# Question on SUEWS sensitivity to vegetation parameters

**URL:** <https://community.suews.io/t/question-on-suews-sensitivity-to-vegetation-parameters/31>\
**Category:** General\
**Created:** [29 January 2026 14:52 UTC](https://community.suews.io/t/question-on-suews-sensitivity-to-vegetation-parameters/31 "2026-01-29T14:52:47Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![sunt05](https://dub1.discourse-cdn.com/flex014/user_avatar/community.suews.io/sunt05/32/5_2.png) [@sunt05](https://community.suews.io/u/sunt05)\
**Post date:** [30 January 2026 13:25 UTC](https://community.suews.io/t/question-on-suews-sensitivity-to-vegetation-parameters/31/6 "2026-01-30T13:25:24Z")

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Glad to hear the `MaxConductance` approach is working — and that the magnitude makes physical sense compared to the surface fraction response.

**Other parameters worth exploring**

For cooling and water use, the most influential parameters in SUEWS tend to be:

- **Surface fractions** (which you’ve already tested — this is the dominant lever)
- **`MaxConductance`** (g\_{s,\max}) — now confirmed working for you
- **Soil storage capacity** (`soildepth`, `soilstorecap`) — controls how long the soil can sustain transpiration between rain events, so it shapes the dry-spell response rather than peak cooling
- **Water distribution** (`waterdist`) — governs how rainfall/irrigation runoff is redistributed between surfaces, which affects soil moisture availability
- **Irrigation parameters** — if you’re comparing irrigated vs non-irrigated green infrastructure scenarios

On **tree height** : you’re right that it doesn’t directly affect stomatal conductance or the g\_\mathrm{LAI} calculation. It enters SUEWS indirectly through the roughness length (z\_{0m}) and displacement height (z\_d), which determine the aerodynamic resistance R\_A. In the Penman-Monteith equation:

Q\_E = \frac{s \cdot Q\_{n,e} + \rho c\_p \cdot \mathrm{VPD} / R\_A}{s + \gamma\left(1 + R\_S / R\_A\right)}

a lower R\_A (from taller roughness elements) increases the VPD-driven term in the numerator and also changes the R\_S/R\_A balance in the denominator. So taller trees can modestly increase QE, but the effect is secondary compared to g\_{s,\max} and surface fractions.

**On T2 and cooling efficiency**

I’ve seen Fredrik’s [separate post](https://community.suews.io/t/how-to-best-model-within-canyon-air-temperature/34) about T2 — I’ll reply there with more detail on the physics settings and profile scheme configuration. The short version: T2 sensitivity in SUEWS depends heavily on which RSL (roughness sub-layer) method you’re using (`rslmethod`) and how the vertical temperature profile is constructed. Your config uses `rslmethod: 2`, which is the more physically-based option, but there are some important considerations for intra-urban comparisons that I’ll cover in that thread.

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