Urban Footprints in the Storm: Land-Use Sensitivity of Extreme Rainfall over Chennai

Urban Footprints in the Storm: Land-Use Sensitivity of Extreme Rainfall over Chennai | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Urban Footprints in the Storm: Land-Use Sensitivity of Extreme Rainfall over Chennai Konduru Rakesh Teja, Anu Gupta, Vivek Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7670619/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urbanization significantly changes land surface features, affecting local weather and increasing extreme rainfall events (EREs) in cities like Chennai. Improving urban resilience to climate-related extremes requires understanding how land-use patterns influence heavy rainfall in fast-growing cities. Greening concrete grids with vegetation or converting them to cropland can reduce convective strength and change moisture flows, helping to moderate extreme rainfall. To explore this, we performed high-resolution simulations using the Weather & Research Forecasting Model (WRF), combined with an urban canopy model, for three land-use scenarios: fully urbanized (UCM), cropland replacement (Crop (UCM)), and flooded surface (Water (UCM)). These experiments showed that urban surfaces increase both the frequency and severity of heavy rainfall, while replacing urban areas with cropland significantly decreases rainfall and surface runoff. The Water (UCM) scenario displayed mixed results, with shifts in rainfall patterns and increased runoff compared to Crop (UCM). Additionally, urbanization boosted sea-breeze circulation and moisture transport, which contributed to localized rainfall intensification. These results emphasize the important role of land-use planning in influencing ERE behavior. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Urbanization Chennai floods rainfall Convection permitting model Extreme rainfall urban climate model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Urbanization, a hallmark of human progress, has significantly altered the natural landscape and atmospheric processes, often intensifying the risk of extreme weather events such as urban floods. As cities expand, impervious surfaces increase, modifying local hydrology and enhancing surface runoff, which in turn exacerbates flood vulnerability during heavy rainfall events (Pielke et al., 2007; Yu et al., 2015; Shepherd et al., 2010 ). Urban areas also influence atmospheric dynamics by altering surface energy balances, increasing heat retention, and modifying boundary layer characteristics, which can lead to enhanced convective activity and localized precipitation (Oke, 1982 ; Bornstein & Lin, 2000 ; Niyogi et al., 2017 ; Konduru et al. 2023 ). These effects are not confined to city centers alone; urban-induced changes can extend to surrounding regions, influencing mesoscale weather patterns (Singh et al., 2020 ; Zhang et al., 2018 ). For instance, the hydrometeorological impact of Hurricane Harvey over Houston was found to be amplified by urban land use, as shown through WRF simulations (Zhang et al., 2018 ). Similarly, long-term analyses over the eastern United States revealed a rising trend in summer heavy rainfall events linked to urbanization (Niyogi et al., 2017 ). In India, with its rapidly growing cities, has witnessed a surge in extreme rainfall events, particularly during the summer monsoon season. Kishtawal et al. ( 2010 ) identified a significant increase in heavy rainfall occurrences over major Indian metropolitan cities, attributing this trend to urbanization. However, the relationship between urbanization and rainfall extremes remains complex and sometimes contested. Ali et al. ( 2014 ), using IMD station data, found that only a few cities among those experiencing extreme rainfall events showed a clear urbanization signal. Singh et al. ( 2016 ) highlighted that urbanization introduces temporal variability in monsoon rainfall, leading to nonstationarity in its patterns. Shastri et al. ( 2015 ) further demonstrated that urban regions in central and western India are particularly susceptible to urban-induced rainfall intensification. Case studies over Mumbai have shown that urbanization can exacerbate monsoon extremes, as evidenced by the 2005 flood event (Paul et al., 2018 ; Zope et al., 2015 ). Remote sensing and topographic analyses have confirmed that land use changes in Mumbai have played a critical role in worsening flood impacts (Zope et al., 2016 ). Recently, over Chennai region Konduru et al. ( 2023 ) presented a comprehensive study on urban climate dynamics over Indian cities, highlighting the role of mesoscale interactions and land surface heterogeneity in shaping rainfall extremes. These findings showcases the need for improved modeling and prediction capabilities to understand and mitigate urban flood risks in India’s megacities. Accurate representation of urban land surface processes in numerical weather prediction (NWP) models is essential for simulating extreme rainfall events in urban settings. Traditional models often fail to capture the complexity of urban-atmosphere interactions unless urban canopy schemes are explicitly included (Lei et al., 2008 ; Kusaka et al., 2001 ; Chen et al., 2011a ). The use of high-resolution models like WRF, coupled with urban schemes such as the Single-Layer Urban Canopy Model (SLUCM), has shown promise in improving rainfall simulations over cities like Mumbai and Hyderabad (Niyogi et al., 2020 ; Patel et al., 2020 ). Incorporating realistic urban land use and energy balance processes helps in better capturing phenomena like Urban Heat Island (UHI) and Urban-Induced Turbulence (UIT), which are critical for convective development (Shepherd, 2005 ; Oleson et al., 2011 ; Konduru et al. 2025a , b ). Moreover, novel approaches such as Local Climate Zone (LCZ) mapping have enhanced the spatial accuracy of urban rainfall simulations (Patel et al., 2020 ). Recent advancements in Large-Eddy Simulation (LES) have further improved the representation of urban boundary layer processes. Notably, Konduru et al. ( 2025a ) demonstrated the effectiveness of LES at 2-meter resolution in simulating urban turbulence impact on the upward momentum transport. While in another Konduru et al. ( 2025b ) showed the improvements in capturing urban-induced turbulence impact on the rainfall enhancement over the Chennai region. Despite these advancements, the role of urban grid modifications such as replacing urban areas with crop or water land use types remains underexplored. Understanding how these changes affect precipitation dynamics can provide valuable insights into urban flood mitigation and planning. Motivated by the increasing vulnerability of Chennai to extreme rainfall and flooding, this study aims to evaluate the impact of urban land use representation on rainfall simulations using the WRF model. Specifically, we conduct ultra-high-resolution (1-km) convection-permitting experiments to compare four scenarios: (1) no urban scheme, (2) urban scheme with, (3) urban grids replaced with crop land use, and (4) urban grids replaced with water bodies. These experiments are designed to isolate the influence of urbanization on rainfall distribution and intensity during a major flood event over Chennai. By analyzing the differences in precipitation patterns across these configurations, we aim to understand the role of urban surface characteristics in modulating extreme weather. Through this approach, we seek to contribute to the growing body of research on urban meteorology and provide actionable insights for urban planning and disaster preparedness in coastal Indian cities. 2. Datasets and methodology 2.1 Regional climate model To investigate the Chennai flood event, a high-resolution simulation was performed using version 3.9.1 of the Weather Research and Forecasting (WRF) model (Skamarack et al. 2008) was configured. The computational domain encompassed Chennai and adjacent regions, as shown in the figure, incorporating terrain and geographic features essential for realistic atmospheric modeling. A nested grid system was applied, with horizontal resolutions of 25 km, 5 km, and 1 km. ERA5 reanalysis data were used to initialize the simulation, ensuring accurate representation of large-scale meteorological conditions. The physics settings used in the simulation, were consistent with those employed in our previous study on extreme rainfall in Chennai (Konduru et al. 2023 ), and are listed in Table T1 (Mellor et al. 1982; Janjić 1994 ; Janjić et al. 2001; Chen and Dudhia 2001 ; Hong et al. 2004 ; Milbrandt and Yau 2005 , 2006 ; Iacono et al. 2008 ; Chen et al. 2011b ). The microphysics schemes tested included WSM6, Thompson, and MYNN, each offering distinct treatments of cloud microphysics and precipitation processes, which are vital for capturing convective systems accurately. For PBL representation, MYNN and MYJ schemes were selected, each contributing differently to the modeling of turbulence and vertical mixing. Cumulus parameterization was disabled across all domains to reduce errors associated with convective parameterization (Konduru et al. 2020), allowing convection to be resolved explicitly. The model time step was adjusted according to grid resolution, ranging from 90 seconds for the 25 km grid to 5 seconds for the 1 km grid, ensuring numerical stability and precision. Initial conditions for the simulation were derived from ERA5 reanalysis data, ensuring that the broader atmospheric environment was accurately captured. 2.2 Urban Canopy Model in WRF The WRF simulation coupled with the single-layer Urban Canopy Model (SLUCM; Kusaka et al. 2001 ; Kusaka and Kimura 2004 ; Chen et al. 2011b ), which conceptualizes urban areas as idealized two-dimensional street canyons with symmetric rows of buildings and infinite length. This approach is based on the original single-layer urban parameterization framework (Kusaka et al. 2001 ) and urban heat island experiments, forming the basis for subsequent urban weather forecasting applications. At each time step, the SLUCM computes separate radiative balances for roofs, walls, and roads, accounting for shadowing effects, multiple reflections, and longwave radiation trapping. A four-layer heat conduction model is applied to these surfaces to simulate thermal behavior. The model advances prognostic variables such as surface temperatures of roofs, walls, and roads, canyon air temperature and humidity, and wind speed at canopy level, in sync with the atmospheric grid. Momentum and heat exchanges are calculated using roughness lengths and drag coefficients that depend on building density. The SLUCM receives atmospheric inputs wind, temperature, humidity, and downward radiation and returns updated fluxes and surface temperatures to the surface layer, radiation, and PBL schemes in the standard WRF physics sequence. Key geometric and thermal parameters such as building height, roughness length, sky-view factor, building fraction, heat capacity, thermal conductivity, albedo, and emissivity are retrieved from a lookup table, while anthropogenic heat flux and layer thickness are set to default values. 2.3 Experiment design We conducted a control run using WRF (Fig. 1 ), where urban grids remained unaltered by uncoupling WRF with SLUCM (NOUCM); UCM, we applied the SLUCM with anthropogenic heat and coupled to WRF. In the Crop (UCM) experiment, where urban grids were replaced with cropland using SLUCM, and in the Water (UCM) experiment, where urban grids were substituted with water bodies using SLUCM. These modifications were applied to all urban grids within the Chennai metropolitan area and its surroundings. The total number of experiments comprised 20 simulations, structured across five ensemble types and four urban land-use configurations. These simulations were designed to examine the sensitivity of the model to urban representation and physical parameterizations. The simulation period spanned four days, from 26 November 00 UTC to 3 December 00 UTC, 2015, coinciding with a major rainfall event that caused severe flooding in Chennai (Konduru et al. 2023 ). Each simulation included a 24-hour spin-up phase to allow the model to stabilize and develop realistic atmospheric conditions before analysis. The reproducibility of the UCM model was earlier assessed in (Konduru et al. 2023 ), and we showed that WRF-SLUCM can simulated more rainfall close to observations. The model experiments each with 5 ensemble simulations were compared with the NOUCM, Crop (UCM), and Water (UCM) experiments. These simulations were designed to isolate the effects of urban land-use representation on rainfall and convection during the Chennai flood event. For consistency across configurations, all model outputs were interpolated to a common resolution of 1-km, enabling direct comparison with observations and minimizing resolution-induced biases. Difference among the experiments were generated by computing the ensemble mean for each urban experiment and subtracting it from the UCM ensemble mean. This method highlights the spatial and structural changes introduced by different urban treatments, particularly the influence of UCM and land-use modifications (Crop (UCM) and Water (UCM)) on simulated rainfall patterns. The approach provides a robust framework for evaluating how urban representation affects model fidelity, especially in reproducing extreme precipitation events. 3. Results 3.1 Impact of urban scheme on the extreme rainfall simulation The mean accumulated rainfall during the ERE event is compared between the two model experiments (NoUCM and UCM) and CHIRPS observations (Fig. 2 ). Observations show more than 250 mm of precipitation along the southeast coast, especially over the Chennai region. NoUCM simulates less than 150 mm/day of precipitation over inland areas around Chennai compared to observations, but shows higher precipitation offshore. In contrast, the UCM experiment simulates a spatial pattern of precipitation that closely matches observations, with precipitation rates exceeding 250 mm/day both inland and offshore of Chennai. The impact of coupling urbanization with the land surface and atmosphere models is evident in simulating the ERE event over Chennai. Using the urban scheme results in 80–100 mm/day more precipitation over the southeast coast and near Chennai. Therefore, incorporating urbanization effects into the atmosphere model is crucial for more accurately simulating urban-related impacts on the ERE. 3.2 Urbanization impact on rainfall sensitivity to land use type To evaluate the effect of urbanization on the extreme rainfall event (ERE) in Chennai, two sensitivity experiments were performed: one in which urban land-use was replaced with cropland (Crop (UCM) ), and another in which the urban area was replaced with flooded surfaces (Water (UCM)). As shown in Fig. 3 , the accumulated precipitation during the ERE reveals a pronounced dependence on land-use type. In the Crop (UCM) experiment, a clear reduction in total rainfall is evident over Chennai and surrounding neighborhoods. The simulated precipitation shows that when urban grids are replaced with croplands, the overall rainfall during the ERE decreases significantly, with the Chennai region receiving less than 100 mm, and weaker intensity precipitation systems compared to the fully urbanized scenario (Figure bb). In contrast, the Water (UCM) experiment, where the urban area is entirely flooded, produces total rainfall amounts similar to the Urban Canopy Model (UCM) experiment. However, the spatial pattern is altered, with much higher rainfall observed in the neighborhoods around Chennai (Fig. 3 c). These findings highlight the crucial role of land-use patterns in influencing the magnitude and distribution of extreme rainfall in urban environments. 3.3 Changes in the extreme rainfall characteristics To further dissect the impacts of urbanization, we analyzed the frequency of heavy precipitation events (defined as hourly rainfall rates exceeding 2 mm/hr) across the different experiments. The spatial distribution of heavy precipitation frequency, as shown in Fig. 4 , exhibits substantial variability depending on the land-use configuration. The urbanized scenario (UCM) is characterized by a significantly higher frequency of heavy rainfall events compared to the Crop (UCM) experiment, which simulates 50–60% fewer heavy precipitation events over Chennai (Fig. 4 d). Similarly, the Water (UCM) scenario yields about 10% fewer heavy precipitation events than the UCM. Notably, the most pronounced differences in heavy rainfall frequency occur in the vicinity of Chennai, underscoring the local amplification of extreme precipitation due to urbanization. The intensity of extreme rainfall events, calculated as the mean rainfall rate for events exceeding 2 mm/hr, also varies markedly across the experiments (Fig. 5 ). Urbanization is found to enhance both the intensity and spatial concentration of heavy precipitation over Chennai and its immediate neighborhood. Relative to the UCM case, the Crop (UCM) scenario exhibits 15–20% lower intensity of extreme rainfall over Chennai, while the Water (UCM) experiment displays about 5% lower intensity. Moreover, the intense precipitation region to the south of Chennai observed in the urbanized experiment becomes more spatially uniform in the Water (UCM) case and is substantially diminished in the Crop (UCM) case. This highlights the role of impervious urban surfaces in fostering more localized and intense precipitation systems. 3.4 Integrated Precipitation Characteristics and Surface Run-off To understand the impact of urbanization on extreme precipitation, we performed a detailed analysis of the frequency and intensity of the 5-ensemble mean simulated extreme rainfall events in the Chennai region. ​Major changes in the frequency of heavy precipitation in UCM are noted with respect to each experiment over the neighborhood of Chennai (Fig. 6 a). A higher frequency of heavy precipitation events was observed in the UCM compared to the experiment Crop (UCM), and simulating 50–60% less heavy precipitation frequency compared to the UCM experiment. ​Additionally, the Water (UCM) experiment simulates 10% less heavy precipitation frequency than UCM. ​In terms of the intensity of heavy precipitation events, urbanization leads to higher intensity precipitation over Chennai and its neighborhood. ​ The UCM case has 15–20% higher intensity of precipitation compared to the Crop (UCM) experiment. ​The water (UCM) experiment simulates 5% less intensified precipitation compared to the UCM case. ​In the Water (UCM) case, the precipitation spatial distribution to the south of Chennai is uniform. ​The increase in precipitation characteristics that intensified rainfall in UCM also impacted surface run-off (Fig. 6 a). The surface run-off is reduced by 26% in the Crop (UCM) experiment, indicating a decrease in the chances of flooding. Also, the precipitation characteristics and run-off are almost 50% increased over the Water (UCM) compared to the Crop (UCM) (crop) region. Overall, the findings suggest (Fig. 6 b) that urbanization has a significant impact on the frequency, intensity, and overall characteristics of extreme rainfall events in the Chennai region. ​ Urbanization leads to higher frequency and intensity of heavy precipitation, while replacing the urban area with croplands reduces these characteristics. ​The experiment with a Water (UCM) city shows mixed results, with higher total rainfall and increased precipitation characteristics compared to the non-urbanized case. 4. Discussion 4.1 Influence of Urbanization on Surface Winds and Water Vapor Earlier sensitivity experiments clearly illustrate significant modifications in surface wind patterns associated with urbanization, which directly influence onshore moisture transport during the extreme rainfall event (ERE), consistent with a study on a coastal city, Houston, by Fan et al. (2020). Before the event, substantial differences were observed in surface wind fields among the urbanized (UCM), cropland (Crop (UCM) ), and flooded (Water (UCM)) scenarios (Fig. 7 a-b). Specifically, in the Crop (UCM) scenario, anomalous southwesterly and northeasterly winds converge weakly near northeastern Chennai, indicating reduced convective potential compared to the urbanized case. In contrast, the Water (UCM) scenario exhibited stronger anomalous southwesterly winds around Chennai, highlighting how flooded urban surfaces can enhance low-level wind speeds due to altered land-sea thermal contrasts. During the ERE, notable differences in wind anomalies persisted (Fig. 7 c-d). In the Crop (UCM) experiment, anomalous southwesterly winds receded, and weaker easterly winds dominated. However, the urbanized scenario sustained intensified anomalous northeasterlies along the eastern coast and over central Chennai, indicative of enhanced sea-to-land breeze circulation. This robust sea-breeze circulation in the urban scenario significantly contributed to increased moisture transport from oceanic regions into Chennai. Enhanced moisture transport facilitated by intensified surface winds thus emerges as a critical mechanism behind increased rainfall in urbanized scenarios consistent with Fan et al. (2020). Figure 8 clearly illustrates that urbanization substantially increased precipitable water vapor over Chennai during the ERE compared to the Crop (UCM) and Water (UCM) experiments. The urbanized scenario demonstrated markedly higher water vapor content, predominantly due to intensified coastal northeasterly winds enhancing ocean-to-land moisture transport. This increase in atmospheric moisture content provided essential fuel for the convection and significantly contributed to intensifying rainfall systems over Chennai. Thus, higher water vapor levels in the urban scenario underscore the importance of urban-induced mesoscale circulation changes in modulating rainfall characteristics. 4.2 Influence on surface heat fluxes Analysis of surface energy fluxes further underscores the critical role of urban surfaces in modifying atmospheric conditions. Before the rainfall event, urbanized surfaces generated significantly higher sensible heat fluxes compared to the Crop (UCM) and Water (UCM) scenarios (Figs. 9 a-b). Higher sensible heat fluxes in the urban scenario indicate greater heating of the surface air layer, creating favorable conditions for enhanced convective activity. Conversely, the latent heat fluxes were lower in urbanized scenarios (Figs. 9 c-d), reflecting the lower evaporative cooling capacity of impervious urban surfaces compared to vegetated or water-covered surfaces. Ground heat fluxes (Figs. 9 e-f) are notably higher in the urbanized scenario. Increased ground heat fluxes in urban regions reflect the enhanced capacity of urban materials to store heat, which is subsequently released during nighttime. This storage and nighttime release of heat support elevated nocturnal temperatures, maintaining atmospheric instability conducive to convective processes. Such urban thermal effects highlight the integral role of land-use characteristics in shaping local atmospheric dynamics during extreme rainfall events. 4.3 Integrated Assessment of Surface Energy and Hydrological Response The comparative analysis of surface energy flux changes in the Crop (UCM) and Water (UCM) scenarios relative to the urbanized case revealed substantial variations (Fig. 10 ). Notably, the non-urbanized crop scenario exhibited a major reduction in ground heat flux (-60%) and sensible heat flux (-5 to -10%) relative to the urban scenario. Similarly, in the Water (UCM) scenario, latent heat fluxes increased slightly due to the higher thermal inertia and evaporation potential of water surfaces. Yet, significant reductions were again observed in ground heat flux (-69%) and sensible heat flux (-25 to -30%). These reductions directly translated to lower surface temperatures and diminished convective potential. The hydrological implications of altered surface fluxes are critical. Our analysis shows a clear reduction in extreme rainfall frequency and intensity over cropland scenarios compared to the urbanized scenario. Consequently, total rainfall accumulation decreased significantly, reducing surface runoff by approximately 26%. Such reductions imply a notable decrease in flooding potential, highlighting the beneficial hydrological impact of maintaining vegetated land surfaces. These results provide robust evidence that urbanization significantly exacerbates extreme rainfall characteristics through complex interactions between surface heat fluxes, moisture transport, and local circulations. Urban landscapes enhance low-level convergence and intensify coastal breezes, promoting greater atmospheric moisture transport and rainfall intensification. The observed increases in extreme rainfall frequency and intensity have profound implications for urban flood risk management, emphasizing the necessity of integrating urban climate resilience into city planning and development. While cropland scenarios markedly reduced flood risk, flooded urban surfaces presented mixed outcomes, emphasizing the complexity of surface-water interactions in modifying urban climates. Therefore, sustainable urban development must carefully consider land-cover choices, balancing impervious surface expansion with green infrastructure and water-sensitive urban design principles. 5. Summary To assess the role of urbanization in modulating extreme rainfall events (EREs) over Chennai, three sensitivity experiments were conducted using the WRF model: a fully urbanized scenario (UCM), a cropland-replacement experiment (Crop (UCM) ), and a flooded-surface experiment (Water (UCM)). The results show that land-use configuration significantly influences both the magnitude and spatial distribution of rainfall. In the Crop (UCM) experiment, replacing urban grids with cropland led to a marked reduction in total rainfall, with Chennai receiving less than 100 mm and weaker convective systems. In contrast, the Water (UCM) experiment produced rainfall totals similar to the UCM case but shifted the spatial pattern, with higher rainfall observed in the surrounding neighborhoods. The frequency and intensity of heavy precipitation events (defined as hourly rainfall > 2 mm/hr) were also analyzed. The UCM scenario showed the highest frequency of such events, while the Crop (UCM) experiment simulated 50–60% fewer occurrences over Chennai. The Water (UCM) experiment showed a moderate reduction of about 10%. In terms of intensity, the UCM case exhibited 15–20% higher rainfall rates than Crop (UCM) and about 5% higher than Water (UCM). The spatial distribution of intense rainfall was more concentrated in the UCM case, while the Water (UCM) scenario produced a more uniform pattern and the Crop (UCM) case showed a significant decline. These findings highlight the sensitivity of urban precipitation to surface characteristics and the amplifying effect of impervious urban surfaces in intensifying both the frequency and spatial concentration of extreme rainfall. An integrated analysis of ensemble mean precipitation characteristics confirmed these trends. The UCM scenario consistently showed higher frequency and intensity of heavy rainfall compared to the other two experiments. Surface runoff, a key indicator of flood risk, was reduced by 26% in the Crop (UCM) case, while the Water (UCM) experiment showed nearly 50% higher runoff than Crop (UCM) , indicating that flooded urban surfaces can still exacerbate flood potential. Due to the absence of real urban morphological features and the use of non-LES configurations, exact UIT effect cannot be resolved. Future work will incorporate detailed urban structure and LES modeling to better resolve urban boundary layer processes and validate results extensively using surface and satellite observations. Declarations Acknowledgement and Funding Information We would like to thank the Osaka Central Advanced Mathematical Institute, Osaka Metropolitan University, for the resources and support. The first and third authors will share the publication cost, supported by the third author’s institution. We would like to thank Dr. C M Kishtawal for his detailed discussion. Also, Would like to thank Prof. Venkatesh Raghavan for his discussions and his recommendations to continue this work for Osaka city. We used large language models to correct English and to improve the readability. Data Availability Statement The simulation results were obtained using the Weather & Research Forecasting Model (WRF v3.9.1), which can be downloaded with registration from the link (https://www2.mmm.ucar.edu/wrf/users/download/get_source.html). The ERA5 dataset, used as initial conditions, can be obtained from the link with registration (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview). Topography data in the WRF model is taken from the Shuttle Radar Topography Mission 3-second (SRTM 3s) dataset, which can be downloaded from the link with registration (https://www2.mmm.ucar.edu/wrf/src/wps_files/geog_high_res_mandatory.tar.gz). Further queries can be directed to corresponding author. 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Synoptic analysis and urban signatures of a heavy rainfall on 7 August 2015 in Beijing. Journal of Geophysical Research: Atmospheres . https://doi.org/10.1002/2016JD025420 Zhang, W., Villarini, G., Vecchi, G.A. and Smith, J.A., 2018. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature , 563 (7731), pp.384-388. Zope, P. E., Eldho, T. I., & Jothiprakash, V. (2015). Hydrological impacts of land use–land cover change in Mumbai, India. Hydrological Sciences Journal , 60(5), 745–760. https://doi.org/10.1080/02626667.2014.967248 Zope, P. E., Eldho, T. I., & Jothiprakash, V. (2016). Urban flood modeling using integrated hydrological–hydraulic modeling approach. Natural Hazards , 84(2), 749–776. https://doi.org/10.1007/s11069-016-2455-1 Table Table 1. Weather Research & Forecasting (WRF v3.9.1) model settings Model configuration Description Horizontal resolution (nested domains) 25 km, 5km, 1 km Cumulus parameterization Off in all domains Planetary boundary layer scheme Mellor-Yamada-Janjic (MYJ), MYNN Microphysics WSM6 Land surface model Noha land surface model Urban scheme Urban Climate Surface physics Monin-Obukhov-Janjic Long and shortwave radiation RRTM and Dudhia scheme Vertical levels 30 terrain following coordinates Land use dataset MODIS Land use land cover dataset Initial and boundary conditions ERA5 6 hourly dataset Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:42:56","extension":"xml","order_by":44,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102213,"visible":true,"origin":"","legend":"","description":"","filename":"a171610f0d17455caa7e178e33e18a031structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/3993eacb21fe3400e2d42688.xml"},{"id":95399538,"identity":"c4774ef4-96c4-46e5-bb81-9fca0627c569","added_by":"auto","created_at":"2025-11-07 15:42:56","extension":"html","order_by":45,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113953,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/65e97c6a7409bfadf1acfafb.html"},{"id":95399496,"identity":"6c2a07b5-9ccd-49ee-8b4a-e91d54210496","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":901493,"visible":true,"origin":"","legend":"\u003cp\u003ea) MODIS Land use land cover (LULC) type in the 1 km horizontal resolution domain used in the simulation settings. The dashed rectangle region indicates Chennai and its surrounding areas. Experiments with b) default urban grids, and replaced these urban 1 km x1 km pixels with c) Crop, and d) Water.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/86e82b3350d2c70263b8f230.png"},{"id":95399498,"identity":"bec9a256-e982-4d05-b1c1-f618a6286dc9","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":614947,"visible":true,"origin":"","legend":"\u003cp\u003eObserved accumulated rainfall (mm) by using a) CHIRPS dataset. Five ensemble mean of accumulated precipitation (mm) for b) no urban climate model coupling (NOUCM) and c) urban climate model coupling (UCM) simulations. Dash circled region points Chennai.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/cd5360d42696854f6d4bb3e9.png"},{"id":95399503,"identity":"18846343-54b7-4326-a4a2-f6d2946bcaf4","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":855272,"visible":true,"origin":"","legend":"\u003cp\u003eFive ensemble mean of accumulated precipitation (mm) over a) urban grids with crop (Crop (UCM) ) and b) urban grids with water (Water (UCM)) simulations. A difference of UCM with respect to c) Crop (UCM) , and d) Water (UCM). Dash circled region points Chennai.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/4337a1a4e2407d6087de5eb1.png"},{"id":95399497,"identity":"f3944320-723a-435d-a6d6-53cedbf52ff0","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1276358,"visible":true,"origin":"","legend":"\u003cp\u003eFive ensemble mean of frequency of heavy rainfall (%) in a) UCM, b) Water, and, c) Crop experiments. A difference of heavy rainfall frequency in UCM with respect to d) Crop (UCM) , and e) Water (UCM). Dash circled region points Chennai.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/71bf888de7c9a96f506535d8.png"},{"id":95526887,"identity":"146bb7ba-759a-4fd6-be44-89334e760dad","added_by":"auto","created_at":"2025-11-10 10:08:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1228987,"visible":true,"origin":"","legend":"\u003cp\u003eFive ensemble mean of rainfall intensity (mm/hr) in a) UCM, b) Water, and, c) Crop experiments. A difference of heavy rainfall intensity in UCM with respect to d) Crop (UCM) , and e) Water (UCM). Dash circled region points Chennai.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/74bd6fe918fa6bbaf16f23d7.png"},{"id":95527108,"identity":"f8edae02-5cd5-414e-bae2-c6fd65464078","added_by":"auto","created_at":"2025-11-10 10:10:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":504928,"visible":true,"origin":"","legend":"\u003cp\u003ea) Relative change of the precipitation characteristics (%) in Crop and Water with respect to the UCM. Error bars shows one standard deviation of change. b) Schematic showing UCM, Crop, and Water land use types and corresponding precipitation characteristics patterns. Here TotalPr refers to total accumulated precipitation (mm); IntenH referes to intensity of heavy rain; FreqH refers to the frequency of heavy rain.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/72bd8ca948fc141ea3ebaac3.png"},{"id":95526654,"identity":"7f241136-3429-460b-94e2-f72da7cba60a","added_by":"auto","created_at":"2025-11-10 10:07:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1447524,"visible":true,"origin":"","legend":"\u003cp\u003eA difference of five ensemble means of surface wind speeds (m/s) in UCM with respect to (a, b) Crop (UCM), and (c, d) Water (UCM) before and after the extreme events. Dash circled the region points in Chennai. Reference wind vector is 2 m/s.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/41f90a7b15d1305499088a0c.png"},{"id":95399499,"identity":"b0950ed2-8d89-4e78-9f43-07545a8d5d94","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":647419,"visible":true,"origin":"","legend":"\u003cp\u003eA difference of five ensemble means of precipitable water vapor (mm) in UCM with respect to a) Crop (UCM), and b) Water (UCM) before and after the extreme events. Dash circled the region points in Chennai.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/a5ddb0efe1ed09e8b270d225.png"},{"id":95399504,"identity":"673c71a2-789f-4ba9-bb92-59bce6342993","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":912390,"visible":true,"origin":"","legend":"\u003cp\u003eA difference of five ensemble means of surface heat fluxes (W/m\u003csup\u003e2\u003c/sup\u003e) before the extreme event in UCM with respect to (a,c,e) Crop (UCM), and (b,d,f) Water (UCM). Dash circled the region points in Chennai.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/7a887c8cd6e8bb52d7f88c2d.png"},{"id":95399502,"identity":"864f4bf1-b7dd-4f36-a02f-43fb5249c9ab","added_by":"auto","created_at":"2025-11-07 15:42:55","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":69320,"visible":true,"origin":"","legend":"\u003cp\u003eRelative change in the surface heat fluxes (%) in Crop and Water with respect to the UCM. Error bars show one standard deviation of change.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/f146ecdc9af91433e338153e.png"},{"id":96363814,"identity":"64db1c29-b4d2-4558-9978-8fae6224086f","added_by":"auto","created_at":"2025-11-20 10:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8676934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7670619/v1/78f294da-9246-4ef1-be6e-f63e463953af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Footprints in the Storm: Land-Use Sensitivity of Extreme Rainfall over Chennai","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanization, a hallmark of human progress, has significantly altered the natural landscape and atmospheric processes, often intensifying the risk of extreme weather events such as urban floods. As cities expand, impervious surfaces increase, modifying local hydrology and enhancing surface runoff, which in turn exacerbates flood vulnerability during heavy rainfall events (Pielke et al., 2007; Yu et al., 2015; Shepherd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Urban areas also influence atmospheric dynamics by altering surface energy balances, increasing heat retention, and modifying boundary layer characteristics, which can lead to enhanced convective activity and localized precipitation (Oke, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Bornstein \u0026amp; Lin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Niyogi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Konduru et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These effects are not confined to city centers alone; urban-induced changes can extend to surrounding regions, influencing mesoscale weather patterns (Singh et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, the hydrometeorological impact of Hurricane Harvey over Houston was found to be amplified by urban land use, as shown through WRF simulations (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, long-term analyses over the eastern United States revealed a rising trend in summer heavy rainfall events linked to urbanization (Niyogi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn India, with its rapidly growing cities, has witnessed a surge in extreme rainfall events, particularly during the summer monsoon season. Kishtawal et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) identified a significant increase in heavy rainfall occurrences over major Indian metropolitan cities, attributing this trend to urbanization. However, the relationship between urbanization and rainfall extremes remains complex and sometimes contested. Ali et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), using IMD station data, found that only a few cities among those experiencing extreme rainfall events showed a clear urbanization signal. Singh et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlighted that urbanization introduces temporal variability in monsoon rainfall, leading to nonstationarity in its patterns. Shastri et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) further demonstrated that urban regions in central and western India are particularly susceptible to urban-induced rainfall intensification. Case studies over Mumbai have shown that urbanization can exacerbate monsoon extremes, as evidenced by the 2005 flood event (Paul et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zope et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Remote sensing and topographic analyses have confirmed that land use changes in Mumbai have played a critical role in worsening flood impacts (Zope et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recently, over Chennai region Konduru et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) presented a comprehensive study on urban climate dynamics over Indian cities, highlighting the role of mesoscale interactions and land surface heterogeneity in shaping rainfall extremes. These findings showcases the need for improved modeling and prediction capabilities to understand and mitigate urban flood risks in India\u0026rsquo;s megacities.\u003c/p\u003e\u003cp\u003eAccurate representation of urban land surface processes in numerical weather prediction (NWP) models is essential for simulating extreme rainfall events in urban settings. Traditional models often fail to capture the complexity of urban-atmosphere interactions unless urban canopy schemes are explicitly included (Lei et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kusaka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e). The use of high-resolution models like WRF, coupled with urban schemes such as the Single-Layer Urban Canopy Model (SLUCM), has shown promise in improving rainfall simulations over cities like Mumbai and Hyderabad (Niyogi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Incorporating realistic urban land use and energy balance processes helps in better capturing phenomena like Urban Heat Island (UHI) and Urban-Induced Turbulence (UIT), which are critical for convective development (Shepherd, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Oleson et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Konduru et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, novel approaches such as Local Climate Zone (LCZ) mapping have enhanced the spatial accuracy of urban rainfall simulations (Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent advancements in Large-Eddy Simulation (LES) have further improved the representation of urban boundary layer processes. Notably, Konduru et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) demonstrated the effectiveness of LES at 2-meter resolution in simulating urban turbulence impact on the upward momentum transport. While in another Konduru et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e) showed the improvements in capturing urban-induced turbulence impact on the rainfall enhancement over the Chennai region. Despite these advancements, the role of urban grid modifications such as replacing urban areas with crop or water land use types remains underexplored. Understanding how these changes affect precipitation dynamics can provide valuable insights into urban flood mitigation and planning.\u003c/p\u003e\u003cp\u003eMotivated by the increasing vulnerability of Chennai to extreme rainfall and flooding, this study aims to evaluate the impact of urban land use representation on rainfall simulations using the WRF model. Specifically, we conduct ultra-high-resolution (1-km) convection-permitting experiments to compare four scenarios: (1) no urban scheme, (2) urban scheme with, (3) urban grids replaced with crop land use, and (4) urban grids replaced with water bodies. These experiments are designed to isolate the influence of urbanization on rainfall distribution and intensity during a major flood event over Chennai. By analyzing the differences in precipitation patterns across these configurations, we aim to understand the role of urban surface characteristics in modulating extreme weather. Through this approach, we seek to contribute to the growing body of research on urban meteorology and provide actionable insights for urban planning and disaster preparedness in coastal Indian cities.\u003c/p\u003e"},{"header":"2. Datasets and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Regional climate model\u003c/h2\u003e\u003cp\u003eTo investigate the Chennai flood event, a high-resolution simulation was performed using version 3.9.1 of the Weather Research and Forecasting (WRF) model (Skamarack et al. 2008) was configured. The computational domain encompassed Chennai and adjacent regions, as shown in the figure, incorporating terrain and geographic features essential for realistic atmospheric modeling. A nested grid system was applied, with horizontal resolutions of 25 km, 5 km, and 1 km. ERA5 reanalysis data were used to initialize the simulation, ensuring accurate representation of large-scale meteorological conditions.\u003c/p\u003e\u003cp\u003eThe physics settings used in the simulation, were consistent with those employed in our previous study on extreme rainfall in Chennai (Konduru et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and are listed in Table T1 (Mellor et al. 1982; Janjić \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Janjić et al. 2001; Chen and Dudhia \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Milbrandt and Yau \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Iacono et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). The microphysics schemes tested included WSM6, Thompson, and MYNN, each offering distinct treatments of cloud microphysics and precipitation processes, which are vital for capturing convective systems accurately. For PBL representation, MYNN and MYJ schemes were selected, each contributing differently to the modeling of turbulence and vertical mixing. Cumulus parameterization was disabled across all domains to reduce errors associated with convective parameterization (Konduru et al. 2020), allowing convection to be resolved explicitly. The model time step was adjusted according to grid resolution, ranging from 90 seconds for the 25 km grid to 5 seconds for the 1 km grid, ensuring numerical stability and precision. Initial conditions for the simulation were derived from ERA5 reanalysis data, ensuring that the broader atmospheric environment was accurately captured.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Urban Canopy Model in WRF\u003c/h2\u003e\u003cp\u003eThe WRF simulation coupled with the single-layer Urban Canopy Model (SLUCM; Kusaka et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kusaka and Kimura \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e), which conceptualizes urban areas as idealized two-dimensional street canyons with symmetric rows of buildings and infinite length. This approach is based on the original single-layer urban parameterization framework (Kusaka et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and urban heat island experiments, forming the basis for subsequent urban weather forecasting applications. At each time step, the SLUCM computes separate radiative balances for roofs, walls, and roads, accounting for shadowing effects, multiple reflections, and longwave radiation trapping. A four-layer heat conduction model is applied to these surfaces to simulate thermal behavior.\u003c/p\u003e\u003cp\u003eThe model advances prognostic variables such as surface temperatures of roofs, walls, and roads, canyon air temperature and humidity, and wind speed at canopy level, in sync with the atmospheric grid. Momentum and heat exchanges are calculated using roughness lengths and drag coefficients that depend on building density. The SLUCM receives atmospheric inputs wind, temperature, humidity, and downward radiation and returns updated fluxes and surface temperatures to the surface layer, radiation, and PBL schemes in the standard WRF physics sequence. Key geometric and thermal parameters such as building height, roughness length, sky-view factor, building fraction, heat capacity, thermal conductivity, albedo, and emissivity are retrieved from a lookup table, while anthropogenic heat flux and layer thickness are set to default values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Experiment design\u003c/h2\u003e\u003cp\u003eWe conducted a control run using WRF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), where urban grids remained unaltered by uncoupling WRF with SLUCM (NOUCM); UCM, we applied the SLUCM with anthropogenic heat and coupled to WRF. In the Crop (UCM) experiment, where urban grids were replaced with cropland using SLUCM, and in the Water (UCM) experiment, where urban grids were substituted with water bodies using SLUCM. These modifications were applied to all urban grids within the Chennai metropolitan area and its surroundings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe total number of experiments comprised 20 simulations, structured across five ensemble types and four urban land-use configurations. These simulations were designed to examine the sensitivity of the model to urban representation and physical parameterizations. The simulation period spanned four days, from 26 November 00 UTC to 3 December 00 UTC, 2015, coinciding with a major rainfall event that caused severe flooding in Chennai (Konduru et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each simulation included a 24-hour spin-up phase to allow the model to stabilize and develop realistic atmospheric conditions before analysis.\u003c/p\u003e\u003cp\u003eThe reproducibility of the UCM model was earlier assessed in (Konduru et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and we showed that WRF-SLUCM can simulated more rainfall close to observations. The model experiments each with 5 ensemble simulations were compared with the NOUCM, Crop (UCM), and Water (UCM) experiments. These simulations were designed to isolate the effects of urban land-use representation on rainfall and convection during the Chennai flood event. For consistency across configurations, all model outputs were interpolated to a common resolution of 1-km, enabling direct comparison with observations and minimizing resolution-induced biases.\u003c/p\u003e\u003cp\u003eDifference among the experiments were generated by computing the ensemble mean for each urban experiment and subtracting it from the UCM ensemble mean. This method highlights the spatial and structural changes introduced by different urban treatments, particularly the influence of UCM and land-use modifications (Crop (UCM) and Water (UCM)) on simulated rainfall patterns. The approach provides a robust framework for evaluating how urban representation affects model fidelity, especially in reproducing extreme precipitation events.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Impact of urban scheme on the extreme rainfall simulation\u003c/h2\u003e\u003cp\u003eThe mean accumulated rainfall during the ERE event is compared between the two model experiments (NoUCM and UCM) and CHIRPS observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Observations show more than 250 mm of precipitation along the southeast coast, especially over the Chennai region. NoUCM simulates less than 150 mm/day of precipitation over inland areas around Chennai compared to observations, but shows higher precipitation offshore. In contrast, the UCM experiment simulates a spatial pattern of precipitation that closely matches observations, with precipitation rates exceeding 250 mm/day both inland and offshore of Chennai. The impact of coupling urbanization with the land surface and atmosphere models is evident in simulating the ERE event over Chennai. Using the urban scheme results in 80\u0026ndash;100 mm/day more precipitation over the southeast coast and near Chennai. Therefore, incorporating urbanization effects into the atmosphere model is crucial for more accurately simulating urban-related impacts on the ERE.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Urbanization impact on rainfall sensitivity to land use type\u003c/h2\u003e\u003cp\u003eTo evaluate the effect of urbanization on the extreme rainfall event (ERE) in Chennai, two sensitivity experiments were performed: one in which urban land-use was replaced with cropland (Crop (UCM) ), and another in which the urban area was replaced with flooded surfaces (Water (UCM)). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the accumulated precipitation during the ERE reveals a pronounced dependence on land-use type. In the Crop (UCM) experiment, a clear reduction in total rainfall is evident over Chennai and surrounding neighborhoods. The simulated precipitation shows that when urban grids are replaced with croplands, the overall rainfall during the ERE decreases significantly, with the Chennai region receiving less than 100 mm, and weaker intensity precipitation systems compared to the fully urbanized scenario (Figure bb). In contrast, the Water (UCM) experiment, where the urban area is entirely flooded, produces total rainfall amounts similar to the Urban Canopy Model (UCM) experiment. However, the spatial pattern is altered, with much higher rainfall observed in the neighborhoods around Chennai (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These findings highlight the crucial role of land-use patterns in influencing the magnitude and distribution of extreme rainfall in urban environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Changes in the extreme rainfall characteristics\u003c/h2\u003e\u003cp\u003eTo further dissect the impacts of urbanization, we analyzed the frequency of heavy precipitation events (defined as hourly rainfall rates exceeding 2 mm/hr) across the different experiments. The spatial distribution of heavy precipitation frequency, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, exhibits substantial variability depending on the land-use configuration. The urbanized scenario (UCM) is characterized by a significantly higher frequency of heavy rainfall events compared to the Crop (UCM) experiment, which simulates 50\u0026ndash;60% fewer heavy precipitation events over Chennai (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Similarly, the Water (UCM) scenario yields about 10% fewer heavy precipitation events than the UCM. Notably, the most pronounced differences in heavy rainfall frequency occur in the vicinity of Chennai, underscoring the local amplification of extreme precipitation due to urbanization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe intensity of extreme rainfall events, calculated as the mean rainfall rate for events exceeding 2 mm/hr, also varies markedly across the experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Urbanization is found to enhance both the intensity and spatial concentration of heavy precipitation over Chennai and its immediate neighborhood. Relative to the UCM case, the Crop (UCM) scenario exhibits 15\u0026ndash;20% lower intensity of extreme rainfall over Chennai, while the Water (UCM) experiment displays about 5% lower intensity. Moreover, the intense precipitation region to the south of Chennai observed in the urbanized experiment becomes more spatially uniform in the Water (UCM) case and is substantially diminished in the Crop (UCM) case. This highlights the role of impervious urban surfaces in fostering more localized and intense precipitation systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Integrated Precipitation Characteristics and Surface Run-off\u003c/h2\u003e\u003cp\u003eTo understand the impact of urbanization on extreme precipitation, we performed a detailed analysis of the frequency and intensity of the 5-ensemble mean simulated extreme rainfall events in the Chennai region. ​Major changes in the frequency of heavy precipitation in UCM are noted with respect to each experiment over the neighborhood of Chennai (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). A higher frequency of heavy precipitation events was observed in the UCM compared to the experiment Crop (UCM), and simulating 50\u0026ndash;60% less heavy precipitation frequency compared to the UCM experiment. ​Additionally, the Water (UCM) experiment simulates 10% less heavy precipitation frequency than UCM. ​In terms of the intensity of heavy precipitation events, urbanization leads to higher intensity precipitation over Chennai and its neighborhood. ​ The UCM case has 15\u0026ndash;20% higher intensity of precipitation compared to the Crop (UCM) experiment. ​The water (UCM) experiment simulates 5% less intensified precipitation compared to the UCM case. ​In the Water (UCM) case, the precipitation spatial distribution to the south of Chennai is uniform. ​The increase in precipitation characteristics that intensified rainfall in UCM also impacted surface run-off (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The surface run-off is reduced by 26% in the Crop (UCM) experiment, indicating a decrease in the chances of flooding. Also, the precipitation characteristics and run-off are almost 50% increased over the Water (UCM) compared to the Crop (UCM) (crop) region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the findings suggest (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) that urbanization has a significant impact on the frequency, intensity, and overall characteristics of extreme rainfall events in the Chennai region. ​ Urbanization leads to higher frequency and intensity of heavy precipitation, while replacing the urban area with croplands reduces these characteristics. ​The experiment with a Water (UCM) city shows mixed results, with higher total rainfall and increased precipitation characteristics compared to the non-urbanized case.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Influence of Urbanization on Surface Winds and Water Vapor\u003c/h2\u003e\u003cp\u003eEarlier sensitivity experiments clearly illustrate significant modifications in surface wind patterns associated with urbanization, which directly influence onshore moisture transport during the extreme rainfall event (ERE), consistent with a study on a coastal city, Houston, by Fan et al. (2020). Before the event, substantial differences were observed in surface wind fields among the urbanized (UCM), cropland (Crop (UCM) ), and flooded (Water (UCM)) scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b). Specifically, in the Crop (UCM) scenario, anomalous southwesterly and northeasterly winds converge weakly near northeastern Chennai, indicating reduced convective potential compared to the urbanized case. In contrast, the Water (UCM) scenario exhibited stronger anomalous southwesterly winds around Chennai, highlighting how flooded urban surfaces can enhance low-level wind speeds due to altered land-sea thermal contrasts. During the ERE, notable differences in wind anomalies persisted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec-d). In the Crop (UCM) experiment, anomalous southwesterly winds receded, and weaker easterly winds dominated. However, the urbanized scenario sustained intensified anomalous northeasterlies along the eastern coast and over central Chennai, indicative of enhanced sea-to-land breeze circulation. This robust sea-breeze circulation in the urban scenario significantly contributed to increased moisture transport from oceanic regions into Chennai. Enhanced moisture transport facilitated by intensified surface winds thus emerges as a critical mechanism behind increased rainfall in urbanized scenarios consistent with Fan et al. (2020).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e clearly illustrates that urbanization substantially increased precipitable water vapor over Chennai during the ERE compared to the Crop (UCM) and Water (UCM) experiments. The urbanized scenario demonstrated markedly higher water vapor content, predominantly due to intensified coastal northeasterly winds enhancing ocean-to-land moisture transport. This increase in atmospheric moisture content provided essential fuel for the convection and significantly contributed to intensifying rainfall systems over Chennai. Thus, higher water vapor levels in the urban scenario underscore the importance of urban-induced mesoscale circulation changes in modulating rainfall characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Influence on surface heat fluxes\u003c/h2\u003e\u003cp\u003eAnalysis of surface energy fluxes further underscores the critical role of urban surfaces in modifying atmospheric conditions. Before the rainfall event, urbanized surfaces generated significantly higher sensible heat fluxes compared to the Crop (UCM) and Water (UCM) scenarios (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea-b). Higher sensible heat fluxes in the urban scenario indicate greater heating of the surface air layer, creating favorable conditions for enhanced convective activity. Conversely, the latent heat fluxes were lower in urbanized scenarios (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec-d), reflecting the lower evaporative cooling capacity of impervious urban surfaces compared to vegetated or water-covered surfaces. Ground heat fluxes (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee-f) are notably higher in the urbanized scenario. Increased ground heat fluxes in urban regions reflect the enhanced capacity of urban materials to store heat, which is subsequently released during nighttime. This storage and nighttime release of heat support elevated nocturnal temperatures, maintaining atmospheric instability conducive to convective processes. Such urban thermal effects highlight the integral role of land-use characteristics in shaping local atmospheric dynamics during extreme rainfall events.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Integrated Assessment of Surface Energy and Hydrological Response\u003c/h2\u003e\u003cp\u003eThe comparative analysis of surface energy flux changes in the Crop (UCM) and Water (UCM) scenarios relative to the urbanized case revealed substantial variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Notably, the non-urbanized crop scenario exhibited a major reduction in ground heat flux (-60%) and sensible heat flux (-5 to -10%) relative to the urban scenario. Similarly, in the Water (UCM) scenario, latent heat fluxes increased slightly due to the higher thermal inertia and evaporation potential of water surfaces. Yet, significant reductions were again observed in ground heat flux (-69%) and sensible heat flux (-25 to -30%). These reductions directly translated to lower surface temperatures and diminished convective potential. The hydrological implications of altered surface fluxes are critical. Our analysis shows a clear reduction in extreme rainfall frequency and intensity over cropland scenarios compared to the urbanized scenario. Consequently, total rainfall accumulation decreased significantly, reducing surface runoff by approximately 26%. Such reductions imply a notable decrease in flooding potential, highlighting the beneficial hydrological impact of maintaining vegetated land surfaces.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results provide robust evidence that urbanization significantly exacerbates extreme rainfall characteristics through complex interactions between surface heat fluxes, moisture transport, and local circulations. Urban landscapes enhance low-level convergence and intensify coastal breezes, promoting greater atmospheric moisture transport and rainfall intensification. The observed increases in extreme rainfall frequency and intensity have profound implications for urban flood risk management, emphasizing the necessity of integrating urban climate resilience into city planning and development. While cropland scenarios markedly reduced flood risk, flooded urban surfaces presented mixed outcomes, emphasizing the complexity of surface-water interactions in modifying urban climates. Therefore, sustainable urban development must carefully consider land-cover choices, balancing impervious surface expansion with green infrastructure and water-sensitive urban design principles.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Summary","content":"\u003cp\u003eTo assess the role of urbanization in modulating extreme rainfall events (EREs) over Chennai, three sensitivity experiments were conducted using the WRF model: a fully urbanized scenario (UCM), a cropland-replacement experiment (Crop (UCM) ), and a flooded-surface experiment (Water (UCM)). The results show that land-use configuration significantly influences both the magnitude and spatial distribution of rainfall. In the Crop (UCM) \u0026nbsp;experiment, replacing urban grids with cropland led to a marked reduction in total rainfall, with Chennai receiving less than 100 mm and weaker convective systems. In contrast, the Water (UCM) experiment produced rainfall totals similar to the UCM case but shifted the spatial pattern, with higher rainfall observed in the surrounding neighborhoods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe frequency and intensity of heavy precipitation events (defined as hourly rainfall \u0026gt; 2 mm/hr) were also analyzed. The UCM scenario showed the highest frequency of such events, while the Crop (UCM) \u0026nbsp;experiment simulated 50\u0026ndash;60% fewer occurrences over Chennai. The Water (UCM) experiment showed a moderate reduction of about 10%. In terms of intensity, the UCM case exhibited 15\u0026ndash;20% higher rainfall rates than Crop (UCM) \u0026nbsp;and about 5% higher than Water (UCM). The spatial distribution of intense rainfall was more concentrated in the UCM case, while the Water (UCM) scenario produced a more uniform pattern and the Crop (UCM) \u0026nbsp;case showed a significant decline. These findings highlight the sensitivity of urban precipitation to surface characteristics and the amplifying effect of impervious urban surfaces in intensifying both the frequency and spatial concentration of extreme rainfall.\u003c/p\u003e\n\u003cp\u003eAn integrated analysis of ensemble mean precipitation characteristics confirmed these trends. The UCM scenario consistently showed higher frequency and intensity of heavy rainfall compared to the other two experiments. Surface runoff, a key indicator of flood risk, was reduced by 26% in the Crop (UCM) \u0026nbsp;case, while the Water (UCM) experiment showed nearly 50% higher runoff than Crop (UCM) , indicating that flooded urban surfaces can still exacerbate flood potential.\u0026nbsp;\u003cstrong\u003eDue to the absence of real urban morphological features and the use of non-LES configurations, exact UIT effect cannot be resolved. Future work will incorporate detailed urban structure and LES modeling to better resolve urban boundary layer processes and validate results extensively using surface and satellite observations.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement and Funding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Osaka Central Advanced Mathematical Institute, Osaka Metropolitan University, for the resources and support. The first and third authors will share the publication cost, supported by the third author\u0026rsquo;s institution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would like to thank Dr. C M Kishtawal for his detailed discussion. Also, Would like to thank Prof. Venkatesh Raghavan for his discussions and his recommendations to continue this work for Osaka city. \u0026nbsp;We used large language models to correct English and to improve the readability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation results were obtained using the Weather \u0026amp; Research Forecasting Model (WRF v3.9.1), which can be downloaded with registration from the link (https://www2.mmm.ucar.edu/wrf/users/download/get_source.html). The ERA5 dataset, used as initial conditions, can be obtained from the link with registration (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview). Topography data in the WRF model is taken from the Shuttle Radar Topography Mission 3-second (SRTM 3s) dataset, which can be downloaded from the link with registration (https://www2.mmm.ucar.edu/wrf/src/wps_files/geog_high_res_mandatory.tar.gz). Further queries can be directed to corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKonduru R.T: conceptualization, simulations, analysis, Investigation, first draft; Gupta A.: Simulation, Analysis, Investigation, revising draft.; Singh V. : Analysis, Investigation, revising draft\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli, H., Mishra, V., \u0026amp; Pai, D. S. (2014). 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An overview of regional land-use and land-cover impacts on rainfall. \u003cem\u003eTellus B: Chemical and Physical Meteorology\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(3), 587-601.\u003c/li\u003e\n\u003cli\u003eShastri, H., Paul, S., Ghosh, S., \u0026amp; Karmakar, S. (2015). Impacts of urbanization on Indian summer monsoon rainfall extremes. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e, 120(2), 495\u0026ndash;516. https://doi.org/10.1002/2014JD022061\u003c/li\u003e\n\u003cli\u003eShepherd, J. M. (2005). A review of current investigations of urban-induced rainfall and recommendations for the future. \u003cem\u003eEarth Interactions\u003c/em\u003e, \u003cem\u003e9 \u003c/em\u003e(12), 1-27.\u003c/li\u003e\n\u003cli\u003eShepherd, J. M., Stallins, J. A., Jin, M. L., \u0026amp; Mote, T. L. (2010). Urbanization: Impacts on clouds, precipitation, and lightning. \u003cem\u003eUrban ecosystem ecology\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 1-28. \u003c/li\u003e\n\u003cli\u003eSingh, J., Vittal, H., Karmakar, S., Ghosh, S., \u0026amp; Niyogi, D. (2016). Urbanization causes nonstationarity in Indian summer monsoon rainfall extremes. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, 43(11), 5730\u0026ndash;5737. https://doi.org/10.1002/2016GL071238\u003c/li\u003e\n\u003cli\u003eSingh, J., Karmakar, S., Paimazumder, D., Ghosh, S. and Niyogi, D., 2020. Urbanization alters rainfall extremes over the contiguous United States. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSkamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., et al. \u0026amp; Powers, J. G. (2008). A description of the advanced research WRF version 3. NCAR Technical Note, 475(July 2008), 113. [https://doi.org/10.5065/D68S4MVH](https://doi.org/10.5065/D68S4MVH)\u003c/li\u003e\n\u003cli\u003eYu, M., Miao, S., \u0026amp; Li, Q. (2016). Synoptic analysis and urban signatures of a heavy rainfall on 7 August 2015 in Beijing. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e. https://doi.org/10.1002/2016JD025420\u003c/li\u003e\n\u003cli\u003eZhang, W., Villarini, G., Vecchi, G.A. and Smith, J.A., 2018. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e563\u003c/em\u003e(7731), pp.384-388.\u003c/li\u003e\n\u003cli\u003eZope, P. E., Eldho, T. I., \u0026amp; Jothiprakash, V. (2015). Hydrological impacts of land use\u0026ndash;land cover change in Mumbai, India. \u003cem\u003eHydrological Sciences Journal\u003c/em\u003e, 60(5), 745\u0026ndash;760. https://doi.org/10.1080/02626667.2014.967248\u003c/li\u003e\n\u003cli\u003eZope, P. E., Eldho, T. I., \u0026amp; Jothiprakash, V. (2016). Urban flood modeling using integrated hydrological\u0026ndash;hydraulic modeling approach. \u003cem\u003eNatural Hazards\u003c/em\u003e, 84(2), 749\u0026ndash;776. https://doi.org/10.1007/s11069-016-2455-1\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Weather Research \u0026amp; Forecasting (WRF v3.9.1) model settings\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel configuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHorizontal resolution (nested domains)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e25 km, 5km, 1 km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulus parameterization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eOff in all domains\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlanetary boundary layer scheme\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eMellor-Yamada-Janjic (MYJ), MYNN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrophysics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eWSM6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand surface model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eNoha land surface model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban scheme\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eUrban Climate \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurface physics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eMonin-Obukhov-Janjic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong and shortwave radiation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eRRTM and Dudhia scheme\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVertical levels\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e30 terrain following coordinates\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand use dataset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eMODIS Land use land cover dataset\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial and boundary conditions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003eERA5 6 hourly dataset\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 267px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urbanization, Chennai floods, rainfall, Convection permitting model, Extreme rainfall, urban climate model","lastPublishedDoi":"10.21203/rs.3.rs-7670619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7670619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanization significantly changes land surface features, affecting local weather and increasing extreme rainfall events (EREs) in cities like Chennai. Improving urban resilience to climate-related extremes requires understanding how land-use patterns influence heavy rainfall in fast-growing cities. Greening concrete grids with vegetation or converting them to cropland can reduce convective strength and change moisture flows, helping to moderate extreme rainfall. To explore this, we performed high-resolution simulations using the Weather \u0026amp; Research Forecasting Model (WRF), combined with an urban canopy model, for three land-use scenarios: fully urbanized (UCM), cropland replacement (Crop (UCM)), and flooded surface (Water (UCM)). These experiments showed that urban surfaces increase both the frequency and severity of heavy rainfall, while replacing urban areas with cropland significantly decreases rainfall and surface runoff. The Water (UCM) scenario displayed mixed results, with shifts in rainfall patterns and increased runoff compared to Crop (UCM). Additionally, urbanization boosted sea-breeze circulation and moisture transport, which contributed to localized rainfall intensification. These results emphasize the important role of land-use planning in influencing ERE behavior.\u003c/p\u003e","manuscriptTitle":"Urban Footprints in the Storm: Land-Use Sensitivity of Extreme Rainfall over Chennai","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 15:42:50","doi":"10.21203/rs.3.rs-7670619/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e126d301-6a2b-48c3-acae-85c187a2ad4e","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57530213,"name":"Earth and environmental sciences/Climate sciences"},{"id":57530214,"name":"Earth and environmental sciences/Environmental sciences"},{"id":57530215,"name":"Earth and environmental sciences/Hydrology"},{"id":57530216,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-11-17T04:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 15:42:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7670619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7670619","identity":"rs-7670619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}