https://orcid.org/0000-0002-2186-9771
ABSTRACT
With rapid urbanization and development, periods of water stress are common in the Nagpur Metropolitan Region (NMR) of India. The domestic and industrial water demands coupled with the demand for irrigation continue to rise in this region due to significant demographic growth and land use changes resulting in the decreasing surface water availability and exploitation of groundwater sources. Amidst the impacts of climate change coupled with rapid development, understanding the subsequent resource availability and future water requirements is critical for allocating vital and finite resources such as water. In this regard, an assessment of water demands under various scenarios is an important step. With this objective, this study has used the Water Evaluation and Planning modelling tool to simulate the water systems for the NMR by employing a scenario analysis approach to determine the future water balance. The findings reveal a water supply requirement of 4.14 bcm by 2040, considering the business-as-usual scenario. The research also reiterated the potential for increased rural–urban conflicts over water resource allocation in the future, underscoring the need for strategies to equitably cater to rural and urban water demands.
HIGHLIGHTS
Future water supply requirements are projected for various blocks of the Nagpur Metropolitan Region (NMR).
Deficits under various scenarios are discussed, and it was found that ‘business as usual’ leads to a deficit of 1.32 billion cubic metres (bcm) by 2040.
Sectoral water demands and deficits are critically assessed under various scenarios to understand the priority areas for action.
INTRODUCTION
Water-related issues have long been discussed at the global, national, and local scales (Cosgrove & Loucks 2015) and have lately intensified the scientific community's concern due to the rising water stress scenarios. Water, being central to achieving a larger sense of sustainability, security, and human well-being (UN Water 2013), emerges as a critical resource that needs to be ingeniously managed. In arid and semi-arid regions, water resource scarcity has become a determinant restricting social and economic development (Zhou et al. 2017). The available fresh water for consumption in these regions is becoming scarce due to the exponential demands (Hadri et al. 2022). Based on current consumption rates, approximately one-third of the global population is projected to experience water scarcity by 2025 (UN 2016). Gradual changes in hydrology and the impacts due to climate change are further exacerbating this phenomenon. This has especially been evident in developing countries like India due to rapid demographic growth and lifestyle changes (Ray & Shaw 2019).
India has witnessed rapid urbanization in the past two decades and currently stands as one of the world's most populous countries. According to the projections made by the United Nations, India's population is further projected to increase to 1.64 billion by 2050 (UN DESA 2022), consequently leading to the rise in freshwater demands for domestic consumption as well as water-intensive resources such as energy and food. Amidst the existing resource shortfalls and unmet freshwater demands in India (Ramesh 2021), it is critical to aptly govern the available freshwater resources. Of late, the repercussions of the inadequately apportioned freshwater resources coupled with variability in rainfalls, rapid population growth, and the consequent urbanization have manifested in the form of severe water crises in larger metropolitan areas of India such as Chennai and Bangalore (Goldman & Narayan 2019; Kalia 2020; Mukherjee 2022). The water crises in these regions have further been largely attributed to the urban expansion into rural areas, the overexploitation of groundwater resources, encroachment over local water bodies, and increased reliance on distant water sources (Goldman & Narayan 2019; Mukherjee 2022).
Empirical studies have shown that urban areas often satisfy their water requirements by accessing water resources beyond their territorial limits (Heard et al. 2017), leading to increased conflict over the distribution of water resources between rural and urban areas (Cullet et al. 2015). The strategy of reallocating water resources from rural to urban regions has become increasingly prevalent as a means of meeting the water demands of urban areas (Garrick et al. 2019), resulting in a compromise with the agricultural demands. Rural areas, therefore, are often compelled to rely on groundwater to meet their water requirements, resulting in the continuous exploitation of groundwater resources (Kulkarni et al. 2015). Concerns regarding groundwater depletion in India have also been highlighted in the global reports by the World Bank, UN-Water, and PRS legislative research (World Bank 2012; Suhag 2016; UN-Water 2022). This makes maintaining water balance across the rural–urban continuum a challenge as well as a priority.
A similar scenario is observed in the Nagpur Metropolitan Region (NMR) of Maharashtra state in India. Although the area does not belong to regions that experience extreme physical water scarcity, it is witnessing serious water stress concerns due to climate variations and upstream development (Deshkar 2019; Sukhwani & Shaw 2020). Seasonal shortages and sectoral conflicts over the rural–urban continuum are also common (Sukhwani et al. 2020). Presently, the water allocation is based on the ‘current situation and demand’ trend (Anparthi 2018; Chakraborty 2022), which is unsustainable in the long run. For maintaining the optimal water balance across the urban–rural continuum, there is an urgent need to factor in the possible demographic change and governance priorities, as well as climate change impacts on water resources.
Despite the emerging concerns, there are limited studies that holistically examine the water balance, demand projections, and viable mitigation strategies for this region. Researchers have often focused on individual dimensions of water, such as groundwater, climate, and urban and industrial growth in the NMR (Sukhwani & Shaw 2022; Swain et al. 2022), and there is a limited focus on the intricate interplay between these aspects. Therefore, this research has endeavoured to address this gap by integrating a thorough analysis of sectoral demands and supply sources. This study offers a comprehensive overview of the water balance landscape in the NMR, providing previously unexplored insights. The research uniquely contributes as the first empirical investigation at computing the water balance for this region using the WEAP simulation and modelling tool.
With the objective of enhancing the decision-making capacities by quantifying water requirements under various scenarios, this study has aimed to comprehensively assess the present and future water availability and demand scenarios by employing WEAP, a robust and methodology-driven tool for water resource assessment. Section 2 (Methods) provides a systematic discussion of the dynamics of water demand and supply in the case study region, emphasizing the need for a simulation-based approach. It highlights the significance of the WEAP tool, along with details of the model setup, datasets, and modelled simulation scenarios. Section 3 (Results and Discussion) presents and discusses the results from the simulations, while Section 4 (Conclusion) concludes the study by outlining the future scope, limitations, and key policy implications.
METHODS
(a) Case study area
Location map of Nagpur City and NMR.
The NMR lies in the Godavari River basin, contributing to the catchments of the Wainganga River. Water, in this region, is largely sourced from the two perennial rivers Kanhan, the right bank tributary of the Wainganga River, and Pench, a tributary of the Kanhan River (Figure 1). Parts of NMR, along with Nagpur City, primarily rely on surface water sources such as rivers, lakes, and reservoirs, while the other rural areas depend mainly on groundwater sources (NIT 2015). The Navegaon-Khairy or the Pench Dam, situated in the northern region of the NMR, is one of the primary sources of drinking water for Nagpur City, providing around 520 million litres of water per day out of the total demand of 670 million litres (NIT 2015). However, as a multi-purpose project, it also serves several other sectors, including agriculture, energy, industry, fisheries. Therefore, with the declining water levels in the Pench Dam, increasing conflicts have been observed between the urban and rural areas along with the allied agriculture and industrial sectors in the upstream regions (Sukhwani et al. 2020). These conflicts have been particularly evident during years of water deficit.
The Vena River is yet another source of water for the region. This river is the main source of water for the industrial areas of Hingna and Nagpur Rural blocks and some parts of Kamptee block (NIT 2015). Additionally, the region receives water from the Kanhan intake works and other small and medium-sized local reservoirs such as the Kanholibara, Zilpi, and Wakeshwar reservoirs (NIT 2015). NMR also benefits from the treated wastewater.
Nagpur City generates around 0.52 million m3/day of wastewater and has an existing wastewater treatment plant (WWTP) within the city with a capacity of 0.1 million m3/day. This WWTP treats the wastewater and releases it into the Nag and Pili Rivers which are not the direct source of water for any activities in the NMR (NIT 2015). In 2015 and 2018, two more WWTPs were commissioned with treatment capacities of 0.13 million m3/day and 0.2 million m3/day, respectively, at Bhandewadi and the treated water from these plants is channelled to the thermal power stations of the Kamptee block. The wastewater treatment and reuse in this region has also been recognized as one of the global best practises (World Bank 2019).
(b) Research method
Water allocation priority towards the city has led to serious water stress in the rural regions of the NMR (Sukhwani & Shaw 2022). At the same time, increasing industrial and agricultural water demands coupled with the uncertain climatic scenarios present yet another set of challenges for managing the available water resources. Events such as extreme population growth and sudden changes in the governance of water resources may potentially complicate the allocation dynamics and therefore require attention prior to their manifestation. Understanding water availability under various scenarios thus becomes important for planning the optimal allocation and ensuring equitable distribution of water resources.
In this regard, modelling and simulation methods provide a powerful approach to understanding water availability under varying scenarios. These methods enable the integration of complex interactions between climatic, hydrological, and human systems, offering a comprehensive overview of water resource dynamics (Yu et al. 2022). By simulating different scenarios, such as changes in climate, population growth, or water management policies, these models help predict future water demands and resource availability to identify potential risks. Unlike static analyses, dynamic simulation models capture the spatio-temporal variations, providing insights into seasonal fluctuations, extreme events, and long-term trends (Ke et al. 2016). This approach also facilitates scenario-based planning, enabling decision-makers to evaluate the effectiveness of various interventions, such as water-saving technologies or policy measures, in addressing challenges related to water security (Yu et al. 2022). Therefore, to gain an understanding of the present and future water resource dynamics in the NMR under various scenarios, this study relies on the modelling and simulation methods.
(c) Significance of WEAP modelling
Globally, researchers have employed diverse modelling and simulation techniques in conjunction with various socioeconomic factors to conduct comprehensive water resource assessments. Among all the water resource planning and allocation models, WEAP (Yao et al. 2021) has been one of the most widely used models in the last few years (Yates et al. 2005; Moncada et al. 2021; RaziSadath et al. 2023). WEAP, developed by the Stockholm Environment Institute, facilitates water resource allocation under different scenarios, including the socioeconomic, governance, and climate change scenarios. Hence, WEAP proves to be a useful tool for visualizing the ideal water balance over rural–urban boundaries under various scenarios. Several studies have reported the effectiveness of WEAP as a modelling tool for understanding demands and managing water supply at the regional and local scales in India (Bhave et al. 2018; Agarwal et al. 2019; Nivesh et al. 2022; Berredjem et al. 2023). WEAP offers an integrated system for effectively managing various processes, including water demand, supply, storage, discharge, flow, and others. It provides a set of processes and model components, such as demand nodes and transmission/return flow links, which facilitate the visualization of management concerns through a scenario-based approach. Therefore, to enhance the decision-making capacities in water resource governance under uncertain anthropic and climatic constraints, this study has utilized the WEAP as a tool for modelling and simulating the water balance in the NMR.
(d) Model setup and datasets
WEAP offers two primary approaches for modelling water systems. The first is catchment-based modelling, which emphasizes natural hydrological processes within a catchment or watershed. This method captures the dynamics of precipitation, runoff, and infiltration across the hydrological boundaries. The second is node and link-based modelling, which centres on water demand, supply, and allocation. This method is ideal for simulating the interactions between the users and resources within a network of nodes and links and for understanding the water use dynamics within the regional administrative boundaries. As this research primarily aims at evaluating the water balance among competing water users in the administrative boundaries of NMR, the node-link-based modelling approach is adopted for further analysis.
The modelling process in WEAP begins with the input of geographic layers containing supply and demand nodes. The schematic view within WEAP establishes connections between these spatial features using nodes and transmission links. Ten demand sites, including the five complete blocks and four partial blocks of NMR and Nagpur City are modelled as the demand nodes. Various domestic urban and rural, agricultural, and industrial demands, including the demands in the power sector for each block, are modelled under the respective demand node.
In WEAP, water balance accounting involves the transmission of water across system links to meet the demand at specific sites (Moncada et al. 2021). Thus, transmission links to each demand node are established based on the specific water sources utilized to meet the demands at the respective nodes. Three main reservoirs, namely the Pench Reservoir, Upper Vena, and Lower Vena over the Pench River, Kanhan River confluence, and the Vena River are modelled in the schematic as these are the major reservoirs of the region. Additionally, the Kanholibara reservoir is also modelled as it is a significant source of water for the southern industrial zone of the NMR. Water from the other medium-to-minor sized reservoirs is aptly included within these four reservoirs.
Details of datasets used as input to the WEAP model
| Dataset | Period | Description | Source |
|---|---|---|---|
| Meteorological data | 2005–2016 | Temperature | Indian Meteorological Department (IMD) |
| Precipitation | |||
| Hydrological data | 2011–2021 | Evaporation | Central Water Commission, Nagpur (CWC) |
| Transpiration | |||
| Streamflow | |||
| River discharge | |||
| GIS data | 2016 | DEM, | USGS Earth explorer |
| Land use, Land cover | National Remote Sensing Centre, India (NRSC) | ||
| Census data | 1981–2011 | Population | Nagpur Improvement Trust (NIT) |
| Draft Development Plan (NIT 2015) | |||
| Water demand and Supply Data | 2000–2015 | Domestic water demand | Nagpur Municipal Corporation (NMC) |
| Draft plan for Water supply (2005) | |||
| Ministry of Urban Development (MoUD) (2015) | |||
| City Development Plan for Nagpur 2041 | |||
| 2018 | Energy water demand | Council on Energy, Environment, and Water, India | |
| 2011 | Agriculture water demand | Department of Agriculture, Government of Maharashtra; | |
| Minor Irrigation Census Data; (2000–2001) | |||
| Indian Agricultural Statistics Research Institute (ICAR) | |||
| Other Water Resource Data | 2009–2013 | Groundwater data | Central Groundwater Board (CGWB) |
| 2011 | Reservoir data | Nagpur Municipal Corporation, Detailed Project Report for 24 × 7 supply (2011) |
| Dataset | Period | Description | Source |
|---|---|---|---|
| Meteorological data | 2005–2016 | Temperature | Indian Meteorological Department (IMD) |
| Precipitation | |||
| Hydrological data | 2011–2021 | Evaporation | Central Water Commission, Nagpur (CWC) |
| Transpiration | |||
| Streamflow | |||
| River discharge | |||
| GIS data | 2016 | DEM, | USGS Earth explorer |
| Land use, Land cover | National Remote Sensing Centre, India (NRSC) | ||
| Census data | 1981–2011 | Population | Nagpur Improvement Trust (NIT) |
| Draft Development Plan (NIT 2015) | |||
| Water demand and Supply Data | 2000–2015 | Domestic water demand | Nagpur Municipal Corporation (NMC) |
| Draft plan for Water supply (2005) | |||
| Ministry of Urban Development (MoUD) (2015) | |||
| City Development Plan for Nagpur 2041 | |||
| 2018 | Energy water demand | Council on Energy, Environment, and Water, India | |
| 2011 | Agriculture water demand | Department of Agriculture, Government of Maharashtra; | |
| Minor Irrigation Census Data; (2000–2001) | |||
| Indian Agricultural Statistics Research Institute (ICAR) | |||
| Other Water Resource Data | 2009–2013 | Groundwater data | Central Groundwater Board (CGWB) |
| 2011 | Reservoir data | Nagpur Municipal Corporation, Detailed Project Report for 24 × 7 supply (2011) |
WEAP schematic for the NMR Region.
Various hydro-climatic and physical datasets are used in this study to assess future water availability. Building on the trends up to the year 2011, water demand is projected from 2012 to 2040 (28 years) under alternative scenarios over the administrative boundaries of the NMR. As the accuracy of model results depends on the quality of input variables, great care is taken in selecting and processing data to represent the system as it was in 2011. Multiple data sources are integrated using the WEAP model for water balance analysis. Table 1 enlists the datasets along with their sources that have been used to construct the WEAP model.
The demand analysis is conducted for the NMR using the disaggregation-based approach within the WEAP model. The water requirements for each sector are specified at disaggregated levels, considering population count, land area in hectares, and so on. These values are subsequently multiplied by the corresponding annual water use rate. Four critical demand nodes, namely:
(1) Domestic water demand; (2) agricultural water demand; (3) industrial water demand; and (4) water demand in the power sector are considered for each block of the NMR. The details of the input datasets for computing the water demands of each node under the base scenario are described as follows.
- • Population growth rate: The population growth rate for projecting future population utilizes the compounded aggregated growth rate method (Tripathi & Mahey 2016) based on the historical datasets of the population for each block for four consecutive decadal census of the years 1981, 1991, 2001, and 2011 (NIT 2015) as mentioned in in the following equation:where Pt is the projected population at time t (future year). P0 is the initial population (base year population). Pn is the population at the most recent census year. n is the number of years between P0 and Pn, and t is the time in years between the base year and the projected year.(1)
- • Domestic water requirements: The population for each block is utilized to estimate the domestic water demand in each block of the NMR. As per the standard water requirement of 135 litres per capita per day (lpcd) in the urban areas and 40 lpcd in the rural areas (IS Code 1993), as mentioned in Equation (2). The domestic water requirements are computed and calibrated after substantiating with the water supply data for the years 2011 and 2015 (NIT 2015; NMC 2015).where Wdom is the total annual domestic water requirement; Pi,urban is the urban population of block i; Pi,rural is the rural population of block i; n is the total number of blocks; 135 is a per capita daily water requirement in urban areas; 40 is a per capita daily water requirement in rural areas; 365 is the number of days in a year.(2)
- • Agricultural water requirements: The agricultural water demand is calculated based on the irrigated area of land under various crops for complete blocks and proportioned for the partial blocks, as mentioned in Equation (3). The crops are segregated as per the cultivation season, namely the Kharif (June–October), Rabi (November–March), and Zaid (March–June) seasons. The agricultural water demands are then computed using the crop coefficient method (Equation (3.1)) considering a standard crop water requirement for all the crops grown in identical seasons (Allen et al. 1998).where Wagri is the total annual agricultural water requirement; Aj,k is an irrigated area under crop k in block j; CWRk is the crop water requirement for crop k; m is the total number of crops considered in block j; n is the total number of blocks.(3)
- Industrial water requirements: Industrial water demand is estimated using domestic water use as a proxy for industrial water use (Equation (4)), as previously done by the Central Pollution Control Board (1989) and Rooijen et al. (2009).where: Wind is the total annual industrial water requirement; α is the proportion factor for industrial water use based on total rural domestic water demands; β is the proportion factor for industrial water use based on total urban domestic water demands; WRdom is the rural domestic water requirement; WUdom is the urban domestic water requirement(4)
- Water requirements in the power sector: Water demand in the power sector is based on the generation capacity for each power plant (NIT 2015) and is computed using the consumptive water requirement per megawatt per hour (Chaturvedi et al. 2018), as mentioned in Equation (5). For modelling purposes, the water demands in the power sector are consolidated with industrial water requirements.where Wpower is the total annual power sector water requirement; Gp is the annual electricity generation capacity of power plant p (MWh); CWRp is the consumptive water requirement per MWh; n is the total number of power plants.(5)
The model is calibrated using historical data for the population spanning from the year 1981 to 2011 (a period of 30 years), followed by validation of the population and the water demand using data from 2001 to 2015, respectively.
(e) Scenarios
The scenarios in the WEAP model include a reference scenario, which represents a base case or the business-as-usual scenario with future climate projections and an average population growth rate continuing at 1.97%. Three scenarios, (a) a high growth scenario, (b) a climate scenario of deficit rainfall conditions, and (c) a governance intervention scenario, are used to assess the water demand situation for the present and future. The details of the scenarios used in the study are mentioned as follows:
(1) Reference scenario: This scenario, also known as the base case or the business-as-usual scenario refers to the current account in which all the real-time datasets are used. The population and water demands are increasing as per the historical trends and estimated projections.
(2) High growth scenario: In this scenario, it is assumed that with the rapid industrial developments, demands for power and workforce would rise. Consequently, more investments in the industries and power infrastructures would be observed in addition to the population influx, and the water demands would increase accordingly. An average annual growth rate of 5% in the industries (over the existing growth rate of 2%) and an average growth rate of 5% (over the existing growth rate of 1.97%) in the total population, along with the consequent growth in the allied water-intensive parameters augmenting the water demands, are modelled in this scenario, and the results therein are used to compare with the reference scenario.
(3) Rainfall deficit scenario: This scenario considers the impacts of deficit rainfall conditions due to historical observations of inconsistent precipitation patterns and declining wet weather conditions (Shravankumar & Vasudeo 2015). Historic trends are evaluated to determine and model the extent of deficit rain conditions. This scenario primarily focuses on the agriculture sector. In this scenario, it is assumed that more areas would require irrigation. A 10% average annual increase in irrigation water demand is modelled in this scenario against the existing rate of 4.17%.
(4) Governance interventions scenario: In this scenario, demand-side management (DSM) interventions are modelled to understand the impact of governance on the water system. A demand-side management saving (DMS) of 15% after 2025 is considered for modelling this scenario.
RESULTS AND DISCUSSION
Model calibration and validation
The WEAP model calibration and validation process for this research specifically focuses on population-based water requirements, as it is one of the critical dynamic nodes influencing the overall water demands in the NMR. However, due to the availability of archives for population, water supply demand, and delivery data only for Nagpur City, this stage specifically focuses on the domestic water demands of Nagpur City. The domestic demands of the city are then considered a proxy for simulating the future rural water demands (Groppo et al. 2019) and the results are validated with the observed rural water demands in 2015–2016. The model parameters related to population growth, water consumption, and demand rates are adjusted during the calibration period of 1981–2011 to minimize discrepancies between the simulated and observed water demand rates for 2015–2016. The model's reliability is evaluated by comparing the observed and the simulated values, with the coefficient of determination (Bobbitt 2021) and the R2 value of 0.845 obtained, indicating a good fit.
Overall water supply requirements and future projections
Overall water supply requirements under various scenarios.
However, significant investments in the Nagpur region for industrial development have been rapidly attracting numerous industries (Deshkar 2019; Sukhwani et al. 2020; TOI 2024). This industrial growth, intrinsically linked to population increase, implies that continued industrial expansion would lead to a rise in population and, consequently, an increased demand for freshwater resources. Therefore, scenario 2, the high growth scenario, assumes a growth of 5% in the population and industries, an escalation of 60% over the growth rates in the reference scenario. The irrigation water demands in this scenario remain unaltered and continue to rise as per the reference scenario. This scenario projects the supply requirements to reach 6.93 bcm by 2040, 68% higher than the reference scenario.
Although rainfall issues are not a significant concern for Nagpur, the region experiences variability in rainfall patterns leading to periods of water scarcity. This has been evident through the inconsistencies in the seasonal precipitations, fluctuating water levels in the reservoirs, and occasional drought conditions observed previously (Sukhwani & Shaw 2022). Therefore, a rainfall deficit scenario is considered to understand the probable impacts of infrequent rains on water supply requirements. Under this scenario, the rising water demands are attributed to increased demand for irrigation in the rainfed agricultural lands to offset the lack of natural precipitation. It is also assumed that a rainfall deficit would exacerbate water scarcity, prompting agricultural, industrial, and domestic users to seek more water to maintain their activities and mitigate the impacts of the drought conditions. Thus, a significant rise in the water supply requirements is considered in this scenario. This scenario projects the supply requirements to reach 5.10 bcm by 2040, around 25% higher than the reference scenario.
Assuming the water requirements range from 4.14 bcm (minimum as per the reference scenario) and 6.93 bcm (maximum as per the high growth scenario) by 2040, significant distress would still be anticipated in water allocation due to the limited supply sources. Therefore, an alternative scenario is examined to determine if present-day interventions could influence future water demands. Subsequently, a governance intervention scenario is modelled assuming that certain DMS strategies encompassing infrastructural, regulatory, and behavioural interventions are employed and become fully functional around the year 2025. The results from this scenario reveal that the supply requirements would reach 3.81 bcm by 2040, around 7% lower than the reference scenario, which would be a significant reduction considering the time frame of 15 years.
Sectoral supply requirements
Sectoral water supply requirements under various scenarios. (a) Sectoral water demands by 2040. (b) Share of each sector in every scenario.
Sectoral water supply requirements under various scenarios. (a) Sectoral water demands by 2040. (b) Share of each sector in every scenario.
In the case of the high growth scenario, where the model accounts for the increased industrial developments and a corresponding increase in the influx of population, the water requirements are observed to soar to 3.53 bcm in the domestic sector, around 1.5 times higher than the reference scenario, followed by 1.80 bcm, in the industrial sector, up to 75% more than the reference scenario, by 2040. In this scenario, the share of the domestic sector is revealed to be higher at 51%, followed by an equal share of the industrial sector at 26% and the agricultural sector at 23%.
Considering the rainfall deficit scenario, where the irrigation requirements are assumed to rise due to unequally distributed precipitation, increased agricultural water requirements are modelled to understand the extent of water supply requirements for each sector. The agriculture water requirements in this scenario increase to 2.40 bcm, 55% higher than the reference scenario. It is found that the domestic as well as industrial demands increased to 1.6 and 1.03 bcm, a rise of 4.7 and 0.5%, respectively, from the reference scenario, by 2040. In this scenario, the agriculture sector clearly had the highest proportion of water demand at 49% followed by the domestic and industrial sectors at 31 and 20%, respectively.
Under the governance intervention scenario, the sectoral share of the water supply requirements remains constant as in the case of the reference scenario, with agriculture, domestic, and industrial sector shares being 38, 37 and 25%, respectively. However, the supply requirements for the respective sectoral demands are curtailed by 8.1, 8.01, and 7.69% compared to the reference scenario at 1.40, 1.46, and 0.95 bcm, respectively, by 2040.
Block-wise water supply requirements
Block-wise water supply requirements under various scenarios. (a) Block-wise water supply requirements by 2040. (b) Sectoral share of requirements in each block by 2040.
Block-wise water supply requirements under various scenarios. (a) Block-wise water supply requirements by 2040. (b) Sectoral share of requirements in each block by 2040.
Water supply reliability
Demand site reliability under various scenarios by 2040.
On average, the lowest demand site reliability is observed for Nagpur City at 17%, whereas the highest demand site reliability is observed for Parseoni at 85%. This means that 100% of the water demands would not be met in Nagpur City for 83% of the time, and the city could experience negligible to significant unmet demands depending on the scenario. From this analysis, it is evident that under every scenario, frequent unmet demands would be observed primarily in Nagpur City followed by the neighbouring rural blocks of Kamptee, Nagpur Rural, Mauda, and Hingna, respectively. However, the governance scenario accounting for DMS strategies shows promise, particularly for Nagpur City and the neighbouring blocks. In this analysis, the blocks of Umred and Saoner are not considered because only a small portion of the blocks is a part of the NMR and shows 100% demand fulfilment in every scenario.
Unmet demands
Unmet demand under various scenarios. (a) Block-wise unmet demand by 2040. (b) Sector-wise unmet demand by 2040.
Unmet demand under various scenarios. (a) Block-wise unmet demand by 2040. (b) Sector-wise unmet demand by 2040.
As the local bylaws of Nagpur district prioritize fulfilling the domestic water demands (Sukhwani et al. 2020), deficits in the industrial and agricultural sectors are foreseeable in every scenario. Furthermore, as the domestic demands are primarily concentrated in the urban part of the NMR, water allocation challenges are projected to intensify for the rural areas of the NMR, resulting in significant unmet demands at the rural end. Based on the findings from this research, the ongoing water conflict between rural and urban areas of NMR, as substantiated by Sukhwani et al. (2020), is projected to exacerbate by 2040.
With the current infrastructure and supply sources, around 1.45 bcm of water is supplied to the NMR annually through surface water sources such as the Navegaon-Khairy Dam constructed over the Pench River (NIT 2015) and the Vena and Kanholi Dams constructed over the Vena River and the other small surface water reservoirs. The Navegaon-Khairy Dam, or the Pench Project, is the main source of surface water for NMR, primarily catering to the domestic water requirements of Nagpur City (NIT 2015). However, the project itself receives water from the Totladoah dam, which is in the upstream area of Madhya Pradesh State and recent upstream developments such as the construction of the Chaurai dam in the Madhya Pradesh State have affected the water levels in the Pench Project (Sukhwani et al. 2020), indicating that the possibility of a transboundary conflict cannot be ruled out. Beyond the 1.45 bcm of water sourced from the surface water sources, the rest is sourced through groundwater sources. Out of the net annual groundwater availability of 0.82 bcm, the current groundwater draft stands at 0.28 bcm, 85% of which is utilized for fulfilling the irrigation water demands and the remaining 15% for fulfilling industrial and rural domestic water demands (NIT 2015). While this indicates that an additional 0.54 bcm of water could potentially be sourced from groundwater, assuming sufficient annual recharge, concerns about the heavy reliance on these resources are growing (Deshkar 2019), underpinning the risks of over-extraction in the NMR. The increasing water losses due to poor infrastructure (Deshkar 2019; Sukhwani & Shaw 2022; Hitavada 2024) also magnify the water concerns in the NMR. All these concerns add a different layer of complexity, which has not been considered in this research.
CONCLUSION
This study sheds light on water supply requirements and availability dynamics of the NMR. Through a meticulous analysis of various scenarios in WEAP, this research presents the potential water supply shortfalls in the NMR by 2040. By analysing multiple scenarios using WEAP, the research identifies potential water supply gaps and emphasizes the urgency of adopting DSM strategies to mitigate these challenges. Despite the absence of comprehensive census data on water consumption, the scenario-based approach highlights actionable pathways to address the impending water crisis.
Key findings: The research indicates that while supply requirements are projected to rise to 4.14 bcm by 2040, implementing DSM strategies as early as 2025 could substantially reduce demand, particularly in the domestic sector, with Nagpur City being a critical focus. Furthermore, targeting the agriculture sector – specifically the green belts in Mouda, Kalmeshwar, and Parseoni – could yield significant savings. Likewise, DSM interventions in rapidly industrializing blocks like Hingna, Kamptee, and Nagpur Rural could address water-intensive growth trends. These strategies could include technological innovations for efficient water use, infrastructure retrofitting, incentives for cross-sectoral cooperation, and rainwater harvesting or on-site wastewater reuse solutions.
Policy implications: This research underscores the need for integrated water management policies that prioritize DSM strategies across sectors. Policymakers should invest in capacity-building programs, develop financial incentives for adopting efficient technologies, and create mechanisms to monitor and regulate water usage equitably. Effective governance frameworks must balance rural and urban demands while addressing risks such as conflicts over shared resources, groundwater overdrafts, and infrastructure deficits.
Future research scope: While this study presents a foundational understanding of water balance in the NMR, future research should focus on evaluating the effectiveness of specific DSM interventions at the local level. Studies exploring alternative water supply sources, such as wastewater recycling and rainwater harvesting, should also be prioritized. Additionally, assessing the socio-economic implications of DSM strategies, such as impacts on marginalized communities, would provide a more holistic perspective.
Research limitations: This research acknowledges the absence of detailed water consumption data and the reliance on scenario-based projections, which may introduce uncertainties. Moreover, while DSM strategies are highlighted as a promising solution, their feasibility and long-term effectiveness require comprehensive validation through pilot studies and stakeholder consultations.
In conclusion, this study reiterates the critical importance of addressing water balance challenges in the NMR through a combination of DSM and alternative supply strategies. By integrating these measures into regional water management frameworks, stakeholders can enhance resilience, promote equitable distribution, and mitigate risks associated with extreme water deficits across the rural–urban continuum.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the Central Water Commission (CWC) Nagpur, Central Ground Water Board (CGWB) Nagpur, and Nagpur Municipal Corporation (NMC) for providing the relevant data to conduct this research. We also thank the Stockholm Environment Institute (SEI) for providing the license for WEAP.
AUTHOR CONTRIBUTIONS
S.J. was involved in the conceptualization, data collection, model development, analysis, and manuscript writing. S.D. was involved in the conceptualization of the study and approval of the manuscript.
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.
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