The article discusses a statistical model to explain the varying performance of water supply schemes under different physical environments in the state of Maharashtra in India, characterized by high spatial and temporal variability in rainfall and climate, and heterogeneity in geological and geomorphological conditions. The factors that enhance the performance of the schemes are effective utilizable recharge rates, extent of surface irrigation, and aquifer storage space. The factors that adversely affect the performance are the extent of the population covered by groundwater-based schemes and irrigation water demand per unit area. Subsequently, the districts in Maharashtra where groundwater-based schemes are likely to succeed were identified. The article also explains the practical and policy relevance of the model results for rural water supply planning.

  • Groundwater-based drinking water supply schemes in Maharashtra perform poorly in most districts.

  • The factors influencing the performance of drinking water supply schemes of the state were identified through statistical modelling like groundwater recharge, stress on aquifers, and aquifer behaviour.

  • Rainfall, infiltration characteristics of the surface formation, aquifer storage space, and extent of gravity irrigation increase the chances of success of rural drinking water sources.

  • High irrigation water demand and higher dependence on groundwater-based schemes increase the chances of failure of rural water supply schemes.

  • Groundwater-based schemes would succeed in districts that have good rainfall and favourable strata for infiltration of the water, good aquifer storage characteristics, large extent of surface irrigation, and relatively low irrigation water demand.

With the successful introduction of hand pumps in remote rural areas of the country in the 1960s, India made a strategic move to invest in groundwater-based rural water supply schemes. The low capital cost of the infrastructure that is capable of meeting the localized water needs and a very low gestation period, the low level of technical sophistication of the system rendering it easy for the local arms of the government could operate and maintain them, and the ease of finding source water to meet the low volumes of water demand that exist at the village level revolutionized the method of improving the access of the rural communities to safe water in a poor country like India. In the initial years, the sustainability of the source water was not a concern, as a sufficient amount of groundwater could be obtained locally to meet the low domestic demand.

With the passing of time, however, finding an adequate quantum of water in the wells to meet the rural drinking water requirements on annual basis proved to be a mounting task. With the intensification of groundwater withdrawal for irrigation in semi-arid and arid areas (Kumar, 2007; Shah, 2009), the drinking water wells either started yielding less or in many cases began to dry up during the summer months (Kumar et al., 2012). The efforts subsequently shifted to scientific prospecting of groundwater – in search of good aquifers and the right locations for drilling wells.

The real magnitude of the resource sustainability challenges was therefore not understood. It was thought that a groundwater regulation with strictly enforced norms on spacing and depth of irrigation wells would help reduce the ‘negative externalities’ of irrigation drafts on drinking water wells. However, no Indian state could come up with a regulation that was both technically meaningful and socially acceptable to check groundwater over-development and to protect the drinking water sources. The ‘groundwater acts’ that were passed in the state legislatures were not effective due to limited implementation owing to a lack of political will and the absence of a platform for convergent planning (Cullet, 2014, 2017).

At the same time, hundreds of thousands of drinking water wells and hand pumps survived the onslaught by well irrigation in many regions and localities of the country that included areas that are traditionally known for widespread well failures such as hard rock areas with poor natural groundwater potential. Clearly, the factors that determine the sustainability of groundwater-based drinking water sources were not fully understood. The crucial point to recognize is that most of the agricultural water demand in the semi-arid and arid areas is during the winter season and this demand exceeds the total available resource, whereas the domestic water demand is throughout the year.

In this article, we present the results of the investigation into the factors that explain the varying performance of rural water supply schemes using data from Maharashtra. The analysis looks at the potential influence of the factors that are physical (geomorphology and climate), technical (dependence on groundwater, extent of gravity-surface irrigation), and socio-economic (cropped area and irrigated area) in nature, in addition to rainfall and geohydrology whose effects are relatively better known.

Sustainability of water wells: a literature review

In India, currently, the majority of the water supply schemes are based on groundwater. Rural households use water supplied by the schemes for many productive purposes (such as for homesteads, small business activity, etc.) in addition to the intended domestic uses (Smits et al., 2010; Kumar et al., 2016). Therefore, at the aggregate level, the water supply levels maintained by the schemes, which are as per traditional norms of per capita domestic needs, are not able to fully meet the demands in most instances.

On the availability side, the most crucial factors for the hard rock areas are the potential infiltration from rainfall, and the storage space available in the aquifer to accommodate the infiltrating water. The combined effect of these variables will be on the amount of water that remains in the aquifer to sustain the drinking water sources which ultimately determines the performance of the scheme (wells, hand pumps, etc.) that tap groundwater. However, a portion of the water that is stored in the aquifer during the monsoon (and that is reflected in the water level fluctuation during the season) is discharged into the streams as the water level in the streams starts receding after the monsoon.

An important factor that has received the least attention in the debate on drinking water security is the role of surface irrigation on the sustainability of drinking water wells. As discussed earlier, the past efforts to improve the sustainability of water supply from wells in the hard rock areas (like in the Saurashtra region of Gujarat, Marathwada, and Vidarbha regions of Maharashtra) had focussed on building artificial recharge schemes to put monsoon runoff into the aquifers. However, the strategy has not been effective due to the fact that during a good monsoon (when runoff is normally available from the local catchments for capturing), the hard rock aquifers get fully replenished with very little extra space in the aquifer to receive additional infiltration caused by the recharge structures (Kumar et al., 2006). Boisson et al. (2014) and Massuel et al. (2014) who studied the performance of tanks in hard rock areas of South India made similar observations, reporting low percolation efficiency of the water stored in the tanks. For recharge to be effective and to have a positive impact on the yield of drinking water wells, infiltration should occur during the lean season.

Research studies undertaken in the Murray basin of Australia and the Indus basin in Pakistan, both having arid climates and deep water table conditions showed a substantial rise in water table conditions after the introduction of canal water for irrigation (Watt, 2008). Analysis by Rotiroti et al. (2019) of the Po plain in the Oglio river basin of northern Italy showed a positive impact of surface irrigation systems on the sustainability of water wells with a rise in water levels, improved yield of water wells and improved quality of groundwater (with a reduction in nitrate levels) through recharge of the unconfined aquifer that sustains water use by humans and the ecosystem.

As regards the use, the most important is the demand for water in irrigation, and the availability of water from other sources (especially surface water) to meet those demands. Excessive demand for water for irrigation threatens the sustainability of drinking water wells in regions in the absence of alternative sources of water to meet such demands (Hemani et al., 2021). Climate is an important factor that influences the demand for irrigation water (Kumar et al., 2021), along with the proportion of the geographical area under cultivation.

Thus, any adverse situation resulting from either reduced availability of water or increased withdrawal of water has the potential to influence the functioning of the scheme. It was found that overall, in about 10–15% of the states in India, habitations covered by the groundwater-based scheme suffer ‘slip-back’ annually (Chaudhuri et al., 2020).

The lack of adequate quantity of water for supply and drying up of the source are identified as among the major reasons for the slippage of the rural habitations (Reddy et al., 2010). Analysis of data from 677 ‘slip-back’ habitations in the Konkan division of Maharashtra revealed that about 32% of them had ‘slip-back’ due to source drying up. In the remaining 68%, schemes were not able to perform satisfactorily even when the design life was yet to be complete. Inadequate supply at the delivery point and improper functioning of the old scheme was found to be the other two main reasons for the failure of the schemes in the region (Source: Based on data presented in IIT CTARA, and UNICEF Maharashtra (IRAP, CTARA, UNICEF, 2018)). Similarly, in Amravati, Aurangabad, Nagpur, and Pune, 50% of the non-functional water supply schemes were because of the drying up of the groundwater-based source (Sakthivel et al., 2015).

Seasonal shortage of water is another reason for the temporary failure of groundwater-based supply schemes in hard rock areas (Reddy et al., 2010). Further, it has been observed that inadequate monetary allocation for interventions related to source sustainability is one of the main causes of widespread slippage in the rural water supply services.

The assessment was done during the year 2020 used statistical modelling to understand the factors influencing the performance of groundwater-based water supply schemes. To determine the factors influencing the sustainability of the scheme, a multiple regression analysis was performed by considering the following independent variables: (1) water demand for irrigation per unit geographical area; (2) effective utilizable recharge per unit area; (3) the proportion of the total geographical area under surface irrigation; (4) the extent of habitation covered by groundwater-based schemes for water supply; and (5) aquifer storage space. The extensive review of scientific literature on the performance of drinking water supply schemes provided the theoretical underpinning for the selection of the independent variables.

For instance, the effect of monsoon rainfall and infiltration properties of the surface formation on the quantum of recharge to groundwater is well documented (Rajaveni et al., 2017; CGWB, 2021). The influence of ‘geomorphology’, which influence the groundwater flow gradient, on the storage of water in the aquifer is also discussed (see Winter et al., 1998; Maggirwar & Umrikar, 2011; Deshpande et al., 2016). The selection of aquifer storage space as a variable was based on analysis by Boisson et al., (2014), Deshpande et al. (2016), Kumar et al., (2006), and Massuel et al., (2014), which illustrated the effect of storage space in the aquifer on recharge efficiency. The selection of the variable ‘total area under surface irrigation’ was based on Perry et al. (2017), Rotiroti et al. (2019), and Watt (2008) which explained the positive effect of gravity irrigation and canal seepage on groundwater recharge and sustainability of irrigation and drinking water wells. Sakthivel et al. (2015) illustrated the negative implications of over-dependence on groundwater on the sustainability of drinking water sources in rural areas. Several authors discussed the adverse impact of the high demand for irrigation water which translates into the excessive withdrawal of groundwater in the absence of alternative sources for meeting on the performance of drinking water wells (Hemani et al., 2021). Kumar et al. (2021) illustrated the effect of climate on irrigation water demand. The number of tankers supplied during summer per 1,000 population was chosen as the dependent variable. The values of the different variables used in the regression model were adjusted for calibration of the model to get the strong coefficient.

To compute the values of the variables used for the regression analysis, district-wise data were collected. These include rainfall, geographical area, net sown area, canal-irrigated area, rural population, habitations covered by groundwater-based schemes, number of tankers supplied, and pre and post monsoon groundwater levels. Maps on geomorphology and the one showing priority areas for artificial recharge (prepared by Groundwater Surveys and Development Agency, Government of Maharashtra) were referred for choosing the recharge coefficient and the specific yield of the aquifer. The data for 2017 were incomplete, and thus not considered for the analysis.

The output model (result) from the regression analysis was used to identify the determinants of the success of the groundwater-based schemes. It analysed the impact of each of the independent variables on the dependence on the tanker water supply and shortlisted those that reduce the dependence on the tanker water supply during the lean season. Various districts in Maharashtra were ranked on the identified factors and the bottom 10 districts under each attribute were categorized into distinct typologies, each requiring a certain type of intervention to improve the sustainability of water supply schemes.

Scheme failure and tanker water supply in rural areas

The factors that determine the performance of a rural water supply scheme are the quantum of water available from the sources that the schemes tap (surface water or groundwater) against the demand for water for various uses that the source water is expected to meet across the year. There are many variables that affect the availability of water from the source and the aggregate demand in different parts of the year. But the data on these supply and demand-related variables are not readily available and it would require an enormous amount of effort to generate them. Hence, it was examined whether the high degree of dependence on groundwater-based sources has any negative bearing on the sustainability of rural water supply schemes. For this, the data on the extent of tanker water supply in different districts of Maharashtra during 2016, 2018, and 2019 was analysed. Since the population varies from district to district, the total number of tankers supplying water was divided by the population in thousand to obtain tankers per 1,000 population in order to normalize the figures.

The first round of analysis of data on tankers per 1,000 people showed that the extent of tanker water dependence was found to be highest in 2019. Across districts, the highest incidence of tanker water dependence was found in the Aurangabad district, followed by Beed, Jalna, Ahmednagar, and Osmanabad.

A frequency analysis was done with a proportion of rural habitations covered by groundwater-based schemes and the extent of use of tanker water supply. A total of 34 districts from the state were chosen for the analysis. The districts were put into three categories, those where the proportion of rural habitations depending on groundwater is less than 80%, those where the proportion of rural habitations depending on groundwater is in the range of 80–90%, and the last one in which more than 90% of rural habitations served by groundwater. The results are presented in Figure 1.
Fig. 1

Water supply scheme characteristics and dependence on tanker water supply. (Source: Authors’ analysis based on data from Ministry of Jal Shakti, GoI, 2020.)

Fig. 1

Water supply scheme characteristics and dependence on tanker water supply. (Source: Authors’ analysis based on data from Ministry of Jal Shakti, GoI, 2020.)

Close modal

It was found that in those districts where the proportion of habitations covered by groundwater-based schemes is less than 80% (there are 5 such districts and the actual % ranges from 34 to 79), the tanker water supply (per 1,000 persons) is very low, 0.27, 0.28, and 1.081, respectively, in 2016, 2018, and 2019. These are districts that have good coverage of schemes based on surface water, in terms of a number of habitations covered. Against this for districts with the proportion of habitations covered by groundwater-based schemes in the range of 80 and 90% (there are 11 such districts), the tanker water supply per 1,000 persons is quite high, 1.39, 0.309, and 1.55, respectively, in 2016, 2018, and 2019. For districts with a proportion of habitations covered by groundwater-based schemes is 90% and above (there are 18 such districts), the tanker water supply per 1,000 persons is quite high, 1.95, 0.67, and 2.505, respectively, in 2016, 2018, and 2019. The above analysis suggests that the higher the proportion of habitations covered by groundwater-based schemes, the higher the chances of water supply becoming undependable. The differences are statistically significant. The analysis brings out one phenomenon very clearly. The district which heavily depends on surface sources for rural water supply, the dependence on tanker water supply is quite low.

Influence of irrigation water demand on sustainability of schemes: theoretical perspective

In India, the demand for irrigation water in a year of normal monsoon mainly starts during winters when most of the soil moisture is used by the crops grown during the Kharif (monsoon) season. The actual demand depends on the climatic conditions of the area. In areas experiencing cold winters (generally in the north, east, north-west, and north east) the demand is generally low.

However, as the same aquifer is shared by many farmers, the overall abstraction often exceeds the annual utilizable groundwater recharge (Anantha, 2009), and the majority of utilizable groundwater is withdrawn for irrigating winter crops. Thus, the wells dry up before the onset of summer leading to seasonal scarcity of groundwater. It has been observed that due to high dependence on groundwater for irrigation, the yield of the wells has decreased (Kumar & Singh, 2008) and the rate of wells failure has increased (Kumar, 2007; Kumar & Singh, 2008) in the hard rock areas. Even, the deeper bore wells have a poor yield in such formations. As high as 60% of about 40,000 deep bore wells (that are in use) in Maharashtra were not able to utilize their potential due to poor discharge (Kumar et al., 2010).

Under such a scenario, the functioning of groundwater-based rural water supply schemes gets adversely affected and they fail to provide water during summer. Every year, newspapers are flooded with the reports on drinking water crisis during the summer months in the hard rock areas of India. The diagram shown in Figure 2 explains the process by which groundwater in hard rock areas gets depleted causing drinking water shortages during the lean season.
Fig. 2

Hypothetical graph groundwater behaviour in a hard rock area over the year.

Fig. 2

Hypothetical graph groundwater behaviour in a hard rock area over the year.

Close modal
The average water requirement for irrigation per unit area based on the evapotranspiration demand, effective rainfall, and the net sown area for different districts of Maharashtra are shown in Figure 3. The highest irrigation requirement per year is 833 mm and the lowest is 78 mm (Gadchiroli).
Fig. 3

Irrigation water requirement per unit geographical area in different districts. (Source: Authors’ analysis using data from rainfall and climate data, and data on net sown area and geographical area.)

Fig. 3

Irrigation water requirement per unit geographical area in different districts. (Source: Authors’ analysis using data from rainfall and climate data, and data on net sown area and geographical area.)

Close modal

Impact of gravity irrigation on rural water supply schemes: theory and empirical evidence

Most of the canals in India run during winters when there is a need for irrigating crops. Though there is a conjunctive use of water in the canal command areas, wells in such settings get recharge benefits during the non-monsoon months from the canal seepage losses. The benefit of groundwater recharge in the command areas is also dependent on whether the canals are lined or unlined (Perry et al. 2017; Watt, 2008). In the hard rock area of the Cauvery River basin in southern India, the contribution of the lined canals to groundwater recharge was up to 20% whereas it was up to 40% in the case of the unlined canals (Mirudhula, 2014).

As of 2018–19, the majority of gravity canal-based surface irrigated area in Maharashtra is confined to the districts of Ahmednagar, Pune, Solapur, Satara, Sangli, and Kolhapur. These districts together constitute about 53% of the total 2.06 million ha of surface irrigated area in the state. Further, these districts along with Gondia, and Bhandara have a higher proportion of arable land under surface irrigation in comparison to other districts. Except for Ahmednagar, these are also the districts where the incidence of tanker dependence is among the lowest in 2016 and 2018. In fact, in Kolhapur, Gondia, and Bhandara, no tanker water supply was requested in 2016 and 2018.

In Aurangabad, Beed, Jalna, and Osmanabad which have the highest incidence of tanker water dependence, the proportion of total arable land under gravity-based surface irrigation is among the lowest and in most cases below 4%. Thus, it can be interpreted that in hard rock areas in the command of gravity-based surface irrigation systems, the groundwater remains available for a longer duration (in some instances lasting the whole year) than in the non-command areas.

An empirical model explaining the varying performance of rural water supply schemes

It was observed that for many districts with coverage of groundwater schemes touching a very high extent – going beyond 90%, the use of tanker water supply is quite low. Examples are Gadchiroli, Nandurbar, Nagpur, Washim, and Ratnagiri. Therefore, a high degree of dependence on groundwater does not always mean that the water supply schemes will have low dependability, and that dependence on tanker water supply will be high. There are many other factors that determine the performance of groundwater-based drinking water supply schemes. The condition in various districts of the state with respect to those factors needs to be understood.

Firstly, the rainfall and geomorphological conditions keep varying from district to district (even within divisions) significantly, both might probably influence the yield of drinking water wells during the summer season. Flat topography and permeable soils will provide favourable conditions for the rainwater to infiltrate. On the other hand, a higher quantum of rainfall not only increases the recharge to aquifers, but also reduces the demand for irrigation which is the largest user of water in almost all districts, except the highly urbanized districts of Mumbai and New Mumbai.

Secondly, the aquifer conditions also vary in the state though 90% of the state's geographical area is underlain by basalt. In the Konkan region with very high rainfall and more favourable geology – with laterite and alluvial sands along with basalt formations – groundwater storage potential is good. In the Tapi basin area (extending over part of five districts, viz., Akola, Amravati, Dhule, Jalgaon, and Buldhana) also, due to alluvial deposits, the groundwater potential is good.

Thirdly, as we have mentioned earlier, surface water availability for irrigation can be a very important determinant influencing the sustainability of groundwater-based drinking water schemes. Since such data are not available readily at the district level, a proxy variable called the proportion of geographical area under surface irrigation was used. The higher the proportion of the area under surface irrigation, the lower will be the dependence on groundwater for irrigation, and the higher would be the recharge to groundwater, particularly during the non-monsoon period (Jagadeesan & Kumar, 2015; Kumar & Perry, 2019). A robust coefficient that can truly represent the intensity of demand for groundwater for irrigation at the aggregate level is the multiple of the Net Sown Area as a proportion of the total geographical area and the difference between the reference evapotranspiration and effective rainfall (). It can be represented as . Here, the and values have to be for the entire length of the cropping season. If there are standing crops for 240 days in a year in a district, the and values shall be estimated for that many days.

To analyze the effect of rainfall, climate, proportion of area under cultivation and surface water availability for irrigation, a multi-variate analysis was performed with the following variables: (1) the water demand for irrigation per unit geographical area, expressed as ; (2) effective utilizable recharge per unit area, which is a multiple of the rainfall, recharge coefficient chosen for the district on the basis of geomorphology, and a coefficient called ‘utilizable recharge fraction’ to arrive at the recharge actually available for use during the year, or to factor out the base flow from aquifer into streams during the non-monsoon period1; (3) the proportion of the total geographical area under surface irrigation (in %); (4) extent of habitation covered by groundwater-based schemes for water supply (in %); and, (5) multiple of specific yield of the aquifer and pre-monsoon depth to water levels (storage space in the aquifer, i.e., , a factor which is found to have significant influence on the total recharge from monsoon.

As regards the last variable, under the same study, a state-wide analysis involving district-wise data of average rainfall, average pre-monsoon depth to water levels, and average water level fluctuations during monsoon for 33 districts showed that the average water level fluctuation during monsoon is a function of the rainfall, the recharge coefficient (which is determined by the infiltration properties of the surface strata and the rainfall), the specific yield of the aquifer and the pre-monsoon depth to water levels. The R2 value was 0.59. The analysis suggested the positive influence of the storage space available in the aquifer prior to monsoon (which is the multiple of pre-monsoon depth to water levels and the specific yield along with rainfall infiltration in deciding the quantum of recharge during monsoon . The regression equation was:
formula
(1)
The values of various coefficients used in the final regression analysis for each district are presented in Table 1. The final regression analysis shows that all the five factors discussed earlier influence the extent of dependence on tanker water supply, which is an indicator of the failure of groundwater-based schemes. The R2 value was estimated to be 0.51 for 2016. The regression equation is provided below (Equation (2)) and the results are presented in Table 2.
formula
(2)
where is the tanker water supply, is the proportion of the total geographical area under surface irrigation, is effective utilizable recharge per unit area, is the aquifer storage space, and is the proportion of habitations covered by the groundwater-based water supply schemes.
Table 1

Values of various coefficients and parameters used for the multi-variate analysis on tanker water supply

DistrictRecharge CoefficientNSA (000 ha)Geographical Area (000 ha)ETo (mm/year)Effective Rainfall (mm)Rainfall (mm)Pre-Monsoon Depth to Water Level (m)Specific YieldStorage Available in Aquifer per unit areaIrrigation Requirement/year (mm)% of Habitations Covered by Groundwater SchemesFraction of geographical area under surface irrigationUtilizable Recharge FractionEff. Utilizable Recharge = Recharge Coeff. X Rainfall X Utilizable Recharge Fraction
AHMEDNAGAR 0.035 1,046.4 1,702 1,156.8 200 688.3 14.30 0.02 0.286 588.2 82.0 0.107 0.5 12.04 
AKOLA 0.08 430 543 1,156.8 250 941.9 13.24 0.1 1.324 718.1 77.3 0.054 0.8 60.28 
AMRAVATI 0.08 751 1,222 1,231.2 275 1,020.2 11.64 0.1 1.164 587.6 67.7 0.060 0.8 65.29 
AURANGABAD 0.05 663.8 1,008 1,291.2 200 714.7 7.66 0.02 0.153 718.6 93.9 0.039 0.8 28.59 
BEED 0.05 757.6 1,069 1,291.2 275 960.5 11.28 0.02 0.226 720.2 94.7 0.009 0.8 38.42 
BHANDARA 0.03 177.6 342 966 300 1,194.2 9.73 0.015 0.146 345.9 89.4 0.256 0.5 17.91 
BULDANA 0.08 656.1 967 1,291.2 225 798.2 11.54 0.1 1.154 723.4 82.1 0.037 0.8 51.08 
CHANDRAPUR 0.03 458.5 1,092 966 300 1,605.6 7.36 0.015 0.110 279.6 92.1 0.075 0.5 24.08 
DHULE 0.08 416.3 733 1,156.8 200 568.1 10.04 0.1 1.004 543.4 93.2 0.010 0.8 36.35 
GARHCHIROLI 0.03 174.6 1,492 966 300 1,735.8 8.10 0.015 0.122 77.9 98.8 0.016 0.5 26.04 
GONDIYA 0.03 181.7 586 966 300 1,391.4 8.64 0.015 0.130 206.5 92.8 0.131 0.5 20.87 
HINGOLI 0.05 329.2 466 1,231.2 275 1,036.5 11.04 0.02 0.221 675.5 90.3 0.029 0.8 41.46 
JALGAON 0.08 849.3 1,164 1,341.6 200 716 13.86 0.1 1.386 833.0 92.7 0.009 0.8 45.82 
JALNA 0.05 572.4 773 1,341.6 250 912.9 13.43 0.02 0.269 808.3 99.5 0.030 0.8 36.52 
KOLHAPUR 0.02 433.6 777 856.8 300 1,293.5 6.13 0.02 0.123 310.7 34.8 0.266 0.5 12.94 
LATUR 0.05 503.1 716 1,341.6 300 1,128.1 14.87 0.02 0.297 731.9 89.4 0.010 0.8 45.12 
NAGPUR 0.03 727.4 1,033 1,077.3 275 977.4 8.84 0.015 0.133 564.9 96.3 0.083 0.5 14.66 
NANDED 0.05 560 986 1,231.2 300 1,250.3 10.71 0.02 0.214 528.9 97.8 0.057 0.8 50.01 
NANDURBAR 0.05 265.9 705 979.2 200 737.7 9.84 0.02 0.197 293.9 99.4 0.006 0.8 29.51 
NASHIK 0.05 872.8 1,563 979.2 275 1,035.8 10.73 0.02 0.215 393.2 89.5 0.045 0.8 41.43 
OSMANABAD 0.05 416.4 749 1,341.6 275 966.5 10.63 0.02 0.213 593.0 97.2 0.010 0.8 38.66 
PARBHANI 0.05 483.9 631 1,341.6 275 978 14.28 0.02 0.286 818.0 95.2 0.056 0.8 39.12 
PUNE 0.035 600.5 1,562 856.8 250 858.9 7.30 0.02 0.146 233.3 85.5 0.152 0.5 15.03 
RAIGARH 0.07 185.1 687 1,144.8 300 4,095.1 4.11 0.08 0.329 227.6 50.2 0.019 0.2 57.33 
RATNAGIRI 0.07 254.5 816 1,144.8 300 3,802.5 8.62 0.08 0.690 263.5 89.4 0.006 0.2 53.24 
SANGLI 0.02 591 861 1,012.2 200 634.6 10.07 0.02 0.201 557.5 86.4 0.143 0.5 6.35 
SATARA 0.02 539.5 1,058 856.8 250 881.5 8.63 0.02 0.173 309.4 85.3 0.157 0.5 8.82 
SINDHUDURG 0.07 140.5 504 874.8 300 3,363 8.20 0.08 0.656 160.2 91.9 0.016 0.2 47.08 
SOLAPUR 0.02 1,044.9 1,488 1,089.9 200 652.9 10.74 0.02 0.215 624.9 92.3 0.120 0.5 6.53 
THANE 0.07 245.9 421.4 1,144.8 300 2,795.1 5.59 0.08 0.447 493.0 73.7 0.025 0.5 97.83 
WARDHA 0.05 345.2 629 1,077.3 300 1,050.3 9.57 0.02 0.191 426.6 95.6 0.054 0.8 42.01 
WASHIM 0.05 381 513 1,231.2 275 993 9.68 0.02 0.194 710.2 86.6 0.070 0.8 39.72 
YAVATMAL 0.05 854.7 1,352 1,231.2 300 1,116.6 9.51 0.02 0.190 588.7 97.4 0.051 0.8 44.66 
DistrictRecharge CoefficientNSA (000 ha)Geographical Area (000 ha)ETo (mm/year)Effective Rainfall (mm)Rainfall (mm)Pre-Monsoon Depth to Water Level (m)Specific YieldStorage Available in Aquifer per unit areaIrrigation Requirement/year (mm)% of Habitations Covered by Groundwater SchemesFraction of geographical area under surface irrigationUtilizable Recharge FractionEff. Utilizable Recharge = Recharge Coeff. X Rainfall X Utilizable Recharge Fraction
AHMEDNAGAR 0.035 1,046.4 1,702 1,156.8 200 688.3 14.30 0.02 0.286 588.2 82.0 0.107 0.5 12.04 
AKOLA 0.08 430 543 1,156.8 250 941.9 13.24 0.1 1.324 718.1 77.3 0.054 0.8 60.28 
AMRAVATI 0.08 751 1,222 1,231.2 275 1,020.2 11.64 0.1 1.164 587.6 67.7 0.060 0.8 65.29 
AURANGABAD 0.05 663.8 1,008 1,291.2 200 714.7 7.66 0.02 0.153 718.6 93.9 0.039 0.8 28.59 
BEED 0.05 757.6 1,069 1,291.2 275 960.5 11.28 0.02 0.226 720.2 94.7 0.009 0.8 38.42 
BHANDARA 0.03 177.6 342 966 300 1,194.2 9.73 0.015 0.146 345.9 89.4 0.256 0.5 17.91 
BULDANA 0.08 656.1 967 1,291.2 225 798.2 11.54 0.1 1.154 723.4 82.1 0.037 0.8 51.08 
CHANDRAPUR 0.03 458.5 1,092 966 300 1,605.6 7.36 0.015 0.110 279.6 92.1 0.075 0.5 24.08 
DHULE 0.08 416.3 733 1,156.8 200 568.1 10.04 0.1 1.004 543.4 93.2 0.010 0.8 36.35 
GARHCHIROLI 0.03 174.6 1,492 966 300 1,735.8 8.10 0.015 0.122 77.9 98.8 0.016 0.5 26.04 
GONDIYA 0.03 181.7 586 966 300 1,391.4 8.64 0.015 0.130 206.5 92.8 0.131 0.5 20.87 
HINGOLI 0.05 329.2 466 1,231.2 275 1,036.5 11.04 0.02 0.221 675.5 90.3 0.029 0.8 41.46 
JALGAON 0.08 849.3 1,164 1,341.6 200 716 13.86 0.1 1.386 833.0 92.7 0.009 0.8 45.82 
JALNA 0.05 572.4 773 1,341.6 250 912.9 13.43 0.02 0.269 808.3 99.5 0.030 0.8 36.52 
KOLHAPUR 0.02 433.6 777 856.8 300 1,293.5 6.13 0.02 0.123 310.7 34.8 0.266 0.5 12.94 
LATUR 0.05 503.1 716 1,341.6 300 1,128.1 14.87 0.02 0.297 731.9 89.4 0.010 0.8 45.12 
NAGPUR 0.03 727.4 1,033 1,077.3 275 977.4 8.84 0.015 0.133 564.9 96.3 0.083 0.5 14.66 
NANDED 0.05 560 986 1,231.2 300 1,250.3 10.71 0.02 0.214 528.9 97.8 0.057 0.8 50.01 
NANDURBAR 0.05 265.9 705 979.2 200 737.7 9.84 0.02 0.197 293.9 99.4 0.006 0.8 29.51 
NASHIK 0.05 872.8 1,563 979.2 275 1,035.8 10.73 0.02 0.215 393.2 89.5 0.045 0.8 41.43 
OSMANABAD 0.05 416.4 749 1,341.6 275 966.5 10.63 0.02 0.213 593.0 97.2 0.010 0.8 38.66 
PARBHANI 0.05 483.9 631 1,341.6 275 978 14.28 0.02 0.286 818.0 95.2 0.056 0.8 39.12 
PUNE 0.035 600.5 1,562 856.8 250 858.9 7.30 0.02 0.146 233.3 85.5 0.152 0.5 15.03 
RAIGARH 0.07 185.1 687 1,144.8 300 4,095.1 4.11 0.08 0.329 227.6 50.2 0.019 0.2 57.33 
RATNAGIRI 0.07 254.5 816 1,144.8 300 3,802.5 8.62 0.08 0.690 263.5 89.4 0.006 0.2 53.24 
SANGLI 0.02 591 861 1,012.2 200 634.6 10.07 0.02 0.201 557.5 86.4 0.143 0.5 6.35 
SATARA 0.02 539.5 1,058 856.8 250 881.5 8.63 0.02 0.173 309.4 85.3 0.157 0.5 8.82 
SINDHUDURG 0.07 140.5 504 874.8 300 3,363 8.20 0.08 0.656 160.2 91.9 0.016 0.2 47.08 
SOLAPUR 0.02 1,044.9 1,488 1,089.9 200 652.9 10.74 0.02 0.215 624.9 92.3 0.120 0.5 6.53 
THANE 0.07 245.9 421.4 1,144.8 300 2,795.1 5.59 0.08 0.447 493.0 73.7 0.025 0.5 97.83 
WARDHA 0.05 345.2 629 1,077.3 300 1,050.3 9.57 0.02 0.191 426.6 95.6 0.054 0.8 42.01 
WASHIM 0.05 381 513 1,231.2 275 993 9.68 0.02 0.194 710.2 86.6 0.070 0.8 39.72 
YAVATMAL 0.05 854.7 1,352 1,231.2 300 1,116.6 9.51 0.02 0.190 588.7 97.4 0.051 0.8 44.66 

Source: NSA and geographical area is from Government of Maharashtra, rainfall data are from Indian Meteorological Department, pre-monsoon depth to water table data is from Central Ground Water Board, and rest are based on authors’ own estimates.

Table 2

Results of multi-variate analysis

CoefficientsStandard errort-statP-value
Intercept 2.67 3.670 0.73 0.472 
Proportion of habitations covered by groundwater −0.0187 0.0312 −0.60 0.554 
Proportion of geographical area under surface irrigation −14.45 7.824 −1.85 0.076 
Eff. utilizable recharge/unit area −0.02245 0.0256 −0.87 0.39 
Storage available in the aquifer −3.046 1.050 −2.90 0.007 
Irrigation demand from year (mm) 0.00689 0.00165 4.17 0.000 
CoefficientsStandard errort-statP-value
Intercept 2.67 3.670 0.73 0.472 
Proportion of habitations covered by groundwater −0.0187 0.0312 −0.60 0.554 
Proportion of geographical area under surface irrigation −14.45 7.824 −1.85 0.076 
Eff. utilizable recharge/unit area −0.02245 0.0256 −0.87 0.39 
Storage available in the aquifer −3.046 1.050 −2.90 0.007 
Irrigation demand from year (mm) 0.00689 0.00165 4.17 0.000 

Source: Authors’ own analysis.

As per the results of the multi-variate regression (refer to Table 3), the coefficients are negative for four variables, viz., storage space available in the aquifer; % of the geographical area covered by surface irrigation; the proportion of habitations covered by groundwater-based schemes; and effective utilizable groundwater recharge per unit geographical area. This trend is along the expected lines. It should also be noted that the level of significance for recharge per unit area, and the proportion of habitations covered by groundwater are low. The level of significance is very high for storage space available in the aquifer, which is the multiple of the pre-monsoon depth to water level and specific yield of the aquifer, and high for ‘% geographical area under surface irrigation’.

Table 3

Ten districts of Maharashtra with lowest score on the four attributes (or unfavourable conditions for successful drinking water wells)

Recharge per unit area (mm)Aquifer storage space (m)Low irrigation water demand (mm/year)Proportion of area under surface water irrigation
Sangli Chandrapur Jalgaon Gadchiroli 
Solapur Gadchiroli Parbhani Gondiya 
Satara Kolhapur Jalna Pune 
Ahmednagar Gondiya Latur Chandrapur 
Kolhapur Nagpur Buldana Nandurbar 
Nagpur Bhandara Beed Satara 
Aurangabad Pune Aurangabad Kolhapur 
Bhandara Aurangabad Akola Bhandara 
Beed Satara Washim Nashik 
Pune Yavatmal Hingoli Wardha 
Recharge per unit area (mm)Aquifer storage space (m)Low irrigation water demand (mm/year)Proportion of area under surface water irrigation
Sangli Chandrapur Jalgaon Gadchiroli 
Solapur Gadchiroli Parbhani Gondiya 
Satara Kolhapur Jalna Pune 
Ahmednagar Gondiya Latur Chandrapur 
Kolhapur Nagpur Buldana Nandurbar 
Nagpur Bhandara Beed Satara 
Aurangabad Pune Aurangabad Kolhapur 
Bhandara Aurangabad Akola Bhandara 
Beed Satara Washim Nashik 
Pune Yavatmal Hingoli Wardha 

Source: Authors’ own analysis based on secondary data.

The coefficient is positive for one variable, i.e., the intensity of groundwater demand for irrigation with a very high level of significance, which again is along the expected lines. Under normal circumstances, in a situation like in Maharashtra, with hard rock aquifers in most parts, increasing demand for irrigation water (with a greater proportion of the geographical area under cultivation, more aridity, and intensive cropping) would ideally lead to faster depletion of the aquifers, with increased dependence on tanker water supply for drinking water supply needs. Conversely, increasing the provision of surface water for meeting irrigation needs will have positive effects, as shown by the model. First, it reduces the groundwater demand for irrigation. Second, it improves groundwater recharge through irrigation return flows. The regression equation suggests that with every 100 mm increase in irrigation demand, the dependence on tanker water supply will increase by 0.68 tankers per 1,000 people. This is a significant effect. Going by the value of the beta coefficient corresponding to that variable (i.e., irrigation water demand), areas that cultivate sugarcane which is a highly water-intensive crop (in terms of the evapotranspiration demand), can pose significant sustainability threats to drinking water wells. Similarly, with every one cubic metre increase in storage space in the aquifer per m2 of the geographical area, the dependence on tanker water supply is likely to reduce by nearly three tankers per 1,000 people.

Factors determining the success of drinking water wells

The easiest way to understand the conditions under which groundwater-based schemes succeed is to understand the conditions that reduce the dependence on tanker water supply during the lean season. In this regard, the analyses presented in the earlier sections suggest the following. In districts where the aquifer has enough storage space before the onset of monsoon (indicated by the multiple of pre-monsoon depth to the water table and specific yield), and if the demand for water for irrigation is low (by virtue of a low proportion of the area under cultivation and high rainfall and low aridity), then groundwater-based schemes will be successful.

Here again, we need to reckon with the fact that the groundwater recharge potential of a district can decline considerably, and the pressure on groundwater to meet irrigation water demand can also go up significantly when the rainfall is low in a particular year. The pattern of occurrence of the rainfall also will have some effect. The same amount of rainfall distributed over a larger number of rainy days will produce a greater quantum of recharge than what would be produced if the same rainfall occurs on fewer rainy days.

Multi-variate analysis also showed that the dependence on tanker water supply is likely to be very less in districts that get considerable surface irrigation.

Overall, the favourable conditions for groundwater-based drinking water supply schemes in rural Maharashtra to be sustainable, are sufficient storage space in the aquifer before the onset of monsoon, due to the high depth to the water table and good specific yield of the aquifer; large extent of surface irrigation; very low irrigation water demand by virtue of the climatic conditions and arable land access; and favourable geomorphology and rainfall that increase the utilizable recharge rates. The districts which score lowest on each of the four key attributes, sufficient recharge rate per unit area; adequate aquifer storage space, limited irrigation demand; and increasing the extent of surface irrigation are given in Table 3. Table 3 shows that Jalgaon has the highest irrigation demand. Similarly, Sangli has the lowest recharge rate. Chandrapur has the lowest aquifer storage space.

There are very few areas in Maharashtra where favourable conditions exist for groundwater-based schemes to perform well. They include the high rainfall tracts of Konkan division in three districts viz., Ratnagiri, Raigad, and Sindhudurg having laterite formations and alluvial deposits; and certain pockets in Amravati division, along the Tapi river valley extending from Dhule to Akola to Amravati to Jalgaon and Buldhana, which receive considerable recharge from the river itself. In Western Maharashtra, though the rainfall is excessively high, the recharge potential is very poor due to the steep terrain and hard rock terrain which result in poor infiltration of the rainwater. Further, due to the steep groundwater flow gradient, the recharge during monsoon does not remain in the aquifer due to heavy groundwater discharge into streams that drain the region.

Nearly 90% of the rural water supply schemes in Maharashtra are based on groundwater. The modelling study carried out to understand the critical factors influencing the performance of rural water supply schemes with district level data shows that there is a high likelihood of good scheme performance under the following conditions: (1) the effective utilizable recharge potential of the aquifer is good; (2) there is sufficient storage space in the aquifer; (3) the extent of coverage of surface irrigation schemes is considerable; (4) the irrigation water demand is low and the overall dependence on groundwater for water supply is less. Further consideration of the condition vis-à-vis the corresponding attributes at the district level shows that there are very few areas in Maharashtra in terms of geographical extent where the condition is favourable for the provision of water supply through wells. Yet, historically most of the rural water supply schemes were planned around groundwater resources.

Comparing the situation in Maharashtra with that of India, it is apparent that there are large regions where the situation is unfavourable for managing the required quantity of water for rural water supply throughout the year, due to unfavourable conditions vis-à-vis groundwater availability and demand. Around two-thirds of the country's geographical area is underlain by hard rock aquifers from basalt to crystalline formations. Most of these regions also experience excessive demand for water for irrigation due to the presence of a large amounts of arable land, low to medium rainfall, and high aridity. Groundwater-based rural water supply schemes are increasingly becoming unsustainable in these regions (Kumar et al., 2021) with either permanent failures, or seasonal drying up, or reduction in the yield of wells. Future investments for rural water supply in these regions will have to be based on more dependable surface water sources.

Probably, one of the most important findings of the study is the positive externality that surface irrigation can induce in drinking water wells in rural areas. Given this scenario, under ‘Jal Jeevan Mission’ the state of Maharashtra should develop a blueprint for drinking water security to gradually shift from ground water-based schemes to reservoir-based schemes with the provision of multiple sources of water and conjunctive use of water during the extended lean periods.

The variables identified as determinants of the success of groundwater-based rural water supply schemes through our analysis can contribute to the selection of broad water supply options and identifying the nature of the interventions required for improving the source sustainability. The specific engineering interventions for dealing with groundwater scarcity in summer months that involve dedicated storage and the use of rain water and runoff during the monsoon can also be explored by undertaking detailed investigations of the geology, hydrogeology, and catchment hydrology of the specific localities. This will also contribute to identifying different options for rural water supply scheme selection.

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

The authors declare there is no conflict.

1

For hilly areas with very high rainfall such as Ratnagiri, Raigarh, and Sindhudurg, the value for utilizable recharge fraction considered is 0.20. For hilly and undulating areas (Chandrapur, Pune, Ahmednagar, etc.), the value considered is 0.50. For remaining areas, which are part of the Deccan plateau, the fraction considered is 0.80. A value of 0.20 means that only 20% of the recharge available during the monsoon will be utilizable.

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