1. Please report issues with the manual on the GitHub page.

  2. Please cite SUEWS with proper information from our Zenodo page.

2. Parameterisations and sub-models within SUEWS

2.1. Net all-wave radiation, Q*

There are several options for modelling or using observed radiation components depending on the data available. As a minimum, SUEWS requires incoming shortwave radiation to be provided.

  1. Observed net all-wave radiation can be provided as input instead of being calculated by the model.

  2. Observed incoming shortwave and incoming longwave components can be provided as input, instead of incoming longwave being calculated by the model.

  3. Other data can be provided as input, such as cloud fraction (see options in RunControl.nml).

  4. NARP (Net All-wave Radiation Parameterization, Offerle et al. 2003 [O2003] , Loridan et al. 2011 [L2011] ) scheme calculates outgoing shortwave and incoming and outgoing longwave radiation components based on incoming shortwave radiation, temperature, relative humidity and surface characteristics (albedo, emissivity).

2.2. Anthropogenic heat flux, QF

  1. Two simple anthropogenic heat flux sub-models exist within SUEWS:

    • Järvi et al. (2011) [J11] approach, based on heating and cooling degree days and population density (allows distinction between weekdays and weekends).

    • Loridan et al. (2011) [L2011] approach, based on a linear piece-wise relation with air temperature.

  2. Pre-calculated values can be supplied with the meteorological forcing data, either derived from knowledge of the study site, or obtained from other models, for example:

    • LUCY (Allen et al. 2011 [lucy], Lindberg et al. 2013 [lucy2]). A new version has been now included in UMEP. To distinguish it is referred to as LQF

    • GreaterQF (Iamarino et al. 2011 [I11]). A new version has been now included in UMEP. To distinguish it is referred to as GQF

2.3. Storage heat flux, ΔQS

  1. Three sub-models are available to estimate the storage heat flux:

    • OHM (Objective Hysteresis Model, Grimmond et al. 1991 [G91OHM], Grimmond & Oke 1999a [GO99QS], 2002 [GO2002]). Storage heat heat flux is calculated using empirically-fitted relations with net all-wave radiation and the rate of change in net all-wave radiation.

    • AnOHM (Analytical Objective Hysteresis Model, Sun et al. 2017 [AnOHM17]). OHM approach using analytically-derived coefficients. Not recommended in this version.

    • ESTM (Element Surface Temperature Method, Offerle et al. 2005 [OGF2005]). Heat transfer through urban facets (roof, wall, road, interior) is calculated from surface temperature measurements and knowledge of material properties. Not recommended in this version.

  2. Alternatively, ‘observed’ storage heat flux can be supplied with the meteorological forcing data.

2.4. Turbulent heat fluxes, QH and QE

  1. LUMPS (Local-scale Urban Meteorological Parameterization Scheme, Grimmond & Oke 2002 [GO2002]) provides a simple means of estimating sensible and latent heat fluxes based on the proportion of vegetation in the study area.

  2. SUEWS adopts a more biophysical approach to calculate the latent heat flux; the sensible heat flux is then calculated as the residual of the energy balance. The initial estimate of stability is based on the LUMPS calculations of sensible and latent heat flux. Future versions will have alternative sensible heat and storage heat flux options.

Sensible and latent heat fluxes from both LUMPS and SUEWS are provided in the Output files. Whether the turbulent heat fluxes are calculated using LUMPS or SUEWS can have a major impact on the results. For SUEWS, an appropriate surface conductance parameterisation is also critical [J11] [W16]. For more details see Differences between SUEWS, LUMPS and FRAISE .

2.5. Water balance

The running water balance at each time step is based on the urban water balance model of Grimmond et al. (1986) [G86] and urban evaporation-interception scheme of Grimmond and Oke (1991) [G91].

  • Precipitation is a required variable in the meteorological forcing file.

  • Irrigation can be modelled [J11] or observed values can be provided if data are available.

  • Drainage equations and coefficients to use must be specified in the input files.

  • Soil moisture can be calculated by the model.

  • Runoff is permitted:

    • between surface types within each model grid

    • between model grids (Not available in this version.)

    • to deep soil

    • to pipes.

2.6. Snowmelt

The snowmelt model is described in Järvi et al. (2014) [Leena2014]. Changes since v2016a: 1) previously all surface states could freeze in 1-h time step, now the freezing surface state is calculated similarly as melt water and can freeze within the snow pack. 2) Snowmelt-related coefficients have also slightly changed (see SUEWS_Snow.txt).

2.7. Convective boundary layer

A convective boundary layer (CBL) slab model (Cleugh and Grimmond 2001 [CG2001]) calculates the CBL height, temperature and humidity during daytime (Onomura et al. 2015 [Shiho2015]).

2.8. Surface Diagnostics

A MOST-based surface diagnostics module is implemented in 2017b for calculating the surface level diagnostics, including:

  • T2: air temperature at 2 m agl

  • Q2: air specific humidity at 2 m agl

  • U10: wind speed at 10 m agl

The details for formulation of these diagnostics can be found in equations 2.54, 2.55 and 2.56 in Brutsaert (2005) [B05]

2.9. Wind, Temperature and Humidity Profiles in the Roughness Sublayer

Wind, temperature and humidity profiles are derived at 30 levels in the surface layer. In order to account for the roughness sublayer and canopy layer, we follow Harman and Finnigan (2007) [HF07], Harman and Finnigan (2008) [HF08], and Theeuwes et al. (2019) [T19].

The 30 levels have a step of 0.1 times the canopy height zh (should still output zh somewhere) dz = 0.1 * zh. However. if 3 x canopy height is less the 10 m steps of 0.3333 m are used:

IF ((3.*Zh) < 10.) THEN
dz = 1./3.
zarray = (/(I, I=1, nz)/)*dz...

Here nz = 30.


All the diagnostic profiles (wind speed, temperature and humidity) are calculated from the forcing data down into the canopy. Therefore it is assumed that the forcing temperature and humidity are above the blending height.