Urban Water Resources Management Research Articles

Assessment of land use transformations and its impact on fluctuations in groundwater quality in Chengalpattu district, Tamil Nadu

ABSTRACT This study examines land use transformations and their impact on groundwater quality fluctuations in Chengalpattu district, Tamil Nadu. Chengalpattu is a rapidly urbanizing coastal region in South India. Landsat 5 and 9 satellite imagery were utilized to create land use land cover (LULC) maps for 2011 and 2021, with overall accuracies of 82.20% and 85.71%, respectively. The analysis showed significant growth in built-up and fallow lands due to urbanization, which has altered LULC. Groundwater Quality Index (GQI) was generated using seven groundwater quality parameters, adhering to World Health Organization (WHO) standards. The mean GQI showed slight fluctuations during pre- and post-monsoon periods, with a declining trend in pre-monsoon seasons. The study area predominantly experiences moderate groundwater quality, with fewer areas of low and high quality. The findings indicate that land use transformation significantly impacts groundwater quality, emphasizing the need for sustainable urban planning and water resource management in the region.

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

Comparative analysis of greywater recycling and rainwater harvesting as supplementary water sources for conventional urban and tourist resort water supplies

Freshwater scarcity combined with high water demand from rapid urbanization, population growth, and changing consumption has resulted in increasing stress on urban water supply systems. Seasonal fluctuations in the population of tourist destinations are especially evident in tourist resorts. Greywater recycling and rainwater harvesting have been proposed as the most widely used valuable strategies to address water scarcity. By taking the conventional water supply system and the water supply of tourist resorts as examples, this study systematically compares and analyzes the advantages and limitations of greywater recycling and rainwater harvesting by enumerating the stress of water supply in different systems, discussing the benefits of both strategies and comparing the differences when using them for tourist destinations. The results showed that pumping systems or elevated tanks used to meet the water supply needs of high-rise buildings pose energy challenges. The water use characteristics of tourist resorts cause it to be closely related to the seasonal influx of tourists. Greywater recycling is more effective than rainwater harvesting in mitigating tourist resorts' water shortage problem. Suggestions for ways to implement these strategies for different regions are also given to make informed decisions about water distribution efforts. This study provides a reference for sustainable urban water resource management.

An integrated urban water resources management approach for infrastructure and urban planning

Transportation projects are crucial for the overall success of major urban, metropolitan, regional, and national development according to their capacity by bringing significant changes in socio-economic and territorial aspects. In this context, sustaining and development of economic and social activities depend on having sufficient Water Resources Management. This research helps to manage transport project planning and construction phases to analyze the surface water flow, high-level streams, wetland sites for development of transportation infrastructure planning, implementing, maintenance, monitoring and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. A case study was carried out using the Arc Hydro extension within ArcGIS for processing and presenting spatially-referenced Stream Model. Geographical information systems have the potential to improve water resources planning and management. The study framework would be useful for water resources problem solving by enabling decision makers to collect qualitative data more effectively and gather them into the water management process by a systematic framework.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

Exploring potential trade-offs in outdoor water use reductions and urban tree ecosystem services during an extreme drought in Southern California

In Southern California cities, urban trees play a vital role in alleviating heat waves through shade provision and evaporative cooling. Trees in arid to semi-arid regions may rely on irrigation, which is often the first municipal water use to be restricted during drought, causing further drought stress. Finding a balance between efficient water use and maintaining tree health will be crucial for long-term urban forestry and water resources management, as climate change will increase drought and extreme heat events. This study aimed to quantify how urban tree water and carbon fluxes are affected by irrigation reductions, and how that relationship changes with tree species and temperature. We used an ecohydrologic model that mechanistically simulates water, carbon, and energy cycling, parameterized for 5 common tree species in a semi-arid urban area. We simulated a range of irrigation reductions based on average outdoor water use data from the city for a recent extreme drought as well as with warmer temperatures. We then analyzed the response of model outcomes of plant carbon fluxes, leaf area index (LAI), and water use. Results show that reducing irrigation up to 25%, a comparable amount as the California state mandate in 2014, has minimal effects on tree primary productivity and water use efficiency. We found that transpiration was linearly related to irrigation input, which could lead to a short-term loss of evaporative cooling with irrigation reductions during drought. However, primary productivity and LAI had a nonlinear response to irrigation, indicating shade provision could be maintained throughout drought with partial irrigation reductions. Results varied across tree species, with some species showing greater sensitivity of productivity to both irrigation reductions and potentially warmer droughts. These results have implications for water resources management before and during drought, and for urban tree climate adaptation to future drought.

POLITICAL AND ECONOMIC ASPECTS OF INTEGRATED URBAN WATER RESOURCES MANAGEMENT (IUWRM) IN SEMARANG CITY INDONESIA

Purpose: This study aims to find out the aspects of politics and economics of integrated urban water resources management in Semarang City and their impact on the practices of qualities of water resources management. Methods: This research used the qualitative method and field research approach. The focus data are the regulation and the implementation of water resources management in Semarang City Indonesia. The data was drawn from documents and interview with some resource persons. Results and Conclusion: The study found that the city government, which should carry out the function of conserving water resources, actually polluted the Kaligarang River through one of its institutions: Local Water Drinking Company, “Tirta Moedal”. This company disposes of waste sludge containing aluminum metal directly into the river without any treatment processes that impacted in pollution in the river and causing siltation of the river downstream. Research implications: the enactment of the comprehensive and integrated water resources management will has a positive impact on the practices of water resources management in Semarang specifically and Indonesia and even world countries generally. The implementation of comprehensive and integrated water resources management will also support the achievement of the sustainable development Goal. Originality/value: Semarang City government must be supported to arrange a comprehensive and integrated water resources management (WRM) policy. There must be a clear legal law for the water resources management.

Assessing the water quality in urban river considering the influence of rainstorm flood: A case study of Handan city, China

The water quality of urban rivers is subject to fluctuation caused by rainstorm flood. The uncertainty of flooding in a dynamic environment brings about changes in river quality, presenting a significant challenge. Water samples were collected at 7 sampling sites in Handan City of China from January 2020 to August 2023, and 9 water quality parameters (WT, pH, Cond, Do, COD, BOD, NH3-N, TP, and TP) were analyzed. Specifically, the spatial and temporal variation of water quality in the Qingzhang River was analyzed. In terms of season, the concentration of water quality parameters in spring and winter was found to be significantly higher than that in summer and autumn. Spatially, the concentration of water quality parameters was lower in the upstream compared to the downstream. Furthermore, it was discovered that the reservoir had a purifying effect on the water quality of the river. Additionally, a comparison of water quality during flood and non-flood periods revealed that the concentration of water quality parameters in the upstream, midstream, and downstream of the Qingzhang River were higher during non-flood periods. These findings indicated that, in comparison to the urbanization factor, the hydrological factor, which results in the runoff carrying nutrients into the Qingzhang River, plays a crucial role in the change of its water quality. Moreover, the autoregressive integrated moving average (ARIMA) method was employed to create a water pollution emergency prediction model for the different sections of the Qingzhang River, and an adaptive urban river water purification strategy was formulated based on its variation patterns. The research results contribute to the theoretical basis for the management of urban rivers and water resources.

Unravelling the sources contributing to the urban water supply: An isotope perspective from Ljubljana, Slovenia

In cities experiencing rapid urbanization, we must continually update our understanding of the partitioning of drinking water sources concerning its supply if it is to be managed sustainably. This need is especially crucial given the pressure on water resources arising from evolving land use patterns and climate change. For this reason, a city-wide study of stable water isotopes (δ2H and δ18O) in precipitation, surface water and groundwater across Ljubljana, Slovenia, was undertaken. The goal was to characterise the temporal dynamics of urban water cycling and trace the various sources contributing to the city’s drinking water supply. Monthly water sampling, combined with hydrogeochemical and in-situ data, permitted the identification of local precipitation and surface water contributions to its two groundwater supply aquifers. In addition, a re-examination of the mean residence times (MRT) of surface waters revealed an MRT of 3–4 years, which is much longer than previously reported. Also, changes in the contributions of surface water and precipitation to groundwater were observed compared to previous studies. These findings improve our understanding of local water partitioning and provide valuable insights for water managers addressing future urban water resource management.

A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings.

Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

In recent years, the utilization of wastewater recycling as an alternative water source has gained significant traction in addressing urban water shortages. Accurate prediction of wastewater quantity is paramount for effective urban river water resource management. There is an urgent need to develop advanced forecasting technologies to further enhance the accuracy and efficiency of water quantity predictions. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms—linear regression (LR), gradient boosting regression (GBR), and LSTM—were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

Study on multiscale-multivariate prediction and risk assessment of urban flood

Few studies have explored the impact of drivers on urban flood at multi-time scales, and few studies have assessed the urban flood risk based on accurate description and quantification of the intrinsic correlation between urban flood and its drivers. Therefore, this study develops a multiscale-multivariate prediction model for flood volume (FV) using wavelet transform, and a flood risk assessment model based on the joint distribution of FV and its key drivers using the Copula method. The downtown area of Zhengzhou City, a typical city in North China, is taken as the study area. The results reveal that land use change is the key driver for variation of urban rainstorm flood, and rapid urbanization led to the variation observed in 2005. The impact of land use change on FV primarily manifests at the long cycle scale, and the multiscale-multivariate prediction model demonstrates effective simulation (NSE = 0.957, R2 = 0.958, MAPE = 13.47%) and prediction capabilities (relative errors are all below 20%). Taking the FV exceeding the threshold corresponding to the frequency of 37.5% as an example, the maximum conditional risk probability under nine combinations of key drivers is 88.34%, while the minimum probability is only 6.83%, intuitively indicating the impact of rainstorm and urbanization on this risk. These findings can provide technical references for urban flood forecasting, urban water resources management and urban development planning.

An efficient wastewater collection model for groundwater resource protection in smart cities

Efficient wastewater collection is a critical concern in the development of smart cities, where sustainable urban infrastructure is paramount. In the context of smart city wastewater optimization, this study highlights the need for a comprehensive approach that considers both surface water and groundwater dynamics. Existing methods often fall short in addressing groundwater-related concerns, with limitations in accounting for potential contamination and aquifer vulnerability. The proposed method integrates a nuanced understanding of groundwater aspects, providing a more robust and sustainable solution for urban water resource management. This research introduces a new approach that harnesses the power of Divide-And-Conquer (DAC) and Variable Neighborhood Search (VNS) to optimize wastewater collection systems in smart cities. The DAC strategy divides the city into manageable zones, simplifying the optimization problem for parallel processing in large-scale urban environments. VNS fine-tunes wastewater collection networks within each zone by exploring diverse neighborhood solutions. The DAC and VNS offers several benefits, including improved efficiency in wastewater transport, reduced energy consumption, and minimized environmental impact. Zoning the urban landscape is a strategic approach to optimize wastewater collection in smart cities. It involves dividing the city into manageable zones based on specific criteria such as geographical features, population density, and infrastructure layout. Each zone is treated as an independent unit for focused analysis and optimization. The optimization process employs a scoring function that combines weighted criteria, yielding a composite score for each geographical location. Applying a threshold or clustering algorithm group's locations into zones, facilitating tailored wastewater management strategies. By optimizing collection routes and infrastructure design, this approach assists smart cities in achieving sustainability goals while efficiently managing wastewater. The study showcases the potential of DAC and VNS as a robust and adaptable solution for wastewater collection in smart cities, contributing to the development of greener, more resource-efficient urban areas.

Enhancing urban water efficiency through digital financial inclusion: Evidence from China

Traditional finance exhibits several limitations in its capacity to drive the advancement of the green economy. In contrast, emerging Digital Inclusive Finance (DIF) uses digitization as a tool to address these shortcomings inherent in traditional financial systems. This research employs panel data encompassing 275 cities in China over the period from 2011 to 2021 to examine the relationship between DIF and Urban Water Efficiency (UWE). The findings of this study demonstrate a compelling positive association between DIF and the enhancement of UWE, intensified by significant spatial spillover effects. These results remain robust across a range of tests. It's worth noting that DIF primarily boosts pure technical efficiency, rather than scale efficiency, which are two important aspects of UWE. Delving deeper into the mechanisms that clarify how DIF reinforces green innovation and enhances the green total factor productivity, thereby contributing to UWE. Further exploration indicates that DIF has a more significant impact on UWE in cities with populations exceeding 5 million, no water resource tax, and strict environmental regulation. The combined efforts of financial and environmental regulation by the Chinese government play a key role in strengthening the positive relationship between DIF and UWE. Consequently, this research provides stronger evidence that financial development enhances environmental efficiency and offers new insights for urban water resource management.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

Strategic Decision-Making in Sustainable Water Management Using Demand Analysis and the Water Evaluation and Planning Model

Water infrastructure management relies on information, communication strategies, and affordable technologies. This paper used demand analysis and modeling to guide strategic decision-making in sustainable water management for the urban cluster in Tlemcen, Algeria. To achieve this, the water supply and demand of the study area were assessed over the past three decades. The Water Evaluation and Planning (WEAP) system was employed considering different future scenarios to help decision-makers consider the best choices for sustainable urban water resources management. The results showed that the average water production and distribution efficiency was only 46% due to the high network loss. Therefore, urgent action should be considered to increase the efficiency of the distribution network. Moreover, the outcome showed severe unmet demand in 2050, which can be managed by improving the water networks, increasing conventional water production, and reducing personal water consumption. In cooperation with key stakeholders, new scenarios can be analyzed to develop efficient water management policies and to implement sustainable water allocation approaches.

Assessment and Comprehensive Evaluation of Large-Scale Reclaimed Water Reuse for Urban River Restoration and Water Resource Management: A Case Study in China

Replenishing reclaimed water into urban rivers, which suffer from reduced flow and deteriorating water quality due to anthropogenic activities, presents an opportunity for water resource management and ecological restoration, while the effect and evaluation need to be considered. This study investigated the feasibility of large-scale reclaimed water reuse in urban rivers, focusing on water quality improvements and reuse scheme evaluation, utilizing modeling software to simulate the water quality after implementing the reclaimed water replenishment scheme. After seven days of reclaimed water replenishment simulated, the water quality in the receiving urban rivers exhibited substantial improvements to different extents, with some rivers showing a decrease of over 90% in chemical oxygen demand (CODMn), ammonia nitrogen (NH3-N), and total phosphorus (TP) concentrations. A comprehensive evaluation method using the physical element extension–analytic hierarchy process (AHP) evaluation model was developed to evaluate the feasibility and efficiency of the large-scale project of reclaimed water reuse in urban rivers. The overall score of the large-scale reclaimed water reuse scenario reaches 89, approaching Level I and indicating a highly scientific and reasonable plan. This study contributes to the field of urban river restoration and water resource management by demonstrating the potential for improving water quality in urban rivers through large-scale reclaimed water reuse. The innovative comprehensive evaluation method offers valuable insights for guiding the implementation of similar projects in other urban river systems, addressing water resource challenges, and promoting ecological restoration in urban areas.

A novel safe-fail framework for the design of urban stormwater drainage infrastructures with minimal failure and flood severity

The urban stormwater drainage structures (SDSs) are conventionally designed for their failure recurrences rather than addressing the consequences of the failures. Various methods that consider the effects of flooding in SDSs design largely rely on the overflow volume and do not consider actual inundation characteristics due to computational challenges. Hence, advanced methods are required to design SDSs while incorporating flood consequences. We introduce a novel method called the 'safe-fail framework' for designing SDSs, which determines the best design solutions that minimize flood consequences with computational efficiency. The safe-fail framework involves event-based simulations of urban flood inundation properties, iteratively to evaluate the flood consequences until the design return period satisfies the safe-fail criteria with acceptable failure consequences. We demonstrate the approach in an urban catchment in Chennai, India, by estimating the optimal design return periods of the SDSs to minimize the flood consequences. A structure-variable approach that reduces the complexities of multivariate problems to univariate to identify flood-causing extreme rainfall events was explored. We show that 12 conduits (among 30) in the catchment require design return periods ranging between 3 and 6 years (vis-a-vis initial 2 years) to achieve the safe-fail condition, evaluated through a newly developed Flood Severity Index. The results reiterate the inappropriateness of using a common design return period across the catchment due to the varying sensitivity of locations to flood flows. The safe-fail framework can potentially improve the SDSs design criteria while accounting for urban flood properties and deriving improved urban water resources management decisions.

Assessing the Influence of Land Cover and Climate Change Impacts on Runoff Patterns Using CA-ANN Model and CMIP6 Data

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.