the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Barriers of urban hydro-meteorological simulation: a review
Abstract. Urban areas, characterized by dense populations and many socio-economic activities, increasingly suffer from floods, droughts, and heat stress due to land use and climate change. Traditionally, the urban thermal environment and water resources management have been studied separately, using urban land surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes clear that hydrological disasters—such as floods, droughts, severe urban thermal environments, and more frequent heat waves—are actually not isolated events but compound events. This underscores the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles to better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydro-meteorological predictions and assessments is essential for better managing climate disasters and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydro-meteorological simulation: similarities, differences, and gaps among different models; existing gaps in physical process implementations; and efforts, challenges, and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show potential for simulating urban hydro-meteorological environments, but face challenges at regional and neighborhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and urban water management communities to enhance urban hydro-meteorological simulation models.
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RC1: 'Comment on egusphere-2024-3988', Zhi-Hua Wang, 25 Jan 2025
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This paper presents a comprehensive review of the state-of-the-art urban climate and urban hydrological modeling. The review is timely and of great interest to the urban study community, especially because urban hydrological modeling has been lagging behind practices for sustainable urban development. While some technical details need to be further clarified (see my specific comments below), overall, the paper is technically sound and well written, and I thoroughly enjoyed reading the manuscript. I therefore recommend the paper to be accepted for publication after the following comments to be adequately addressed.
Specific comments
1. Lines 112: on the compound urban climate mitigation mechanisms, the following study provides a mathematical formalism that may worth considering:
Wang, Z.H. (2021). Compound environmental impact of urban mitigation strategies: Co-benefits, trade-offs, and unintended consequence. Sustainable Cities and Society, 75, 103284. https://doi.org/10.1016/j.scs.2021.103284
2. Line 160-161: “However, the multilayer models solve the vertical profiles of the atmospheric conditions”, the phrase “atmospheric conditions” is too general to describe the multilayer UCM, it is more precise to use “canopy-layer flows and momentum transport”.
3. Table 1: under SLUCM, the citation Wang et al., 2021 should be referring to
Wang, C., Wang, Z.H., & Ryu, Y.H. (2021). A single-layer urban canopy model with transmissive radiation exchange between trees and street canyons. Building and Environment, 191, 107593. https://doi.org/10.1016/j.buildenv.2021.107593
which is not included in the reference list.
4. Figure 1: I wonder if it is necessary to separately indicate the surface temperature and net radiation for heterogeneous landuse. For by the same token, sensible (and latent) heat fluxes for these facets are also different and should also be separately indicated. In addition, there is a downwelling Rnet and 7 upwelling Rnets, while the net radiation from the urban canopy should be the combination of them, and none of the individual components can be called Rnet I’d suggestion to keep the separate representation of surface temperatures as it is, but combine the radiation into a single Rnet (or with a downwelling radiation as Rdown and a upwelling component as Rup).
5. Lines 226-229, “The single-layer urban canopy model developed by Kusaka et al. (2001) (SLUCM) is very similar to TEB. The only differences are that the SLUCM in this version (Kusaka et al., 2001) includes the canyon orientation and diurnal change of solar azimuth angle, and the surface consists of several canyons with different orientations.” This statement is incomplete. In fact, Kusaka’s SLUCM, as implemented into WRF, contains a different parameterization scheme of radiation by discretizing the canyon facet and computing radiation on individual gridcells, whereas TEB uses the analytical formulae for in-canyon view factors. For simple rectangular canyons with only walls and roads (and short vegetation), Kusaka’s radiation scheme is a setback to the analytical formulation, but it opens the possibility to include radiative exchange by roughness elements presented in street canyons such as trees/blocks/vehicles.
6. Table 3, I don’t really understand how the temporal resolution of different ULSMs is determined. If the temporal resolution refers to the time intervals/steps used to solve the parameterization scheme, it varies widely depending on the discretizing (forward- or central-in-time finite difference) schemes, running platforms (offline or imbedded in regional climate models such as WRF), and applications. The time steps used to solve parameterization schemes can be as small as 1s (e.g. WRF-UCM for it used both spatial-temporal discretization for land-atmosphere interactions), or as large as 30 min. If the temporal resolution refers to the time scale for sampling the output, it is a rather arbitrary choice of the users. For instance, output of WRF-UCM is often sampled in hourly scale, like what is indicated in the table, but it can also be sampled at 3-hourly or 6-hrouly intervals for longterm (monthly to annual) simulations, but it can also be, in theory, sampled at 1s interval. My understanding is that the temporal resolution of all UCMs have no essential difference as their parameterization schemes represented by partial differential equations of land surface processes are all similar. The spatial scale for them does vary for SLUCM resolves the physical structure of the canyon, and building-resolving models has to resolve individual buildings, while slab models represent the aggregated urban landscapes.
7. Table 5: SLUMC should be SLUCM (same typo in Tables 6 and 7). Also, I am concerned about the naming of the urban canopy models. The discussion of the single-layer urban canopy models in this paper is largely based TEB, Kusaka’s UCM implemented in WRF, and the Arizona Single Layer Urban canopy Model (ASLUM, the name is used in Wang et al., 2021, Lipson et al., 2024, and Jongen et al., 2024). Yet TEB is separately discussed in this table, and SLUCM seemingly groups ASLUM and Kusaka’s model. Given the fact that Kusaka’s single-layer model is not further developed in a separate line, the representative SLUCM should be more properly named after ASLUM, as the latter is a coherent family of models developed by the same group of model developers in a continuous manner (Wang et al., 2013; Wang, 2014; Yang et al., 2015; Ryu et al., 2016; Wang et al., 2024).
8. Section 6: this part presents some thought-provoking questions that need to be pursued in future development of urban climate and urban environment modeling in depth. I would suggest the authors also include a brief discussion of the potential of AI and machine learning application in the field, given that these tools are increasingly adopted and some promising results generated from pioneering work in this field.
Citation: https://doi.org/10.5194/egusphere-2024-3988-RC1
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