Assessing low-impact development strategies using synthetic rainfall under climate change across different urbanization densities Open Access

Urban flooding presents a significant global challenge, with yearly damages exceeding $40 billion and an estimated impact on two billion people by 2050 due to climate change (Water Directors of the European Union 2003; Programme & UN-Water 2012). Floods, comprising 48% of global disasters, are exacerbated by factors like unplanned urbanization and extreme rainfall events (Douris & Kim 2021). The Intergovernmental Panel on Climate Change (IPCC) (Ed.) (2023) Sixth Assessment Report highlights the heightened risk of more frequent and intense precipitation events, particularly in urban areas (Saha et al. 2017; Kim et al. 2018).

Efforts to understand the potential impact of climate change on Indian monsoons have gained traction among scientists and policymakers, underscoring the need for reliable climate simulations under various scenarios (Mitra 2021). Pyke et al. (2011) emphasized the sensitivity of stormwater runoff to changes in impervious cover and precipitation volume, particularly in urban settings like South Boston, Massachusetts. Similarly, Zahmatkesh et al. (2015) utilized CMIP5 precipitation projections to demonstrate substantial increases in future urban runoff volume and peak flow rate in a New York City watershed, highlighting the imperative of integrating climate change considerations into stormwater management and flood control planning.

However, the uncertainties associated with climate change present significant challenges to the planning and design of water resources engineering systems (Hallegatte 2009; Wardekker et al. 2010). While there have been numerous studies focusing on urban flooding in the context of environmental changes, many have examined the impacts of climate change and land use change separately on flood risk and mitigation strategies (Binesh et al. 2019; Feng et al. 2021; Xu et al. 2023). Hassani et al. (2023) have proposed a comprehensive decision framework aimed at guiding the practices using low-impact developments (LIDs), and Kourtis & Tsihrintzis (2021) have introduced reliability indices to assist in flood risk mitigation planning by adapting urban drainage systems to climate change. However, integrated assessments considering the dual impacts remain scarce, thereby impeding effective urban flood risk management.

Synthetic rainfall datasets play a crucial role in urban flood quantification and design, offering a means to incorporate maximum rainfall intensities into runoff analysis (Grimaldi & Serinaldi 2006). Various methodologies, including the Poisson cluster rainfall model (Kim & Onof 2020) and stochastic weather generators (Lu & Qin 2020), have been employed to generate synthetic rainfall scenarios for urban hydrological modeling.

In 2018, Kerala experienced devastating multi-day extreme rainfall in July and August, accumulating approximately 1,600 mm of rainfall, resulting in an estimated economic loss of 200 billion USD, while in Hyderabad, a severe flooding event in October 2020 claimed over 80 lives, displaced about 40,000 families, and caused an economic loss of 0.57 billion Euros. These events underscore the urgent need to address urban flooding risks, emphasizing the importance of understanding multi-day extreme rainfall patterns for future impact studies. Studies have shown variations in maximum monsoon rainfall durations, highlighting the complexity of urban drainage design and flood modeling. Parameters such as inter-event times, storm depth, and duration need to be accurately linked with frequency or return periods in modeling approaches like personal computer stormwater management model (PCSWMM), considering available data and computational resources for effective flood risk mitigation strategies (Ramasamy et al. 2019).

A recent devastating event in Hyderabad during the 2020 monsoon season, including substantial loss of life, displacement of families, and significant economic losses, highlights the immediate necessity for an effective flood predictive model. The city's rapid urbanization, with a growth rate of 16.5% over the past two decades, has resulted in challenges such as the reduction of storage bodies and the conversion of both natural and man-made streams into artificial stormwater systems (O'Driscoll et al. 2010). While previous flood modeling studies (Naresh et al. 2021) in Hyderabad primarily concentrated on single-day precipitation occurrences employing models such as SWMM (Vemula et al. 2020), hydrologic engineering center–river analysis system (HEC–RAS) (Naresh & Naik 2023b), and HEC–hydrologic modeling system (HMS) (Rangari et al. 2020; Naresh & Naik 2023a) primarily considering the influence of climate change under varying scenarios. However, there exists a gap in research focusing on the integrated assessment of how both climate and land use changes affect urban flood management (Bibi & Kara 2023) strategies in Indian urban areas. Currently, no urban center in India has undergone examination regarding the implementation of LIDs for flood mitigation, considering variations in climate and changes in land use and land cover (LULC) alongside synthetic rainfall scenarios.

LID techniques have emerged as effective tools for mitigating adverse hydrologic and water quality effects of urbanization (Elliott & Trowsdale 2007). These practices, such as bioretention facilities and green roofs, aim to mimic natural hydrological processes, offering sustainable stormwater management solutions (Li et al. 2019).

The Environmental Protection Agency Storm Water Management Model (SWMM) and PCSWMM are widely utilized for urban flood modeling, enabling the assessment of various factors influencing flood inundation (Rossman 2015). Despite their utility, challenges persist in accurately assessing LID impacts, particularly in densely populated urban areas (Custódio & Ghisi 2023).

In response, this study aims to assess the impact of climate and land use changes on urban flood management in Hyderabad, India, by integrating synthetic rainfall data with PCSWMM modeling. By quantifying LID responses to various storm patterns, including extreme rainfall events, the study seeks to enhance urban flood resilience and inform sustainable development decision-making processes. The proposed methodology involves simulating floods based on synthetic rainfall scenarios using PCSWMM, coupled with an assessment of LID effectiveness under changing environmental conditions. This integrated approach holds promise for developing adaptive urban flood management strategies, addressing the multifaceted challenges of climate change and urbanization.

The differing urbanization densities in the northern and southern parts of Hyderabad significantly impact these stormwater zones. The northern GHMC area, including zone-XII, has higher urbanization density, leading to increased impermeable surfaces and higher runoff volumes, exacerbating stormwater management challenges. In contrast, the southern GHMC zones IV&V have lower urbanization densities, resulting in different water flow and drainage patterns. These variations require tailored flood management strategies to address each area's specific needs and challenges, highlighting the importance of understanding urbanization density differences for effective and sustainable solutions to Hyderabad's flooding issues.

Table S1 demonstrates land use area statistics of GHMC stormwater zones, particularly the growth of built-up areas from 2000 to 2020. In 2000, built-up areas covered 139.81 km² (17.87% of total area), increasing to 390.03 km² (49.85%) in 2020, and the built-up area's substantial growth would increase from 49.85% in 2020. The zone-XII, zone-IV&V of built-up areas expansion can be observed in Figure S2. Zone V, channeling stormwater through Zone IV, was combined with Zone IV for accurate hydrological modeling, highlighting the need for effective flood management.

The implementation of LID techniques within the PCSWMM framework plays a crucial role in enhancing flood resilience and mitigating runoff. To facilitate the PCSWMM simulation, data pertaining to an 11-day extreme rainfall event occurring between 10 and 21 October 2020, were utilized. The highest rainfall was observed on 14 October 2020, with a weighted average of 237.5 mm recorded by GHMC during this event. Moreover, the study aims to address climate change considerations by incorporating data from a simulated extreme precipitation event of 1–10 days of synthetic rainfall, corresponding to the four future shared socioeconomic pathways (SSP) scenarios based on GCM–CMIP6 of Ec-Earth3-Veg and MPI-ESM-1-2-HR. For more details, one can refer to Sagar Kumar & Umamahesh (2024). The synthetic hyetographs of 1–10 days of nine events E1–E9 are shown in supplementary figures as Figures S3–S5. Similarly, another synthetic hyetograph is collected by using design precipitation data. Precipitation records with a 1-h interval between the years 1969 and 2022 were utilized to ensure realistic results. Synthetic rainfall hyetographs for Hyderabad city were developed by deriving precipitation depth for each time interval and organizing the values using the alternate block method, as detailed by Rangari et al. (2020). This approach enhances accuracy and realistic simulations by utilizing historical precipitation data. However, it is noted that the application of bio-retention was deemed unfeasible for reducing peak runoff during higher return period storm events, such as those occurring every 50 and 100 years (Ekmekcioglu et al. 2021). Therefore, the present study did not consider LID implementation for higher return periods. The synthetic hyetographs for 2, 5, and 10-year events are presented in the Supplementary Material as Figure S6.

The PCSWMM was employed to simulate surface runoff within a catchment (Manchikatla & Umamahesh 2022). Table S2 presents the summary of datasets used for urban flood modeling in the supplementary material. Figure S7 in the supplementary material illustrates the stormwater zones of XII, IV&V, including sub-catchments and depicting junctions, conduits, storages, and outfalls (Outlet).

To examine the impacts of LIDs amidst projected dual impacts, the present study utilized a combined approach integrating a Markov–CA model with geographic information system (GIS) to forecast land use land cover (LULC) changes. Furthermore, a Markov chain methodology-based weather generator was employed to get the climate change extremes. The dual impacts were then incorporated into the PCSWMM software to evaluate urban runoff across multiple time frames: present (2020) and three future periods: (2023–2048) 2030s, (2049–2074) 2050s, and (2075–2100) 2075s. This research analyzed four SSPs' climate change scenarios to compare rainfall at the Begumpet station in GHMC during the 2030, 2050, and 2075 time slices, relative to 2020. Special emphasis was placed on two scenarios: synthetic rainfall events of 1–10 days durations, and synthetic rainfall occurrences from 2-year, 5-year, and 10-year return periods. Additionally, the study focused on flood modeling in two zones, by strategically implementing LIDs. This analysis is pivotal for comprehending future rainfall–runoff scenarios and enhancing urban flood management strategies.

In this study, nine extreme events (E1–E9) of synthetic rainfall under CMIP6-based four SSP scenarios, with different LULC data, and corresponding synthetic rainfall derived from observed rainfall data, were established to discern the effects of LIDs in reducing flood impacts using PCSWMM. Further details regarding the simulation of future rainfall events and LULC changes can be found in the study by Sagar Kumar & Umamahesh (2024). Figure 3 illustrates the methodological flowchart used in the study to assess low-impact development (LID) strategies, integrating climate and land use changes under synthetic rainfall scenarios within the PCSWMM framework.

PCSWMM, developed by Computational Hydraulics International, is a proprietary software that extends SWMM by incorporating additional GIS-based decision support features. PCSWMM, a proprietary software program, is widely utilized for various hydrologic and hydraulic applications, particularly in urban settings. It is proficient in simulating both single-event-based and continuous rainfall–runoff processes. In flood modeling with PCSWMM, the initial step involves simulating hydrologic models, followed by hydraulic models. Runoff and flow depth in conduits are computed using storm drainage network data, created using ArcMap, and refined using Bing satellite's base map, integrated into PCSWMM. Table 1 outlines the different scenarios of synthetic rainfall utilized for PCSWMM simulation options.

PCSWMM extracts the drainage network from the DEM, incorporating parameters such as catchment surface area, impervious area percentage, slope, average width, Manning coefficients, and infiltration. An overview of PCSWMM parameters is shown in Table 2. Infiltration losses are computed using the Green-Ampt method, with parameters derived based on soil characteristics. The watershed delineation tool in PCSWMM divides sub-catchments into multiple zones using DEM. Sub-catchment attributes are summarized for both zones to facilitate urban flood model scenarios. Refer to Tables S3 and S4 in the Supplementary Material for future details of imperviousness for both stormwater zones in the LULC analysis. Key parameters adjusted during the calibration process included Manning's roughness coefficients for conduits and overland flow, sub-catchment imperviousness, infiltration parameters (e.g., initial, and saturated infiltration rates), and storage area characteristics. The calibration aimed to minimize discrepancies between observed and simulated hydrographs, focusing on peak flow rates and flood inundation depths. Parameter estimation for urban flood modeling (Beven & Binley 1992; Choi & Ball 2002) suggests that multiple sets of parameter values can produce similar model outputs.

Due to the absence of gauge data at the outlets for calibration and validation, inundation maps were generated by employing the PCSWMM model. These maps were subsequently superimposed with water logging points (WLPs) collected from field documented during the flood event to verify the precision of the PCSWMM model outcomes. Moreover, WLPs were identified at flood event sites using social media markers (SMMs) extracted from platforms like news channels, newspapers, Instagram, and YouTube, to further assess the inundation model's efficiency. The model setup includes detailed configurations for these zones: Zone XII contains 1,114 conduits and 1,115 junctions, while Zones IV and V include 568 conduits and 569 junctions. The validation of the model's results is detailed in the study by Manchikatla & Umamahesh (2022). Model validation involved comparing flood depths estimated by PCSWMM with observed WLPs photographed during the event, providing additional insights where gauge station data was lacking. Additionally, a comparison was made between flood depths estimated by PCSWMM and those reported by the GHMC for zone-XII.

The significance of climate change in urban flooding is highlighted, emphasizing its role in exacerbating flood risks. Climate change is projected to alter rainfall patterns and intensities, leading to more frequent and intense flooding events. This effect is further compounded by urbanization, which increases impermeable surfaces, amplifying flood risks.

The study incorporates climate change projections by using synthetic rainfall data derived from Hyderabad's intensity–duration–frequency (IDF) curve and Markov chain methodology. This enables the simulation of future rainfall scenarios (from 2020 to 2075) and the assessment of their impact on urban flood dynamics under changing climatic conditions. By integrating both current and future climate scenarios, the study illustrates how climate change influences flood trends and identifies vulnerabilities in flood-prone zones (XII, IV, and V), which are crucial for effective flood risk management and mitigation.

This focus on climate change provides valuable insights into long-term flood risks, underscoring the importance of considering future climate scenarios in urban flood management. The study proposed five primary LID controls: bioretention cells, infiltration trenches (IT), permeable pavement (PP), green roofs, and rain barrel storage facilities. These measures aim to mitigate the adverse effects of urbanization and climate change on flood risk, offering a multifaceted approach to urban flood management.

The possible layers in typical LIDs are as follows:

• i. The surface layer consists of the pavement or ground surface, with plant-growing depressions in an engineered soil combination on top of a gravel drainage bed. Direct rainfall as well as runoff from the surrounding area are stored, absorbed, and evaporated by this layer.

• ii. The pavement layer consists of porous material such as concrete or asphalt, which facilitates water permeation. These systems can be continuous porous pavement systems or filler material within modular systems.

• iii. The soil layer is made up of a specially designed soil mixture that is utilized in bio-retention cells to promote plant development and make water penetration easier.

• iv. The storage layer is composed of gravel and crushed rock, serving as a reservoir for water storage in sustainable drainage systems controls.

• v. The underdrain system facilitates the transfer of water from the gravel storage layer to a common outlet pipe or chamber by means of slotted or perforated pipes inside bio-retention cells, porous pavement systems, and IT. The drain valve located at the bottom of the rain barrel serves as a representation of the underdrain system.

• i. Bio retention cells: Bio retention cells, also known as bioswales, are vegetated channels designed to slow, collect, and filter stormwater runoff, facilitating infiltration and reducing erosion. They feature depressions where plants grow in an engineered mix of soil atop a gravel drainage bed. These cells store, infiltrate, and evaporate rainwater, directly reducing runoff volume. The size of a bio-retention cell varies depending on factors such as drainage area, soil type, and land use intensity. Figure S8 shows the representation of layers in the bio-retention cell in Supplementary Material.

• ii. Infiltration trenches: IT are narrow storage areas beneath granular media that intercept runoff from upslope impervious areas. Their design function is to capture and hold runoff to enhance infiltration into the native soil below. An excavation lined with geotextile is backfilled with stone to create an underground reservoir. Stormwater gradually infiltrates the subsoil as it flows into the trench, with excess runoff potentially overflowing during intense rainfall events. Figure S9 in the Supplementary Material shows the representation of layers in IT.

• iii. Porous pavement: Rainfall permeates the pavement and enters a gravel storage layer below, where it infiltrates into the native soil. Permeable pavers or porous materials allow water to infiltrate, reducing surface runoff and replenishing groundwater. Figure S10 in the Supplementary Material shows the representation of layers in PP.

• iv. Rain barrels: Rain barrels collect rainwater from rooftops during storms for subsequent release or reuse. They mitigate runoff volume by storing rainwater, which can be reused for irrigation or other purposes during dry periods. Rain barrels typically overflow to a safe disposal location and contribute to reducing demand for municipal water supplies. Rainwater harvesting (RWH) collects rainwater from rooftops for reuse in irrigation, toilet flushing, or groundwater recharge reducing demand on municipal water supplies and decreasing runoff. Figure S11 in the Supplementary Material shows the representation of layers in the rain barrel.

• v. Green roofs: Green roofs are vegetated roofs that absorb rainwater, reducing runoff while providing insulation and improving air quality. Rain gardens, like green roofs, are landscaped depressions that capture and treat stormwater, filtering out pollutants and replenishing groundwater. Figure S12 shows the representation of layers in the green roof in Supplementary Material.

Table S5 indicates the combination of layers applicable to each type of LID control and the design parameters and design considerations for the LIDs in Supplementary Material from Table S6–S9. Each LID control contributes to rainfall–runoff storage and water storage evaporation, with infiltration into native soil occurring in bio-retention cells, porous pavement systems, and IT if an impermeable bottom layer is not used. The performance of installed LID controls in sub-catchments is evaluated based on computed runoff, infiltration, and evaporation rates. Surface overflow from LID controls is directed to the hydrological component of sub-catchments, with underdrain flow directed to specified outlets. Rain barrels are assumed to be installed across all buildings, while bio-retention cells are allocated to open spaces.

In this study, PCSWMM was employed to evaluate the impact of urbanization on runoff and the effectiveness of LID practices in reducing floods (Abdelrahman et al. 2018). PCSWMM facilitates the modeling of green infrastructure units such as LID controls, primarily focusing on runoff from impervious areas. LID controls are represented by vertical layers, with parameters like thickness and infiltration rate defined for each unit area. Sub-catchments are selected based on their compatibility with these LID controls.

To prioritize critical sub-catchments for effective LID implementation, a combination of PCSWMM and the compromise programming method (CPM) was utilized. The prioritization process considers factors such as observed WLPs in the field, impervious area extent within each sub-catchment, as well as slope and surface area (Manchikatla & Umamahesh 2022). These criteria are used to calculate the priority value (L_p) for each sub-catchment, with the weights determined using the entropy technique. This approach aids in identifying critical sub-catchments, facilitating informed urban planning and flood management decisions. The ranks of critical sub-catchments based on CPM for both stormwater zones are shown in the Supplementary Material as Tables S10 and S11. Zone-IV&V and zone-XII were selected for simulation based on hydrological features and the prioritization of critical sub-catchments (Manchikatla & Umamahesh 2022).

Here, multiple controls and one or more controls are used to implement LID controls in sub-catchments. Previous studies have already shown the effectiveness of multiple and one or more controls in reducing the impact of the flood (Taji & Regulwar 2019; Pour et al. 2020; Bhusal et al. 2024). When incorporating LIDs into sub-catchments, the total area comprises both non-LID and LID portions, while parameters like percent imperviousness and width apply only to the non-LID portion. The properties of LID control objects' different layers were modified using the LID Control Editor, and the LID Usage Editor specified the size of the control and the fraction of impervious area it captures. Detailed sub-catchment parameters for various LID placement scenarios are provided below for different scenarios.

Based on the availability of suitable sites, LID controls are placed in both study areas. LID storage capacity or overflow components allow surface runoff from impermeable surfaces to flow into and out of them. The attributes of LIDs are defined per-unit area basis in the model, which is represented as many vertical layers. Based on Rossman (2015) approach, the LID parameter values were developed. Once these LIDs have been selected, they can be put in any sub-catchment at any desired size using aerial coverage. Design parameters for each layer as specified in Tables S6–S9 were given for different LID controls. Tables S12 and S13 present the proportion of LID area coverage within sub-catchments located in zone-XII and zone-IV&V, respectively, in the Supplementary Material. Table S14 displays the area (in square meters) allocated to each type of LID.

To develop the urban flood model to reduce the runoff for the stormwater zones of GHMC zone-IV&V and zone-XII are considered for the placements of LID controls. The No LIDs, LIDs placed in impervious area scenarios are simulated for 2, 5, and 10 return periods as per the IDF curves generated by Rangari et al. (2020) study in the Hyderabad city. The synthetic hyetographs of 2, 5, and 10-year return periods are shown in Figure S6. Similarly, the simulated 1–10 days of extreme event scenarios of present and future scenarios of 2020–2075 are studied for runoff reduction strategies and variations are studied.

Approximately 94.7% of WLPs (36 out of 38) in zones IV&V and 60% of WLP locations (6 out of 10) in zone XII match the maps of inundation. As a result, there is a good level of agreement between the model and the WLPs. Validating the results involved using the SMMs of flooding during the 2020 flood event.

The analysis of synthetic rainfall events spanning from 2020 to 2075, ranging from 1 to 10 days in duration, offers valuable insights into the effectiveness of LID practices in mitigating maximum flow reduction after LID installation in zone-IV&V and zone-XII. Comparing the results across different types of rainfall events and return periods illuminates the impacts of LID implementation and control strategies on stormwater management outcomes.

Table S15 of average flow values and Table S16 of maximum flow values show that implementing LID strategies reduces both average and maximum flow rates compared to scenarios without LIDs, demonstrating their effectiveness in mitigating floods. However, despite these reductions, both flow rates generally increase over time due to climate and land use changes, highlighting the escalating challenges in urban flood management. Although LIDs mitigate some of this increase, they do not completely offset it. Therefore, while LID strategies are beneficial, they must be part of a broader, comprehensive flood management approach to effectively address future urban flood risks in Hyderabad.

Tables S17 and S18 present the average flow, and maximum flow reduction percentage at outlet point for return periods of 2, 5, and 10 years. Additionally, the PCSWMM model outcomes illustrate fluctuating maximum flow rates at outfall points across various synthetic rainfall events influenced by climate and land use change scenarios, alongside scenarios without land use changes, as detailed in Table S19 for different return periods and CMIP6 events. Comparisons between scenarios with combined impacts of climate and LULC changes versus those with no LULC change reveal instances where maximum flow values are lower, indicating potential mitigation effects of LULC changes on stormwater runoff. Notably, when LIDs are integrated with combined impacts of climate and LULC changes, additional reductions in maximum flow are observed, underscoring the efficacy of integrated flood management strategies. This suggests promising avenues for addressing urban hydrology complexities and achieving sustainable flood risk mitigation. However, the effectiveness of such approaches may vary based on event characteristics and the specific design and implementation of LID measures, highlighting the importance of comprehensive planning and adaptive strategies in urban flood management.

Similarly, the LID-based PCSWMM simulations were performed by comparing the impacts of synthetic rainfall events across different return periods revealing variations based on duration and intensity. The efficacy of mitigation measures varies based on the severity and duration of synthetic rainfall events, underlining the necessity of tailored strategies. The results underscore the critical role of LID measures in reducing maximum outflow and improving flood resilience. In this study, the 2–10-year return periods were used to align with standard stormwater design practices in India, where urban drainage systems are typically designed using these intervals. To account for climate change impacts, rainfall projections from the CMIP6 models were used to generate synthetic rainfall events for future scenarios. While the concept of return periods assumes stationarity, this study integrates non-stationarity by applying climate model projections to assess future conditions. Also, the return periods are considered 2, 5, and 10 years only because, from the literature, Bioretention, as a LID technique, proved ineffective in mitigating peak runoff during extreme storm events with return periods of 50 and 100-years (Ekmekcioglu et al. 2021). Hence in the present study, the higher return periods of the LIDs are not implemented.

Examining the LID controls implemented in each sub-catchment provides insights into the effectiveness of specific strategies in mitigating runoff. Zone-IV&V employs Bioretention, RWH, PPs, and IT, while zone-XII utilizes Green Roofs, RWH, PP, IT, and Bioretention. The diversity of controls emphasizes the need for tailored approaches considering local hydrological conditions and land use patterns.

The observed reductions in maximum flow after LID implementation indicate the potential of LIDs to alleviate drainage issues and enhance urban resilience. zone-IV&V generally exhibits higher reduction percentages than zone-XII, possibly due to differing implementation strategies and local factors. The complexity of stormwater management underscores the importance of prioritizing LID installations based on effectiveness and feasibility. The findings emphasize the significance of considering local factors in developing sustainable stormwater management strategies. The maximum outfall peak flow reduction is 8.59% after LIDs implementation in zone-IV&V for the 2030-SSP370-10-day scenario shown in Figure S13, which is the sample result for understanding the maximum outfall peak flow reduction.

In conclusion, the study showcases the effectiveness of LID practices in urban flood management under changing climate and land use scenarios. By integrating synthetic rainfalls and the PCSWMM approach, valuable insights are gained for sustainable urban planning and resilience enhancement against flooding in Hyderabad City, India. Comparatively, zone-IV&V exhibits higher reduction percentages than zone-XII, suggesting that the combination of LIDs installed in zone-IV&V sub-catchments might be more effective in managing stormwater runoff. The variations in reduction percentages across different return periods and installation years highlight the complexity of stormwater management and the need for tailored approaches to address specific challenges.

In the present study, future rainfall intensities were adjusted using CMIP6 climate projections; however, it is important to note that the traditional interpretation of return periods assumes climate stationarity. Under a changing climate, return periods are expected to evolve dynamically over time. Additionally, this study focused on changes in rainfall alone and did not account for potential changes in catchment characteristics such as infiltration rates, antecedent soil moisture, and evapotranspiration, which could further influence urban flood responses. Future research is recommended to incorporate these aspects for a more comprehensive understanding of urban system resilience under climate change.

The present study focused on the effectiveness of LID techniques for flood control under climate and land change scenarios in critical stormwater zones of Hyderabad City, India. Through the simulation of five LID types on maximum flow at outlets under different synthetic rainfalls, the reduction effect in both zone-XII and zone-IV&V was examined. Conclusively, the study highlights several key findings and policy implications:

• LIDs prove to be effective methods for mitigating floods in urban areas, with infiltration facilities significantly reducing surface runoff. The combination of LID designs shows the best results in both zones, particularly during shorter and heavier rainfall events.

• The CMIP6 scenarios suggest a notable reduction in maximum flow at the outfall of both zone-IV&V and zone-XII, indicating improvements in average and maximum flow reduction across different return periods. Reduction percentages vary between zones, emphasizing the influence of localized factors such as topography and land use.

• Integrating LID practices into urban planning and design strategies is crucial for enhancing resilience against changing climate patterns and mitigating the impacts of extreme weather events. Policymakers and urban planners can leverage a range of rainfall scenarios to make informed decisions about LID interventions.

• Future research should focus on evaluating the long-term effectiveness and scalability of LID interventions, as well as their potential synergistic effects when integrated with traditional drainage infrastructure. Additionally, exploring the cost-effectiveness and co-benefits of LID interventions is essential for sustainable urban flood management.

• Policymakers should prioritize integrated approaches to urban flood management, provide financial incentives for LID adoption, foster collaborative governance models, and invest in capacity-building initiatives. Adaptive management frameworks should be institutionalized to refine flood mitigation strategies in response to evolving climate conditions.

• The results align with findings from previous studies (Taji & Regulwar 2019; Pour et al. 2020; Bhusal et al. 2024), demonstrating the potential of LIDs to reduce runoff volumes and peak flows in highly urbanized areas. Such practices provide a sustainable approach to urban flood management.

• While this research serves as a catalyst for investing in LID strategies, further exploration is needed to address economic considerations, especially in developing countries. The PCSWMM model demonstrates the potential for supporting decision-makers in assessing resilience measures and LID practices, although improvements could be made by incorporating resilience index measures and comparing results with other specialized models.

• The study's simulations offer insights into the optimal arrangement of LID facilities in Hyderabad City, Telangana. For instance, in areas characterized by low-lying impermeable surfaces, prioritizing storage units proves beneficial. Conversely, for expansive squares dominated by concrete or asphalt pavements, opting for PP emerges as a more suitable choice. Moreover, by integrating the costs associated with each LID technique and considering factors such as real-time rainfall data, a more comprehensive approach can be devised. This approach aims to provide tailored and cost-effective solutions, thereby offering valuable technical support for the construction of sponge cities. Additionally, increasing the quantity and distribution of various LIDs may lead to a reduction in flooding. However, this approach is not advisable due to limitations in space availability and cost considerations.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.