Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways

Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways | 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 Short Report Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways Monomoy Goswami, Avijit Paul, Meghraj Goswami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8901153/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Increased incidences of pluvial floods from rainfall affect key economic and cultural hubs of many countries of the world. Future-proofing India’s 100 designated smart cities against pluvial flooding through Integrated Urban Water Management (IUWM) is an important requirement that calls for assessments of the future characteristics of rainfall in these cities and the likely impacts of alterations of these characteristics under Shared Socio-economic Pathways (SSPs) of the Sixth Assessment Report (2021) of the Intergovernmental Panel on Climate Change. In this study, potential future alterations in Intensity-Duration-Frequency (IDF) characteristics of precipitation were explored by using reanalysis outputs of hourly precipitation of the period 1955–2014 from a selected Global Climate Model (GCM), namely MIROC6. In this study, the MIROC6-simulated precipitation data of three future periods, namely 2015–2040, 2041–2070 and 2071–2100, under four SSPs, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, at a 0.25°×0.25° grid over India were used. It emerged from the analyses that, generally, for all four SSPs, the north-central, eastern and western parts of the country encompassing 69 smart city locations and the southern peninsular region containing 29 locations would have high future precipitation intensities. The number of smart cities having significant positive trends of 1-hour annual maximum precipitation was found to increase from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively. The historical 1-hr precipitations of 100-year return period and their % deviations in the three future periods under four SSPs showed that the aerial extents of positive deviations generally increase from SSP1-2.6 to SSP4-8.5, particularly during 2041-70 and 2071–2100, and that 81 smart city locations exhibit high positive deviations under SSP5-8.5 during 2071–2100. These results indicate that future precipitation intensities, and hence pluvial floods, are likely to increase over the designated smart cities, and accounting for the increasing vulnerabilities of these cities to urban pluvial flooding due to increased short-duration rainfall would be vital for effective IUWMs. Pluvial flood Smart Cities Intensity-Duration-Frequency Shared Socio-Economic Pathways GCM Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Increased incidences of pluvial floods caused by precipitation in the form of rainfall coupled with inadequate infrastructure and shortfall in management practices affect key economic and cultural hubs in urban areas of many countries of the world. Generally, these floods occur from the accumulation of water from rain over urbanized surfaces and the inadequacies of conveyance systems to carry the accumulated water out of the urban catchments. In India, the effects of pluvial floods in urban areas include inundation of densely inhabited areas, disruption of infrastructures and economic activities, impacts on civic amenities, peoples’ exposure to water borne diseases, psychological trauma, sufferings and other health risks, and in some cases, loss of lives. The National Disaster Management Authority of the Government of India, in describing the difference of urban flooding from rural flooding (NDMA, 2010 ), stated that high intensity rainfall increases the flood peaks to 1.8 to 8 times and flood volumes up to 6 times in urban areas in comparison with those in rural areas. India has 100 designated smart cities, hereinafter referred to as DSCs, including eight Tier I Urban Conglomerates (UCs) that are Ahmedabad, Bengaluru, Chennai, Delhi, Hyderabad, Kolkata, Mumbai and Pune. Many of the DSCs are historically flood-prone, and many experience increased incidences of flooding, primarily due to the impacts of (i) increased occurrences of small-duration rainfall under a changing climate (Westra et al., 2014 ), (ii) the ‘new and intensified phase of urbanization during 2001–2011 coupled with spatial expansion of urban extents’ (MoUD, 2017 ), and (iii) the ‘urbanization signature’ on ‘rainfall climatology’ (Kishtawal et al., 2010 ). Recent notable floods in some of the DSCs are the floods of Bengaluru in 2022 and 2024, Hyderabad in 2000, 2020 and 2021, Ahmedabad in 2001 and 2020, Delhi in 2002, 2003, 2009, 2010 and 2023, Chennai in 2004, 2015 and 2021, Mumbai in 2005, 2020 and 2023, Surat in 2006, Jamshedpur in 2008, Pune in 2019, Guwahati in 2010 and Srinagar in 2014 (MoUD, 2017 ), besides the floods of Kolkata in 2007, 2017 and 2023 and of Ahmedabad in 2017. These observations on increased frequency of occurrence of flood in India are generally similar to the observations of increased occurrences of small duration precipitations, such as those lasting for less than a day, under a changing climate (Goswami et al., 2006 ). Future-proofing the DSCs of India against pluvial flooding is an important requirement for ensuring that the benefits of implementing the National Smart Cities Mission of 2015 of the Government of India and the resulting contributions to the nation’s economy and well-being are accrued in the desired pace and level as envisaged without hindrance from urban floods. Steps to achieve this call for adoption of (a) structural measures of constructing new and upgrading existing storm water drainage systems, flood-protection infrastructures, etc. (b) non-structural measures of operationalizing early warning systems, creating resilience, etc. and (c) Integrated Urban Water Management (IUWM) practices. Essentially, these require reliable inputs of intensity, duration and frequency (IDF) of future precipitation, particularly short-duration rainfall, in the focus areas. There are several mathematical frameworks for deriving IDF relationships ((Koutsoyiannis et al., 1998 )), and several studies exploring the impacts on future IDF characteristics in different regions of the world under a changing climate. (Zhao et al., 2021 ) used multi-model simulations for projecting the IDF characteristics of two Vietnamese cities by considering a near future period from 2026 to 2045 and a far one from 2066 to 2085. They found that the intensity of rarer rainfall events is likely to increase, particularly for longer durations, in the future, and recommended the updating of the existing urban drainage systems in these two cities. (Cheng and AghaKouchak, 2014 ) presented a generalized framework to account for non-stationarity of IDF characteristics for designing infrastructure under a changing climate. Applying IDF analysis on historical and climate projection data of three cities, one each in Ethiopia, Tanzania and Cameroon in Africa, (De Paola et al., 2014 ) found that, although future rainfall intensities in a study area might decrease or increase depending on the location of the area, frequencies of intense rainfalls would increase in the future. Simulating future precipitations using Canadian Regional Climate Model (Mailhot et al., 2007 ) estimated that the frequencies of 12- and 24-hour precipitations would increase three times and those of 2- and 6-hour precipitations would increase two times over the Southern Quebec region during the future period of 2041–2070. A 16–27% increase of IDF ordinates for different return periods over Europe was found by (Hosseinzadehtalaei et al., 2020 ) from a study of Climate change impacts on short-duration extreme precipitation and IDF characteristics. Using record of daily rainfall, (Choi et al., 2019 ) derived future IDF curves over South Korea, and concluded that the intensity of design rainfall is likely to increase in the future in most areas excluding east coastal region. Increasing trends of precipitation intensities were also found over selected regions in China (Zhang et al., 2022 ), Brazil (De Souza Costa et al., 2020 ), Turkey (Şen and Kahya, 2021 ), Malaysia (Kuok et al., 2016 ; Shukor et al., 2020 ), and several other countries. In the case of India, the future IDF characteristics were studied individually for several cities including three DSCs, namely Bengaluru (Chandra et al., 2015 ), Hyderabad (Agilan and Umamahesh, 2016 ) and Chennai (Andimuthu et al., 2019 ), Roorkee (Singh et al., 2016 ), etc., and an extensive study of changes in future precipitation intensities across the whole country under different climate change scenarios was carried out by (Maity and Maity, 2022 ). Noting the dearth of literature on studies of IDF characteristics involving the DSCs distributed across India, the objective of this study was set to investigate the trend and deviations of future IDF characteristics under different climate change scenarios that would have potential to aggravate the future pluvial flooding scenarios in the DSCs. 2. Study Area and Data Noting that there are 7933 urban settlements across India (MoUD, 2017 ) and that regional attributes of rainfall are relevant for comprehending the dynamics of location-specific rainfall characteristics, this study was conducted by exploring the future IDF characteristics of rainfall at grid points over mainland India in a 0.25°×0.25° geographic grid into which the country is conventionally divided for climate related research. An outline of mainland India showing the major grids of latitudes and longitudes is provided in Fig. 1 (a). The locations of the 100 DSCs including the locations, names and populations as per 2011 Census of the eight UCs amongst these DSCs are also included in this map in order to show the spread of the DSCs across India. The names of the DSCs are not given for the clarity of the map. Readers may find details of the DSCs in the official webpages of the Smart Cities Mission of the Government of India (smartcities.gov.in). Since precipitation of a region is influenced, inter alia , by the region’s physiography, hence, the elevation map showing the Indo-Gangetic-Brahmaputra plains spreading across the north-central, eastern and western parts of the country, and the country’s southern peninsular region is shown in Fig. 1 (b). Meaningful studies of IDF characteristics require short-duration, preferably hourly, data of precipitation. However, the unavailability of long records of historical data of observed hourly precipitation in many parts of the world including India compels researchers either to apply statistical techniques, such as temporal downscaling, scale invariance method, Equidistance Quantile Matching Method etc., to daily data of precipitation for producing data of sub-daily temporal resolutions, or to use a reference set of data in lieu of recorded data. Because the use of statistical techniques imparts increased uncertainty to the assessment of future IDF characteristics, and records of hourly observed data are not readily available for the study area, the current study was undertaken by using a set of hourly reanalysis precipitation data of the fifth generation produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), called ECMWF Re-Analysis 5, i.e. ERA5, as reference data. As described in the Climate Data Guide of the National Center for Atmospheric Research of the USA (NCAR, 2023 ), these reanalysis data were produced by assimilating multiple observational data of the atmosphere, land and ocean are assimilated into a forecast model and yielding a ‘dynamically consistent estimate’ of the state of climate. The adoption of ERA5 hourly data of precipitation as reference data for this study is supported by the observations from an earlier study (Mahto and Mishra, 2019 ) in which the authors found the ERA5 data as being the best amongst five different reanalysis datasets in representing the monsoon precipitation of India. Accordingly, ERA5 reanalysis outputs for a historical 60-year period from 1955 to 2014 were used for determining the observed IDF characteristics at grid points across the study area. For producing IDF characteristics of future precipitations, the hourly precipitation outputs from one General Circulation Model, also called a Global Climate Model (GCM), namely MIROC6 of the Coupled Model Intercomparison Project phase 6, were taken for an 86-year projection divided into three periods, namely 2015–2040 for immediate-future, 2041–2070 for near-future and 2071–2100 for far-future. The choice of MIROC6 was because of it being the only GCM that provided hourly output of precipitation for all SSPs considered in this study. The future MIROC6-simulated data were obtained for four of the five climate change and greenhouse gas emission scenarios under Shared Socio-economic Pathways (SSPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 of the variant level r1i1p1f1 as defined in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change of the UN. These SSPs represent future climate change scenarios with SSP1-2.6 representing a sustainability-focused low greenhouse gas emission scenario, SSP2-4.5 corresponding to a ‘middle-of-the-road’ moderate emission scenario, SSP3-7.0 indicating a high emissions scenario with regional rivalries, and SSP5-8.5 representing a worst-case scenario of a ‘business as usual’ world with heavy reliance on fossil fuels and absence of any future climate policy. 3. Methodology Due to the fact that GCM outputs often exhibit large systematic errors or biases arising out of inadequate comprehension of physical processes, inaccurate input data, limitations of computing resources, etc. in regional scales (Chen et al., 2021 ), and that the use of these outputs for the assessment of future impacts of climate change on hydro-climatological variables is likely to yield less reliable results (Kotlarski et al., 2014 ; Maraun and Widmann, 2018 ), it becomes pertinent to correct the bias of the GCM outputs for aligning the statistical characteristics of these outputs with those of the observed data (Maraun, 2013 ). For the study described herein, the Quantile Delta Mapping (QDM) for bias correction (Cannon et al., 2015 ) through matching of Cumulative Distribution Functions (Panofsky and Brier, 1968 ) was applied to the GCM outputs of hourly precipitation data. The mathematical formulation of this method is described by Eq. ( 1 ) $$\:{x}_{i}^{maf}={x}_{i}^{mf}+{F}_{{x}^{oh}}^{-1}\left({F}_{{x}^{mf}}\left({x}^{mf}\right)\right)-{F}_{{x}^{mh}}^{-1}\left({F}_{{x}^{mf}}\left({x}^{mf}\right)\right)$$ 1 where i denotes a grid point, superscripts mf denotes GCM model outputs of the future, maf denotes bias-corrected GCM outputs of the future, mh denotes GCM-simulated historical values, and oh denotes observed historical values or reference data of precipitation. The Annual Maximum (AM) Series of precipitation intensities of durations 1-, 2-, 6-, 12-, 24- and 48-hours for the historical data period as well as for each future period were produced from the respective time series data of precipitation by using moving windows of the selected durations across the data series. The AM series of precipitation of different durations for different periods were thus generated for each grid point over the mainland India. The intensities of AM precipitation for different durations were then estimated for each of 5-, 10-, 25-, 50- and 100-year return periods by employing Gumbel’s extreme value distribution (Gumbel, 1958 ). Having obtained these probabilistically derived data of precipitation, the IDF curves of the form of an empirical equation of Bernard (Bernard, 1932 ) as given by Eq. ( 2 ) were fitted to the data at each grid point. $$\:I=\frac{a{T}^{b}}{{d}^{c}}$$ 2 where I is the precipitation intensity, d is the duration, T is the return period, and a , b , and c are parameters. Accordingly, the precipitation intensity of a given duration and a given return period would increase with increase of a and b , and decrease of c . For the AMS at each grid point, the values of the three IDF parameters were estimated for 1-, 2-, 6-, 12-, 24- and 48-hr durations for 5-, 10-, 25-, 50- and 100-year return periods. The parameters of the equation were evaluated by Ordinary Least Squares estimation method by employing the Generalized Reduced Gradient search algorithm for automatic optimization. Mann-Kendall trend test (Mann, 1945 ) was performed and Sen’s slope (Sen, 1968 ) was estimated at each grid point by using 60 years of historical data from 1955 to 2014 and an 86-year projection into the future from 2015 to 2100, focusing on AMS of 1-hour duration across all four SSPs. Subsequent to trend analyses, the precipitation intensities of the selected durations and return periods were estimated at all grid points and their percentage (%) deviations under each of the four selected SSPs from the corresponding historical values were assessed. Inferences were then drawn from the values of trends, magnitudes of intensities and the deviations of short-duration precipitations at all grid points for three future periods under four climate change scenarios with particular references to the observations pertaining to the DSCs of India. 4. Results and Discussions It was found from the analyses of Bernard’s parameters that, generally for all four SSPs, the plains in the north-central, eastern and western parts of India encompassing 69 DSCs show relatively high values of a and b , and the southern peninsular region containing 29 DSCs exhibits relatively low values of c in the three future periods under all four SSPs. For brevity, representative graphical results for only one SSP, namely SSP1-2.6, for each of the three parameters and each of the three periods are exhibited in Fig. 2 . The sampled values of the parameters at the locations of the eight Tier I UCs for SSP1-2.6 are also provided in this figure using the same legends for the UCs as provided in Fig. 1 (a) for visual assessment of the relative values of these parameters in the regions around the respective UCs, and across the whole country. Since the higher values of a and b and lower values of c indicate larger precipitation intensities, hence the results of the analyses of Bernard’s parameters indicate that higher precipitation intensities would be likely in almost all DSCs in the future relative to the corresponding historical values under all greenhouse gas emission scenarios. It was also found from the comparison of values of the three IDF parameters for different SSP scenarios across the grid points that, generally, the magnitudes of the precipitation intensities would increase with the increase of severity of greenhouse gas emission from the low emission scenario SSP1-2.6 to the very high emission scenario SSP5-8.5. The above observations are similar to those made in respect of the whole of India by (Maity and Maity, 2022 ) who found that future precipitation intensities would increase by about 17–21% in areas covering about 70–90% area under SSP2-4.5 and by about 40–48% in areas covering almost the whole country, and that the increase in precipitation intensity is likely to be more in the Central and northeastern parts of the country and the mountainous and Terai regions of the Gangetic basin on the north, and moderate in the alluvial plains of the north, the southern peninsula and the desert region. The historical 1-hour intensities of AM precipitations of 100-year return period and their deviations in the three periods under four SSPs from the corresponding historical values are presented in Fig. 3 . These figures show that the aerial extents of positive deviations generally increase with the increase of severity of greenhouse gas emissions from the sustainability-focused scenario SSP1-2.6 to the business-as-usual scenario SSP4-8.5, particularly, in the near-future and far-future periods of 2041-70 and 2071–2100 respectively. It was found from the results of the deviation analysis that 85 of the 100 DSCs exhibit high positive deviations under SSP5-8.5 during 2071–2100 implying large increase in short-duration precipitation intensities in most of the DSCs of India. These observations further substantiate that the impacts of precipitation on pluvial floods in the DSCs are likely to increase in the future due to increase in the volume of short-duration precipitation. The analysis of trends of the AM precipitation of 1-hour duration at 95% significance level at all grid points revealed that the trends in terms of both the Sen’s slope and the Mann-Kendall test statistic worked out as being insignificant for the historical and the SSP1-2.6-projected future periods in considerably large expanse of the country. These observations are also reflected in the maps showing the trends of Sen’s slope in Fig. 4 (a) and 4(b). However, from a look at the significant trends of the AM precipitation of 1-hour duration for different SSPs as presented in Fig. 4 (b-e), it may be seen that increasing numbers of grid points exhibit significant positive trends from SSP1-2.6 to SSP5-8.5. From the results of trend analyses, it was also found that the number of DSCs having significant positive trends of AM precipitation intensities of 1-hour duration increases from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively. As regards the UCs in particular, the values of the trend indicator, i.e. the Sen’s slope, provided on the maps of Fig. 4 reveal that the trends of the AM precipitation of 1-hour duration at 95% significance level are insignificant under the sustainability-focused low greenhouse gas emission scenario SSP1-2.6 in seven of the eight UCs, and negative in the UC of Mumbai on the western coast. However, the trends change to significantly positive in two relatively closely located UCs of Mumbai and Pune under the moderate emission scenario SSP2-4.5. As the scenario turns to high emission, five UCs, namely Delhi, Hyderabad, Kolkata, Ahmedabad and Chennai, exhibit significant positive trends, whereas the UCs of Delhi, Bengaluru, Hyderabad, Mumbai and Pune show significantly positive trends under the very high emission scenario SSP5-8.5. Although the above results from trend analysis of the AM precipitation of 1-hour duration over the DSCs and the whole country generally indicate increasing trends of short-duration AM precipitations, the results in respect of the UCs in particular reveal that the impacts of greenhouse gas emissions of increasing severities might not always translate to significantly increasing trends of short-duration AM precipitations in some DSCs. Similar results were found by (Mirhosseini et al., 2013 ) in a study of the impacts of climate change on rainfall IDF curves in Alabama in the US who observed that the patterns of future precipitation of Alabama are likely to be less intense for short-duration events, and that the results were not consistent for precipitations of relatively longer duration of more than four hours. The reasons for the apparent inconsistencies that could be found from the above trend analysis results for some of the UCs of India are likely to be (i) the impacts of greenhouse gas emissions and the resulting rise in temperature on a large variety of physical processes that complexly and non-linearly influence the pattern of occurrence of precipitation, (ii) the uncertainties in the GCM-projected values of future precipitations, and (iii) consideration of reanalysis products of precipitation instead of observed shot-duration precipitations as reference sets of historical data. The differences in the values of trend indicators, e.g. in the case of the UC of Mumbai as exhibited on the maps in Fig. 3 ., indicate the necessity of undertaking scenario-specific analysis for determining the expected IDF characteristics for efficient design of urban pluvial flood control structures and IUWM. However, although this approach would be idealistic, the practical problem would be to assess the likely severity of the greenhouse gas emission of the future that would help identify an appropriate SSP scenario. A way of overcoming this difficulty of adopting an SSP scenario for design in absence of better estimates of precipitation intensity would be that suggested for urban stormwater infrastructure design in Toronto-Niagara Region of Canada by (Watt et al., 2003 ) to adopt a design storm 15% larger than the present estimate. 5. Conclusions In this study, the trends and deviations of future IDF characteristics, particularly of short-duration annual maximum precipitation, that are likely to aggravate the future pluvial flooding scenarios of 100 DSCs of India were studied at grid points of a 0.25°×0.25° grid covering mainland India. By using hourly data of AM precipitations of the historical past from the year 1955 to 2014 and the bias-corrected GCM-projected future values of 1-hour AM precipitations under four Shared Socioeconomic Pathways (SSPs) during three periods from 2015–2040, 2041–2070 and 2071 to 2100, it was found that Intensity wise, the plains in the north-central, eastern and western parts of the country in which 69 DSCs are located and the southern peninsular region containing 29 DSCs are likely to experience intensities of short-duration precipitation higher than the corresponding historical values in the three future periods under all four SSPs Magnitude wise, the aerial extents of positive deviations of short-duration AM precipitation from the corresponding historical values would generally increase from SSP1-2.6 to SSP4-8.5, particularly, during 2041–2100, and 85 of the 100 DSCs are likely to experience high positive deviations under SSP5-8.5 during 2071–2100. Trend wise, the number of DSCs having significant positive trends of AM precipitation intensities of 1-hour duration increases from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively. In the case of some DSCs, the impacts of greenhouse gas emissions of increasing severities might not always indicate significantly increasing trends of short-duration AM precipitations. Whereas such an apparent inconsistency is possible, it would necessitate the consideration of a realistic future greenhouse gas emission scenario for each DSC for designing efficient urban pluvial flood protection structures and undertaking effective IUWM practices for the DSC. However, this approach of adopting DSC-specific and climate change scenario specific values of future IDF values of precipitations for design would be impractical, and the suggestion of (Watt et al., 2003 ) to adopt a design storm 15% larger than the present estimate for design appear as being pragmatic. Generally, however, the results of this study show that most of the DSCs are expected to experience larger volumes of short-duration precipitation, and hence increased incidences of pluvial flooding, in the future. These findings would facilitate the assessment of the increasing vulnerabilities of the 100 DSCs located across India to urban pluvial floods, and provide an objective basis for efficient planning and design of urban stormwater drainage systems and flood protection infrastructures. The results of this study would also contribute to the framing of effective IUWM practices for sustainable, citizen-friendly developments of these DSCs. Declarations Acknowledgments. The authors express their sincere gratitude to the anonymous reviewers for their valuable suggestions and insightful comments. Disclosure of Interests. The authors have no competing interests to declare that are relevant to the content of this article. Funding This research received no funding from external sources. ORCID Monomoy Goswami: 0000-0003-1380-6276; Avijit Paul: 0000-0001-9388-9571; Meghraj Goswami: 0009-0007-4895-2747 Data Availability Statement Raw simulated outputs of GCMs of the CMIP6 phase are available at https://esgf-node.llnl.gov/projects/cmip6/ Author Contributions The first author suggested improvements on the methodology and presentation of results, and prepared the manuscript. The second and third author conceptualized, analysed, and prepared graphical outputs of the research. References Agilan V, Umamahesh NV. 2016. Is the covariate based non-stationary rainfall IDF curve capable of encompassing future rainfall changes? Journal of Hydrology 541 : 1441–1455 DOI: 10.1016/j.jhydrol.2016.08.052 Andimuthu R, Kandasamy P, Mudgal BV, Jeganathan A, Balu A, Sankar G. 2019. Performance of urban storm drainage network under changing climate scenarios: Flood mitigation in Indian coastal city. Scientific Reports 9 (1): 7783 DOI: 10.1038/s41598-019-43859-3 Bernard MM. 1932. Formulas For Rainfall Intensities of Long Duration. Transactions of the American Society of Civil Engineers 96 (1): 592–606 DOI: 10.1061/TACEAT.0004323 Cannon AJ, Sobie SR, Murdock TQ. 2015. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate 28 (17): 6938–6959 DOI: 10.1175/JCLI-D-14-00754.1 Chandra R, Saha U, Mujumdar PP. 2015. Model and parameter uncertainty in IDF relationships under climate change. Advances in Water Resources 79 : 127–139 DOI: 10.1016/j.advwatres.2015.02.011 Chen C-A, Hsu H-H, Liang H-C. 2021. Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the seasonal extreme precipitation in the Western North Pacific and East Asia. Weather and Climate Extremes 31 : 100303 DOI: 10.1016/j.wace.2021.100303 Cheng L, AghaKouchak A. 2014. Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design in a Changing Climate. Scientific Reports 4 (1): 7093 DOI: 10.1038/srep07093 Choi J, Lee O, Jang J, Jang S, Kim S. 2019. Future intensity–depth–frequency curves estimation in Korea under representative concentration pathway scenarios of Fifth assessment report using scale‐invariance method. International Journal of Climatology 39 (2): 887–900 DOI: 10.1002/joc.5850 De Paola F, Giugni M, Topa ME, Bucchignani E. 2014. Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities. SpringerPlus 3 (1): 133 DOI: 10.1186/2193-1801-3-133 De Souza Costa CEA, Blanco CJC, De Oliveira-Júnior JF. 2020. IDF curves for future climate scenarios in a locality of the Tapajós Basin, Amazon, Brazil. Journal of Water and Climate Change 11 (3): 760–770 DOI: 10.2166/wcc.2019.202 Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK. 2006. Increasing Trend of Extreme Rain Events Over India in a Warming Environment. Science 314 (5804): 1442–1445 DOI: 10.1126/science.1132027 Gumbel EJ. 1958. Statistics of Extremes. In Statistics of Extremes Columbia University Press. DOI: 10.7312/gumb92958 Hosseinzadehtalaei P, Tabari H, Willems P. 2020. Climate change impact on short-duration extreme precipitation and intensity–duration–frequency curves over Europe. Journal of Hydrology 590 : 125249 DOI: 10.1016/j.jhydrol.2020.125249 Kishtawal CM, Niyogi D, Tewari M, Pielke RA, Shepherd JM. 2010. Urbanization signature in the observed heavy rainfall climatology over India. International Journal of Climatology 30 (13): 1908–1916 DOI: 10.1002/joc.2044 Kotlarski S, Keuler K, Christensen OB, Colette A, Déqué M, Gobiet A, Goergen K, Jacob D, Lüthi D, van Meijgaard E, et al. 2014. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development 7 (4): 1297–1333 DOI: 10.5194/gmd-7-1297-2014 Koutsoyiannis D, Kozonis D, Manetas A. 1998. A mathematical framework for studying rainfall intensity-duration-frequency relationships. Journal of Hydrology 206 (1–2): 118–135 DOI: 10.1016/S0022-1694(98)00097-3 Kuok KK, Mah YS, Imteaz MA, Kueh SM. 2016. Comparison of future intensity duration frequency curve by considering the impact of climate change: case study for Kuching city. International Journal of River Basin Management 14 (1): 47–55 DOI: 10.1080/15715124.2015.1082478 Mahto SS, Mishra V. 2019. Does ERA‐5 Outperform Other Reanalysis Products for Hydrologic Applications in India? Journal of Geophysical Research: Atmospheres 124 (16): 9423–9441 DOI: 10.1029/2019JD031155 Mailhot A, Duchesne S, Caya D, Talbot G. 2007. Assessment of future change in intensity–duration–frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM). Journal of Hydrology 347 (1–2): 197–210 DOI: 10.1016/j.jhydrol.2007.09.019 Maity SS, Maity R. 2022. Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change. Water Resources Management 36 (14): 5371–5399 DOI: 10.1007/s11269-022-03313-y Mann HB. 1945. Nonparametric Tests Against Trend. Econometrica 13 (3): 245 DOI: 10.2307/1907187 Maraun D. 2013. Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue. Journal of Climate 26 (6): 2137–2143 DOI: 10.1175/JCLI-D-12-00821.1 Maraun D, Widmann M. 2018. Statistical Downscaling and Bias Correction for Climate Research . Cambridge University Press: Cambridge. DOI: 10.1017/9781107588783 Mirhosseini G, Srivastava P, Stefanova L. 2013. The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama. Regional Environmental Change 13 (S1): 25–33 DOI: 10.1007/s10113-012-0375-5 MoUD. 2017. Standard Operating Procedure for Urban Flooding. Ministry of Urban Development, Government of India Available at: https://mohua.gov.in/upload/uploadfiles/files/SOP%20Urban%20flooding_5%20May%202017.pdf NCAR. 2023. Climate data Guide (CDG) Available at: https://climatedataguide.ucar.edu/climate-data/reanalysis [Accessed 12 July 2023] NDMA. 2010. National Disaster Management Guidelines - Management of Urban Flooding. National Disaster Management Authority, Government of India . Available at: https://nidm.gov.in/pdf/guidelines/new/management_urban_flooding.pdf Panofsky HA, Brier GW. 1968. Some Applications of Statistics to Meteorology . Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences. Paul A, Goswami M. 2024. Does CMIP5 Still Have Value Over CMIP6? A Case of Mean and Extreme Temperature Simulation Over Mainland India. International Journal of Environmental Science and Development 15 (2) DOI: 10.18178/ijesd.2024.15.2.1472 Şen O, Kahya E. 2021. Impacts of climate change on intensity–duration–frequency curves in the rainiest city (Rize) of Turkey. Theoretical and Applied Climatology 144 (3–4): 1017–1030 DOI: 10.1007/s00704-021-03592-2 Sen PK. 1968. Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association 63 (324): 1379–1389 DOI: 10.1080/01621459.1968.10480934 Shukor MSA, Yusop Z, Yusof F, Sa’adi Z, Alias NE. 2020. Detecting Rainfall Trend and Development of Future Intensity Duration Frequency (IDF) Curve for the State of Kelantan. Water Resources Management 34 (10): 3165–3182 DOI: 10.1007/s11269-020-02602-8 Singh R, Arya DS, Taxak AK, Vojinovic Z. 2016. Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee, India. Water Resources Management 30 (13): 4603–4616 DOI: 10.1007/s11269-016-1441-4 Watt WE, Waters D, McLean R. 2003. Climate Change and Urban Stormwater Infrastructure in Canada: Context and Case Studies. Toronto-Niagara Region Study Report and Working Paper Series. Meteorological Service of Canada, Waterloo, Ontario. 2003–1. Westra S, Fowler HJ, Evans JP, Alexander LV, Berg P, Johnson F, Kendon EJ, Lenderink G, Roberts NM. 2014. Future changes to the intensity and frequency of short-duration extreme rainfall. Reviews of Geophysics 52 (3): 522–555 DOI: 10.1002/2014RG000464 Zhang B, Wang S, Moradkhani H, Slater L, Liu J. 2022. A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales. Water Resources Research 58 (11): e2022WR032658 DOI: 10.1029/2022WR032658 Zhao W, Kinouchi T, Nguyen HQ. 2021. A framework for projecting future intensity-duration-frequency (IDF) curves based on CORDEX Southeast Asia multi-model simulations: An application for two cities in Southern Vietnam. Journal of Hydrology 598 : 126461 DOI: 10.1016/j.jhydrol.2021.126461 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 17 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8901153","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":598691753,"identity":"85fc994e-0e4b-4932-b7af-ef48c9d46c4f","order_by":0,"name":"Monomoy Goswami","email":"","orcid":"","institution":"Central Institute of Technology Kokrajhar","correspondingAuthor":false,"prefix":"","firstName":"Monomoy","middleName":"","lastName":"Goswami","suffix":""},{"id":598691755,"identity":"fca30b96-e904-4e35-98a9-516fce0c744e","order_by":1,"name":"Avijit Paul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBADHsZmCEOGH0QmFBClhRnCkGwAaTEgyiKoFoMDIAqPFn7p9msfGGoOyzC38x9gLqg5zGN8fnXihwcGDPL8YgewapGcc6Z4BsOxw2CHMc8AMsxuvN0sAXSY4czZCVi1GNzISWZgYEuDaOFhA2k5uwGkJcHgNnYt9mAt/2Ba/gEdNuPs5h/4tBhIpB9mYGyzgWjhbTvMY8Dfuw2vLRI3cpgZEvvAWgwOz+xL55G4wbvNIsFAAqdf+GekP2b48E3C3rD/4MPHBd+s5fj7z26++aPCRp5fGrsWUEQwgKQMGxgYDkMsBquUwKEcBNgfgCl5Blhk8h/Ao3oUjIJRMApGIgAAKINVms6tTPkAAAAASUVORK5CYII=","orcid":"","institution":"Central Institute of Technology Kokrajhar","correspondingAuthor":true,"prefix":"","firstName":"Avijit","middleName":"","lastName":"Paul","suffix":""},{"id":598691757,"identity":"00e1c5ba-2709-4036-b3ff-b2507174f466","order_by":2,"name":"Meghraj Goswami","email":"","orcid":"","institution":"Birla Institute of Technology and Science - Hyderabad Campus","correspondingAuthor":false,"prefix":"","firstName":"Meghraj","middleName":"","lastName":"Goswami","suffix":""}],"badges":[],"createdAt":"2026-02-17 12:39:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8901153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8901153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104400172,"identity":"4cc93bbd-3920-4845-ba43-b1b6725f15c4","added_by":"auto","created_at":"2026-03-11 12:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181178,"visible":true,"origin":"","legend":"\u003cp\u003eMap of mainland India: (a) Country outline showing the locations of 100 DSCs, and the locations and bubble plots of population in Million (as per Census 2011) of eight Tier I UCs, and (b) Elevation map (Paul and Goswami, 2024)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8901153/v1/dcd55022b4e28deaa482606e.png"},{"id":103772353,"identity":"95740a5c-1b92-4607-b728-8315ce1fbe50","added_by":"auto","created_at":"2026-03-02 17:34:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":474509,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of Bernard’s Parameters \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e and \u003cem\u003ec \u003c/em\u003ein three 3 consecutive future periods under one of the SSPs, namely SSP1-2.6\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8901153/v1/89f7afd67fdc6b06f8bc1a4a.png"},{"id":104400736,"identity":"31d9330e-a78c-479a-a83b-f01d2e4834c2","added_by":"auto","created_at":"2026-03-11 12:10:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":525416,"visible":true,"origin":"","legend":"\u003cp\u003ePrecipitation (a) in the historical period, and (b)-(m) the percentage deviations in three future periods under the four SSPs\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8901153/v1/4cf5aa2de3fc57e6328180d1.png"},{"id":103772356,"identity":"2b9e5563-992a-42d4-a549-0bfcf6fdbc01","added_by":"auto","created_at":"2026-03-02 17:34:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":318554,"visible":true,"origin":"","legend":"\u003cp\u003eTrend Analysis of (a) historical and (b-e) future precipitation under four SSPs. Color indices indicating no trend (blue), positive trend (red) and negative trend (green) in Fig. (a) apply in Figs. (b-e), and I.T, P.T and N.T. in legends on all figures indicate Insignificant, Positive and Negative trends of the Sen’s slope respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8901153/v1/c6994e52c76659d99524cc19.png"},{"id":104407859,"identity":"eb8091e1-6012-4a65-9da9-5748b30f0a4a","added_by":"auto","created_at":"2026-03-11 12:40:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8901153/v1/de8c1169-5d09-4fca-b395-282235bf5ed1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIncreased incidences of pluvial floods caused by precipitation in the form of rainfall coupled with inadequate infrastructure and shortfall in management practices affect key economic and cultural hubs in urban areas of many countries of the world. Generally, these floods occur from the accumulation of water from rain over urbanized surfaces and the inadequacies of conveyance systems to carry the accumulated water out of the urban catchments. In India, the effects of pluvial floods in urban areas include inundation of densely inhabited areas, disruption of infrastructures and economic activities, impacts on civic amenities, peoples\u0026rsquo; exposure to water borne diseases, psychological trauma, sufferings and other health risks, and in some cases, loss of lives. The National Disaster Management Authority of the Government of India, in describing the difference of urban flooding from rural flooding (NDMA, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), stated that high intensity rainfall increases the flood peaks to 1.8 to 8 times and flood volumes up to 6 times in urban areas in comparison with those in rural areas. India has 100 designated smart cities, hereinafter referred to as DSCs, including eight Tier I Urban Conglomerates (UCs) that are Ahmedabad, Bengaluru, Chennai, Delhi, Hyderabad, Kolkata, Mumbai and Pune. Many of the DSCs are historically flood-prone, and many experience increased incidences of flooding, primarily due to the impacts of (i) increased occurrences of small-duration rainfall under a changing climate (Westra et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), (ii) the \u0026lsquo;new and intensified phase of urbanization during 2001\u0026ndash;2011 coupled with spatial expansion of urban extents\u0026rsquo; (MoUD, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and (iii) the \u0026lsquo;urbanization signature\u0026rsquo; on \u0026lsquo;rainfall climatology\u0026rsquo; (Kishtawal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Recent notable floods in some of the DSCs are the floods of Bengaluru in 2022 and 2024, Hyderabad in 2000, 2020 and 2021, Ahmedabad in 2001 and 2020, Delhi in 2002, 2003, 2009, 2010 and 2023, Chennai in 2004, 2015 and 2021, Mumbai in 2005, 2020 and 2023, Surat in 2006, Jamshedpur in 2008, Pune in 2019, Guwahati in 2010 and Srinagar in 2014 (MoUD, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), besides the floods of Kolkata in 2007, 2017 and 2023 and of Ahmedabad in 2017. These observations on increased frequency of occurrence of flood in India are generally similar to the observations of increased occurrences of small duration precipitations, such as those lasting for less than a day, under a changing climate (Goswami et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture-proofing the DSCs of India against pluvial flooding is an important requirement for ensuring that the benefits of implementing the National Smart Cities Mission of 2015 of the Government of India and the resulting contributions to the nation\u0026rsquo;s economy and well-being are accrued in the desired pace and level as envisaged without hindrance from urban floods. Steps to achieve this call for adoption of (a) structural measures of constructing new and upgrading existing storm water drainage systems, flood-protection infrastructures, etc. (b) non-structural measures of operationalizing early warning systems, creating resilience, etc. and (c) Integrated Urban Water Management (IUWM) practices. Essentially, these require reliable inputs of intensity, duration and frequency (IDF) of future precipitation, particularly short-duration rainfall, in the focus areas.\u003c/p\u003e \u003cp\u003eThere are several mathematical frameworks for deriving IDF relationships ((Koutsoyiannis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)), and several studies exploring the impacts on future IDF characteristics in different regions of the world under a changing climate. (Zhao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used multi-model simulations for projecting the IDF characteristics of two Vietnamese cities by considering a near future period from 2026 to 2045 and a far one from 2066 to 2085. They found that the intensity of rarer rainfall events is likely to increase, particularly for longer durations, in the future, and recommended the updating of the existing urban drainage systems in these two cities. (Cheng and AghaKouchak, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) presented a generalized framework to account for non-stationarity of IDF characteristics for designing infrastructure under a changing climate. Applying IDF analysis on historical and climate projection data of three cities, one each in Ethiopia, Tanzania and Cameroon in Africa, (De Paola et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that, although future rainfall intensities in a study area might decrease or increase depending on the location of the area, frequencies of intense rainfalls would increase in the future. Simulating future precipitations using Canadian Regional Climate Model (Mailhot et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) estimated that the frequencies of 12- and 24-hour precipitations would increase three times and those of 2- and 6-hour precipitations would increase two times over the Southern Quebec region during the future period of 2041\u0026ndash;2070. A 16\u0026ndash;27% increase of IDF ordinates for different return periods over Europe was found by (Hosseinzadehtalaei et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) from a study of Climate change impacts on short-duration extreme precipitation and IDF characteristics. Using record of daily rainfall, (Choi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) derived future IDF curves over South Korea, and concluded that the intensity of design rainfall is likely to increase in the future in most areas excluding east coastal region. Increasing trends of precipitation intensities were also found over selected regions in China (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Brazil (De Souza Costa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Turkey (Şen and Kahya, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Malaysia (Kuok et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shukor et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and several other countries. In the case of India, the future IDF characteristics were studied individually for several cities including three DSCs, namely Bengaluru (Chandra et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Hyderabad (Agilan and Umamahesh, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Chennai (Andimuthu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Roorkee (Singh et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), etc., and an extensive study of changes in future precipitation intensities across the whole country under different climate change scenarios was carried out by (Maity and Maity, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNoting the dearth of literature on studies of IDF characteristics involving the DSCs distributed across India, the objective of this study was set to investigate the trend and deviations of future IDF characteristics under different climate change scenarios that would have potential to aggravate the future pluvial flooding scenarios in the DSCs.\u003c/p\u003e"},{"header":"2. Study Area and Data","content":"\u003cp\u003eNoting that there are 7933 urban settlements across India (MoUD, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and that regional attributes of rainfall are relevant for comprehending the dynamics of location-specific rainfall characteristics, this study was conducted by exploring the future IDF characteristics of rainfall at grid points over mainland India in a 0.25\u0026deg;\u0026times;0.25\u0026deg; geographic grid into which the country is conventionally divided for climate related research. An outline of mainland India showing the major grids of latitudes and longitudes is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a). The locations of the 100 DSCs including the locations, names and populations as per 2011 Census of the eight UCs amongst these DSCs are also included in this map in order to show the spread of the DSCs across India. The names of the DSCs are not given for the clarity of the map. Readers may find details of the DSCs in the official webpages of the Smart Cities Mission of the Government of India (smartcities.gov.in). Since precipitation of a region is influenced, \u003cem\u003einter alia\u003c/em\u003e, by the region\u0026rsquo;s physiography, hence, the elevation map showing the Indo-Gangetic-Brahmaputra plains spreading across the north-central, eastern and western parts of the country, and the country\u0026rsquo;s southern peninsular region is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeaningful studies of IDF characteristics require short-duration, preferably hourly, data of precipitation. However, the unavailability of long records of historical data of observed hourly precipitation in many parts of the world including India compels researchers either to apply statistical techniques, such as temporal downscaling, scale invariance method, Equidistance Quantile Matching Method etc., to daily data of precipitation for producing data of sub-daily temporal resolutions, or to use a reference set of data in lieu of recorded data. Because the use of statistical techniques imparts increased uncertainty to the assessment of future IDF characteristics, and records of hourly observed data are not readily available for the study area, the current study was undertaken by using a set of hourly reanalysis precipitation data of the fifth generation produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), called ECMWF Re-Analysis 5, i.e. ERA5, as reference data. As described in the Climate Data Guide of the National Center for Atmospheric Research of the USA (NCAR, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), these reanalysis data were produced by assimilating multiple observational data of the atmosphere, land and ocean are assimilated into a forecast model and yielding a \u0026lsquo;dynamically consistent estimate\u0026rsquo; of the state of climate. The adoption of ERA5 hourly data of precipitation as reference data for this study is supported by the observations from an earlier study (Mahto and Mishra, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in which the authors found the ERA5 data as being the best amongst five different reanalysis datasets in representing the monsoon precipitation of India. Accordingly, ERA5 reanalysis outputs for a historical 60-year period from 1955 to 2014 were used for determining the observed IDF characteristics at grid points across the study area. For producing IDF characteristics of future precipitations, the hourly precipitation outputs from one General Circulation Model, also called a Global Climate Model (GCM), namely MIROC6 of the Coupled Model Intercomparison Project phase 6, were taken for an 86-year projection divided into three periods, namely 2015\u0026ndash;2040 for immediate-future, 2041\u0026ndash;2070 for near-future and 2071\u0026ndash;2100 for far-future. The choice of MIROC6 was because of it being the only GCM that provided hourly output of precipitation for all SSPs considered in this study. The future MIROC6-simulated data were obtained for four of the five climate change and greenhouse gas emission scenarios under Shared Socio-economic Pathways (SSPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 of the variant level r1i1p1f1 as defined in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change of the UN. These SSPs represent future climate change scenarios with SSP1-2.6 representing a sustainability-focused low greenhouse gas emission scenario, SSP2-4.5 corresponding to a \u0026lsquo;middle-of-the-road\u0026rsquo; moderate emission scenario, SSP3-7.0 indicating a high emissions scenario with regional rivalries, and SSP5-8.5 representing a worst-case scenario of a \u0026lsquo;business as usual\u0026rsquo; world with heavy reliance on fossil fuels and absence of any future climate policy.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eDue to the fact that GCM outputs often exhibit large systematic errors or biases arising out of inadequate comprehension of physical processes, inaccurate input data, limitations of computing resources, etc. in regional scales (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and that the use of these outputs for the assessment of future impacts of climate change on hydro-climatological variables is likely to yield less reliable results (Kotlarski et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Maraun and Widmann, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), it becomes pertinent to correct the bias of the GCM outputs for aligning the statistical characteristics of these outputs with those of the observed data (Maraun, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For the study described herein, the Quantile Delta Mapping (QDM) for bias correction (Cannon et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) through matching of Cumulative Distribution Functions (Panofsky and Brier, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) was applied to the GCM outputs of hourly precipitation data. The mathematical formulation of this method is described by Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{x}_{i}^{maf}={x}_{i}^{mf}+{F}_{{x}^{oh}}^{-1}\\left({F}_{{x}^{mf}}\\left({x}^{mf}\\right)\\right)-{F}_{{x}^{mh}}^{-1}\\left({F}_{{x}^{mf}}\\left({x}^{mf}\\right)\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ei\u003c/em\u003e denotes a grid point, superscripts mf denotes GCM model outputs of the future, \u003cem\u003emaf\u003c/em\u003e denotes bias-corrected GCM outputs of the future, \u003cem\u003emh\u003c/em\u003e denotes GCM-simulated historical values, and \u003cem\u003eoh\u003c/em\u003e denotes observed historical values or reference data of precipitation.\u003c/p\u003e \u003cp\u003eThe Annual Maximum (AM) Series of precipitation intensities of durations 1-, 2-, 6-, 12-, 24- and 48-hours for the historical data period as well as for each future period were produced from the respective time series data of precipitation by using moving windows of the selected durations across the data series. The AM series of precipitation of different durations for different periods were thus generated for each grid point over the mainland India. The intensities of AM precipitation for different durations were then estimated for each of 5-, 10-, 25-, 50- and 100-year return periods by employing Gumbel\u0026rsquo;s extreme value distribution (Gumbel, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Having obtained these probabilistically derived data of precipitation, the IDF curves of the form of an empirical equation of Bernard (Bernard, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1932\u003c/span\u003e) as given by Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were fitted to the data at each grid point.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:I=\\frac{a{T}^{b}}{{d}^{c}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e is the precipitation intensity, \u003cem\u003ed\u003c/em\u003e is the duration, \u003cem\u003eT\u003c/em\u003e is the return period, and \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e, and \u003cem\u003ec\u003c/em\u003e are parameters. Accordingly, the precipitation intensity of a given duration and a given return period would increase with increase of \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e, and decrease of \u003cem\u003ec\u003c/em\u003e. For the AMS at each grid point, the values of the three IDF parameters were estimated for 1-, 2-, 6-, 12-, 24- and 48-hr durations for 5-, 10-, 25-, 50- and 100-year return periods. The parameters of the equation were evaluated by Ordinary Least Squares estimation method by employing the Generalized Reduced Gradient search algorithm for automatic optimization.\u003c/p\u003e \u003cp\u003eMann-Kendall trend test (Mann, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1945\u003c/span\u003e) was performed and Sen\u0026rsquo;s slope (Sen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) was estimated at each grid point by using 60 years of historical data from 1955 to 2014 and an 86-year projection into the future from 2015 to 2100, focusing on AMS of 1-hour duration across all four SSPs. Subsequent to trend analyses, the precipitation intensities of the selected durations and return periods were estimated at all grid points and their percentage (%) deviations under each of the four selected SSPs from the corresponding historical values were assessed. Inferences were then drawn from the values of trends, magnitudes of intensities and the deviations of short-duration precipitations at all grid points for three future periods under four climate change scenarios with particular references to the observations pertaining to the DSCs of India.\u003c/p\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003eIt was found from the analyses of Bernard\u0026rsquo;s parameters that, generally for all four SSPs, the plains in the north-central, eastern and western parts of India encompassing 69 DSCs show relatively high values of \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e, and the southern peninsular region containing 29 DSCs exhibits relatively low values of \u003cem\u003ec\u003c/em\u003e in the three future periods under all four SSPs. For brevity, representative graphical results for only one SSP, namely SSP1-2.6, for each of the three parameters and each of the three periods are exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sampled values of the parameters at the locations of the eight Tier I UCs for SSP1-2.6 are also provided in this figure using the same legends for the UCs as provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a) for visual assessment of the relative values of these parameters in the regions around the respective UCs, and across the whole country. Since the higher values of \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eb\u003c/em\u003e and lower values of \u003cem\u003ec\u003c/em\u003e indicate larger precipitation intensities, hence the results of the analyses of Bernard\u0026rsquo;s parameters indicate that higher precipitation intensities would be likely in almost all DSCs in the future relative to the corresponding historical values under all greenhouse gas emission scenarios. It was also found from the comparison of values of the three IDF parameters for different SSP scenarios across the grid points that, generally, the magnitudes of the precipitation intensities would increase with the increase of severity of greenhouse gas emission from the low emission scenario SSP1-2.6 to the very high emission scenario SSP5-8.5. The above observations are similar to those made in respect of the whole of India by (Maity and Maity, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who found that future precipitation intensities would increase by about 17\u0026ndash;21% in areas covering about 70\u0026ndash;90% area under SSP2-4.5 and by about 40\u0026ndash;48% in areas covering almost the whole country, and that the increase in precipitation intensity is likely to be more in the Central and northeastern parts of the country and the mountainous and Terai regions of the Gangetic basin on the north, and moderate in the alluvial plains of the north, the southern peninsula and the desert region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe historical 1-hour intensities of AM precipitations of 100-year return period and their deviations in the three periods under four SSPs from the corresponding historical values are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These figures show that the aerial extents of positive deviations generally increase with the increase of severity of greenhouse gas emissions from the sustainability-focused scenario SSP1-2.6 to the business-as-usual scenario SSP4-8.5, particularly, in the near-future and far-future periods of 2041-70 and 2071\u0026ndash;2100 respectively. It was found from the results of the deviation analysis that 85 of the 100 DSCs exhibit high positive deviations under SSP5-8.5 during 2071\u0026ndash;2100 implying large increase in short-duration precipitation intensities in most of the DSCs of India. These observations further substantiate that the impacts of precipitation on pluvial floods in the DSCs are likely to increase in the future due to increase in the volume of short-duration precipitation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of trends of the AM precipitation of 1-hour duration at 95% significance level at all grid points revealed that the trends in terms of both the Sen\u0026rsquo;s slope and the Mann-Kendall test statistic worked out as being insignificant for the historical and the SSP1-2.6-projected future periods in considerably large expanse of the country. These observations are also reflected in the maps showing the trends of Sen\u0026rsquo;s slope in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) and 4(b). However, from a look at the significant trends of the AM precipitation of 1-hour duration for different SSPs as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b-e), it may be seen that increasing numbers of grid points exhibit significant positive trends from SSP1-2.6 to SSP5-8.5. From the results of trend analyses, it was also found that the number of DSCs having significant positive trends of AM precipitation intensities of 1-hour duration increases from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively. As regards the UCs in particular, the values of the trend indicator, i.e. the Sen\u0026rsquo;s slope, provided on the maps of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal that the trends of the AM precipitation of 1-hour duration at 95% significance level are insignificant under the sustainability-focused low greenhouse gas emission scenario SSP1-2.6 in seven of the eight UCs, and negative in the UC of Mumbai on the western coast. However, the trends change to significantly positive in two relatively closely located UCs of Mumbai and Pune under the moderate emission scenario SSP2-4.5. As the scenario turns to high emission, five UCs, namely Delhi, Hyderabad, Kolkata, Ahmedabad and Chennai, exhibit significant positive trends, whereas the UCs of Delhi, Bengaluru, Hyderabad, Mumbai and Pune show significantly positive trends under the very high emission scenario SSP5-8.5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough the above results from trend analysis of the AM precipitation of 1-hour duration over the DSCs and the whole country generally indicate increasing trends of short-duration AM precipitations, the results in respect of the UCs in particular reveal that the impacts of greenhouse gas emissions of increasing severities might not always translate to significantly increasing trends of short-duration AM precipitations in some DSCs. Similar results were found by (Mirhosseini et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in a study of the impacts of climate change on rainfall IDF curves in Alabama in the US who observed that the patterns of future precipitation of Alabama are likely to be less intense for short-duration events, and that the results were not consistent for precipitations of relatively longer duration of more than four hours. The reasons for the apparent inconsistencies that could be found from the above trend analysis results for some of the UCs of India are likely to be (i) the impacts of greenhouse gas emissions and the resulting rise in temperature on a large variety of physical processes that complexly and non-linearly influence the pattern of occurrence of precipitation, (ii) the uncertainties in the GCM-projected values of future precipitations, and (iii) consideration of reanalysis products of precipitation instead of observed shot-duration precipitations as reference sets of historical data. The differences in the values of trend indicators, e.g. in the case of the UC of Mumbai as exhibited on the maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e., indicate the necessity of undertaking scenario-specific analysis for determining the expected IDF characteristics for efficient design of urban pluvial flood control structures and IUWM. However, although this approach would be idealistic, the practical problem would be to assess the likely severity of the greenhouse gas emission of the future that would help identify an appropriate SSP scenario. A way of overcoming this difficulty of adopting an SSP scenario for design in absence of better estimates of precipitation intensity would be that suggested for urban stormwater infrastructure design in Toronto-Niagara Region of Canada by (Watt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to adopt a design storm 15% larger than the present estimate.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, the trends and deviations of future IDF characteristics, particularly of short-duration annual maximum precipitation, that are likely to aggravate the future pluvial flooding scenarios of 100 DSCs of India were studied at grid points of a 0.25\u0026deg;\u0026times;0.25\u0026deg; grid covering mainland India. By using hourly data of AM precipitations of the historical past from the year 1955 to 2014 and the bias-corrected GCM-projected future values of 1-hour AM precipitations under four Shared Socioeconomic Pathways (SSPs) during three periods from 2015\u0026ndash;2040, 2041\u0026ndash;2070 and 2071 to 2100, it was found that\u003c/p\u003e \u003cp\u003eIntensity wise, the plains in the north-central, eastern and western parts of the country in which 69 DSCs are located and the southern peninsular region containing 29 DSCs are likely to experience intensities of short-duration precipitation higher than the corresponding historical values in the three future periods under all four SSPs\u003c/p\u003e \u003cp\u003eMagnitude wise, the aerial extents of positive deviations of short-duration AM precipitation from the corresponding historical values would generally increase from SSP1-2.6 to SSP4-8.5, particularly, during 2041\u0026ndash;2100, and 85 of the 100 DSCs are likely to experience high positive deviations under SSP5-8.5 during 2071\u0026ndash;2100.\u003c/p\u003e \u003cp\u003eTrend wise, the number of DSCs having significant positive trends of AM precipitation intensities of 1-hour duration increases from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively.\u003c/p\u003e \u003cp\u003eIn the case of some DSCs, the impacts of greenhouse gas emissions of increasing severities might not always indicate significantly increasing trends of short-duration AM precipitations. Whereas such an apparent inconsistency is possible, it would necessitate the consideration of a realistic future greenhouse gas emission scenario for each DSC for designing efficient urban pluvial flood protection structures and undertaking effective IUWM practices for the DSC. However, this approach of adopting DSC-specific and climate change scenario specific values of future IDF values of precipitations for design would be impractical, and the suggestion of (Watt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to adopt a design storm 15% larger than the present estimate for design appear as being pragmatic.\u003c/p\u003e \u003cp\u003eGenerally, however, the results of this study show that most of the DSCs are expected to experience larger volumes of short-duration precipitation, and hence increased incidences of pluvial flooding, in the future. These findings would facilitate the assessment of the increasing vulnerabilities of the 100 DSCs located across India to urban pluvial floods, and provide an objective basis for efficient planning and design of urban stormwater drainage systems and flood protection infrastructures. The results of this study would also contribute to the framing of effective IUWM practices for sustainable, citizen-friendly developments of these DSCs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003eThe authors express their sincere gratitude to the anonymous reviewers for their valuable suggestions and insightful comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Interests.\u0026nbsp;\u003c/strong\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research received no funding from external sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonomoy Goswami: 0000-0003-1380-6276; Avijit Paul: 0000-0001-9388-9571; Meghraj Goswami: 0009-0007-4895-2747\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003eRaw simulated outputs of GCMs of the CMIP6 phase are available at https://esgf-node.llnl.gov/projects/cmip6/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author suggested improvements on the methodology and presentation of results, and prepared the manuscript. The second and third author conceptualized, analysed, and prepared graphical outputs of the research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgilan V, Umamahesh NV. 2016. Is the covariate based non-stationary rainfall IDF curve capable of encompassing future rainfall changes? \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e541\u003c/strong\u003e: 1441\u0026ndash;1455 DOI: 10.1016/j.jhydrol.2016.08.052\u003c/li\u003e\n\u003cli\u003eAndimuthu R, Kandasamy P, Mudgal BV, Jeganathan A, Balu A, Sankar G. 2019. Performance of urban storm drainage network under changing climate scenarios: Flood mitigation in Indian coastal city. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e (1): 7783 DOI: 10.1038/s41598-019-43859-3\u003c/li\u003e\n\u003cli\u003eBernard MM. 1932. Formulas For Rainfall Intensities of Long Duration. \u003cem\u003eTransactions of the American Society of Civil Engineers\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e (1): 592\u0026ndash;606 DOI: 10.1061/TACEAT.0004323\u003c/li\u003e\n\u003cli\u003eCannon AJ, Sobie SR, Murdock TQ. 2015. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? \u003cem\u003eJournal of Climate\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e (17): 6938\u0026ndash;6959 DOI: 10.1175/JCLI-D-14-00754.1\u003c/li\u003e\n\u003cli\u003eChandra R, Saha U, Mujumdar PP. 2015. Model and parameter uncertainty in IDF relationships under climate change. \u003cem\u003eAdvances in Water Resources\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e: 127\u0026ndash;139 DOI: 10.1016/j.advwatres.2015.02.011\u003c/li\u003e\n\u003cli\u003eChen C-A, Hsu H-H, Liang H-C. 2021. Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the seasonal extreme precipitation in the Western North Pacific and East Asia. \u003cem\u003eWeather and Climate Extremes\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e: 100303 DOI: 10.1016/j.wace.2021.100303\u003c/li\u003e\n\u003cli\u003eCheng L, AghaKouchak A. 2014. Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design in a Changing Climate. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e (1): 7093 DOI: 10.1038/srep07093\u003c/li\u003e\n\u003cli\u003eChoi J, Lee O, Jang J, Jang S, Kim S. 2019. Future intensity\u0026ndash;depth\u0026ndash;frequency curves estimation in Korea under representative concentration pathway scenarios of Fifth assessment report using scale‐invariance method. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e (2): 887\u0026ndash;900 DOI: 10.1002/joc.5850\u003c/li\u003e\n\u003cli\u003eDe Paola F, Giugni M, Topa ME, Bucchignani E. 2014. Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities. \u003cem\u003eSpringerPlus\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (1): 133 DOI: 10.1186/2193-1801-3-133\u003c/li\u003e\n\u003cli\u003eDe Souza Costa CEA, Blanco CJC, De Oliveira-J\u0026uacute;nior JF. 2020. IDF curves for future climate scenarios in a locality of the Tapaj\u0026oacute;s Basin, Amazon, Brazil. \u003cem\u003eJournal of Water and Climate Change\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (3): 760\u0026ndash;770 DOI: 10.2166/wcc.2019.202\u003c/li\u003e\n\u003cli\u003eGoswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK. 2006. Increasing Trend of Extreme Rain Events Over India in a Warming Environment. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e314\u003c/strong\u003e (5804): 1442\u0026ndash;1445 DOI: 10.1126/science.1132027\u003c/li\u003e\n\u003cli\u003eGumbel EJ. 1958. Statistics of Extremes. In \u003cem\u003eStatistics of Extremes\u003c/em\u003eColumbia University Press. DOI: 10.7312/gumb92958\u003c/li\u003e\n\u003cli\u003eHosseinzadehtalaei P, Tabari H, Willems P. 2020. Climate change impact on short-duration extreme precipitation and intensity\u0026ndash;duration\u0026ndash;frequency curves over Europe. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e590\u003c/strong\u003e: 125249 DOI: 10.1016/j.jhydrol.2020.125249\u003c/li\u003e\n\u003cli\u003eKishtawal CM, Niyogi D, Tewari M, Pielke RA, Shepherd JM. 2010. Urbanization signature in the observed heavy rainfall climatology over India. \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e (13): 1908\u0026ndash;1916 DOI: 10.1002/joc.2044\u003c/li\u003e\n\u003cli\u003eKotlarski S, Keuler K, Christensen OB, Colette A, D\u0026eacute;qu\u0026eacute; M, Gobiet A, Goergen K, Jacob D, L\u0026uuml;thi D, van Meijgaard E, et al. 2014. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. \u003cem\u003eGeoscientific Model Development\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (4): 1297\u0026ndash;1333 DOI: 10.5194/gmd-7-1297-2014\u003c/li\u003e\n\u003cli\u003eKoutsoyiannis D, Kozonis D, Manetas A. 1998. A mathematical framework for studying rainfall intensity-duration-frequency relationships. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e206\u003c/strong\u003e (1\u0026ndash;2): 118\u0026ndash;135 DOI: 10.1016/S0022-1694(98)00097-3\u003c/li\u003e\n\u003cli\u003eKuok KK, Mah YS, Imteaz MA, Kueh SM. 2016. Comparison of future intensity duration frequency curve by considering the impact of climate change: case study for Kuching city. \u003cem\u003eInternational Journal of River Basin Management\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (1): 47\u0026ndash;55 DOI: 10.1080/15715124.2015.1082478\u003c/li\u003e\n\u003cli\u003eMahto SS, Mishra V. 2019. Does ERA‐5 Outperform Other Reanalysis Products for Hydrologic Applications in India? \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e (16): 9423\u0026ndash;9441 DOI: 10.1029/2019JD031155\u003c/li\u003e\n\u003cli\u003eMailhot A, Duchesne S, Caya D, Talbot G. 2007. Assessment of future change in intensity\u0026ndash;duration\u0026ndash;frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM). \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e347\u003c/strong\u003e (1\u0026ndash;2): 197\u0026ndash;210 DOI: 10.1016/j.jhydrol.2007.09.019\u003c/li\u003e\n\u003cli\u003eMaity SS, Maity R. 2022. Changing Pattern of Intensity\u0026ndash;Duration\u0026ndash;Frequency Relationship of Precipitation due to Climate Change. \u003cem\u003eWater Resources Management\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e (14): 5371\u0026ndash;5399 DOI: 10.1007/s11269-022-03313-y\u003c/li\u003e\n\u003cli\u003eMann HB. 1945. Nonparametric Tests Against Trend. \u003cem\u003eEconometrica\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (3): 245 DOI: 10.2307/1907187\u003c/li\u003e\n\u003cli\u003eMaraun D. 2013. Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue. \u003cem\u003eJournal of Climate\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e (6): 2137\u0026ndash;2143 DOI: 10.1175/JCLI-D-12-00821.1\u003c/li\u003e\n\u003cli\u003eMaraun D, Widmann M. 2018. \u003cem\u003eStatistical Downscaling and Bias Correction for Climate Research\u003c/em\u003e. Cambridge University Press: Cambridge. DOI: 10.1017/9781107588783\u003c/li\u003e\n\u003cli\u003eMirhosseini G, Srivastava P, Stefanova L. 2013. The impact of climate change on rainfall Intensity\u0026ndash;Duration\u0026ndash;Frequency (IDF) curves in Alabama. \u003cem\u003eRegional Environmental Change\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (S1): 25\u0026ndash;33 DOI: 10.1007/s10113-012-0375-5\u003c/li\u003e\n\u003cli\u003eMoUD. 2017. Standard Operating Procedure for Urban Flooding. Ministry of Urban Development, Government of India Available at: https://mohua.gov.in/upload/uploadfiles/files/SOP%20Urban%20flooding_5%20May%202017.pdf\u003c/li\u003e\n\u003cli\u003eNCAR. 2023. \u003cem\u003eClimate data Guide (CDG)\u003c/em\u003e Available at: https://climatedataguide.ucar.edu/climate-data/reanalysis [Accessed 12 July 2023]\u003c/li\u003e\n\u003cli\u003eNDMA. 2010. \u003cem\u003eNational Disaster Management Guidelines - Management of Urban Flooding. National Disaster Management Authority, Government of India\u003c/em\u003e. Available at: https://nidm.gov.in/pdf/guidelines/new/management_urban_flooding.pdf\u003c/li\u003e\n\u003cli\u003ePanofsky HA, Brier GW. 1968. \u003cem\u003eSome Applications of Statistics to Meteorology\u003c/em\u003e. Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences.\u003c/li\u003e\n\u003cli\u003ePaul A, Goswami M. 2024. Does CMIP5 Still Have Value Over CMIP6? A Case of Mean and Extreme Temperature Simulation Over Mainland India. \u003cem\u003eInternational Journal of Environmental Science and Development\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2) DOI: 10.18178/ijesd.2024.15.2.1472\u003c/li\u003e\n\u003cli\u003eŞen O, Kahya E. 2021. Impacts of climate change on intensity\u0026ndash;duration\u0026ndash;frequency curves in the rainiest city (Rize) of Turkey. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e (3\u0026ndash;4): 1017\u0026ndash;1030 DOI: 10.1007/s00704-021-03592-2\u003c/li\u003e\n\u003cli\u003eSen PK. 1968. Estimates of the Regression Coefficient Based on Kendall\u0026rsquo;s Tau. \u003cem\u003eJournal of the American Statistical Association\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e (324): 1379\u0026ndash;1389 DOI: 10.1080/01621459.1968.10480934\u003c/li\u003e\n\u003cli\u003eShukor MSA, Yusop Z, Yusof F, Sa\u0026rsquo;adi Z, Alias NE. 2020. Detecting Rainfall Trend and Development of Future Intensity Duration Frequency (IDF) Curve for the State of Kelantan. \u003cem\u003eWater Resources Management\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e (10): 3165\u0026ndash;3182 DOI: 10.1007/s11269-020-02602-8\u003c/li\u003e\n\u003cli\u003eSingh R, Arya DS, Taxak AK, Vojinovic Z. 2016. Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee, India. \u003cem\u003eWater Resources Management\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e (13): 4603\u0026ndash;4616 DOI: 10.1007/s11269-016-1441-4\u003c/li\u003e\n\u003cli\u003eWatt WE, Waters D, McLean R. 2003. Climate Change and Urban Stormwater Infrastructure in Canada: Context and Case Studies. Toronto-Niagara Region Study Report and Working Paper Series. Meteorological Service of Canada, Waterloo, Ontario. 2003\u0026ndash;1.\u003c/li\u003e\n\u003cli\u003eWestra S, Fowler HJ, Evans JP, Alexander LV, Berg P, Johnson F, Kendon EJ, Lenderink G, Roberts NM. 2014. Future changes to the intensity and frequency of short-duration extreme rainfall. \u003cem\u003eReviews of Geophysics\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e (3): 522\u0026ndash;555 DOI: 10.1002/2014RG000464\u003c/li\u003e\n\u003cli\u003eZhang B, Wang S, Moradkhani H, Slater L, Liu J. 2022. A Vine Copula‐Based Ensemble Projection of Precipitation Intensity\u0026ndash;Duration\u0026ndash;Frequency Curves at Sub‐Daily to Multi‐Day Time Scales. \u003cem\u003eWater Resources Research\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e (11): e2022WR032658 DOI: 10.1029/2022WR032658\u003c/li\u003e\n\u003cli\u003eZhao W, Kinouchi T, Nguyen HQ. 2021. A framework for projecting future intensity-duration-frequency (IDF) curves based on CORDEX Southeast Asia multi-model simulations: An application for two cities in Southern Vietnam. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e598\u003c/strong\u003e: 126461 DOI: 10.1016/j.jhydrol.2021.126461\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pluvial flood, Smart Cities, Intensity-Duration-Frequency, Shared Socio-Economic Pathways, GCM","lastPublishedDoi":"10.21203/rs.3.rs-8901153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8901153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIncreased incidences of pluvial floods from rainfall affect key economic and cultural hubs of many countries of the world. Future-proofing India\u0026rsquo;s 100 designated smart cities against pluvial flooding through Integrated Urban Water Management (IUWM) is an important requirement that calls for assessments of the future characteristics of rainfall in these cities and the likely impacts of alterations of these characteristics under Shared Socio-economic Pathways (SSPs) of the Sixth Assessment Report (2021) of the Intergovernmental Panel on Climate Change. In this study, potential future alterations in Intensity-Duration-Frequency (IDF) characteristics of precipitation were explored by using reanalysis outputs of hourly precipitation of the period 1955\u0026ndash;2014 from a selected Global Climate Model (GCM), namely MIROC6. In this study, the MIROC6-simulated precipitation data of three future periods, namely 2015\u0026ndash;2040, 2041\u0026ndash;2070 and 2071\u0026ndash;2100, under four SSPs, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, at a 0.25\u0026deg;\u0026times;0.25\u0026deg; grid over India were used. It emerged from the analyses that, generally, for all four SSPs, the north-central, eastern and western parts of the country encompassing 69 smart city locations and the southern peninsular region containing 29 locations would have high future precipitation intensities. The number of smart cities having significant positive trends of 1-hour annual maximum precipitation was found to increase from 35 under SSP2-4.5 to 68 and 72 under SSP3-7.0 and SSP5-8.5 respectively. The historical 1-hr precipitations of 100-year return period and their % deviations in the three future periods under four SSPs showed that the aerial extents of positive deviations generally increase from SSP1-2.6 to SSP4-8.5, particularly during 2041-70 and 2071\u0026ndash;2100, and that 81 smart city locations exhibit high positive deviations under SSP5-8.5 during 2071\u0026ndash;2100. These results indicate that future precipitation intensities, and hence pluvial floods, are likely to increase over the designated smart cities, and accounting for the increasing vulnerabilities of these cities to urban pluvial flooding due to increased short-duration rainfall would be vital for effective IUWMs.\u003c/p\u003e","manuscriptTitle":"Future precipitation characteristics of eight Tier I Urban Conglomerates amongst designated smart cities of India under selected Shared Socio-economic Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 17:34:54","doi":"10.21203/rs.3.rs-8901153/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T05:43:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236605829073132616478992291435802263948","date":"2026-05-17T05:08:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202445082788704304370942441260149092645","date":"2026-05-13T15:35:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52637091194632032155119085733909407613","date":"2026-05-11T18:01:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T05:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80710509968992280265550501558271699425","date":"2026-03-23T19:54:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211157214239413570764090939267460883528","date":"2026-03-19T15:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T15:24:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T12:44:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T01:42:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Processes","date":"2026-02-17T12:28:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-processes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enpr","sideBox":"Learn more about [Environmental Processes](https://www.springer.com/journal/40710)","snPcode":"40710","submissionUrl":"https://submission.nature.com/new-submission/40710/3","title":"Environmental Processes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7ac2e5f7-170d-469a-b8d2-be7fa44bc6ab","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T05:43:23+00:00","index":65,"fulltext":""},{"type":"reviewerAgreed","content":"236605829073132616478992291435802263948","date":"2026-05-17T05:08:43+00:00","index":64,"fulltext":""},{"type":"reviewerAgreed","content":"202445082788704304370942441260149092645","date":"2026-05-13T15:35:59+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"52637091194632032155119085733909407613","date":"2026-05-11T18:01:48+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T17:34:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 17:34:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8901153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8901153","identity":"rs-8901153","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}