A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration

Sun, Ting; Grimmond, Sue

doi:10.5194/gmd-12-2781-2019

Cited by 34 publications

(26 citation statements)

References 65 publications

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“…With the parameters derived from urban observations the local urban climate is accounted for (Appendix ). T air and Δ q from the model input or local values at 2‐m height calculated within the model (Sun & Grimmond, ) can be used with the latter allowing for spatial variability of F pho . Δ θ is spatially variable with simulated response from both the energy and water flux exchanges within SUEWS.…”

Section: Model Descriptionmentioning

confidence: 99%

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

et al. 2019

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There is a growing need to simulate the effect of urban planning on both local climate and greenhouse gas emissions. Here, a new urban surface carbon dioxide (CO 2 ) flux module for the Surface Urban Energy and Water Balance Scheme is described and evaluated using eddy covariance observations at two sites in Helsinki in 2012. The spatial variability and magnitude of local-scale anthropogenic and biogenic CO 2 flux components at high spatial (250 m × 250 m) and temporal (hourly) resolution are examined by combining high-resolution (down to 2 m) airborne lidar-derived land use data and mobility data to account for people's movement. Urban effects are included in the biogenic components parameterized using urban eddy covariance and chamber observations. Surface Urban Energy and Water Balance Scheme reproduces the seasonal and diurnal variability of the CO 2 flux well. Annual totals deviate 3% from observations in the city center and 2% in a suburban location. In the latter, traffic is the dominant CO 2 source but summertime vegetation partly offsets traffic-related emissions. In the city center, emissions from traffic and human metabolism dominate and the vegetation effect is minor due to the low proportion of vegetation surface cover (22%). Within central Helsinki, human metabolism accounts for 39% of the net local-scale emissions and together with road traffic is to a large extent responsible for the spatial variability of the emissions. Annually, the biogenic emissions and sinks are in near balance and thus the effect of vegetation on the carbon balance is small in this high-latitude city.

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Section: Model Descriptionmentioning

confidence: 99%

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

et al. 2019

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“…Observed local standard time air temperature (solid line) and shortwave downward radiation (dashed line) for the three cities on 2016 July 19. Arrows indicate time for satellite overpasses model with SUEWS as its computation core(Sun and Grimmond 2019).…”

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Urban storage heat flux variability explored using satellite, meteorological and geodata

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Olofson

Sun

et al. 2020

Theor Appl Climatol

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The storage heat flux (ΔQ S) is the net flow of heat stored within a volume that may include the air, trees, buildings and ground. Given the difficulty of measurement of this important and large flux in urban areas, we explore the use of Earth Observation (EO) data. EO surface temperatures are used with ground-based meteorological forcing, urban morphology, land cover and land use information to estimate spatial variations of ΔQ S in urban areas using the Element Surface Temperature Method (ESTM). First, we evaluate ESTM for four "simpler" surfaces. These have good agreement with observed values. ESTM coupled to SUEWS (an urban land surface model) is applied to three European cities (Basel, Heraklion, London), allowing EO data to enhance the exploration of the spatial variability in ΔQ S. The impervious surfaces (paved and buildings) contribute most to ΔQ S. Building wall area seems to explain variation of ΔQ S most consistently. As the paved fraction increases up to 0.4, there is a clear increase in ΔQ S. With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of ΔQ S .

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“…In the stand-alone version of this model no spatial variations in these are considered. If coupled to a meteorological model, these outdoor variables can be spatially dynamic and respond to Q F emissions locally (Sun and Grimmond, 2019). DHW is considered as a sensible heat gain only (no latent), with hot water to drains unaccounted for in Q F,B .…”

Section: Building Energy (Q Fb )mentioning

confidence: 99%

“…The Ener-gyPlus BESTEST model downloaded from the EnergyPlus helpserve website (EnergyPlus, 2020) is modified to run with v9.3.0. Observed London weather data for 2012 (Kotthaus and Grimmond, 2014) are generated using SuPy (Sun and Grimmond, 2019) at an hourly resolution for EnergyPlus and STEBBS. Although EnergyPlus indicates it interpolates subhourly weather data for consistency, we use both with a 1 h time step.…”

Section: Appendix A: Creation Of Markov Chainsmentioning

confidence: 99%

Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation

et al. 2020

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Abstract. Thermal emissions – or anthropogenic heat fluxes (QF) – from human activities impact urban climates at a local and larger scale. DASH considers both urban form and function in simulating QF through the use of an agent-based structure that includes behavioural characteristics of urban residents. This allows human activities to drive the calculation of QF, incorporating dynamic responses to environmental conditions. The spatial resolution of simulations depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating–cooling demand, and release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF, but the annual average is smaller than published energy data. Overall, the model generally performs well, including for domestic appliance energy use. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models.

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A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration

Cited by 34 publications

References 65 publications

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

Spatial Modeling of Local‐Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki

Urban storage heat flux variability explored using satellite, meteorological and geodata

Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation

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