The team evaluated two configurations of EcoRoof, the green roof module embedded in the building energy simulation program EnergyPlus, alongside a multilayer green roof parameterization coupled to the Weather Research and Forecasting model, known as WRF-MLGR. Rather than simply comparing predicted rooftop temperatures with observations, the researchers dissected the underlying physics: how well each model reproduced latent heat flux, soil heat flux, surface temperature, and substrate water content. This process-based approach matters because a model can sometimes land on the right answer for the wrong reasons, and only by examining the individual energy balance components can scientists identify where the physics itself breaks down.

The observational benchmark came from a green roof test array installed on the rooftop of Talbot College at Western University in London, Ontario. The array consists of elevated modules planted exclusively with Sedum spurium, a hardy succulent common on extensive green roofs, growing in a homogeneous 0.15 meter layer of substrate. Instruments including lysimeters, rain gauges, radiation sensors, and heat flux plates recorded energy balance components and microclimate variables during the summer and fall of 2014, from July through October. The researchers divided the record into two 62-day analysis periods, a summer window and a fall window, and further stratified the data into dry versus rainy days and high versus low net radiation conditions to probe how model performance shifts with environmental context.

The observed energy balance itself provided a valuable reference point. During summer, net radiation peaked around 450 watts per square meter at midday, with roughly half of that energy leaving the surface as sensible heat, about 46 percent converted into latent heat through evapotranspiration, and only about 4 percent conducted into the substrate. Surface temperatures climbed to nearly 30 degrees Celsius. In fall, all components weakened, with midday net radiation near 300 watts per square meter and latent heat accounting for about 41 percent of the partitioning. Notably, the elevated configuration of the modules, which creates an insulating air gap between the growing media and the roof deck, appeared to reduce conductive heat transfer compared with roofs in direct contact with the deck.

When the simulations were compared against these measurements, the two EcoRoof versions, an original configuration and a modified one following refinements proposed by earlier researchers, performed similarly and generally tracked observations well. Across the study periods, their root mean square errors averaged roughly 56 to 60 watts per square meter for latent heat flux, 3 to 4 degrees Celsius for surface temperature, about 22 watts per square meter for soil heat flux, and 0.03 cubic meters per cubic meter for substrate water content. The WRF multilayer green roof scheme matched EcoRoof on latent heat flux but fared far worse elsewhere, with soil heat flux errors exceeding 100 watts per square meter, surface temperature deviations near 5 degrees Celsius, and moisture errors roughly twice as large as EcoRoof’s.

The diurnal patterns of error were especially revealing. EcoRoof tended to underestimate evapotranspiration in the early morning and overestimate it in the afternoon, with positive biases peaking around 50 watts per square meter. WRF-MLGR, by contrast, persistently underestimated latent heat flux through most of the day, with morning biases reaching 100 watts per square meter. For surface temperature, EcoRoof showed a modest but persistent warm bias of roughly 1 to 3 degrees Celsius, while WRF-MLGR swung from morning underestimation to substantial afternoon overestimation. The most dramatic failures appeared in soil heat flux, where WRF-MLGR overestimated midday values by more than 200 watts per square meter under high radiation conditions, at times producing a diurnal pattern that resembled a bare, unvegetated substrate more than the living roof it was meant to simulate.

The physical explanations for these divergences lie in the structural differences between the models. EcoRoof’s overestimation of latent heat traces back to its simplified treatment of vegetation physiology. The model scales transpiration strongly with leaf area index and allows stomata to remain open until vapor pressure deficit becomes high, but Sedum is a CAM plant that conserves water through early stomatal closure, a feed-forward behavior the model does not capture. WRF-MLGR, conversely, represents latent heat solely through transpiration, with no substrate evaporation term at all, and applies strong atmospheric controls on stomatal resistance that suppress transpiration prematurely. Its lack of an explicit vegetation canopy means all incoming radiation is absorbed at a combined substrate-vegetation surface, pushing energy into the high thermal admittance substrate rather than allowing foliage to shade it and buffer the surface.

Moisture dynamics told a similar story. All three model configurations correctly reproduced the timing of rainfall events, showing sharp increases in substrate water content after precipitation, but the magnitudes diverged. The modified EcoRoof, which adds canopy interception and a moisture-dependent runoff formulation, tracked observed wetting and drying cycles most closely. The original version produced larger post-rain peaks and drained too quickly, while WRF-MLGR consistently underpredicted water content because its free-drain bottom boundary and nonlinear hydraulic conductivity allow water to percolate out of the substrate column very efficiently, producing a persistent dry bias. Separating days by moisture regime and radiative forcing showed that dry, high-radiation conditions amplified each model’s characteristic biases, while wet and cloudy conditions reduced mean errors but increased variability.

The implications reach well beyond one rooftop in Ontario. Because EcoRoof systematically overestimates evapotranspiration, earlier studies relying on it may have overstated the evaporative cooling potential of green roofs, particularly under high radiation and low moisture availability, although the model appears more trustworthy for subsurface thermal dynamics and building energy applications. WRF-MLGR’s structural limitations, meanwhile, mean that city-scale simulations of green roof heat mitigation built on this scheme warrant caution, since persistent underestimation of cooling fluxes and overestimation of surface heating could distort adaptation planning. Encouragingly, the research team has already developed an upgraded version of the WRF scheme that addresses the main deficiencies and performs substantially better against field observations.

More broadly, the study delivers a pointed message to the urban climate modeling community: rigorous, process-level evaluation should be a prerequisite for any model deployed to assess heat mitigation strategies. Most prior applications of green roof schemes in WRF and EnergyPlus were never validated against surface energy balance measurements, and those that were evaluated focused narrowly on temperatures. As cities worldwide invest billions in rooftop greening to combat extreme heat, the accuracy of the models guiding those decisions becomes a matter of public consequence. The authors argue that future model development should prioritize vegetation-specific physiology, canopy and substrate heat storage, explicit radiative absorption by foliage, and the distinct pathways of evaporation and transpiration, ensuring that the virtual green roofs guiding real-world policy behave like the living ones above our heads.

Subject of Research: Evaluation of green roof energy balance models in WRF and EnergyPlus against field observations

Article Title: Process-based evaluation of green roof models for assessment of heat mitigation efficacy in WRF (v4.3.1) and EnergyPlus (v8.6.0)

Article References: Martinez Mendoza, M., Saeedi, A., Voogt, J. A., & Krayenhoff, E. S. (2026). Process-based evaluation of green roof models for assessment of heat mitigation efficacy in WRF (v4.3.1) and EnergyPlus (v8.6.0). Geoscientific Model Development, 19(19), 9729-9749. https://doi.org/10.5194/gmd-19-9729-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9729-2026

Keywords: green roofs, urban heat island, WRF, EnergyPlus, EcoRoof, surface energy balance, latent heat flux, model evaluation, evapotranspiration, urban climate modeling, Sedum, Geoscientific Model Development

Land surface models are the bridge between the atmosphere and the ground in climate simulations. They calculate how sunlight, heat, and water move between the soil, the vegetation, and the air, and they feed those exchanges upward into the atmospheric component of a climate model. Historically, the models built for global circulation ran on grids tens to hundreds of kilometers wide, where a city was too small to matter. But computing power has changed the game. Global models are now pushing toward kilometer-scale grids, and regional models are running climate-length simulations over huge domains. At those resolutions, cities are no longer invisible specks; they are resolved features whose energy and water budgets can distort temperature, rainfall, and runoff if they are represented badly.

ORCHIDEE is a heavyweight in this world. It simulates coupled water, energy, and carbon cycles and serves as the land component of the IPSL climate model. Its architecture divides each grid cell into a mosaic of plant functional types, with separate soil columns for bare ground, high vegetation, and low vegetation. Until now, however, anything urban was simply assigned to the bare-soil category. That meant no reflective rooftops, no tall buildings stirring the wind, no concrete storing the afternoon sun, and no sealed asphalt blocking rain from soaking into the earth. The model could simulate a forest or a wheat field with sophistication, but a metropolis looked like a patch of exposed dirt.

The new scheme is deliberately parsimonious. Rather than adding an elaborate three-dimensional canyon geometry with separate energy budgets for roofs, walls, and roads, the team introduced a single urban tile, or PFT, into the existing mosaic. This tile carries urban-specific values for albedo, building height, thermal conductivity, and heat capacity, all of which can be prescribed from global or local datasets. The approach follows a lesson from the Urban-PLUMBER intercomparison project, which evaluated thirty urban land surface models and found that simpler slab schemes often outperform more complex ones, provided that vegetation, water availability, and imperviousness are represented realistically. Geometry, in other words, is not everything; what the surface is made of matters just as much.

The genuine novelty lies in how the scheme handles imperviousness. Most urban models treat sealed ground as a surface phenomenon: rain hits the pavement and runs off. Lalonde and colleagues went deeper, literally. Instead of splitting the urban tile into pervious and impervious sub-tiles, they modify the saturated hydraulic conductivity of the entire soil column beneath the city. Two options were tested. The first, called Urban1, slashes conductivity to ten percent of its reference value throughout the column, representing a heavily sealed city. The second, Urban2, scales the reduction linearly with the impervious fraction, so a city that is fifty-five percent sealed sees its conductivity cut proportionally. This single parameter change ripples through the whole hydrology: it suppresses infiltration, boosts surface runoff, reduces soil water storage, and limits the water available for evaporation, all through the model’s existing Richards-equation soil physics rather than any bolted-on machinery.

The thermal side of the scheme is equally consequential. Urban materials conduct and store heat very differently from soil. In grid cells where urban cover exceeds fifty percent, the model now prescribes a thermal conductivity of 3.24 watts per meter per kelvin and a heat capacity of 1890 kilojoules per cubic meter per kelvin, values borrowed from the Noah-MP land surface model. Compare that with the median thermal conductivity of natural soils in the study region, around 1.20 watts per meter per kelvin, and the physical implication is clear: cities soak up far more energy during the day and release it slowly through the night. That stored heat is the engine of the urban heat island, and the model now captures it.

To test the scheme, the team ran standalone simulations at twenty urban flux tower sites drawn from the harmonized Urban-PLUMBER dataset, spanning thirteen countries and climates from tropical Singapore to dry Phoenix and continental Europe. Each simulation was spun up for forty years until soil moisture and temperature reached equilibrium, then run over twelve to sixteen years of observations, with fluxes computed every thirty minutes. The benchmark was the old bare-soil treatment of cities. The verdict, measured by mean absolute error against tower observations, was unambiguous: the new Urban1 configuration reduced the average error in latent heat flux from 30.3 to 27.3 watts per square meter and in sensible heat flux from 41.9 to 36.9 watts per square meter across all sites. At sites with more than fifty percent impervious cover, the gains were larger still, with sensible heat error falling from 46.3 to 38.2 watts per square meter.

The detailed physics behind those numbers is telling. At the reference site of AU-Preston in Melbourne, the urban configurations reproduced the observed summer sensible heat flux far better than the old scheme, both during the day and at night. The improved daytime heat storage, driven by the higher thermal conductivity, left less energy to be dumped into the atmosphere as sensible heat, cutting a summer overestimation that reached a median bias of 77 watts per square meter in the bare-soil runs down to roughly 14 to 18 watts per square meter in the urban runs. Nocturnal sensible heat, which cities keep elevated by releasing stored warmth, also improved in both seasons. Not everything was fixed: daytime sensible heat remained underestimated in winter, and latent heat stayed generally underestimated in summer, likely because the scheme lacks active urban vegetation, irrigation, and anthropogenic heat from traffic and buildings, which can reach nearly 93 watts per square meter at highly urbanized sites like Seoul’s Jungnang district.

The hydrological results reveal both the promise and the limits of the new imperviousness parameterization. Under the strong Urban1 hypothesis, annual surface runoff roughly doubled at most stations, and the balance flipped so that surface runoff exceeded subsurface runoff at fifteen of the twenty sites, exactly the signature urbanization leaves on real catchments. But the moderate Urban2 option produced smaller and sometimes non-monotonic responses, and the simulated reduction in subsurface runoff sits awkwardly with observational studies showing that urban groundwater recharge can actually increase, thanks to leaking pipes and irrigation. The authors are candid that without runoff or soil moisture observations at the tower sites, the water-budget analysis remains a sensitivity assessment rather than a validation.

What comes next is where the story gets ambitious. The team plans convection-permitting simulations over Paris at resolutions below three kilometers, where cities are explicitly resolved and an inaccurate urban energy balance would feed wrong signals straight into the atmosphere. They also intend to add anthropogenic heat fluxes, refine the thermal parameters against measured data, embed active vegetation with seasonal phenology inside the urban tile, and exploit datasets like WUDAPT and the Copernicus Urban Atlas to make albedo, building height, and imperviousness vary across real cityscapes. Notably, most Earth system models contributing to CMIP still ignore cities altogether, a gap that becomes untenable as climate risk assessments increasingly focus on the built environment. A computationally cheap, physically grounded urban tile that works from the neighborhood scale to the globe is exactly the kind of tool that can close it, and ORCHIDEE now has one.

Subject of Research: Development and benchmarking of a new urban land surface scheme with imperviousness-dependent soil hydraulics in the ORCHIDEE 2.2 land surface model

Article Title: Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model

Article References: Lalonde, M., Bastin, S., Oudin, L., Arboleda-Obando, P. F., & Ducharne, A. (2026). Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model. Geoscientific Model Development, 19(19), 9463-9488. https://doi.org/10.5194/gmd-19-9463-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9463-2026

Keywords: ORCHIDEE, land surface model, urban climate, imperviousness, saturated hydraulic conductivity, surface energy balance, Urban-PLUMBER, sensible heat flux, latent heat flux, urban hydrology, convection-permitting simulations, Geoscientific Model Development

The starting point of the analysis is a striking statistic. In 2004, built-up areas accounted for just 4.3 percent of the land surface in and around Hyderabad. By 2024, that figure had climbed to 11.7 percent, nearly tripling in two decades as the city and its periphery absorbed waves of new residential, commercial, and industrial development. This is not merely a change in what the ground looks like from above. Every hectare converted from vegetated or bare soil to impervious surface alters the exchange of energy and water between the land and the atmosphere, changing how sunlight is absorbed, how heat is stored, and how much moisture evaporates back into the air.

To isolate the meteorological fingerprint of this transformation, the team ran a full year of simulations, from December 2023 to November 2024, under two contrasting land use and land cover scenarios. One scenario represented the less urbanized landscape of 2004 to 2005, while the other represented the highly urbanized present day. By holding everything else constant, including the large-scale weather patterns of the simulation year, the researchers could attribute any differences in temperature, humidity, wind, and atmospheric structure directly to the change in land cover. This experimental design is a powerful way of separating the signal of urbanization from the noise of natural climate variability.

The most visible consequence is the urban heat island, the well-known phenomenon in which cities run warmer than their rural surroundings. The simulations confirm that Hyderabad exhibits a persistent heat island across all seasons, with urban areas remaining approximately 2 to 3 degrees Celsius warmer than nearby rural regions. What makes the finding particularly important for a tropical city is its timing. The nocturnal heat island is especially pronounced during winter and the post-monsoon season, when clear skies and weak winds allow the heat absorbed by urban materials during the day to be released slowly back into the air through the night. For residents, this means that the relief of nightfall is measurably diminished precisely during the seasons when people are least prepared for heat stress.

The physics behind this nighttime warming lies in the thermal properties of urban materials. Concrete, brick, and asphalt store far more of the incoming solar energy than soil or vegetation, and they release that stored heat with a delay that stretches well into the night. In the older, less urbanized scenario, more of the daytime energy budget was consumed by evaporation and transpiration, processes that cool the surface but are largely absent once vegetation is replaced by pavement. The new simulations quantify this shift directly: urbanization increases the sensible heat flux, the portion of energy that goes directly into warming the air, while reducing the latent heat flux, the portion consumed by evaporating water.

That redistribution of energy has a second, less obvious consequence that the authors highlight as an urban dry island. Because less water is evaporated and transpired over the built-up surface, the near-surface air over the city and its expanding periphery holds significantly less water vapor than it would under the older landscape. The atmosphere above Hyderabad is not only warmer but drier than it would otherwise be. This drying effect compounds the discomfort of heat, since humid heat is harder for the human body to dissipate, and it also has implications for cloud formation, fog occurrence, and the local water balance. Similar urban dry island effects have been documented in cities from Turin to southern China, but documenting the effect in a semi-arid tropical inland city adds an important data point to the global picture.

Perhaps the most technically significant result concerns the planetary boundary layer, the lowest part of the atmosphere where the air responds directly to the surface within hours. The boundary layer is where pollutants disperse, where clouds initiate, and where the day’s heat is mixed downward from the surface. The simulations show that two decades of urbanization have deepened this layer, with daytime boundary layer heights increasing by roughly 100 meters, particularly during winter and the post-monsoon season. The mechanism is straightforward: stronger surface heating drives stronger convective mixing, pushing the top of the mixed layer higher. A deeper boundary layer can dilute pollutants over a larger volume, but it also changes the dynamics of cloud formation and can alter the dispersion pathways of everything from vehicle exhaust to industrial emissions.

Critically, the largest changes were not found over the historic urban core, which had already been densely built decades ago, but over the rapidly urbanizing peripheral regions. These are the neighborhoods and satellite towns at the city’s edge where farmland and scrubland have been converted to development most recently and most rapidly. The finding carries a clear planning message: the meteorological cost of urbanization is paid most heavily at the frontier of growth, often in areas whose infrastructure and populations are least equipped to cope with intensifying heat. It also suggests that land use decisions made today at the periphery will determine the climate burden of tomorrow.

The study sits within a growing body of evidence that land cover change is a first-order driver of regional climate in India, not a secondary effect. Previous work has linked urbanization signatures to heavy rainfall climatology across Indian cities, shown that irrigation and agriculture exert a dominant control on heat island intensity, and documented strong nighttime surface heat islands across the country’s major population centers. What distinguishes the Hyderabad analysis is its explicit pairing of a two-decade land cover contrast with a full annual cycle of simulations, allowing the seasonal structure of the urban effect to emerge. The finding that winter and post-monsoon nights bear the strongest warming is directly relevant to heat stress management, since these are the seasons in which nighttime cooling is most relied upon for recovery.

For a city that has positioned itself as a global technology destination, the results are a reminder that the atmosphere keeps its own ledger. Every square kilometer of new built-up area adds heat, removes moisture, and deepens the boundary layer, and these changes accumulate in ways that standard weather records, which blend urban and rural influences, tend to obscure. The authors’ scenario-based approach demonstrates that the tools now exist to forecast the climatic consequences of alternative development pathways before the concrete is poured. As Indian cities continue to expand at some of the fastest rates in the world, studies of this kind offer a quantitative basis for choices about green cover, surface materials, and growth corridors that could determine whether the next two decades of urbanization repeat the thermal trajectory of the last.

Subject of Research: The influence of land use and land cover change on surface meteorology and planetary boundary layer characteristics over Hyderabad, India

Article Title: Assessing the influence of land use/land cover on regional meteorology over the tropical inland urban city of Hyderabad, India

Article References: Reddy, T. R., & Rao, T. N. (2026). Assessing the influence of land use/land cover on regional meteorology over the tropical inland urban city of Hyderabad, India. Theoretical and Applied Climatology, 157(10), Article 649. https://doi.org/10.1007/s00704-026-06539-7

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06539-7

Keywords: urban heat island, Hyderabad, land use land cover, urbanization, planetary boundary layer, WRF model, sensible heat flux, latent heat flux, urban dry island, tropical climate, India, Theoretical and Applied Climatology