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Using a detailed model, sensitivity analyses are conducted to identify the leading physical determinants and heat fluxes that control energy exchange between surface runoff and urban pavements during rainfall. These analyses confirm that pavement characteristics, such as albedo and thermal effusivity, strongly influence the initial temperature of the pavement before rain starts. Moreover, this sensitivity propagates to the runoff and pavement temperatures as well as to sensible heat and evaporation fluxes during and after rainfall. Heat transfer inside the runoff and pavement during rainfall is also very sensitive to the rain temperature and is the leading process in surface cooling (the classically important sensible and latent heat fluxes to the atmosphere are minor contributors). Finally, based on the findings from the sensitivity analyses, using a bulk energy approach, a reduced version of the full model is proposed. This simple model uses the spatially averaged temperatures of the runoff and pavement and can predict their temperatures and the associated energy fluxes almost as accurately as the full model. The reduced model has the added advantages of computational efficiency and simplicity of implementation in coarse earth system models.

 

The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements.

 

Large-scale circulations around a city are co-modulated by the urban heat island and by regional wind patterns. Depending on these variables, the circulations fall into different regimes ranging from advection-dominated (plume regime) to convection-driven (bubble regime). Using dimensional analysis and large-eddy simulations, this study investigates how these different circulations scale with urban and rural heat fluxes, as well as upstream wind speed. Two dimensionless parameters are shown to control the dynamics of the flow: (1) the ratio of rural to urban thermal convective velocities that contrasts their respective buoyancy fluxes and (2) the ratio of bulk inflow velocity to the convection velocity in the rural area. Finally, the vertical flow velocities transecting the rural to urban transitions are used to develop a criterion for categorizing different large-scale circulations into plume, bubble or transitional regimes. The findings have implications for city ventilation since bubble regimes are expected to trap pollutants, as well as for scaling analysis in canonical mixed-convection flows. 

Large Eddy Simulations (LES) of neutral flow over regular arrays of cuboids are conducted to explore connections between momentum (z 0m ) and scalar (z 0s ) roughness lengths in urban environments, and how they are influenced by surface geometry. As LES resolves the obstacles but not the micro‐scale boundary layers attached to them, the aforementioned roughness lengths are analyzed at two distinct spatial scales. At the micro‐scale (roughness of individual facets, e.g. roofs), it is assumed that both momentum and scalar transfer are governed by accepted arguments for smooth walls that form the basis for the LES wall model. At the macro‐scale, the roughness lengths are representative of the aggregate effects of momentum and scalar transfer over the resolved roughness elements of the whole surface, and hence they are directly computed from the LES. The results indicate that morphologically‐based parameterizations for macro‐scale z 0m are adequate overall. The relation between the momentum and scalar macro‐roughness values, as conventionally represented by log(z 0m /z 0s ) and assumed to scale with urn:x-wiley:00359009:media:qj3839:qj3839-math-0001 (where Re * is a roughness Reynolds number), is then interpreted using surface renewal theory (SRT). SRT predicts n = 1/4 when only Kolmogorov‐scale eddies dominate the scalar exchange, whereas n = 1/2 is predicted when large eddies limit the renewal dynamics. The latter is found to better capture the LES results. However, both scaling relations indicate that z 0s decreases when z 0m increases for typical urban geometries and scales. This is opposite to how their relation is usually modeled for urban canopies (i.e. z 0s /z 0m is a fixed value smaller than unity). 

Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences ($\mathrm{\Delta }{T}_s$) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of$\mathrm{\Delta }{T}_s$. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce$\mathrm{\Delta }{T}_s$during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation.

 

Large-eddy simulations are conducted to contrast momentum and passive scalar transport over large, three-dimensional roughness elements in a turbulent channel flow. Special attention is given to the dispersive fluxes, which are shown to be a significant fraction of the total flux within the roughness sublayer. Based on pointwise quadrant analysis, the turbulent components of the transport of momentum and scalars are found to be similar in general, albeit with increasing dissimilarity for roughnesses with low frontal blockage. However, strong dissimilarity is noted between the dispersive momentum and scalar fluxes, especially below the top of the roughness elements. In general, turbulence is found to transport momentum more efficiently than scalars, while the reverse applies to the dispersive contributions. The effects of varying surface geometries, measured by the frontal density, are pronounced on turbulent fluxes and even more so on dispersive fluxes. Increasing frontal density induces a general transition in the flow from a wall bounded type to a mixing layer type. This transition results in an increase in the efficiency of turbulent momentum transport, but the reverse occurs for scalars due to reduced contributions from large-scale motions in the roughness sublayer. This study highlights the need for distinct parameterizations of the turbulent and dispersive fluxes, as well as the importance of considering the contrasts between momentum and scalar transport for flows over very rough surfaces.