ```text burned 8,039 river cells by 5 m | 0 unresolved interior pits | fill, resolve flats, D8 and accumulation in 11.6 s | 6.0% of cells raised by depression filling, deepest fill 82.1 m ``` With the rounding, no interior pits remain. The deepest fill, 82 m, is a closed hollow in the north-east of the box, well away from the rivers. ### Choosing the drainage network This is the most important decision in HAND mapping. A 16 km DTM cannot see the roughly 2,000 km² the Severn drains before it enters the box. Accumulation alone would therefore not call the Severn a river until well downstream. The network is the **mapped rivers** plus every **derived channel** that drains at least 3 km² inside the box. The Flood Map for Planning’s river modelling generally covers watercourses with catchments of about 3 km² or more, so this threshold compares like with like: `hand_flood_mapping.py` ```python acc_km2 = np.asarray(acc) * RES ** 2 / 1e6 BOOL_VF = ViewFinder(affine=grid.affine, shape=grid.shape, crs=grid.crs, nodata=False) # for mask rasters STREAM_KM2 = 3.0 def hand_for(threshold_km2): streams = (acc_km2 >= threshold_km2) | river_px h = grid.compute_hand(fdir, dem, Raster(streams, viewfinder=BOOL_VF), dirmap=DIRMAP) return streams, np.asarray(h, dtype="float64") streams, hand_raw = hand_for(STREAM_KM2) ``` `Output — drainage network` ```text streams draining >= 3 km2: 18,304 cells, about 92 km of channel | HAND computed in 1.3 s ``` Note that `compute_hand` takes the **unburned** `dem`. The burned and filled copy only decides where water goes. Heights are measured on the real ground surface. ![Three maps of the study area over a grey hillshade. 1: depression fill depth, with small yellow and orange patches across the floodplain and a few dark patches up to 3 m or more, including a large one in the north-east. 2: flow accumulation as a branching network of channels, thin yellow in the headwaters and thick dark purple along the Severn and Tern. 3: the drainage network, with the OSM rivers in dark blue and derived channels draining at least 3 km² in orange, mostly tributaries from the south and north.](https://www.technicalgis.com/_astro/02-conditioning-and-drainage.Cqq4ZglS_rlAnQ.webp) _Conditioning and drainage: depression fill depth after burning the rivers, D8 flow accumulation (cells draining at least 0.1 km²), and the final drainage network of mapped rivers plus derived channels._ ## Step 4: height above nearest drainage pysheds traces each cell’s D8 flow path down to the first stream cell, and subtracts that cell’s elevation from its own. A few cells sit below their drainage cell, because they are in filled hollows. These are clipped to zero. Cells whose flow path leaves the grid before it meets a channel have no HAND value, and are treated as dry: `hand_flood_mapping.py` ```python hand = np.where(np.isfinite(hand_raw), np.clip(hand_raw, 0, None), np.nan) ``` `Output — HAND` ```text evaluation box: 5,200,000 cells, 20,946 without a HAND value (they drain off the grid before meeting a channel and count as dry) HAND percentiles 10/25/50/75/90: 2.9 / 10.6 / 26.7 / 47.4 / 81.7 m | 0.39% of cells sit below their drainage cell (set to 0) Flood Zone 3 11.28 km2 | median HAND 2.2 m | 90th percentile 3.6 m Flood Zone 2 13.44 km2 | median HAND 2.3 m | 90th percentile 4.1 m outside Flood Zone 2 116.04 km2 | median HAND 29.9 m | 90th percentile 86.1 m ``` This is the first sign that HAND works here. Nine-tenths of Flood Zone 3 lies within 3.6 m of its drainage, while the median HAND outside the flood zones is 29.9 m. ![A map of HAND over a hillshade, coloured from purple and blue at 0 m through green and yellow to red at 20 m or more. The broad Severn floodplain and the Tern valley are blue, narrow blue ribbons follow the tributary valleys, and the higher ground between them is red. An extensive area of green and yellow, 5 to 12 m above drainage, lies on the low ground west of the Severn and Tern confluence.](https://www.technicalgis.com/_astro/03-hand.Cy7iCIbq_Z159BTf.webp) _Height above nearest drainage for the whole DTM, with the drainage network drawn in dark blue._ ### HAND and the Buildwas gauge A HAND threshold is a height above the river surface **as it was during the LIDAR survey**, not above the gauge datum. To relate them, the script finds the lowest stream cell near the gauge and converts the gauge’s levels into heights above that surface: `Output — gauge` ```text LIDAR water surface at Buildwas 38.06 m AOD = stage 2.06 m on the gauge typical high stage 3.40 m -> 1.34 m above the LIDAR water surface highest recent (2014) stage 6.03 m -> 3.96 m above the LIDAR water surface maximum on record (2000) stage 7.04 m -> 4.98 m above the LIDAR water surface ``` On the day of the survey the Severn at Buildwas stood at a stage of about 2.06 m. The February 2014 flood peaked about 4 m above that, and the November 2000 flood, the highest on record, about 5 m above it. These heights apply at Buildwas only. Upstream the water surface is higher, but because HAND is measured from the local channel, a single threshold is still a fair first approximation along one reach. ## Step 5: inundation extents for several stage heights An inundation extent for a stage height _h_ is simply `hand <= h`. The script steps _h_ from 0.5 m to 12 m and scores each extent against the reference inside the evaluation box. Hits (_a_) are flooded in both, false alarms (_b_) only in HAND, and misses (_c_) only in the reference: `hand_flood_mapping.py` ```python def scores(pred, ref, mask): a = (pred & ref & mask).sum(); b = (pred & ~ref & mask).sum(); c = (~pred & ref & mask).sum() return {"area_km2": (pred & mask).sum() * RES ** 2 / 1e6, "hit": a / (a + c), "far": b / (a + b), "csi": a / (a + b + c), "bias": (a + b) / (a + c)} STAGES = np.round(np.arange(0.5, 12.01, 0.25), 2) table = {z: [scores(hand <= h, ref, ev) for h in STAGES] for z, ref in (("FZ3", FZ3), ("FZ2", FZ2))} ``` The **critical success index** is the standard score for flood maps. It ignores the correctly dry cells, which make up most of any map and would otherwise inflate the accuracy. A map that floods everything has a hit rate of 1 but a poor CSI. For more on agreement measures, see [land-cover accuracy assessment in Python](https://www.technicalgis.com/tutorials/land-cover-accuracy-assessment-python/). `Output — agreement by stage height` ```text stage | area km2 | FZ3 hit FAR CSI | FZ2 hit FAR CSI 1.0m | 5.13 | 0.33 0.27 0.29 | 0.30 0.22 0.28 2.0m | 8.09 | 0.46 0.36 0.37 | 0.43 0.29 0.36 3.0m | 13.56 | 0.77 0.36 0.53 | 0.70 0.30 0.54 4.0m | 17.53 | 0.95 0.39 0.59 | 0.88 0.32 0.62 5.0m | 20.46 | 0.99 0.46 0.54 | 0.95 0.38 0.60 6.0m | 22.65 | 1.00 0.50 0.49 | 0.97 0.43 0.56 7.0m | 24.64 | 1.00 0.54 0.46 | 0.98 0.47 0.53 8.0m | 26.53 | 1.00 0.58 0.42 | 0.98 0.50 0.49 10.0m | 30.83 | 1.00 0.63 0.36 | 0.99 0.57 0.43 12.0m | 35.38 | 1.00 0.68 0.32 | 1.00 0.62 0.38 best match to FZ3: HAND <= 3.75 m | CSI 0.59 | hit rate 0.92 | false-alarm ratio 0.38 | bias 1.48 | 16.74 km2 (reference 11.28 km2) best match to FZ2: HAND <= 4.25 m | CSI 0.63 | hit rate 0.91 | false-alarm ratio 0.33 | bias 1.36 | 18.30 km2 (reference 13.44 km2) ``` ![Three maps of the evaluation box, each showing a blue HAND inundation extent over a hillshade with the Flood Zone 3 outline in black. At 2 m the blue covers only the river channels and the lowest parts of the floodplain, well inside the outline. At 3.75 m it fills most of the outline along the Severn and Tern, with extra blue along tributaries and a large patch in the north. At 6 m the blue spills beyond the outline along the valley sides and tributaries.](https://www.technicalgis.com/_astro/04-inundation-stages.Be8hRQ2O_ZN4iIu.webp) _Inundation extents for HAND ≤ 2 m, 3.75 m (best match to Flood Zone 3) and 6 m, against the Flood Zone 3 outline._ The best match to Flood Zone 3 is **HAND ≤ 3.75 m**, with a CSI of 0.59. It finds 92% of the zone, but 38% of what it floods lies outside it. Above 4 m the hit rate barely rises, while false alarms keep growing. Against the larger 0.1% extent, the best threshold rises to 4.25 m, as you would expect for a rarer flood. The best-fitting stage for Flood Zone 3, 3.75 m, is close to the 2014 flood at Buildwas (3.96 m). That is a reassuring check, but only a rough one: the 2014 flood was not a 1% event at every point of the reach. ## Step 6: where it agrees, and the stream threshold The agreement map shows where the errors are: ![Left: a map of the evaluation box with hits in blue along the Severn and Tern floodplains, misses in red mainly along the Severn's southern edge towards Buildwas in the east, and false alarms in orange along almost every tributary, plus a large orange block in the north beside the Tern and a smaller one west of the Severn. Right: histograms of HAND for cells inside Flood Zone 3 (peaks near 0 and 2.5 m), Flood Zone 2 only (centred near 4 m) and outside Flood Zone 2 (flat, spread to 20 m), with a red line at the 3.75 m threshold.](https://www.technicalgis.com/_astro/06-agreement-map.BN3veuHk_Z12oE0d.webp) _Hits, misses and false alarms for HAND ≤ 3.75 m against Flood Zone 3, and the distribution of HAND inside and outside the flood zones._ - **Hits** cover the Severn and Tern floodplains. Along the main rivers, HAND and the EA’s hydraulic modelling largely agree. - **False alarms** line almost every tributary, and cover a large area of flat ground in the north, beside the Tern. A single threshold assumes every channel rises 3.75 m in a flood. The Severn does, but a stream draining 5 km² rises perhaps a metre. Flat ground beside a small derived channel can be low in HAND even though a flood never reaches it. - **Misses** cluster along the southern edge of the Severn floodplain towards Buildwas, and in a few meander loops downstream of Shrewsbury. Where the valley narrows, water stands deeper for the same flow, so a single threshold is too low. If small streams cause most false alarms, the channel threshold should matter, and it does: `Output — stream threshold` ```text streams >= 0.5 km2: 224 km of channel | best FZ3 CSI 0.39 at HAND <= 3.25 m | false-alarm ratio 0.59 streams >= 1.0 km2: 160 km of channel | best FZ3 CSI 0.45 at HAND <= 3.50 m | false-alarm ratio 0.53 streams >= 3.0 km2: 92 km of channel | best FZ3 CSI 0.59 at HAND <= 3.75 m | false-alarm ratio 0.38 streams >= 10.0 km2: 50 km of channel | best FZ3 CSI 0.75 at HAND <= 4.25 m | false-alarm ratio 0.18 ``` With channels from 0.5 km², the network more than doubles, every gully gets a “floodplain”, and the best CSI falls to 0.39. With channels from 10 km², the best CSI rises to 0.75 and the false-alarm ratio halves to 0.18. However, the 10 km² network leaves out tributaries that the EA does map, so part of that gain comes from tuning to this reach. The honest conclusion is that HAND with one threshold is a good map of the **main-river** floodplain, and a poor one for small streams. Production systems solve this with a separate stage, or a synthetic rating curve, for each reach. ![Three line charts against HAND threshold from 0 to 12 m. Left, against Flood Zone 3: the hit rate rises steeply to nearly 1 by 5 m, the false-alarm ratio climbs slowly from 0.2 to 0.68, and the CSI peaks at 0.59 near 3.75 m. Vertical lines mark the Buildwas typical high, 2014 and 2000 levels at about 1.3, 4 and 5 m. Middle: the same against Flood Zone 2, with the CSI peaking at 0.63 near 4.25 m. Right: CSI curves for stream thresholds of 0.5, 1, 3 and 10 km², peaking at 0.39, 0.45, 0.59 and 0.75.](https://www.technicalgis.com/_astro/05-agreement-scores.29v0k3zs_ulcMB.webp) _Hit rate, false-alarm ratio and CSI against Flood Zones 3 and 2 for every stage height, with the Buildwas gauge levels, and the CSI for four stream thresholds._ ## Pitfalls and limitations - **No hydraulics, no volume.** HAND floods every connected cell below the threshold, as if there were unlimited water and a flat water surface relative to each channel. It cannot represent backwater, constrictions, embankments, flood storage or the timing of a flood. That is one reason the misses cluster where the valley narrows. - **The reference is a model too.** The Flood Map for Planning combines detailed local hydraulic models with generalised national modelling. It ignores defences, and it is not an observed flood. A CSI against it measures agreement between two models. For an observed extent, map a real flood from radar as in [Sentinel-1 SAR flood mapping](https://www.technicalgis.com/tutorials/sentinel-1-sar-flood-mapping/). - **Stage is relative to the LIDAR water surface.** The LIDAR composite is a mosaic of surveys flown on different dates and at different river levels. HAND = 0 is the water surface on survey day, which can change abruptly at a survey boundary. - **The box edge.** Flow paths that leave the grid get no HAND value (0.4% of the evaluation box here), and the box cannot see the upstream catchment. Burning the mapped rivers solves the second problem only where the rivers are mapped. - **Burning and filling.** Stream burning trusts the OSM lines. A misplaced river line carves a false channel, and the 5 m burn can attract flow from nearby low ground. Filling also removes real hollows, so a closed basin next to the river has a HAND value even though water cannot flow out of it. - **Resolution and embankments.** At 5 m, flood embankments, road and rail embankments and walls only a few metres wide are partly smoothed out. Coarser DEMs make this much worse, and global DSMs such as Copernicus GLO-30 add trees and buildings. For floodplains, use a lidar DTM when one exists. ## Frequently asked questions ### What is height above nearest drainage (HAND)? HAND is a terrain index introduced by Rennó, Nobre and colleagues in 2008. For each cell of a DEM, it is the vertical distance to the stream cell that the cell drains to, along its flow path. Unlike elevation, it is normalised to the local river. A floodplain is near zero whether the river is at 30 m or 300 m above sea level, which makes HAND useful for floodplain mapping, soil and wetland mapping, and flood inundation forecasting. ### How do you make a flood map from HAND? Compute HAND, choose a stage height _h_ (the water level above the normal river surface), and mark every cell with HAND ≤ _h_ as flooded. Then polygonise the mask if you need vectors, as shown in [raster to vector in Python](https://www.technicalgis.com/tutorials/raster-to-vector-python/). The stage can come from a gauge, a forecast, or a rating curve that converts river flow into a water level. Better systems use a different stage for each river reach. ### How accurate is HAND flood mapping? It depends on the DEM, the drainage network and the river. Here, with 5 m LIDAR and one threshold, the best agreement with the Environment Agency’s Flood Zone 3 was a CSI of 0.59 (hit rate 0.92, false-alarm ratio 0.38). Leaving out channels draining less than 10 km² raised it to 0.75. Published comparisons with hydraulic models vary widely: agreement is usually best on large rivers with well-defined floodplains, and worst on small streams and very flat ground. ### How do I calculate HAND in Python? With pysheds: condition the DEM (`fill_pits`, `fill_depressions`, `resolve_flats`), compute `flowdir` and `accumulation`, and build a boolean stream raster. Then call `grid.compute_hand(fdir, dem, streams)`. Wrap the stream mask in a `ViewFinder` with `nodata=False`, as in Step 3. WhiteboxTools offers the same index as `ElevationAboveStream`. ### What is the difference between HAND and a hydraulic flood model? HAND is a static terrain index: it only knows how high each cell is above its river. A hydraulic model, such as HEC-RAS, LISFLOOD-FP or the models behind the EA flood map, solves equations for the flow of water. It accounts for flow volume, channel and floodplain roughness, constrictions, embankments and how the flood changes through time. HAND takes seconds and needs only a DEM, so it is used for rapid and national-scale mapping. Hydraulic models are used where decisions depend on the result. ### Can I use HAND instead of the Environment Agency flood map? No. For planning, insurance or any decision about a property in England, use the Environment Agency’s flood maps, which account for hydraulics and flood records. HAND is useful for screening, for areas with no official map, for quick scenario maps from a gauge level, and for checking or explaining other maps. ## Next steps - Delineate the catchment above Buildwas and learn the conditioning steps in detail in [watershed delineation in Python](https://www.technicalgis.com/tutorials/watershed-delineation-python/). - Compare HAND with an observed flood: map a real Severn flood from radar with [Sentinel-1 SAR flood mapping](https://www.technicalgis.com/tutorials/sentinel-1-sar-flood-mapping/), and score it with the same CSI code. - Count the buildings in each inundation extent with [zonal statistics in Python](https://www.technicalgis.com/tutorials/zonal-statistics-python/) or a [spatial join in GeoPandas](https://www.technicalgis.com/tutorials/spatial-join-geopandas/). - Build a DTM from a point cloud yourself with [LiDAR point clouds in Python with laspy](https://www.technicalgis.com/tutorials/lidar-point-clouds-laspy/). - Use a DEM to plan routes rather than floods with [least-cost path analysis in Python](https://www.technicalgis.com/tutorials/least-cost-path-analysis-python/), which builds a cost surface from slope and land cover. ## Data and licences - **DTM**: Environment Agency LIDAR Composite DTM 1 m, © Environment Agency copyright and/or database right. All rights reserved. Licensed under the [Open Government Licence v3.0](https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). Accessed through the Defra Data Services Platform WCS on 3 November 2026. - **Flood zones**: Environment Agency Flood Map for Planning, Flood Zones 2 and 3 (Rivers and Sea), © Environment Agency copyright and/or database right, licensed under the Open Government Licence v3.0. Accessed through the OGC API Features service on 3 November 2026. - **Gauge**: this uses Environment Agency flood and river level data from the real-time data API (Beta), station 2134 (Buildwas), licensed under the Open Government Licence v3.0. Accessed on 3 November 2026. - **Rivers**: © OpenStreetMap contributors, available under the [Open Database Licence](https://www.openstreetmap.org/copyright), queried through the Overpass API on 3 November 2026. - Code: [`hand_flood_mapping.py`](https://www.technicalgis.com/code/height-above-nearest-drainage-python/), tested with Python 3.12.0, pysheds 0.5, numba 0.68.1, numpy 2.5.4, scipy 1.18.1, rasterio 1.5.2, geopandas 1.2.0, shapely 2.2.0 and pyproj 3.8.0. ## Key terms - **Height above nearest drainage (HAND)**: For every cell, its elevation minus the elevation of the stream cell where its flow path first meets the drainage network. It normalises a DEM so that floodplains of large and small rivers both sit near zero. - **Stage height**: The water level of a river above a reference. Here it is the height above the river surface in the LIDAR survey; at a gauge it is measured above the gauge datum. - **Flood Zone 3 and Flood Zone 2**: The Environment Agency's Flood Map for Planning zones. Flood Zone 3 has a 1% or greater annual chance of flooding from rivers (0.5% from the sea). Flood Zone 2 covers 0.1% to 1%. Both ignore flood defences. - **Critical success index (CSI)**: Hits divided by hits plus misses plus false alarms. It ranges from 0 to 1 and ignores the correctly dry cells, so it is not inflated by a large dry area. - **Hit rate and false-alarm ratio**: The hit rate is the share of the reference flood extent that the model also floods. The false-alarm ratio is the share of the modelled extent that the reference does not flood. - **Stream burning**: Lowering the DEM along mapped river lines so that flow routing follows the real channels across flat floodplains. ## Data for this tutorial - [EA LIDAR Composite DTM 1 m](https://www.technicalgis.com/data-sources/united-kingdom/#src-ea-lidar-composite-dtm-1m) · The bare-earth DTM, read at 5 m through the WCS - [EA Flood Map for Planning: Flood Zones](https://www.technicalgis.com/data-sources/united-kingdom/#src-ea-flood-map-for-planning) · Flood Zones 2 and 3, the reference extents - [EA real-time flood monitoring API](https://www.technicalgis.com/data-sources/united-kingdom/#src-ea-flood-monitoring-api) · Gauge datum and record levels for Buildwas - [OpenStreetMap](https://www.technicalgis.com/data-sources/#src-openstreetmap) · River centrelines for stream burning Licences and formats for each source are listed in the [GIS data sources catalogue](https://www.technicalgis.com/data-sources/). --- Source: https://www.technicalgis.com/tutorials/height-above-nearest-drainage-python/