Setup_lulcmaps and related methods#

Description#

To prepare land use / land cover related maps for Wflow, HydroMT provides several methods. The basis of these methods is that they use lookup tables with parameters values for each land use / land cover class and then map these values to the model grid using land use / land cover maps.

The parameters are mapped at the original LULC map resolution before being resampled to the model grid resolution (using either averaging or majority mapping depending on the parameter type). This ensures that most of the details of the original resolution of the LULC map are preserved in the final model maps.

The following methods are available:

setup_lulcmaps() and setup_lulcmaps(): Main method to setup LULC maps using lookup tables.
setup_lulcmaps_from_vector() and setup_lulcmaps_from_vector(): Similar to the above but starts with rasterizing the LULC vector data to a user-defined resolution.
setup_lulcmaps_with_paddy(): Specific method if paddies are present in your catchment. The LULC map can directly contain a paddy class or an additional paddy map can be provided and will be merged into the landuse map before deriving parameters. Additional parameters related to paddy management (minimum/optimal/maximum water levels) are also added based on user defined values. Finally, to allow for water to pool on the surface (for paddy/rice fields), the layers in the model can be updated to new depths, such that we can allow a thin layer with limited vertical conductivity. These updated layers means that the soil_brooks_corey_c parameter needs to be calculated again. Next, the soil_ksat_vertical_factor layer corrects the vertical conductivity (by multiplying) such that the bottom of the layer corresponds to a target_conductivity for that layer.

Parameter lookup tables#

The parameter values for each land use / land cover class need to be defined in lookup tables. HydroMT provides some default lookup tables for the following LULC classification systems:

corine: CORINE Land Cover (CLC)
glcnmo: GLCNMO
globcover: GlobCover
esa_worldcover: ESA WorldCover
vito: Copernicus Global Dynamic Land Cover
paddy: specific lookup table for paddy fields (if using the setup_lulcmaps_with_paddy method)

You can find these tables in the HydroMT-Wflow repository or create your own based on these examples or literature values to better reflect the specific vegetation and soil characteristics of your study area.

The lookup tables are simple CSV files with the first column containing the land use / land cover class identifiers (matching those in the LULC map), the second column containing the description of the class and the other columns containing the parameter values for each class. The last line of the table should contain the nodata value for the LULC map (e.g. -9999) and the corresponding parameter values for nodata areas.

The columns names should match the HydroMT names of each Wflow parameter. These are:

landuse: landuse class ID
vegetation_kext: Extinction coefficient in the canopy gap fraction equation [-]
land_manning_n: Manning Roughness [m-1/3 s]
soil_compacted_fraction: The fraction of compacted or urban area per grid cell [-]
vegetation_root_depth: Length of vegetation roots [mm]
vegetation_leaf_storage: Specific leaf storage [mm]
vegetation_wood_storage: Fraction of wood in the vegetation/plant [-]
land_water_fraction: The fraction of open water per grid cell [-]
vegetation_crop_factor: Crop coefficient [-]
vegetation_feddes_alpha_h1: Root water uptake reduction at soil water pressure head h1 (0 or 1) [-]
vegetation_feddes_h1: Soil water pressure head h1 at which root water uptake is reduced (Feddes) [cm]
vegetation_feddes_h2: Soil water pressure head h2 at which root water uptake is reduced (Feddes) [cm]
vegetation_feddes_h3_high: Soil water pressure head h3 (high) at which root water uptake is reduced (Feddes) [cm]
vegetation_feddes_h3_low: Soil water pressure head h3 (low) at which root water uptake is reduced (Feddes) [cm]
vegetation_feddes_h4: Soil water pressure head h4 at which root water uptake is reduced (Feddes) [cm]
erosion_usle_c (sediment): USLE cover management factor [-]

Example lookup table (for ESA WorldCover):

esa	description	landuse	vegetation_kext	land_manning_n	soil_compacted_fraction	vegetation_root_depth	vegetation_leaf_storage	vegetation_wood_storage	land_water_fraction	vegetation_crop_factor	vegetation_feddes_alpha_h1	vegetation_feddes_h1	vegetation_feddes_h2	vegetation_feddes_h3_high	vegetation_feddes_h3_low	vegetation_feddes_h4	erosion_usle_c
10	Tree cover	10	0.8	0.5	0	406	0.23	0.09	0	1.1	1	0	-100	-400	-1000	-16000	0.0012
20	Shrubland	20	0.7	0.5	0	410	0.1	0.05	0	1.05	1	0	-100	-400	-1000	-16000	0.06
30	Grassland	30	0.6	0.2	0	106.8	0.1	0.01	0	1	1	0	-100	-400	-1000	-16000	0.04
40	Cropland	40	0.6	0.15	0	390.4	0.077	0.005	0	1.1	0	0	-100	-400	-1000	-16000	0.3
50	Built-up	50	0.6	0.015	0.9	257.4	0.1	0.03	0	1	1	0	-100	-400	-1000	-16000	0.001
60	Bare / sparse vegetation	60	0.6	0.015	0	10.7	0.1	0.03	0	-999	1	0	-100	-400	-1000	-16000	0.35
70	Snow and Ice	70	0	0.01	0	0	0	0	0	-999	1	0	-100	-400	-1000	-16000	0
80	Permanent water bodies	80	0	0.01	0	0	0	0	1	-999	1	0	-100	-400	-1000	-16000	0
90	Herbaceous wetland	90	0.6	0.125	0	106.8	0.1	0.01	0	1.2	1	0	-100	-400	-1000	-16000	0.001
95	Mangroves	95	0.8	0.5	0	369	0.23	0.09	0.5	1.05	1	0	-100	-400	-1000	-16000	0.008
100	Moss and lichen	100	0.6	0.085	0	136.9	0.09	0	0	1.05	1	0	-100	-400	-1000	-16000	0.001
0	No data	0	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999

Example usage#

Here is an example of how to use the setup_lulcmaps and related methods when updating a model:

Command Line Interface (CLI)

The definition of the method and the arguments is done in a workflow file (YAML format). The workflow file can then be used to build or update a model from the command line interface. For example, using the pre-defined artifact_data catalog:

$ hydromt update wflow_sbm "./path/to/model_to_update" -o "./path/to/model_with_landuse" -d "artifact_data" -i "./path/to/update_landuse.yaml" -v

A minimal example of how to use the setup_lulcmaps method in a workflow file:

steps:
  - setup_lulcmaps:
      lulc_fn: globcover
      lulc_mapping_fn: globcover_mapping_default

Another example using the setup_lulcmaps_from_vector. Here we will also save the rasterized version of the landuse map to a file and only prepare a couple of parameters:

steps:
  - setup_lulcmaps_from_vector:
      lulc_vector_fn: local_landuse_vector.shp
      lulc_mapping_fn: local_landuse_mapping.csv
      lulc_res: 50
      save_raster_lulc: true
      lulc_vars:
        - land_manning_n
        - vegetation_crop_factor

Final example using the setup_lulcmaps_with_paddy method to include paddy fields. Here another raster file is provided with the paddy field locations (GLCNMO where paddy class is number 12). As class numbers for irrigated and rainfed cropland are 11 and 14 in globcover we can keep twelve as the paddy class value in the merged landuse map.

Adding paddies also requires to add extra parameters related to paddy management and update some of the soil parameters.

steps:
  - setup_lulcmaps_with_paddy:
      lulc_fn: globcover
      lulc_mapping_fn: globcover_mapping_default
      paddy_fn: glcnmo
      paddy_mapping_fn: paddy_mapping_default
      paddy_class: 12
      output_paddy_class: 12
      paddy_waterlevels:
        demand_paddy_h_min: 20
        demand_paddy_h_opt: 50
        demand_paddy_h_max: 80
      wflow_thicknesslayers: [50, 100, 50, 200, 800]
      target_conductivity: [null, null, 5, null, null]

Python API

For python, you need to first instantiate a Wflow model and then call the setup methods directly:

from hydromt_wflow import WflowSbmModel

model = WflowSbmModel(
  root="path/to/model_to_update",
  mode="r+",
  data_libs=["artifact_data"]
)

A minimal example of how to use the setup_lulcmaps method:

model.setup_lulcmaps(
  lulc_fn="globcover",
  lulc_mapping_fn="globcover_mapping_default"
)

Another example using the setup_lulcmaps_from_vector. Here we will also save the rasterized version of the landuse map to a file and only prepare a couple of parameters:

model.setup_lulcmaps_from_vector(
  lulc_vector_fn="local_landuse_vector.shp",
  lulc_mapping_fn="local_landuse_mapping.csv",
  lulc_res=50,
  save_raster_lulc=True,
  lulc_vars=[
    "land_manning_n",
    "vegetation_crop_factor"
  ]
)

Final example using the setup_lulcmaps_with_paddy method to include paddy fields. Here another raster file is provided with the paddy field locations (GLCNMO where paddy class is number 12). As class numbers for irrigated and rainfed cropland are 11 and 14 in globcover we can keep twelve as the paddy class value in the merged landuse map.

Adding paddies also requires to add extra parameters related to paddy management and update some of the soil parameters.

model.setup_lulcmaps_with_paddy(
  lulc_fn="globcover",
  lulc_mapping_fn="globcover_mapping_default",
  paddy_fn="glcnmo",
  paddy_mapping_fn="paddy_mapping_default",
  paddy_class=12,
  output_paddy_class=12,
  paddy_waterlevels={
    "demand_paddy_h_min": 20,
    "demand_paddy_h_opt": 50,
    "demand_paddy_h_max": 80
  },
  wflow_thicknesslayers=[50, 100, 50, 200, 800],
  target_conductivity=[None, None, 5, None, None]
)

More examples can be found in the following notebooks:

Parameter estimation#

The estimates in the above table are based on literature reviews done by Imhoff et al., 2020

Here are some references to help you estimate parameter values for your own lookup tables.

Interception parameters#

Parameters related to vegetation interception and storage of rainfall on leaves and branches.

Parameter	Description	Range	Reference
kext	Extinction coefficient in the canopy gap fraction equation [-]	0.2-0.9	Van Dijk and Bruijnzeel (2001) Van Heemst (1988)
leaf_storage	Specific leaf storage [mm]	0.02-0.2	Zhong et al. (2022)
wood_storage	Fraction of wood in the vegetation/plant [-]	0.0-0.5	Zhong et al. (2022)

kext#

Extract from Van Dijk and Bruijnzeel (2001):

The value of kext for a particular radiation wavelength depends on leaf distribution and inclination angle and for PAR usually ranges between 0.6 and 0.8 in forests (Ross, 1975). For a number of agricultural crops, van Heemst (1988) reported kext values between 0.2 and 0.8 with values of 0.5-0.7 being the most common.

Values for different crops from van Heemst (1988):

Crop	kext
Wheat	0.42 - 0.54
Barley	0.44
Rice	0.29 - 0.43
Millet	0.5 - 0.6
Sorghum	0.4 - 0.7
Maize	0.6 - 0.64
Soybean	0.787 - 0.804
Peanut	0.6
Oilseed rape	0.54
Sunflower	0.8 - 0.9
Cassava	0.7 - 0.88
Sweet Potato	0.45
Potato	0.48
Sugar beet	0.65
Sugar cane	0.48
Cotton	0.62

Leaf and wood storage#

Previous values were derived from Pitman (1989) and Liu (1998) . Starting from version 1, the default lookup tables use updated values based on a literature review by Zhong et al. (2022) (supplement values with more details are available).

Note that for land use types with mixed (e.g urban) or sparse vegetation, the actual values will be scaled with LAI.

Vegetation / Crop type	Leaf storage [mm]	Wood storage [-]
Needleleaf forest	0.29	0.09
Evergreen broadleaf forest	0.20	0.09
Deciduous broadleaf forest	0.18	0.09
Mixed forest	0.20	0.09
All forest	0.23	0.09
Short vegetation (crops, grass, shrub)	0.10	0.03 (0.01 - 0.05)
Maize	0.077	0.005
Rice	0.042	0.005

Evapotranspiration parameters#

Parameters related to vegetation evaporation and transpiration.

Parameter	Description	Range	Reference
crop_factor	Crop coefficient [-]	0.3 - 1.25	Allen et al. (1998)
root_depth	Length of vegetation roots [mm]	100 - 5000	Fan et al. (2016) Schenk and Jackson (2002)
feddes_alpha_h1	Root water uptake reduction at pressure head h1 [-]	0 (crop) - 1 (other)	van Dam et al. (1997) Singh et al. (2003)
feddes_h1	Critical pressure head h1 - anorexic condition [cm]	100 (paddy) - 0 (other)	van Dam et al. (1997) Singh et al. (2003)
feddes_h2	Critical pressure head h2 - field capacity [cm]	55 (paddy) - -100 (other)	van Dam et al. (1997) Singh et al. (2003)
feddes_h3_high	Critical pressure head h3 (high) [cm]	-160 (paddy) - -400 (other)	van Dam et al. (1997) Singh et al. (2003)
feddes_h3_low	Critical pressure head h3 (low) [cm]	-250 (paddy) - -1000 (other)	van Dam et al. (1997) Singh et al. (2003)
feddes_h4	Critical pressure head h4 - wilting point [cm]	-15000 (paddy) - -16000 (other)	van Dam et al. (1997) Singh et al. (2003)

Crop factor#

The factor or FAO-56 crop coefficient the crop factor is used to scale reference evapotranspiration (\(ET_0\)) to crop evapotranspiration (ETc) as follows: \(ET_c = (K_{cb} + K_e) * ET_0\), where \(K_{cb}\) is the basal crop coefficient and \(K_e\) is the soil evaporation coefficient. As Wflow takes care of the soil evaporation component, the crop coefficient needed is then \(K_{cb full}\) which is the basal crop coefficient during the mid-season (at peak plant size or height) for vegetation having full ground cover or LAI > 3. Within Wflow, \(K_{cb full}\) will be scaled further based on the actual vegetation cover fraction (using LAI) to get the actual crop coefficient used for \(ET_c\) calculation.

In sub-humid and calm wind conditions, \(K_{cb full}\) is equal to the FAO-56 mid-season crop coefficient \(K_{cb mid}\). Detailed values of \(K_{cb mid}\) can be found for different crop types in the FAO guidelines. As most LULC maps do not distinguish between crop types, an average value representing the most common crops in your study area should be used. In the default lookup tables, 1.15 is used for cropland areas (based on an average value for cereals and oil crops), and 1.2 for paddy/rice fields.

Detailed values of kc can be found for different crop types in the FAO guidelines. As most LULC maps do not distinguish between crop types, an average value representing the most common crops in your study area should be used. In the default lookup tables, 1.10 is used for cropland areas (based on an average value for cereals and oil crops), and 1.15 for paddy/rice fields.

For natural vegetation, \(K_{cb full}\) can be estimated from the vegetation height and climate conditions. For example, the FAO guidelines provide the following equations to estimate \(K_{cb full}\) for natural vegetation:

..math:

K_{cb full} = K_{cb,h} + [0.04(u_2 - 2) - 0.004(RH_{min} - 45)] * (h/3)^0.3
K_{cb,h} = 1.0 + 0.1 * h \quad \text{for } h \leq 2 \text{ m}

where \(u_2\) is the wind speed at 2 m height [m/s], \(RH_{min}\) is the minimum relative humidity [%] and \(h\) is the mean maximum plant height [m].

Finally, for land use type with no vegetation (e.g. bare soil, waterbodies), the nodata value should be used (e.g. -9999) to avoid underestimation of \(K_{cb full}\) during reprojection of the land use parameter at original resolution to the model grid resolution.

For mixed land use types (e.g. urban areas), the value should be scaled based on the vegetation type in the area (e.g. sparse vegetation should use the value for grass or shrubland). In the default lookup tables, such areas (urban, sparse vegetation) use a value of 1.0 for grass.

Root depth#

Values for different crops from Fan et al. (2016) and different other vegetation from Schenk and Jackson (2002):

Vegetation / Crop type	Root depth D50 [mm]	Root depth D95 [mm]
Tundra	90	290
Boreal forest	120	580
Cool temperate forest	210	1040
Warm temperate forest	230	1210
Meadows	50	400
Prairie	70	910
Semi arid steppe	160	1200
Temperate savanna	230	1400
Mediterranean woodland and shrub	190	1710
Semi-desert shrubland	280	1310
Desert	270	1120
Dry tropical savannas	280	1440
Humid tropical savannas	140	940
Tropical semi-deciduous and deciduous forest	160	950
Tropical evergreen forest	150	910
Wheat	168	1038
Maize	144	889
Oat	112	777
Barley	115	996
Cereals	141	929
Soybean	109	1380
Oilseed crops	94	1063
All crops	146	1027

Feddes root water uptake#

Critical pressure heads for rice are taken after Singh et al. (2003). For other vegetation, the default values from Wflow.jl are used. These are now vegetation independent and are taken as the default complete saturation (h1=0 cm), field capacity (h2=-100 cm) and wilting point (h4=-16000 cm). The h3 values are set to -400 cm (high) and -1000 cm (low) but these are largely dependent on the type of vegetation.

Examples can be found in annexes C and D of Van Dam et al. (1997). Here are examples for the most common crops [cm]:

Crop	h1	h2	h3_high	h3_low	h4
Potatoes	-10	-25	-320	-600	-16000
Sugar beet	-10	-25	-320	-600	-16000
Wheat	0	-1	-500	-900	-16000
Pasture	-10	-25	-200	-800	-8000
Corn	-15	-30	-325	-600	-8000

Manning Roughness#

Manning’s N values are used to represent roughness of the land surface for overland flow. Estimations per landuse class can be found in literature such as:

Parameter	Description	Range	Reference
manning_n	Manning Roughness [m-1/3 s]	0.008-0.96	Engman (1986) Kilgore (1997) Cronshey (1986)

Example of values from different sources:

Landuse	Cronshey	Kilgore	Engman
Smooth surfaces (concrete, gravel, bare)	0.011	0.015 (residential/commercial) / 0.020 (gravel road)	0.01 (smooth bare soil or bare sand) / 0.011 (concrete) - 0.020 (gravel)
Fallow (no residue)	0.05	0.05	0.05
Cropland	0.06 - 0.17 (depending on residue cover)	0.032 (wheat) / 0.08 (corn) - 0.2 (depending on tillage)	0.1 - 0.4 (small grain) / 0.07 - 0.2 (row crops)
Grassland	0.15 (short) - 0.24 (dense)	0.046 (grass) / 0.1 (pasture)	0.15 (short) - 0.24 (dense)
Forest	0.4 - 0.8 (depending on underbrush)	0.6
Range (natural)	0.13		0.13
Wetland		0.125
Waterway, pond		0.08

Land cover parameters#

The land cover parameters represent fractions of different land cover types within each grid cell. For these parameters, it may matter to take into account the resolution of the original LULC map when estimating the values. E.g. a coarse resolution may for example represent a cell that is majority urban but still contains a significant fraction of vegetation or water.

Parameter	Description	Estimate
soil_compacted_fraction	Fraction of compacted or paved area per grid cell [-]	> 0 if urban or compacted / paved surfaces are present
land_water_fraction	Fraction of open water per grid cell [-]	> 0 if water (water bodies, ponds, waterways etc.) is present

Soil erosion#

For soil erosion, the soil cover-management factor USLE C can be estimated for different land use / vegetation type.

Parameter	Description	Range	Reference
erosion_usle_c	USLE cover management factor [-]	0.001 - 1.0	Panagos et al. (2015) Bosco et al. (2015) Gericke et al. (2015)

Examples of USLE C values for different land use types different sources:

Land use	Panagos	Bosco	Gericke
Wheat	0.20
Maize	0.38
Rice	0.15	0.15	0.05
Potatoes or sugar beet	0.34
Oilseeds	0.28
All crops	0.233 (0.2 - 0.5)	0.2 (irrigated) / 0.335 (rainfed)	0.18 - 0.24 (irrigated) / 0.3 - 0.4 (rainfed)
Vineyards	0.3527 (0.15 - 0.45)	0.45	0.5
Fruit trees and berries	0.2188 (0.1 - 0.3)	0.35	0.4
Olive groves	0.2273 (0.1 - 0.3)	0.35	0.4
Agro-forestry areas	0.0881 (0.03 - 0.13)	0.2	0.23 - 0.3
Broad-leaved forest	0.0013 (0.0001 - 0.003)	0.0025	0.005 - 0.008
Coniferous forest	0.0011 (0.0001 - 0.003)	0.0015	0.005 - 0.008
Mixed forest	0.0011 (0.0001 - 0.003)	0.002	0.005 - 0.008
Pastures	0.0903 (0.05 - 0.15)	0.01	0.01 - 0.005
Natural grasslands	0.0435 (0.01 - 0.08)	0.005	0.01 - 0.05
Moors and heathland	0.0420 (0.01 - 0.1)	0.05	0.01 - 0.05
Shrubland	0.0623 (0.01 - 0.1)	0.04	0.01 - 0.05
Bare rocks	0		0
Sparse vegetation	0.2652 (0.1 - 0.45)	0.3	0.35
Burnt areas	0.3427 (0.1 - 0.55)	0.3	0.35
Glaciers and perpetual snow	0	0.001	0

References#

Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrig Drain Pap 56. FAO, Rome, p 300
Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., and Panagos, P. (2015). Modelling soil erosion at European scale: towards harmonization and reproducibility, Nat. Hazards Earth Syst. Sci., 15, 225–245, https://doi.org/10.5194/nhess-15-225-2015
Corbari, C., Ravazzani, G., Galvagno, M., Cremonese, E., & Mancini, M. (2017). Assessing crop coefficients for natural vegetated areas using satellite data and eddy covariance stations. Sensors, 17(11), 2664.
Cronshey, R. (1986). Urban hydrology for small watersheds (No. 55). US Department of Agriculture, Soil Conservation Service, Engineering Division.
van Dam, J.C., Huygen, J., Wesseling, J.G., Feddes, R.A., Kabat, P., van Walsum, P.E.V., Groenendijk, P., and van Diepen, C.A., 1997. Theory of SWAP version 2.0: Simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment. Wageningen Agricultural University, The Netherlands, Report 71.
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Feddes, R.A., Kowalik, P.J. and Zaradny, H., 1978, Simulation of field water use and crop yield, Pudoc, Wageningen, Simulation Monographs.
Gericke, A. (2015). Soil loss estimation and empirical relationships for sediment delivery ratios of European river catchments. International Journal of River Basin Management, 13(2), 179–202. https://doi.org/10.1080/15715124.2014.1003302
van Heemst, H.D.J. (1988). Plant data values required for simple crop growth simulation models, review and bibliography. Simulation report CABO-TT No 17. Wageningen.
Imhoff, R.O, van Verseveld, W.J., van Osnabrugge, B., Weerts, A.H., 2020. Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River. Water Resources Research, 56, e2019WR026807. https://doi.org/10.1029/2019WR026807.
Kilgore, J. L. (1997). Development and evaluation of a GIS-based spatially distributed unit hydrograph model (MSc thesis). Retrieved from http://hdl.handle.net/10919/35777
Liu, S. (1998). Estimation of rainfall storage capacity in the canopies of cypress wet lands and slash pine uplands in North-Central Florida. Journal of Hydrology, 207(1-2), 32–41. https://doi.org/10.1016/S0022-1694(98)00115-2
Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. (2015). Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48, 38–50. https://doi.org/10.1016/j.landusepol.2015.05.021
Pereira, L.S., Paredes, P. & Espírito-Santo, D. (2024a). Crop coefficients of natural wetlands and riparian vegetation to compute ecosystem evapotranspiration and the water balance. Irrig Sci 42, 1171–1197. https://doi.org/10.1007/s00271-024-00923-9
Pereira, L.S., Paredes, P., Espírito-Santo, D. et al. (2024b). Actual and standard crop coefficients for semi-natural and planted grasslands and grasses: a review aimed at supporting water management to improve production and ecosystem services. Irrig Sci 42, 1139–1170. https://doi.org/10.1007/s00271-023-00867-6
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