Author

Corey T. White

Modified

October 22, 2026

Coweeta, NC – Soil-Informed Overland Flow Simulation

The Coweeta Hydrologic Laboratory sits in the southern Blue Ridge Mountains of western North Carolina, where steep slopes, dense mixed-hardwood canopy, and deep well-drained loams route most rainfall through subsurface pathways long before it reaches a channel. That makes Coweeta a demanding test for overland-flow models: soil and cover heterogeneity fundamentally reshape where runoff forms and how fast it travels. Typical shallow-water runs apply a single uniform rainfall rate to every cell, but real rainfall excess – the fraction of precipitation that actually runs off – varies pixel by pixel. This notebook imports USDA SSURGO soil data, derives a spatially varying rainfall-excess surface via SCS curve-number analysis, and feeds it into the SIMWE particle-based shallow-water model. We then compare the soil-informed simulation against a naive one that ignores soil variability, and animate the walker cloud colored by flow speed.

The workflow below (@fig_ssurgo_flowchart) traces data from SSURGO and NLCD inputs through curve number, time of concentration, and Manning’s \(n\) to SIMWE’s depth, discharge, and walker outputs.

%% label: fig_ssurgo_flowchart
%% echo: false
%% eval: true
%% fig-cap: "Flowchart of SSURGO SIMWE workflows"
flowchart TD
    A[r.in.ssurgo] --> B("Soils map vector")
    A[r.in.ssurgo] --> HG("Hydrologic group map")
    A[r.in.ssurgo] --> KSAT("Ksat maps rasters")
    A[r.in.ssurgo] --> E("MUKEY map raster")
    B --> F("Soil attributes in vector attribute table")
    G[r.curvenumber] --> H("Curve number map")
    HG --> G
    LC(NLCD land cover map) --> G
    ELEV[Elevation raster] --> W[r.watershed]
    W --> I("Flow direction")
    W --> J("Streams")
    W --> K("Basins")
    TC[r.timeofconcentration] --> TCR("Time of concentration")
    ELEV --> TC
    I --> TC
    J --> TC
    H --> R[r.runoff]
    I --> R
    TCR --> R
    R --> RD("Runoff depth")
    R --> RV("Runoff volume")
    R --> TP("Time to peak")
    R --> PD("Peak discharge raster")
    R --> URD("Upstream runoff depth")
    R --> URV("Upstream runoff volume")
    R --> UA("Upstream area")
    LC --> MC["r.manning"]
    MC --> MN("Manning's n map")
    ELEV --> RSA["r.slope.aspect"]
    RSA --> DX("dx")
    RSA --> DY("dy")
    ELEV --> SIMWE["r.sim.water"]
    DX --> SIMWE
    DY --> SIMWE
    MN --> SIMWE
    HG --> SIMWE
    KSAT --> SIMWE
    SIMWE --> DEPTH("Water depth raster")
    SIMWE --> DISCHARGE("Discharge raster")

Objectives

  1. Import SSURGO soil properties (hydrologic groups, saturated hydraulic conductivity, MUKEYs) from either a local gSSURGO archive or the USDA Soil Data Access service.
  2. Derive a curve-number surface by combining hydrologic soil groups with NLCD 2024 land cover.
  3. Design a 100-year storm whose duration matches the watershed’s time of concentration, ensuring peak-runoff conditions.
  4. Convert SCS curve-number runoff depths into a spatially varying rainfall-excess rate (mm/hr) suitable for SIMWE.
  5. Compute Manning’s \(n\) from NLCD to parameterize surface roughness for the shallow-water solver.
  6. Run SIMWE with and without soil-informed rainfall excess, then visualize the walker cloud colored by flow speed (\(v = q/h\)) from the model’s own depth and discharge outputs.

Coweeta Hydrologic Laboratory (35.0667 N, 83.4333 W) is a long-term USDA Forest Service research site near Otto, NC, with ~2700 ft (~820 m) of relief across forested headwater catchments. Hydrologic soil groups here are dominated by B (moderate infiltration) with pockets of C and D on thinner ridge soils, in contrast to the B/C/D cropland mosaic at the Clay Center site. Work happens inside a GRASS mapset called ssurgo_demo in a project aligned to the site’s 3 m elevation raster.

Import SSURGO Data

r.in.ssurgo can pull soil survey data in two modes: from a local gSSURGO state archive, or on-demand from the Soil Data Access (SDA) web service, which avoids the multi-GB download at the cost of network latency. Both modes produce the same outputs – a soil-polygon vector plus categorical rasters for hydrologic group (HSG) and MUKEY, and low/representative/high saturated hydraulic conductivity (\(K_{sat}\)) rasters – which downstream tools use as inputs to curve-number and infiltration models.

Source for the r.in.ssurgo addon lives in tools/r.soildb/r.in.ssurgo in this repository; install it via g.extension pointing at that path (or from the GRASS Addons repository once it’s merged).

Computational Region

We align the computational region to the 3 m elevation raster so every derived layer – soil rasters, curve number, Manning’s \(n\), SIMWE outputs – shares the same grid. A companion shaded-relief raster provides the cartographic backdrop for every map that follows.

Code 
region_text = tools.g_region(raster="elevation", flags="p").text
print(region_text)
projection: 1 (UTM)
zone:       17
datum:      nad83
ellipsoid:  grs80
north:      3884960
south:      3876990
west:       272970
east:       280150
nsres:      10
ewres:      10
rows:       797
cols:       718
cells:      572246
Code 
tools.r_relief(input="elevation", output="relief")

Loading the soil data

The local tab below imports from a downloaded gSSURGO North Carolina archive; the SDA tab queries the web service directly (no prior download). The horizon window (hzdept_r=0, hzdepb_r=100 cm) and master designation filter (desgnmaster="A") select the top 100 cm of A-horizon material, since rainfall-excess generation on forested slopes is dominated by near-surface soil properties. Outputs are suffixed _sda when imported via SDA so both versions can coexist.

SDA Import

Code 
tools.r_in_ssurgo(
    soils="soil_areas_sda",
    hydgrp="hydgrp_sda",
    ksat_l="ksat_l_sda",
    ksat_r="ksat_r_sda",
    ksat_h="ksat_h_sda",
    mukey="mukey_sda",
    hzdept_r=0,
    hzdepb_r=100,
    desgnmaster="A",
    nprocs=30
)
Code 
tools.r_in_ssurgo(
    ssurgo_path="data/gSSURGO_NC.zip",
    soils="soil_areas",
    hydgrp="hydgrp",
    ksat_l="ksat_l",
    ksat_r="ksat_r",
    ksat_h="ksat_h",
    mukey="mukey",
    hzdept_r=0,
    hzdepb_r=100,
    desgnmaster="A",
    flags="s",
    nprocs=30
)

tools.r_in_ssurgo(
    ssurgo_path="data/gSSURGO_NC.zip",
    soils="soil_areas",
    hydgrp="hydgrp",
    ksat_r="ksat_r",
    depths="0,15,30,60,100",
    desgnmaster="A",
    flags="s",
    nprocs=30
)
Code 
import grass.script.array as garray
import matplotlib.pyplot as plt

a = garray.array3d(mapname="ksat_r")  # shape (z, y, x)
fig, axes = plt.subplots(
    1, a.shape[0], figsize=(4 * a.shape[0], 4), squeeze=False
)
for i, ax in enumerate(axes[0]):
    ax.imshow(a[i], cmap="viridis")
    ax.set_title(f"ksat_r slice {i}")
plt.show()

Visualize Imported Data

Before feeding the soil surfaces into hydrologic models, it’s worth confirming the imports look right. The soil polygons (soil_areas_sda) carry the full SSURGO attribute table – MUKEY, component percentages, hydrologic group, texture, and permeability by horizon – while the companion rasters are pre-rendered MUKEY, HSG, and three \(K_{sat}\) percentiles (low / representative / high) for fast map algebra.

MUKEY Map

MUKEY (map-unit key) is the atomic identifier that ties every raster cell back to a row in the SSURGO attribute database. Distinct MUKEY patches mark where component-level differences – texture, depth, drainage class, percent rock – vary even when the aggregated hydrologic group stays the same.

MUKEY patches at Coweeta with soil polygon boundaries overlaid. The mosaic reflects distinct component assemblages even where the aggregated hydrologic group is the same.

MUKEY patches at Coweeta with soil polygon boundaries overlaid. The mosaic reflects distinct component assemblages even where the aggregated hydrologic group is the same.
Code 
data_json = tools.db_select(
    table="soil_areas",
    format="json",
).json
print(data_json)
df_soils = pd.DataFrame(data_json['records'])
df_soils.head()
{'info': {'table': 'soil_areas', 'columns': [{'name': 'cat', 'sql_type': 'INTEGER', 'is_number': True}, {'name': 'mukey', 'sql_type': 'INTEGER', 'is_number': True}, {'name': 'mukey_int', 'sql_type': 'INTEGER', 'is_number': True}, {'name': 'cokey', 'sql_type': 'INTEGER', 'is_number': True}, {'name': 'comppct_r', 'sql_type': 'INTEGER', 'is_number': True}, {'name': 'hydgrp', 'sql_type': 'TEXT', 'is_number': False}, {'name': 'compname', 'sql_type': 'TEXT', 'is_number': False}, {'name': 'drainagecl', 'sql_type': 'TEXT', 'is_number': False}, {'name': 'slope_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'ksat_l', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'ksat_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'ksat_h', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'sandtotal_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'silttotal_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'claytotal_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'awc_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'om_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'dbthirdbar_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'ph1to1h2o_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'cec7_r', 'sql_type': 'DOUBLE PRECISION', 'is_number': True}, {'name': 'hsg', 'sql_type': 'INTEGER', 'is_number': True}]}, 'records': [{'cat': 64, 'mukey': 545793, 'mukey_int': 545793, 'cokey': 26331583, 'comppct_r': 80, 'hydgrp': 'C', 'compname': 'Braddock', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 5.11199951171875, 'ksat_r': 10.1879990945651, 'ksat_h': 15.2279999500185, 'sandtotal_r': 34.1999997116449, 'silttotal_r': 32.2999993271715, 'claytotal_r': 33.4999985582247, 'awc_r': 0.169999993692233, 'om_r': 0.999999984981507, 'dbthirdbar_r': 1.35000003604438, 'ph1to1h2o_r': 5, 'cec7_r': None, 'hsg': 3}, {'cat': 72, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 73, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 74, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 78, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 79, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 80, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 82, 'mukey': 545800, 'mukey_int': 545800, 'cokey': 26331605, 'comppct_r': 45, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 86, 'mukey': 545801, 'mukey_int': 545801, 'cokey': 26331505, 'comppct_r': 40, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 87, 'mukey': 545801, 'mukey_int': 545801, 'cokey': 26331505, 'comppct_r': 40, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 88, 'mukey': 545801, 'mukey_int': 545801, 'cokey': 26331505, 'comppct_r': 40, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 97, 'mukey': 545801, 'mukey_int': 545801, 'cokey': 26331505, 'comppct_r': 40, 'hydgrp': 'B', 'compname': 'Burton', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.20000005707027, 'ph1to1h2o_r': 4.8000002282811, 'cec7_r': 36.5999981737512, 'hsg': 2}, {'cat': 108, 'mukey': 545803, 'mukey_int': 545803, 'cokey': 26331507, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Cashiers', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999969482422, 'ksat_r': 100.799993896484, 'ksat_h': 151.2, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.129999995231628, 'om_r': 10, 'dbthirdbar_r': 1.4, 'ph1to1h2o_r': 5.3, 'cec7_r': 14.8, 'hsg': 1}, {'cat': 111, 'mukey': 545803, 'mukey_int': 545803, 'cokey': 26331507, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Cashiers', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999969482422, 'ksat_r': 100.799993896484, 'ksat_h': 151.2, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.129999995231628, 'om_r': 10, 'dbthirdbar_r': 1.4, 'ph1to1h2o_r': 5.3, 'cec7_r': 14.8, 'hsg': 1}, {'cat': 114, 'mukey': 545803, 'mukey_int': 545803, 'cokey': 26331507, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Cashiers', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999969482422, 'ksat_r': 100.799993896484, 'ksat_h': 151.2, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.129999995231628, 'om_r': 10, 'dbthirdbar_r': 1.4, 'ph1to1h2o_r': 5.3, 'cec7_r': 14.8, 'hsg': 1}, {'cat': 115, 'mukey': 545803, 'mukey_int': 545803, 'cokey': 26331507, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Cashiers', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999969482422, 'ksat_r': 100.799993896484, 'ksat_h': 151.2, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.129999995231628, 'om_r': 10, 'dbthirdbar_r': 1.4, 'ph1to1h2o_r': 5.3, 'cec7_r': 14.8, 'hsg': 1}, {'cat': 119, 'mukey': 545803, 'mukey_int': 545803, 'cokey': 26331507, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Cashiers', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.3999969482422, 'ksat_r': 100.799993896484, 'ksat_h': 151.2, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.129999995231628, 'om_r': 10, 'dbthirdbar_r': 1.4, 'ph1to1h2o_r': 5.3, 'cec7_r': 14.8, 'hsg': 1}, {'cat': 139, 'mukey': 545805, 'mukey_int': 545805, 'cokey': 26331510, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Chandler', 'drainagecl': 'Somewhat excessively drained', 'slope_r': 23, 'ksat_l': 50.3999962439904, 'ksat_r': 100.799992487981, 'ksat_h': 151.199998121995, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.119999995598426, 'om_r': 4.5, 'dbthirdbar_r': 1.39999991196852, 'ph1to1h2o_r': 5.3000001173753, 'cec7_r': 9.8000001173753, 'hsg': 1}, {'cat': 152, 'mukey': 545805, 'mukey_int': 545805, 'cokey': 26331510, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Chandler', 'drainagecl': 'Somewhat excessively drained', 'slope_r': 23, 'ksat_l': 50.3999962439904, 'ksat_r': 100.799992487981, 'ksat_h': 151.199998121995, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.119999995598426, 'om_r': 4.5, 'dbthirdbar_r': 1.39999991196852, 'ph1to1h2o_r': 5.3000001173753, 'cec7_r': 9.8000001173753, 'hsg': 1}, {'cat': 160, 'mukey': 545805, 'mukey_int': 545805, 'cokey': 26331510, 'comppct_r': 80, 'hydgrp': 'A', 'compname': 'Chandler', 'drainagecl': 'Somewhat excessively drained', 'slope_r': 23, 'ksat_l': 50.3999962439904, 'ksat_r': 100.799992487981, 'ksat_h': 151.199998121995, 'sandtotal_r': 67.5, 'silttotal_r': 21, 'claytotal_r': 11.5, 'awc_r': 0.119999995598426, 'om_r': 4.5, 'dbthirdbar_r': 1.39999991196852, 'ph1to1h2o_r': 5.3000001173753, 'cec7_r': 9.8000001173753, 'hsg': 1}, {'cat': 172, 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60, 'hydgrp': 'A', 'compname': 'Udorthents', 'drainagecl': 'Well drained', 'slope_r': 3, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': 1}, {'cat': 17008, 'mukey': 545857, 'mukey_int': 545857, 'cokey': 26331545, 'comppct_r': 60, 'hydgrp': None, 'compname': 'Rock outcrop', 'drainagecl': None, 'slope_r': None, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': None}, {'cat': 17009, 'mukey': 545875, 'mukey_int': 545875, 'cokey': 26331558, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Trimont', 'drainagecl': 'Well drained', 'slope_r': 63, 'ksat_l': 50.399997877038, 'ksat_r': 100.799995754076, 'ksat_h': 151.199993631114, 'sandtotal_r': 44.800001061481, 'silttotal_r': 41.2000015922215, 'claytotal_r': 14, 'awc_r': 0.129999990048616, 'om_r': 8, 'dbthirdbar_r': 1.48000003980554, 'ph1to1h2o_r': 5.30000006634256, 'cec7_r': None, 'hsg': 2}, {'cat': 17013, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17028, 'mukey': 545857, 'mukey_int': 545857, 'cokey': 26331545, 'comppct_r': 60, 'hydgrp': None, 'compname': 'Rock outcrop', 'drainagecl': None, 'slope_r': None, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': None}, {'cat': 17080, 'mukey': 545874, 'mukey_int': 545874, 'cokey': 26331552, 'comppct_r': 85, 'hydgrp': 'B', 'compname': 'Trimont', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.399997877038, 'ksat_r': 100.799995754076, 'ksat_h': 151.199993631114, 'sandtotal_r': 44.800001061481, 'silttotal_r': 41.2000015922215, 'claytotal_r': 14, 'awc_r': 0.129999990048616, 'om_r': 8, 'dbthirdbar_r': 1.48000003980554, 'ph1to1h2o_r': 5.30000006634256, 'cec7_r': None, 'hsg': 2}, {'cat': 17089, 'mukey': 545857, 'mukey_int': 545857, 'cokey': 26331545, 'comppct_r': 60, 'hydgrp': None, 'compname': 'Rock outcrop', 'drainagecl': None, 'slope_r': None, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': None}, {'cat': 17092, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17167, 'mukey': 545863, 'mukey_int': 545863, 'cokey': 26331839, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.38, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17173, 'mukey': 545885, 'mukey_int': 545885, 'cokey': 26331718, 'comppct_r': 85, 'hydgrp': 'A', 'compname': 'Wayah', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.10000002102589, 'ph1to1h2o_r': 4.49999981977808, 'cec7_r': 28.7999983659879, 'hsg': 1}, {'cat': 17206, 'mukey': 545885, 'mukey_int': 545885, 'cokey': 26331718, 'comppct_r': 85, 'hydgrp': 'A', 'compname': 'Wayah', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.10000002102589, 'ph1to1h2o_r': 4.49999981977808, 'cec7_r': 28.7999983659879, 'hsg': 1}, {'cat': 17209, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17231, 'mukey': 545885, 'mukey_int': 545885, 'cokey': 26331718, 'comppct_r': 85, 'hydgrp': 'A', 'compname': 'Wayah', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.10000002102589, 'ph1to1h2o_r': 4.49999981977808, 'cec7_r': 28.7999983659879, 'hsg': 1}, {'cat': 17249, 'mukey': 545854, 'mukey_int': 545854, 'cokey': 26331779, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 72, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17258, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17263, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17288, 'mukey': 545854, 'mukey_int': 545854, 'cokey': 26331779, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 72, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17291, 'mukey': 545854, 'mukey_int': 545854, 'cokey': 26331779, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 72, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17297, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17314, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17321, 'mukey': 545857, 'mukey_int': 545857, 'cokey': 26331545, 'comppct_r': 60, 'hydgrp': None, 'compname': 'Rock outcrop', 'drainagecl': None, 'slope_r': None, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': None}, {'cat': 17327, 'mukey': 545874, 'mukey_int': 545874, 'cokey': 26331552, 'comppct_r': 85, 'hydgrp': 'B', 'compname': 'Trimont', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.399997877038, 'ksat_r': 100.799995754076, 'ksat_h': 151.199993631114, 'sandtotal_r': 44.800001061481, 'silttotal_r': 41.2000015922215, 'claytotal_r': 14, 'awc_r': 0.129999990048616, 'om_r': 8, 'dbthirdbar_r': 1.48000003980554, 'ph1to1h2o_r': 5.30000006634256, 'cec7_r': None, 'hsg': 2}, {'cat': 17376, 'mukey': 545857, 'mukey_int': 545857, 'cokey': 26331545, 'comppct_r': 60, 'hydgrp': None, 'compname': 'Rock outcrop', 'drainagecl': None, 'slope_r': None, 'ksat_l': None, 'ksat_r': None, 'ksat_h': None, 'sandtotal_r': None, 'silttotal_r': None, 'claytotal_r': None, 'awc_r': None, 'om_r': None, 'dbthirdbar_r': None, 'ph1to1h2o_r': None, 'cec7_r': None, 'hsg': None}, {'cat': 17383, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17394, 'mukey': 545855, 'mukey_int': 545855, 'cokey': 26331542, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Reddies', 'drainagecl': 'Moderately well drained', 'slope_r': 2, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 67.5, 'silttotal_r': 20.9999985582247, 'claytotal_r': 11.5000002402959, 'awc_r': 0.139999999774723, 'om_r': 5.99999975970411, 'dbthirdbar_r': 1.39999990388164, 'ph1to1h2o_r': 6.49999963955616, 'cec7_r': 15.199999471349, 'hsg': 2}, {'cat': 17396, 'mukey': 545854, 'mukey_int': 545854, 'cokey': 26331779, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 72, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17402, 'mukey': 545886, 'mukey_int': 545886, 'cokey': 26331719, 'comppct_r': 85, 'hydgrp': 'A', 'compname': 'Wayah', 'drainagecl': 'Well drained', 'slope_r': 23, 'ksat_l': 50.3999965397392, 'ksat_r': 100.799993079478, 'ksat_h': 151.199999231053, 'sandtotal_r': 65.6999963475025, 'silttotal_r': 22.7999998077633, 'claytotal_r': 11.5000002402959, 'awc_r': 0.18999998963724, 'om_r': 15, 'dbthirdbar_r': 1.10000002102589, 'ph1to1h2o_r': 4.49999981977808, 'cec7_r': None, 'hsg': 1}, {'cat': 17408, 'mukey': 545863, 'mukey_int': 545863, 'cokey': 26331839, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.38, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17418, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17423, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17460, 'mukey': 545853, 'mukey_int': 545853, 'cokey': 26331772, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 40, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17463, 'mukey': 545863, 'mukey_int': 545863, 'cokey': 26331839, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 12, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.38, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}, {'cat': 17469, 'mukey': 545854, 'mukey_int': 545854, 'cokey': 26331779, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Plott', 'drainagecl': 'Well drained', 'slope_r': 72, 'ksat_l': 50.3999968937465, 'ksat_r': 100.799993787493, 'ksat_h': 151.200000217983, 'sandtotal_r': 60.9999997275216, 'silttotal_r': 24.0000002724784, 'claytotal_r': 15, 'awc_r': 0.166696427123887, 'om_r': 10, 'dbthirdbar_r': 0.990000026566642, 'ph1to1h2o_r': 4.80000002043588, 'cec7_r': None, 'hsg': 2}, {'cat': 17475, 'mukey': 545860, 'mukey_int': 545860, 'cokey': 26331828, 'comppct_r': 80, 'hydgrp': 'B', 'compname': 'Saunook', 'drainagecl': 'Well drained', 'slope_r': 22, 'ksat_l': 50.4, 'ksat_r': 100.8, 'ksat_h': 151.2, 'sandtotal_r': 45, 'silttotal_r': 41, 'claytotal_r': 14, 'awc_r': 0.17, 'om_r': 8, 'dbthirdbar_r': 1.25, 'ph1to1h2o_r': 4.80000030517578, 'cec7_r': None, 'hsg': 2}]}
cat mukey mukey_int cokey comppct_r hydgrp compname drainagecl slope_r ksat_l ... ksat_h sandtotal_r silttotal_r claytotal_r awc_r om_r dbthirdbar_r ph1to1h2o_r cec7_r hsg
0 64 545793 545793 26331583 80 C Braddock Well drained 12.0 5.112000 ... 15.228000 34.200000 32.299999 33.499999 0.17 1.0 1.35 5.0 NaN 3.0
1 72 545800 545800 26331605 45 B Burton Well drained 23.0 50.399997 ... 151.199999 65.699996 22.800000 11.500000 0.19 15.0 1.20 4.8 36.599998 2.0
2 73 545800 545800 26331605 45 B Burton Well drained 23.0 50.399997 ... 151.199999 65.699996 22.800000 11.500000 0.19 15.0 1.20 4.8 36.599998 2.0
3 74 545800 545800 26331605 45 B Burton Well drained 23.0 50.399997 ... 151.199999 65.699996 22.800000 11.500000 0.19 15.0 1.20 4.8 36.599998 2.0
4 78 545800 545800 26331605 45 B Burton Well drained 23.0 50.399997 ... 151.199999 65.699996 22.800000 11.500000 0.19 15.0 1.20 4.8 36.599998 2.0

5 rows × 21 columns

Ksat Maps

SSURGO reports saturated hydraulic conductivity as a low / representative / high percentile triplet that captures within-map-unit variability. Importing all three (\(K_{sat,l}\), \(K_{sat,r}\), \(K_{sat,h}\)) lets downstream infiltration models run at the representative value and be stress-tested against the bracketing bounds.

Low

Low

Representative

Representative

High

High

SSURGO saturated hydraulic conductivity at Coweeta, shown for the low, representative, and high estimates within each map unit. The same absolute color ramp (browns = low \(K_{sat}\), blues = high) is applied to all three panels so they are directly comparable – the high-estimate panel is visibly more permeable because it uses the upper-bound \(K_{sat}\) within each component.

Hydrologic Group

Hydrologic soil groups (HSG) are a four-class condensation of SSURGO’s detailed soil properties into categories A (high infiltration, sand/gravel) through D (low infiltration, clay/shallow bedrock), with A/D marking soils whose behavior depends on drainage. HSG is the direct input to the SCS curve-number lookup table, so any coarseness in this raster propagates into every subsequent runoff calculation.

Hydrologic soil groups at Coweeta. Group B dominates the ridgeline forest with pockets of C on thinner soils; no group A (high infiltration) appears in this catchment.

Hydrologic soil groups at Coweeta. Group B dominates the ridgeline forest with pockets of C on thinner soils; no group A (high infiltration) appears in this catchment.

Calculate Curve Number

The SCS curve number (CN) is an empirical index, from 0 to 100, that captures how much of a given rainfall depth becomes runoff. High CNs mean runoff-prone surfaces (impervious, bare, hydrologic group D); low CNs mean infiltration-dominant surfaces (forested, group A). CN is computed from an HSG × land cover lookup table, so it’s the natural bridge between SSURGO’s soil classes and NLCD’s cover classes.

Install r.curvenumber from the GRASS Addons repository:

# | label: install_curvenumber_text
# | eval: false
tools.g_extension(
    extension="r.curvenumber",
)

Land Cover

CN requires a land cover raster alongside the HSG raster. We use NLCD 2024, the most recent national land cover product, imported as a cloud-optimized GeoTIFF so the r.import call only reads the pixels that fall inside the current computational region.

Code 
tools.r_import(
    output="nlcd_2024",
    input="<path_to_nlcd_cog>",
    extent="region",
    resolution="region",
)

The imported raster carries numeric class codes (21, 41, 42…) but no human-readable labels – the legend would render as a column of bare numbers. Since nlcd_2024 lives in PERMANENT and r.category only writes to the current mapset, we copy it into ssurgo_demo first, then attach the standard NLCD 2024 class names inline.

NLCD 2024 land cover at Coweeta. The canopy is dominated by deciduous and mixed forest with narrow ribbons of developed, open space along valley floors – a land-cover distribution that pushes most cells toward low curve-number (infiltration-friendly) values in the next step.

NLCD 2024 land cover at Coweeta. The canopy is dominated by deciduous and mixed forest with narrow ribbons of developed, open space along valley floors – a land-cover distribution that pushes most cells toward low curve-number (infiltration-friendly) values in the next step.

Deriving the CN raster

With HSG and NLCD in hand, r.curvenumber applies the standard SCS lookup. landcover_source="nlcd" tells the tool to use the NLCD-specific class-to-CN table (rather than the default NCDS or custom tables), which matters because class codes differ across land-cover datasets.

Code 
tools.r_curvenumber(
    landcover="nlcd_2024",
    soil="hydgrp_sda",
    landcover_source="nlcd",
    output="curvenumber",
)

Curve number at Coweeta derived from NLCD 2024 and hydrologic soil group. Values span 32 (forested soils, heavy infiltration) to 89 (developed patches on group-C soils).

Curve number at Coweeta derived from NLCD 2024 and hydrologic soil group. Values span 32 (forested soils, heavy infiltration) to 89 (developed patches on group-C soils).

Runoff and Time of Concentration

A CN raster alone doesn’t yet tell us how much runoff a particular storm produces – we also need a design storm depth (how much rain fell) and the watershed’s time of concentration \(T_c\) (how long the longest flow path takes to drain). Pairing \(T_c\) with an IDF curve identifies the storm duration that generates the peak runoff response, which is the storm we want to simulate with SIMWE.

Extension installs

r.timeofconcentration, r.stream.distance, and r.noaa.atlas14 are needed for this section and are pulled from a local checkout of the grass-addons fork.

Design storm from NOAA Atlas 14

NOAA Atlas 14 provides point-based precipitation frequency estimates indexed by return period (1, 2, 5, … 1000 yr) and duration (5 min to 60 day). A mode="point" query at the Coweeta Experimental Station coordinates fetches the local IDF table without triggering the multi-GB regional grid download that mode="grid" would.

Code 
outjson = tools.r_noaa_atlas14(
    mode="point",
    statistic="intensity",
    vector_output="noaa_atlas14_station",
    coordinates=[-83.4333, 35.0667],
    unit="metric",
    series="pds",
    bound="expected",
    resample="bilinear",
).json

Watershed analysis

r.runoff and r.timeofconcentration both need a flow-direction raster, a stream network, and (optionally) basin boundaries. r.watershed derives all three from the DEM in a single call.

Code 
tools.r_watershed(
    elevation="elevation",
    drainage="flow_direction",
    accumulation="accumulation",
    stream="stream",
    basin="basins",
    threshold=1000,
)

Flow accumulation

Flow accumulation

Sub-basins

Sub-basins

Watershed products derived from the DEM by r.watershed. The NOAA Atlas 14 station coordinate is marked on the flow-accumulation panel.

Time of concentration

r.timeofconcentration traces every cell’s flow path downstream using the flow_direction and stream rasters, accumulating travel time to produce a per-cell \(T_c\) estimate. length_min=100 discards flow paths shorter than 100 m as numerical noise. The maximum of this raster is the watershed-scale \(T_c\) that drives the design-storm duration below.

Code 
tools.r_timeofconcentration(
    elevation="elevation",
    direction="flow_direction",
    stream="stream",
    length_min=100,  # minimum length of flow path
    tc="time_concentration",
)

Per-cell time of concentration at Coweeta. Values accumulate downstream along flow paths; the maximum sets the storm duration used to size rainfall intensity.

Per-cell time of concentration at Coweeta. Values accumulate downstream along flow paths; the maximum sets the storm duration used to size rainfall intensity.

A quick univariate summary confirms the range of \(T_c\) values – the maximum drives the storm duration used to size rainfall intensity in the next step.

Table 1: Time of Concentration — Summary Statistics
Statistic Value
n 55,231.000
null_cells 517,015.000
cells 572,246.000
min 0.011
max 0.783
range 0.772
mean 0.056
mean_of_abs 0.056
stddev 0.079
variance 0.006
coeff_var 141.229
sum 3,077.783
first_quartile 0.022
median 0.032
third_quartile 0.054
percentile_percentile 90.000
percentile_value 0.102

Matching storm duration to Tc

The storm that produces the peak runoff response at a given return period has a duration equal to \(T_c\). We convert the watershed’s \(T_c\) from hours to minutes, look up the NOAA Atlas 14 intensity-duration-frequency (IDF) table, and interpolate the 100-year intensity at that duration.

Code 
df_tc_min = df_tc[["min", "mean", "max"]].apply(
    lambda x: round(x * 60, 2)
)  # convert to minutes
max_tc = df_tc_min["max"].max()
print(max_tc)

_rf_table = outjson["table"]
_rf_rps = [str(rp) for rp in _rf_table["return_periods_years"]]
df_rf_intensity = pd.DataFrame(
    [
        {"duration": row["duration"], **{rp: row["values"].get(rp) for rp in _rf_rps}}
        for row in _rf_table["rows"]
    ],
    columns=["duration"] + _rf_rps,
)
display(
    df_rf_intensity.style.hide(axis="index")
    .set_caption("Rainfall Intensity — NOAA Atlas 14, Coweeta, NC")
    .format(
        {
            "Value": lambda v: f"{v:,.3f}"
            if isinstance(v, (int, float, np.number))
            else v
        }
    )
)
46.97
Table 2: Rainfall Intensity — NOAA Atlas 14, Coweeta, NC
duration 1 2 5 10 25 50 100 200 500 1000
5-min 123.000000 144.000000 166.000000 192.000000 220.000000 246.000000 269.000000 293.000000 323.000000 353.000000
10-min 98.000000 115.000000 133.000000 154.000000 175.000000 196.000000 214.000000 232.000000 256.000000 278.000000
15-min 82.000000 96.000000 112.000000 130.000000 148.000000 165.000000 180.000000 195.000000 214.000000 232.000000
30-min 56.000000 66.000000 80.000000 94.000000 110.000000 124.000000 138.000000 152.000000 171.000000 188.000000
60-min 35.000000 42.000000 51.000000 61.000000 73.000000 84.000000 95.000000 107.000000 122.000000 137.000000
2-hr 21.000000 25.000000 30.000000 36.000000 43.000000 49.000000 56.000000 63.000000 72.000000 81.000000
3-hr 15.000000 18.000000 22.000000 26.000000 31.000000 36.000000 41.000000 46.000000 54.000000 61.000000
6-hr 10.000000 12.000000 14.000000 17.000000 20.000000 23.000000 26.000000 30.000000 35.000000 39.000000
12-hr 7.000000 8.000000 9.000000 11.000000 13.000000 14.000000 16.000000 18.000000 20.000000 23.000000
24-hr 4.000000 5.000000 6.000000 7.000000 8.000000 10.000000 11.000000 12.000000 13.000000 15.000000
2-day 3.000000 3.000000 4.000000 4.000000 5.000000 6.000000 6.000000 7.000000 8.000000 8.000000
3-day 2.000000 2.000000 3.000000 3.000000 3.000000 4.000000 4.000000 5.000000 5.000000 6.000000
4-day 1.000000 2.000000 2.000000 2.000000 3.000000 3.000000 3.000000 4.000000 4.000000 4.000000
7-day 1.000000 1.000000 1.000000 2.000000 2.000000 2.000000 2.000000 2.000000 3.000000 3.000000
10-day 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 2.000000 2.000000 2.000000 2.000000
20-day 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
30-day 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
45-day 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
60-day 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

NOAA Atlas 14 rainfall intensity by storm duration for Coweeta, NC, with the maximum time of concentration marked.

NOAA Atlas 14 rainfall intensity by storm duration for Coweeta, NC, with the maximum time of concentration marked.
Table 3: Interpolated rainfall intensity at maximum time of concentration
return_period max_tc_min intensity_mm_hr
100 46.970000 110.190000 
100-year storm intensity at max Tc (46.97 min): 110.19 in/hr

Rainfall depth for r.runoff

r.runoff takes a total rainfall depth (mm) over the storm duration rather than an intensity rate, so we convert the interpolated intensity back to a depth using the \(T_c\)-matched storm duration.

Code 
# storm_intensity_100yr 46.97 min = 0.7828 hr
duration_min = 46.97
intensity_in_hr = 4.34
intensity_mm_hr = intensity_in_hr * 25.4

duration_hr = duration_min / 60.0
rain_mm = intensity_mm_hr * duration_hr

print(f"Storm duration: {duration_hr:.4f} hr")
print(f"Rainfall intensity: {intensity_mm_hr:.3f} mm/hr")
print(f"Rain depth: {rain_mm:.3f} mm")
tools.r_mapcalc(
    expression=f"rain = {rain_mm}",  # mm of precipitation
)
Storm duration: 0.7828 hr
Rainfall intensity: 110.236 mm/hr
Rain depth: 86.296 mm
Code 
tools.r_runoff(
    rainfall="rain",
    direction="flow_direction",
    duration=duration_hr,  # 0.7828 hrs
    curvenumber="curvenumber",
    time_concentration="time_concentration",
    runoff_depth="runoff_depth",
    runoff_volume="runoff_volume",
    upstream_area="upstream_area",
    upstream_runoff_depth="upstream_runoff_depth",
    upstream_runoff_volume="upstream_runoff_volume",
    time_to_peak="time_to_peak",
    peak_discharge="peak_discharge",
)

Runoff depth

With the CN raster, rainfall depth, \(T_c\), and flow-direction raster in hand, r.runoff produces the full suite of SCS-method outputs: per-cell runoff depth and volume, upstream-accumulated runoff, peak discharge, and time to peak. The runoff_depth raster is the critical input to the next step – once normalized by duration, it becomes the rainfall-excess rate that SIMWE routes across the surface.

Runoff depth (mm) from the SCS method for the T_c-matched 100-year storm. Low-CN forest patches shed little runoff; higher-CN valley patches shed several centimeters.

Runoff depth (mm) from the SCS method for the \(T_c\)-matched 100-year storm. Low-CN forest patches shed little runoff; higher-CN valley patches shed several centimeters.

Peak discharge and time-to-peak maps give complementary information: discharge highlights where flow concentrates, while time-to-peak reveals the temporal response of different sub-basins.

Peak discharge (m³/s)

Peak discharge (m³/s)

Time to peak (hours)

Time to peak (hours)

SCS runoff response for the design storm.

Manning’s n Map

SIMWE routes surface flow using the shallow-water momentum equation, with surface friction parameterized by Manning’s \(n\). Coefficient values depend strongly on land cover: open water has \(n \approx 0.03\), dense forest \(n \approx 0.4\) or higher. r.manning assigns \(n\) per NLCD class using a lookup table; here we use the values from Kalyanapu et al. (2009), which are calibrated for overland flow rather than channel flow.

Install r.manning if it isn’t already available:

Code 
tools.g_extension(
    extension="r.manning"
)

A fixed seed=42 keeps the per-class stochastic jitter reproducible across runs.

Code 
tools.r_manning(
    input="nlcd_2024",
    output="mannings_n",
    landcover="nlcd",
    source="kalyanapu",
    seed=42,
)


mannings_n_colors = io.StringIO(
    "0.008 #F0E68C\n"   # Barren Land (low end)
    "0.025 #6BAED6\n"   # Open Water
    "0.040 #A1D99B\n"   # Developed, Open Space / Open Water (med)
    "0.068 #BDBDBD\n"   # Developed, Low/Med Intensity
    "0.160 #F4A460\n"   # Cultivated Crops
    "0.183 #66C2A4\n"   # Emergent Herbaceous Wetlands
    "0.325 #D4B86A\n"   # Pasture/Hay
    "0.368 #A8C25C\n"   # Grassland/Herbaceous
    "0.400 #2D8C2D\n"   # Mixed Forest / Shrub
    "0.530 #1A5C1A\n"   # Dense Forest (high end)
)

with gs.RegionManager(raster="mannings_n", s="s-1500", flags="a"):
    tools.r_colors(map="mannings_n", rules=mannings_n_colors)
    m = gj.Map(use_region=True, width=600)
    m.d_shade(shade="relief", color="mannings_n")
    m.d_legend(
        raster="mannings_n",
        title="Manning's n",
        at="5, 10, 20, 80",
        font="Fira Sans Condensed Light",
        fontsize=16,
        flags="sn",
    )
    # m.d_barscale(
    #     at=(67, 5),
    #     font="Fira Sans Condensed Light",
    #     fontsize=11,
    #     length=2,
    #     units="kilometers",
    #     width_scale=1,
    #     bgcolor="none",
    #     color="#4f4e4e",
    #     flags="n",
    # )
    m.show()

Run SIMWE

SIMWE (r.sim.water) solves the shallow-water momentum equation using a particle-based (walker) approach. Each walker carries a fraction of the total discharge along flow paths computed from the DEM gradient, depth, and Manning’s \(n\). The model needs four inputs: DEM, slope components (\(dx\), \(dy\)), a rainfall-excess rate (mm/hr), and Manning’s \(n\). Outputs are per-timestep depth and unit-discharge rasters plus an optional walker vector cloud for visualization.

Precipitation and Rainfall Excess

The key difference between soil-aware and naive simulations is the rainfall-excess input. A naive run applies a uniform rate to every cell. The soil-informed version divides the per-cell runoff depth produced by the curve-number analysis by the storm duration, yielding a spatially varying mm/hr rate that implicitly encodes soil infiltration capacity and land-cover abstractions.

Location Information Value
Name Otto, North Carolina, USA*
Station name COWEETA EXP STN
Site ID 31-2102
Latitude 35.0667°
Longitude -83.4333°
Elevation 2700 ft**

Coweeta Hydrologic Lab

Coweeta Hydrologic Lab

The resulting rainfall_excess map below is in mm/hr and drives SIMWE’s interior source term.

Note

The runoff depth is divided by the \(T_c\)-matched storm duration (0.7828 hr = 46.8 min), so the resulting rate corresponds to the 100-year design storm delivered over the watershed’s time of concentration.

Code 
# Time of Concentration: 46.8 min
tools.r_mapcalc(
    expression=f"rainfall_excess = runoff_depth /  0.7828",  # 0.7828 hr = 46.8 min
)


with gs.RegionManager(raster="rainfall_excess", s="s-1500", flags="a"):
    tools.r_colors(map="rainfall_excess", color="haxby", flags="en")
    m = gj.Map(use_region=True, width=600)
    m.d_shade(shade="relief", color="rainfall_excess")
    m.d_legend(
        raster="rainfall_excess",
        title="Rainfall Excess (mm/hr)",
        at="5, 10, 20, 80",
        font="Fira Sans Condensed Light",
        fontsize=16,
        flags="n",
    )
    # m.d_barscale(
    #     at=(67, 5),
    #     font="Fira Sans Condensed Light",
    #     fontsize=11,
    #     length=2,
    #     units="kilometers",
    #     width_scale=1,
    #     bgcolor="none",
    #     color="#4f4e4e",
    #     flags="n",
    # )
    m.show()

Soil-informed simulation

The first SIMWE run uses the soil-derived rainfall_excess map and simulates 48 min (covering the \(T_c\)-matched 100-year storm) with 100,000 walkers, writing depth, discharge, and walker snapshots every 2 min. The flags="t" option emits a time-indexed series rather than summary totals. Outputs are registered into a space-time raster dataset (STRDS) so downstream visualization tools can treat the run as a single animated object.

Code 
# duration_min = 46.97 round to 47 min for simulation
tools.r_slope_aspect(elevation="elevation", dx="dx", dy="dy")
tools.r_sim_water(
    elevation="elevation",
    depth="tc_100yr_depth",
    discharge="tc_100yr_discharge",
    walkers_output="tc_100yr_walkers",
    rain="rainfall_excess",  # Infiltration / Veg Intercept
    man="mannings_n",  # Mixed forest Kalyanapu et al. (2009)
    nwalkers=100000,
    # infil="ksat_r_sda", # Infiltration of flowing water
    # niterations=1440 / 2,
    observation="sample_outlets",
    logfile="coweeta_100yr_simulation.log",
    duration=48, # Bump it to 48 min to cover the full storm duration (rounded up from 46.97 min)
    output_step=2,
    nprocs=0,  # auto-detect
    flags="t",
)

# Register the output maps into a space time dataset
gs.run_command(
    "t.create",
    output="tc_100yr_depth_sum",
    type="strds",
    temporaltype="absolute",
    title="Runoff Depth",
    description="Runoff Depth in [m]",
    overwrite=True,
)

# Get the list of depth maps
depth_list = gs.read_command(
    "g.list", type="raster", pattern="tc_100yr_depth.*", separator="comma"
).strip()

# Register the maps
gs.run_command(
    "t.register",
    input="tc_100yr_depth_sum",
    type="raster",
    start="2024-01-01",
    increment="2 minutes",
    maps=depth_list,
    flags="i",
    overwrite=True,
)

Final-timestep overland flow depth draped over NAIP 2022 imagery. Sample outlet points are marked in orange.

Final-timestep overland flow depth draped over NAIP 2022 imagery. Sample outlet points are marked in orange.

The final-timestep depth map above captures the accumulated flood extent once rainfall has routed through the full watershed. To see the temporal evolution – where water ponds first, which channels fill late, and how the drainage pattern develops – we render the full STRDS as an animated GIF.

Code 
with gs.RegionManager(raster="accumulation", s="s-1500", flags="a"):
    depth_list = gs.read_command(
        "g.list", type="raster", pattern="tc_100yr_depth.*", separator="comma"
    ).strip()

    max_depth = depth_list.split(",")[-1]
    stats = tools.r_info(map=max_depth, format="json").json
    m = gj.TimeSeriesMap(use_region=True, width=700)
    m.d_rast(map="relief")
    m.add_raster_series("tc_100yr_depth_sum", values=f"0.025-{stats['max']}")
    m.d_legend(
        raster=max_depth,
        title="Depth [m]",
        flags="ls",
        border_color="none",
        range=[0.025, stats["max"]],
        at="5, 10, 20, 80",
    )

    m.save("output/tc_100yr_simwe_depth_animation.gif")
    m.show()

Animated overland flow depth during the 100-year storm, soil-informed run.

Animated overland flow depth during the 100-year storm, soil-informed run.

Walker Velocity Animation

The walkers_output from r.sim.water stores walker positions but no velocity attribute. SIMWE already emits both depth (\(h\)) and unit discharge (\(q\)) per timestep, and by definition \(q = h \cdot v\), so the per-cell flow speed is simply \(v = q / h\). Using SIMWE’s own outputs keeps the velocity field consistent with the model’s internal physics (diffusion wave + Manning + infiltration) rather than re-deriving it from Manning’s equation externally. We then sample this velocity raster at each walker position to color the Lagrangian particle cloud.

Code 
# Build an STRDS of per-timestep flow-speed rasters directly from SIMWE's own
# discharge and depth outputs: v = q / h.  This stays self-consistent with the
# model's internal physics (diffusion-wave + Manning + infiltration) rather
# than re-deriving velocity from Manning's equation externally.

depth_list = gs.read_command(
    "g.list", type="raster", pattern="tc_100yr_depth.*", separator=","
).strip().split(",")

depth_list = [d.split("@", 1)[0] for d in depth_list]
print(f"Found depth maps: {depth_list}")

vel_list = []
for depth in depth_list:
    suffix = depth.split(".", 1)[1]
    discharge = f"tc_100yr_discharge.{suffix}"
    vel = f"tc_100yr_velocity.{suffix}"
    tools.r_mapcalc(
        expression=(
            f"{vel} = if({depth} > 0.001, {discharge} / {depth}, null())"
        ),
        overwrite=True,
    )
    vel_list.append(vel)

gs.run_command(
    "t.create", output="tc_100yr_velocity_sum", type="strds",
    temporaltype="absolute", title="Overland Flow Velocity",
    description="Flow speed (m/s) from SIMWE discharge / depth", overwrite=True,
)
gs.run_command(
    "t.register", input="tc_100yr_velocity_sum", type="raster",
    start="2024-01-01", increment="2 minutes",
    maps=",".join(vel_list), flags="i", overwrite=True,
)
gs.run_command(
    "t.rast.colors", input="tc_100yr_velocity_sum", color="blues", flags="e",
)
Code 
# Subsample walker vectors, attach an attribute table with a velocity column
# sampled from the v = q/h raster for the matching timestep,
# store a per-walker color via v.colors, and register an STVDS.

# Walker maps: tc_100yr_walkers_02, _04, ...  Velocity rasters: tc_100yr_velocity.02, .04, ...
walker_maps = (
    gs.read_command(
        "g.list", type="vector", pattern="tc_100yr_walkers_*", separator=","
    )
    .strip()
    .split(",")
)
# Strip any "@<mapset>" qualifier so suffix parsing and the derived output
# names stay unqualified (same fix as compute_velocity_rasters).
walker_maps = [w.split("@", 1)[0] for w in walker_maps]

step = 50  # keep every Nth walker

sampled = []
for wm in walker_maps:
    suffix = wm.rsplit("_", 1)[1]  # "02"
    print(f"Processing walker map {wm} with suffix {suffix}")
    vel_raster = f"tc_100yr_velocity.{suffix}"
    s = f"{wm}_sub"

    # Walker vectors have no attribute table, so `where=` SQL is unavailable.
    # Get the point count from v.info and pass an explicit cats list instead.
    vinfo = gs.parse_command("v.info", map=wm, flags="t")
    n_points = int(vinfo.get("points", 0))
    cats = ",".join(str(c) for c in range(1, n_points + 1, step))
    print(f"Processing {wm} ({n_points} pts -> {n_points // step} sampled)")

    gs.run_command(
        "v.extract",
        input=wm,
        output=s,
        cats=cats,
        overwrite=True,
    )

    layers = gs.parse_command(
        "v.db.connect", map=s, flags="p", format="csv"
    )

    if not layers:
        print(f"Warning: no attribute table found for {s}, creating one with cats only")
        gs.run_command(
            "v.db.addtable",
            map=s,
            columns="velocity double precision, GRASSRGB varchar(11)",
            overwrite=True,
        )
    gs.run_command(
        "v.what.rast",
        map=s,
        raster=vel_raster,
        column="velocity",
    )
    gs.run_command(
        "v.colors",
        map=s,
        use="attr",
        column="velocity",
        color="blues",
        rgb_column="GRASSRGB",
    )
    sampled.append(s)

gs.run_command(
    "t.create",
    output="tc_100yr_walkers_stvds",
    type="stvds",
    temporaltype="absolute",
    title="Walker Positions (2% sample)",
    description="Subsampled Lagrangian walker particles with velocity attribute",
    overwrite=True,
)

# SIMWE uses adaptive timestepping, so the output suffixes aren't on a uniform
# 2-min grid (e.g. _10, _11, _13). Register with an explicit file of
# map|start|end rows derived from the suffix so STVDS granularity is well-defined.
import tempfile

ordered = sorted(sampled, key=lambda s: int(s.split("_")[-2]))
starts = [int(s.split("_")[-2]) for s in ordered]

with tempfile.NamedTemporaryFile("w", suffix=".txt", delete=False) as f:
    for i, s in enumerate(ordered):
        t_start = starts[i]
        t_end = starts[i + 1] if i + 1 < len(starts) else t_start + 2
        start_ts = f"2024-01-01 {t_start // 60:02d}:{t_start % 60:02d}:00"
        end_ts = f"2024-01-01 {t_end // 60:02d}:{t_end % 60:02d}:00"
        f.write(f"{s}|{start_ts}|{end_ts}\n")
    register_file = f.name

gs.run_command(
    "t.register",
    input="tc_100yr_walkers_stvds",
    type="vector",
    file=register_file,
    overwrite=True,
)
gs.run_command("t.support", input="tc_100yr_walkers_stvds", type="stvds", flags="u")
print(gs.read_command("t.info", input="tc_100yr_walkers_stvds", type="stvds"))
Code 
# Walkers are colored by their per-timestep velocity via the GRASSRGB column.
# A final-timestep velocity raster is shown only to drive the legend's scale.
with gs.RegionManager(raster="accumulation", s="s-1500", flags="a"):
    vel_list = (
        gs.read_command(
            "g.list", type="raster", pattern="tc_100yr_velocity.*", separator=","
        )
        .strip()
        .split(",")
    )
    vmax = tools.r_info(map=vel_list[-1], format="json").json["max"]

    m = gj.TimeSeriesMap(use_region=True, width=700)
    m.d_shade(shade="relief", color="naip_2022_rgb@naip")
    m.add_vector_series(
        "tc_100yr_walkers_stvds",
        rgb_column="GRASSRGB",
        icon="basic/circle",
        size=2,
    )
    m.d_legend(
        raster=vel_list[-1],
        title="Velocity (m/s)",
        at="5, 10, 20, 80",
        font="Fira Sans Condensed Light",
        fontsize=16,
        range=[0.01, vmax],
        flags="btn",
        border_color="none",
    )
    # m.d_barscale(
    #     at=(67, 5),
    #     font="Fira Sans Condensed Light",
    #     fontsize=11,
    #     length=2,
    #     units="kilometers",
    #     width_scale=1,
    #     bgcolor="none",
    #     color="#ffffff",
    #     flags="n",
    # )
    m.save("output/tc_100yr_walker_velocity_animation.gif")
    display(m.show())

Walker positions colored by SIMWE flow speed (v = q/h)

Walker positions colored by SIMWE flow speed (\(v = q/h\))

Baseline: Uniform Rainfall (No Soil Data)

To isolate the effect of soil heterogeneity, we run SIMWE a second time with the same DEM, Manning’s \(n\), and storm intensity, but replace the spatially varying rainfall_excess map with a single uniform rate (rain_value=rain_mm_per_hour). Any difference between the two runs is attributable solely to the soil-informed rainfall-excess surface rather than changes in terrain or roughness.

Code 
tools.r_sim_water(
    elevation="elevation",
    depth="no_soil_depth",
    discharge="no_soil_discharge",
    walkers_output="no_soil_walkers",
    rain_value=rain_mm_per_hour,
    man="mannings_n",  # Mixed forest Kalyanapu et al. (2009)
    nwalkers=100000,
    duration=48, # Bump it to 48 min to cover the full storm duration (rounded up from 46.97 min)
    observation="sample_outlets",
    logfile="coweeta_100yr_no_soil_simulation.log",
    output_step=2,
    nprocs=0,
    flags="t",
)

gs.run_command(
    "t.create",
    output="no_soil_depth_sum",
    type="strds",
    temporaltype="absolute",
    title="Runoff Depth",
    description="Runoff Depth in [m]",
    overwrite=True,
)

# Get the list of depth maps
depth_list = gs.read_command(
    "g.list", type="raster", pattern="no_soil_depth.*", separator="comma"
).strip()

# Register the maps
gs.run_command(
    "t.register",
    input="no_soil_depth_sum",
    type="raster",
    start="2024-01-01",
    increment="2 minutes",
    maps=depth_list,
    flags="i",
    overwrite=True,
)
Code 
with gs.RegionManager(raster="accumulation", s="s-1500", flags="a"):
    m = gj.Map(use_region=True, width=700)
    stats = tools.r_info(map="no_soil_depth.47", format="json").json

    m.d_shade(shade="relief", color="naip_2022_rgb@naip")
    m.d_rast(map="no_soil_depth.47", values=f"0.025-{stats['max']}")
    m.d_legend(
        raster="no_soil_depth.47",
        title="Depth (m)",
        at="5, 10, 20, 80",
        range=[0.025, stats["max"]],
        font="Fira Sans Condensed Light",
        fontsize=16,
        flags="ls",
        border_color="none",
    )
    m.show()

Temporal evolution without soils

Animating the no-soil depth series over the same timeline makes the comparison direct – watch where, and how quickly, ponded water appears in the uniform-rainfall case versus the soil-informed run above.

Code 
with gs.RegionManager(raster="accumulation", s="s-1500", flags="a"):
    m = gj.TimeSeriesMap(use_region=True, width=700)
    stats = tools.r_info(map="no_soil_depth.47", format="json").json
    m.d_rast(map="relief")
    m.add_raster_series("no_soil_depth_sum", values=f"0.025-{stats['max']}")
    m.d_legend(
        raster="no_soil_depth.47",
        title="Depth [m]",
        flags="ls",
        range=[0.025, stats["max"]],
        border_color="none",
        at=(5, 10, 20, 80),
    )
    m.save("output/no_soil_simwe_depth_animation.gif")
    m.show()

Animated overland flow depth during the 100-year storm, uniform-rainfall (no-soil) baseline.

Animated overland flow depth during the 100-year storm, uniform-rainfall (no-soil) baseline.