Global climate change poses a significant threat to today’s global world and has the potential to cause massive damage to the drainage system (Willems et al., 2013). Global warming is characterized by rising temperatures and increasing frequency of severe weather including persistent rainfall and storms which impede the development of the urban structure (Di Capua & Rahmstorf, 2023). Our investigations will therefore evaluate adaptations of these systems in the present and look for more advanced adaptations that would increase their ability to counter progressive forms of climate adversities and protect built landscapes against flooding and other related hazards.

For a long time, static assumptions have been applied in stormwater drainage systems to forecast and manage storm occurrence (Teshome, 2020). However, these assumptions are constrained by the fact the weather patterns have been made uncertain by climate change characteristics (Ullah et al., 2023). For this reason, it is important to evaluate the consequences of climate change on stormwater management since these changes will trigger more severe and repeated cases of sewerage overflows and flooding (Mohammed, Zwain, & Al-Mussawi, 2021). These conditions have to be addressed to design a more robust infrastructure that can address the changes that can effectively manage precipitation's heightened variability and intensity.

Many new design guidelines require consideration of the estimated life of structures considering climate change and the expected intensity and frequency of storms (Martel, Brissette, Lucas-Picher, Troin, & Arsenault, 2021). Large-scale drainage systems in the past have provided support to urban resilience when design parameters are stressed, but at the same time amplify the risk (Mabrouk, Han, Gamal, Abdrabo, & Yousry, 2024). This illustrates an increase in extreme precipitation all over the world owing to global warming. The Intergovernmental Panel on Climate Change (IPCC) has stated that the average of heavy rainstorms has increased due to global warming and for the twenty-first century it can occur with a probability of ninety percent (Pörtner et al., 2022). As a consequence of greenhouse gases produced by human actions, the hydrological cycle has thus been intensified, a factor that has led to erratic and persistent precipitation (Kundzewicz, 2008). This fact is of ‘catastrophe’ importance for urban floods because the seaside regions were relatively a poor barrier against typical and abnormal climate conditions (Tayyab et al., 2021). More frequent and intensive rainfall is due to global warming that has facilitated the expansion of the hydrological cycle and as such there is a need to address the design and development of water control inherent in urban flood management (Wang & Liu, 2023). GCMs are helpful in reconstructive climates and future changes by using complex computer-based models (Anwar et al., 2024). Nevertheless, due to the coarse grid cell area, they cannot be used directly in regional hydrological modeling where high-resolution outputs are needed and different down-scaling techniques have to be employed (Baron et al., 2005). This process poses some difficulties because it should be made more limited and reflect local features to estimate the climate effect on the hydrological systems adequately.

Applying climate change considerations in the infrastructure design is vital, this is especially for flood risk protection (Auld, 2008). There is a necessity to adapt to the rise in the incidence and intensity of erratic climate conditions (Fankhauser, 2017). But, one can easily appreciate that quantitatively relating climate change impacts to appropriate financial resources for infrastructure investment is not without its challenges (Olsen et al., 2015). It has been a challenge to achieve good modeling of future climate scenarios and their effects on structures hence achieving high precisions for cost estimates (Palmer & Stevens, 2019). This requires a strong, flexible method of investment management and incorporation of climate risk into infrastructure investment to attain sustainable investment for the long term. Storm impact tools like Intensity Duration Frequency (IDF) curves and the Storm Water Management Model (SWMM) are very vital in evaluating and controlling rain-related risks in urban settings (Rangari & Prashanth, 2018). It is easier to forecast future rainfall by the use of meteorological data and data extracted from the General Circulation Model. This information is important for developing adaptive infrastructure and effective water resource management strategies (Rahman & Rahman, 2011).

This research comprehensively evaluates the effects of climate change on drainage infrastructure systems and the costs that are related to these impacts. The one currently being planned is to establish IDF curves for the observed and projected precipitation data and then, perform the assessments using the SWMM under various climate change scenarios to detect the potential rise in infrastructure costs due to climate change. This multi-disciplinary approach aims at enabling decision-makers to make evidence-based decisions and help the resilience of urban drainages due to climate change.

Hayatabad Phase 5 is situated in the western region of Peshawar, Khyber Pakhtunkhwa, between latitudes 33° 59ʹ 12ʺ N and longitudes 71° 44ʹ 27ʺ E. The terrain is characterized by a rough, sandy clay texture with a gentle slope (Ahmad, Iqbal, & Khan, 2013). The area's altitude ranges from approximately 405 meters above sea level as shown in Fig. 01.

Peshawar experiences moderate winters and very hot summers. Winter typically spans from November to late March, sometimes extending into mid-April. Summers prevail from mid-May to mid-September (Nisa, 2012). The mean annual precipitation in Peshawar, recorded over the period from 1980 to 2009, stands at 338.01 mm (Tajbar et al., 2018). Additionally, the average annual mean temperature in the region is approximately 23°C (Abbas et al., 2024).

Data Acquisition

This study developed IDF curves for the Peshawar district based on observed and SSP scenarios using yearly maximum precipitation data. Rainfall data spanning from 2013 to 2022 was obtained from the Peshawar Meteorological Department. The rainfall data is shown in Fig. 02.

This research centers on climate modeling with nine global climate models (GCMs) gathered from the Copernicus website, emphasizing only the CMIP6 models for the SSP4.5 and SSP8.5 scenarios as shown in Fig. 3. In this regard, the projected change in precipitation under both emissions scenarios reveals the importance of long-term strategies for water resource management. Efficient understanding and management of the effects on catchment areas becomes an essential agenda. This study alerts the public to the future challenges of climate change while highlighting the preventive approaches that should be taken to prevent more occurrences of climate change on water resources and its ramifications on ecosystems and human beings.

The research methodology specifically concerned evaluating future climate conditions under different socioeconomic pathways. Started from preprocessing of a raw model output format where data from all models and each of the presented scenarios were aligned uniformly. Then the downscaling method is used to increase spatial detail and bias correction for correcting systematic errors. Moreover, the application and analysis of the different socioeconomic scenarios in the form of SSP 4.5 and SSP 8.5 scenarios were considered to assess the impact of climate. Then the processed climate data is used to determine consequent impacts on various sectors including ecosystems, water, and economics which acted as a framework on how to handle the effects of climate change.

Downscaling and Bias Correction

Downscaling and bias correction are significant methods in climate modeling and hydrological investigations when dealing with data obtained from global climate models (GCMs) (Shrestha, Acharya, & Shrestha, 2017). Downscaling aims at transferring the climate information from the GCMs or RCMs, which contain broad-scale data, into high-resolution data suitable for local or regional applications (Hewitson & Crane, 1996). Bias correction is a technique that is applied to change the climate model output to minimize systematic error (or bias) between the climate model simulations and observational data (Berg, Feldmann, & Panitz, 2012).

Here in this study, the CMHYD tool is employed to perform downscaling and bias correction of the climate data (Haider et al., 2020). After downscaling and bias correction, the general procedures of the multimodal ensemble computation are performed using a general arthritic mean (Mohan & Sinha, 2023).

Multimodal ensemble computation

A multi-model ensemble (MME) is an approach in climate modeling, hydrological forecasting, and various scientific fields where multiple models are combined to improve the accuracy and reliability of predictions (Rozante, Moreira, Godoy, & Fernandes, 2014). Instead of relying on a single model, an ensemble uses outputs from several different models to provide a more comprehensive representation of uncertainty and variability in projections (Richardson, 2001).

In the current study, climate data under two different Shared Socioeconomic Pathways (SSPs), SSP 4.5 and SSP 8.5 using nine climate models were assessed, and from those nine climate models, three models were taken for the multi-model ensemble based on the NSE and R². These modes are CM5-0, MIROC6, MRI-ESM2-0 models for SSP 4. 5, and INM-CM4-8, INM-CM5-0 and CNRM-CM6-1 models for SSP8. 5 were used. Thus, our research specifically evaluates future climate conditions under different socioeconomic pathways.

In this research, multi-model ensemble analysis was performed using the arithmetic mean technique. This approach turns out to be most useful when trying to get a big picture or managing to achieve a majority opinion in terms of the decisions made by the ensemble of models. The future precipitation data is then distributed accordingly. In addition, the IDF curves are reconstructed for these two scenarios. This comprehensive process culmination provides tremendous insights and comprehensive information into how rainfall intensity is impacted by climate change.

Development of IDF curves

An IDF curve (Intensity-Duration-Frequency curve) is a kind of graph that presents and analyzes the relationship between the rainfall intensity, duration, and frequency of the rainfalls at a certain spot (Faradiba, 2021). To develop Intensity Duration Frequency (IDF) curves precipitation data is used. Precipitation records which include the period of 2013–2022 were collected from the Pakistan Meteorology Department. The daily rainfall was disaggregated into hourly time series A more detailed description of the disaggregation is provided in Section 4. As per the reduction formula recommended by the Indian Meteorological Department, the maximum of 24 hours was further made into short spells (1, 2, 3. ..24 hours) using the formula: P_t = P₂₄ * (t / 24) ^ (1/3) where P_t is the required rainfall depth in millimeter for t hours duration, P₂₄ is the daily rainfall in millimeters and t is the duration for which rainfall depth required in hours (Nwaogazie, Sam, & Ikebude, 2021). Hydrological data was used to plot IDF curves for the return periods of 2, 5, 10, 25, 50, and 100 years using rainfall data of the past decade.

SWMM Simulation for Observed and Future Data

The Storm Water Management Model (SWMM) is a dynamic hydrologic and hydraulic modeling tool developed by the U.S. Environmental Protection Agency (EPA) for simulating the quantity and quality of runoff in urban areas (Gironás, Roesner, Rossman, & Davis, 2010).

In this study, the SWMM version 5.2 simulation model is used to evaluate drainage system efficiency under stormwater from climatic events in the study area. Phase 5 Hayat Abad Peshawar with a total area of 1.48 km² is analyzed and it is divided into eighteen sub-catchments with a surface area of 5–25 hectares. SWMM utilizes sub-catchments as hydrologic units to manage surface runoff, directing it to specific discharge points known as nodes. In urban areas, the drainages change the way that natural water flows, by directing it to pass through the designed exits and not follow its original natural pathways (Yazdanfar & Sharma, 2015). In this study, different nodes were put where the change in elevation or change in direction of the street occurs. Then join these nodes utilizing conduits and in SWMM by an auto length ON and length of different pipes and invert elevations are created. Twenty-one nodes have a range in invert elevation from 396-407m and twenty-six conduits with a maximum depth ranging from 0.5–2.2 m and pipe lengths vary between 46–400m.

Physical characteristics of sub-catchments

The rainfall-runoff model utilizes the physical characteristics of the sub-catchment to simulate the hydrological processes in the sub-catchment and predict the ability of the sub-catchment to produce runoff in response to rainfall events. These parameters consist of pervious area, impervious area, Manning roughness coefficient, average Manning coefficient overland flow, and slope of the study area (Hossain, Hewa, & Wella-Hewage, 2019). Hayat Abad Phase 5 has commercial, undeveloped, and grassy areas. So according to the SWMM manual, different values for sub-catchment properties are mentioned in Table 01. This table shows the values which are required for these parameters which are significant inputs for the rainfall-runoff model.

Outlines of the drainage conduits

The study adopted the dynamic wave routing technique, whereby the Hazen–William formula was used to solve the entire Saint–Venant flow routing equation (Hamid, Kadhim, al-taee, & Albazaz, 2014). Several considerations were taken into account in making this decision; the areas under study have gently undulating topography with channel slopes ranging from as low as 1% to as high as 3%, the method is well suited for modeling flow in urban watersheds and stormwater situations and the all-important question of backwater effects downstream. In addition, hydrological data characterized by the Intensity Duration Frequency (IDF) curve in the form of time series allowed providing the simulation model with the necessary inputs: various rainfall intensity values at different time steps for each return period; this allowed for the accurate assessment of the pipeline network’s behavior under different rainfall conditions (Sun, Wendi, Kim, & Liong, 2019). This paper aims to develop a layout plan for drainage systems in a residential area and to model its performances using the EPA SWMM 5. 2 Application focuses on the reliability of development for a 10-year return period (Narzis, Shiraj, & Amin, 2023).

This methodological approach includes data collection, dynamic wave routing, and integration of the hydrological data to improve knowledge of stormwater management in urban areas. The research not only studies the characteristics of the pipeline network in detail and estimates the system response to different rainfall intensities, but also contributes to how infrastructure and urban communities are prepared and optimized for stormwater and efficient water management.

Cost Analysis of Drainage System

Cost analysis of a drainage system involves evaluating all aspects of the financial factors that can surround the planning, implementation, and maintenance of a drainage system (Mohd Sidek et al., 2004). This process helps stakeholders understand some knowledge of the economic consequences of drainage systems and can get this information and make reasonable decisions relating to financing and managing drainage systems. This paper therefore seeks to describe a detailed description of the cost estimation process of a drainage system using precast concrete pipes. For these pipes cost estimation, the Market Rate System (MRS) KPK 2022 Bi-Annual is used. This schedule provides the cost framework for concrete pipes with diameters ranging from 12” to 72” under codes 23-03-a-04 to 23-03-a-14. However, larger diameters of 84”, 90”, and 96” are not covered in MRS. The pipes under consideration are manufactured under ASTM C-76 Type-II with Wall B (Erdogmus, Skourup, & Tadros, 2010). Using a concrete mix ratio of 1:1:2 (cement: coarse aggregate: fine aggregate).

The total cost of materials for pipes concrete works is calculated taking into account the analysis of market prices for cement, coarse aggregate, and fine-aggregate, as well as an estimated reinforcement content (0.3–0.5%). The costing procedure involves the calculation of quantities of these materials based on a certain mix design, establishing reinforcement requirements as percentage volume (of concrete), and then computing their costs. Labor and equipment costs are differentiated depending on the difficulty of the task, as well as time requirements with special needs for machinery expenditures. Next, the contractor's profit and overhead currently is set at 22%.

IDF CURVE FOR OBSERVED DATA AND SSP SCENARIOS

After the downscaling and bias correction of climate models under both SSP 4.5 and SSP 8.5 scenarios, IDF curves are developed to understand the projected changes in extreme precipitation events. IDF curve provides essential information for efficient drainage infrastructure systems that can effectively manage stormwater events of varying magnitudes. The result shows that the intensity of the SSP8.5 is higher than the SSP4.5 as shown in Fig. 6–8

SWMM MODELS FOR OBSERVED DATA AND SSP SCENARIO

In SWMM previously constructed IDF curves of observed data and SSP scenarios were used. The IDF curve of the SSP scenario indicates anticipated changes in rainfall intensity and frequency due to different climate changes. First of all, the IDF curve of observed data was used. In the observed data, significant flooding was recorded at several nodes within the drainage system when the pipe diameter was 0.5 meters. The specific nodes and their corresponding flood magnitudes are as follows:

These nodes are critical and help in understanding the system's vulnerabilities and performance under current conditions. Figure 9 illustrates these nodes and their respective flood magnitudes, providing a visual representation of the affected areas

To mitigate flooding observed at nodes J1, J8, J26, and J18 with magnitudes of 0.8 CMS, 0.57 CMS, 0.51 CMS, and 0.63 CMS respectively, a trial and error method was employed to adjust the pipe diameters within the drainage system. Initially, the pipe diameter was set at 0.5 meters, which resulted in the recorded flood magnitudes as detailed earlier. Gradual modifications were then done to expand the diameters of the pipes in the critical nodes. The trial and error process involved iteratively increasing the pipe diameters until flooding at these nodes was effectively mitigated, achieving zero flood magnitudes. This was based on empirical testing and observation of the flow characteristics to ascertain the diameter size that can accommodate the flow rates and volumes during storm events. The results of the diameter adjustment strategy at nodes where flooding was identified are shown in Fig. 10 for the pipe diameters at nodes J1, J8, J26, and J18. The adjustments resulted in a complete reduction of flood magnitudes to zero, demonstrating the effectiveness of adapting infrastructure to enhance resilience against flooding events.

Under the SSP 4.5 scenario, flooding was identified in the drainage system at certain nodes the pipe diameter was 0.5 m indicating that the systems did not remove all the water as illustrated in Fig. 11. The observed flood magnitudes at these nodes were as follows: Node J1: 0.76 CMS

Through a trial and error method, we extended the pipe diameters at nodes that were flooded under the SSP 4.5 scenario. The flood magnitudes at nodes J1, J7, J12, and J18 were successfully reduced to zero, demonstrating the effectiveness of this approach in mitigating flooding.

As was anticipated, the usage of this strategy enabled the reduction of the values of flood magnitudes at nodes J1, J7, J12, and J18 to zeros demonstrating the effectiveness of this methodology in avoiding floods. Figure 12 shows the specific diameter adjustments that resulted in the elimination of flooding in these critical nodes.

Under the SSP 8.5 scenario, flood was observed at several nodes within the drainage system, this showed the vulnerability of the most sensitive areas within the system. From the above result, this study found that when the pipe diameter was maintained at 0. 5 meters, significant flooding was observed at the following nodes:

Figure 13 shows the effect of the SSP 8.5 scenario on the performance of the drainage system. The recorded flood magnitudes at these nodes underscore the necessity for infrastructure adaptations to manage increased precipitation and prevent urban flooding.

After the identification of the critical node, the trial and error method was adopted to increase the diameter of the pipe, and the flood magnitudes at these nodes were successfully reduced to zero. Figure 14, explains the adjustment of resizing drainage pipes to mitigate flooding at critical points in the system.

By adjusting the pipe diameters, the flooding issues at the specified nodes were effectively resolved, at the given nodes, highlighting a practical solution for managing and preventing flooding in the drainage system. This approach therefore highlights the need for flexibility to work through the stresses resulting from climate change in infrastructure construction.

Effect of climate change on drainage infrastructure system

In this research, the impacts of climate change using the IDF curves under SSP 4.5 and SSP8.5 scenarios were assessed, and the result showed that in all scenarios there is an increase in the maximum flow. As such, modifications of the maximum depth in the conduit become mandatory to accommodate this increased flow. But at the same time, it is pertinent to emphasize that all these amendments are going to escalate the total cost of the modeling activity. This holds the key to the trade-off analyses involved in accommodating changes to drainage structures under climate variability. However, it is crucial to increase the depth of the conduit to tackle highly complex flows, it also poses financial challenges that must be carefully considered in the decision-making process. The drainage system of Phase 5 in Peshawar is depicted in Table 2, outlining various attributes of the junction points within the system. These attributes include their lengths (in meters), shapes, and maximum diameter (in meters). This information provides a comprehensive overview of the infrastructure, aiding in understanding the capacity and functionality of the drainage system in Phase 5 of Peshawar.

Cost Performance Evaluation

The cost analysis compared the differences among the assessed scenarios, with each model having distinct financial ramifications. The results of this study underscore the necessity for informed decision-making to maximize resource allocation and avoid potential financial risks to promote more effective and efficient project implementation.

As per the observed data scenario, its implementation will be as expensive too, which sums up to 41 Million PKR. This scenario acts as a baseline for cost estimation and reflects actual observed conditions. The cost provides a realistic estimate based on past data and the observable character of the performed activities. They make provision for the cost of acquiring materials, wages, and salaries, cost of equipment, and other related expenses.

On the other hand, the cost based on SSP 4.5 scenarios for the implementation of the drainage system is expected to be 49 million PKR. SSP 4.5 is a moderate emissions scenario where global endeavors are made to lessen the effects of global warming at least to some extent. The higher cost in comparison to the existing cost is a result of the additional expenses needed to modify design criteria to account for expected changes in precipitation regime and to enhance the measures to make the infrastructures more climate-resilient under the conditions of SSP 4. 5.

The entire cost estimate rises dramatically to 63 million PKR based on the SSP 8.5 scenario to enhance performance accordingly. SSP 8.5 shows a high emissions scenario whereby the greenhouse gas concentration continues to rise with dire consequences on climate such as enhanced in the frequency and intensity of extreme weather events. This is why this scenario is costlier, first, due to the need for more dramatic changes to infrastructure to protect them against further climate volatility together with the problems of infrastructure eradication and floods.

The cost implications of modifying urban drainage infrastructure to account for climate change scenarios are revealed by the study's cost analysis. Under the observed conditions, the baseline cost was 41 million PKR. Under the SSP4.5 and SSP8.5 scenarios, the cost climbed to 49 million PKR and 63 million PKR, respectively as shown in Fig. 7. These differences in cost highlight the necessity of proactive adaptation techniques to increase resistance to stresses brought on by climate change. Urban planners can benefit greatly from the research's conclusions, which emphasize the need to balance adaptation costs against potential risks and losses brought on by a lack of resilient infrastructure. Urban stormwater management can benefit from a practical framework that integrates hydrological data integration, dynamic wave routing, and extensive data gathering. This method promotes long-term sustainability and informed decision-making.

This comparison shows how it is important to incorporate climate change considerations when designing infrastructure. The differences shown in costs indicate how more effective approaches to adaptation that are aligned with various climate possibilities may greatly affect the projects’ costs. Those who are involved in the planning of urban drainage systems need to be aware of these costs against the risks and losses that may stem from a lack of sufficient robustness. This study has pointed out the fact that it is always important to use strong and flexible approaches. When analyzed, such scenarios make it possible for the interested parties to determine the most appropriate approach toward the allocation of resources to enhance the prospects of the project and at the same time promote the long-term sustainability of the developments made toward enhancing the development of urban infrastructures given changing climate conditions into consideration.

This study underscores the enhancement of the approaches to urban drainage management with a specific focus on Hayatabad Phase 05, Peshawar due to the changing climate conditions. The study assesses the vulnerabilities of existing and future drainage systems by using IPCC data, and state-of-the-art methodologies including CMHYD and IDF curves to model potential future climate impacts. By analyzing past and projected precipitation patterns, and understanding how climate change exacerbates hydrologic cycles, this study offers valuable information on how the urban drainage systems can be impacted under moderate and high-emission scenarios. The study uses SWMM to design drainage systems for observed and SSP scenarios. After obtaining different design models cost of different pipes was calculated. The study reveals a significant escalation in the costs for infrastructure adaptation as the cost estimate is expected to rise from 41 million PKR in its present setting to 49 million PKR in the SSP 4.5 scenarios and even to PKR 63 million in the SSP 8.5 scenario. This indicates not only significant financial loss but at the same time resonates with the need to design and construct more robust structures to cope with the intensified and more frequent extreme weather events.

This study calls for the integration of climate change considerations into the design of urban planning and infrastructure to enhance resilience against future climate stressors. It goes further to put forward practical guidelines for building stronger and more adaptable drainage systems while pointing out that there is an urgency for a proactive adaptation strategy to reduce both financial and ecological risks. This broad-based framework of the study encompasses a wide range from analyzing hydrological data and routing by dynamic wave to extensive data gathering and contributes much towards decision-making by policymakers and urban planners. The study supports informed decision-making in pursuit of sustainable urban development and mitigating adverse climatic change impacts on the urban environment by aligning infrastructure development to projected climate scenarios.