sustainability

Article Groundwater Level Dynamics in Bengaluru City,

M. Sekhar 1, Sat Kumar Tomer 2 ID , S. Thiyaku 2, P. Giriraj 1, Sanjeeva Murthy 1 and Vishal K. Mehta 3,* 1 Department of Civil Engineering and Indo-French Cell for Water Sciences, Indian Institute of Science, 560012, India; [email protected] (M.S.); [email protected] (P.G.); [email protected] (S.M.) 2 Aapah Innovations, Gachibowli 500032, Hyderabad, Telangana 50003, India; [email protected] (S.K.T.); [email protected] (S.T.) 3 Stockholm Environment Institute, 400 F St, Davis, CA 95616, USA * Correspondence: [email protected]; Tel.: +1-530-753-30353

Received: 17 November 2017; Accepted: 19 December 2017; Published: 22 December 2017

Abstract: Groundwater accounts for half of Indian urban water use. However, little is known about its sustainability, because of inadequate monitoring and evaluation. We deployed a dense monitoring network in 154 locations in Bengaluru, India between 2015 and 2017. Groundwater levels collected at these locations were analyzed to understand the behavior of the city’s groundwater system. At a local scale, groundwater behavior is non-classical, with valleys showing deeper groundwater than ridge-tops. We hypothesize that this is due to relatively less pumping compared to artificial recharge from leaking pipes and wastewater in the higher, city core areas, than in the rapidly growing, lower peripheral areas, where the converse is true. In the drought year of 2016, groundwater depletion was estimated at 27 mm, or 19 Mm3 over the study area. The data show that rainfall has the potential to replenish the aquifer. High rainfall during August–September 2017 led to a mean recharge of 67 mm, or 47 Mm3 for the study area. A rainfall recharge factor of 13.5% was estimated from the data for 2016. Sustainable groundwater management in Bengaluru must account for substantial spatial socio-hydrological heterogeneity. Continuous monitoring at high spatial density will be needed to inform evidence-based policy.

Keywords: groundwater dynamics; Bengaluru; monitoring; groundwater balance; India

1. Introduction Groundwater is an important, decentralized source of drinking water for millions of people in India. It meets nearly 85% of rural domestic water needs and 50% of urban water needs [1]. Dependence on groundwater for water-supply in urban towns and cities is increasing due to accelerated growth, increasing per capita water use and poor reliability of imported surface water from distant sources [2,3]. In a review of urban recharge, Lerner [4] describes how urbanization affects the groundwater cycle by way of changes to both the total water budget, and to pathways recharge. The total imported water supply of many temperate European cities is comparable to the annual rainfall endowment, while, in many dense cities in arid areas, water supply imports are greater than annual rainfall. Leakage through water supply pipes then becomes a major source of recharge. In cities with inadequate sewerage systems, leakage through septic tanks and soakpits, and untreated wastewater released into drainage channels, are also major components of recharge quality. While these anthropogenic flows increase groundwater recharge, natural recharge rainfall can decrease in dense built-up areas of towns and cities, where a significant proportion of land surface is altered through soil compaction, roofing and paving, all of which reduce normal infiltration. The net effect on the groundwater budget also

Sustainability 2018, 10, 26; doi:10.3390/su10010026 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 26 2 of 22 depends on the level of pumping from the aquifer. Spatio-temporal variation in the anthropogenic components and spatial variation hydrogeological properties such as specific yield and transmissivity add to the complexity of urban groundwater systems. Thus, reflecting the diversity of development pathways, geographies and geohydrology, urbanization induced changes to the groundwater system may include sharp changes of groundwater levels [5–7]; reduced well yields and deterioration in quality of groundwater [5,8–10]; poor quality base flow [1]; and migration of polluted urban groundwater into surrounding areas [11,12]. In rapidly growing Indian cities, both imported surface water and groundwater pumping have been increasing apace [13]. At the same time, sewer line connections, and wastewater treatment in Indian cities are severely lacking, with the Government of India acknowledging that some 70% of wastewater is estimated to be released without treatment into drainage features [14]. Increased recharge from anthropogenic sources (leaking water supply and wastewater systems), and increased pumping from the aquifer are likely to be occurring simultaneously. One of the goals of groundwater management plans in such circumstances is to safeguard groundwater levels in urban aquifers by influencing the magnitude and use of groundwater pumping. This requires a scientific understanding of urban groundwater systems based on a comprehensive database of groundwater levels, availability and use at appropriate spatial scale. Depending upon the groundwater monitoring network, spatial variations of recharge and of hydrogeological properties such as specific yield can also be estimated (e.g., [15]). Such monitoring is essential to help inform hydrogeological models that could be used for support towards sustainable urban groundwater management. However, the majority of studies utilizing simple to complex groundwater models have been in large agricultural catchments (e.g., [4,16]). Investigations of urban groundwater systems, especially in developing countries, are limited [10,17–21]. In India, only two comprehensive urban groundwater studies have combined monitoring with models. Sekhar and Kumar [22] monitored 472 wells to study and model the groundwater dynamics of a central, 27 km2 portion of Bengaluru, to assess the possible groundwater impacts related to the underground section of the city’s metro rail system. A second study comprehensively evaluated groundwater for the small city of Mulbagal, 95 km from Bengaluru [21]. Groundwater levels in 272 wells covering 50 km2 were monitored for three years. A distributed groundwater model was built based on this dataset, and applied to investigate climate and demographic scenarios for the city. These investigations highlight the fact that models can be built only if there is an adequate spatial representation of the groundwater monitoring network. In India, the nationwide long-term groundwater level monitoring by the Central Groundwater Monitoring Board (CGWB) is on average at a density of one station for approximately every 100 km2. This density is very coarse if one wants to model urban systems, which usually cover areas of a few tens to few hundreds of square kilometers. Heterogeneous characteristics (e.g., hydraulic conductivity, transmissivity, and specific yield) of the aquifer in hard rock areas that underlie much of peninsular India add another good reason for a dense network of wells for small scale urban systems. In this paper, we focus on understanding the groundwater patterns and dynamics in Bengaluru (earlier known as Bangalore), India. Bengaluru is one of the fastest growing cities in Asia, where population has grown from 180,366 in 1891 [23] to 8.5 million in 2011 [24]. The area of city limits, as by the municipal boundaries, has also grown substantially—ten times from a mere 75 km2 in 1901 [25]. Water supply from the utility, Bengaluru Water Supply and Sewerage Board (BWSSB) has grown, but not managed to keep up with the rapid growth in demand. The inability of utility water supply growth to keep up with the city’s growth is well documented (e.g., [24,26]). As a result, similar to all Indian cities, groundwater is heavily used to make up the deficit. Although based on a sparse monitoring network, the CGWB has estimated that groundwater is more than 100% developed in Bengaluru, which means that abstraction (pumping) is more than recharge to the aquifer [25]. In this paper, we report on the establishment of an innovative, dense monitoring network across the city, with the objectives of: (i) collecting a time series of groundwater table depths and using the data to study the spatial patterns in groundwater levels; (ii) estimating net groundwater storage changes Sustainability 2018, 10, 26 3 of 22

Sustainabilityand using2018 the, 10 ,data 26 to study the spatial patterns in groundwater levels; (ii) estimating3 ofnet 22 groundwater storage changes during drought year 2016 and during extreme rainfall periods; and (iii) estimating the rainfall recharge factor net groundwater flux. Our intention in this paper is to during drought year 2016 and during extreme rainfall periods; and (iii) estimating the rainfall recharge describe the patterns observed and to estimate the net groundwater storage change and aggregate factor net groundwater flux. Our intention in this paper is to describe the patterns observed and to flows, based on the water table method. In the Conclusions Section, we describe a more elaborate estimate the net groundwater storage change and aggregate flows, based on the water table method. modeling framework that will be used in a forthcoming paper to estimate separate components of In the Conclusions Section, we describe a more elaborate modeling framework that will be used in a the groundwater budget. forthcoming paper to estimate separate components of the groundwater budget.

2.2. MaterialsMaterials andand MethodsMethods

2.1.2.1. StudyStudy AreaArea BengaluruBengaluru citycity isis situatedsituated withinwithin 1212°◦45′0N–N–1313°10◦10′N0N and and 77° 7725◦25′E–0E–7777°45◦′E,45 0andE, and covers covers an area an area of 720 of 720km2 km (Figure2 (Figure 1). The1). Thecity citycore coreis at is900 at to 900 930 to m 930 amsl m amsl(above (above mean meansea level), sea level),on a divide on a dividewith a roughly with a roughlyNorth–South North–South axis, with axis, the withArkavathi the Arkavathi River drainage River drainageWestward Westward to the River to theCauvery, River Cauvery,and the South and thePennar South drainage Pennar to drainage the East to (Figure the East 2) (Figure[24]. The2)[ climate24]. The is semiarid climate iswith semiarid normal with rainfall normal of 820 rainfall mm, ofSeptember 820 mm, Septemberbeing the peak being rainfall the peak month. rainfall Close month. to 60% Close of the to 60%rainfall of theon rainfallaverage on occurs average during occurs the duringsouthwest the monsoon southwest from monsoon June to from September June to [27]. September The retreating, [27]. The northeast retreating, monsoon northeast also brings monsoon rain alsofrom brings October rain to from December. October The to December. dry period The extends dry period from extendsJanuary fromto May, January although to May, convectional although convectionalthunderstorms thunderstorms occur from March occur fromthrough March May. through Typically, May. January Typically, and January February and receive February almost receive no almostrain. no rain.

FigureFigure 1.1. Spatial distribution (ward-wise)(ward-wise) ofof thethe populationpopulation inin BengaluruBengaluru city.city. TheThe streams,streams, 5 5 km km ×× 5 5km km grid grid and and Telemetric Telemetric Rainfall Rainfall Gauge Gauge (TRG) (TRG) of ofKSNDMC KSNDMC are are also also shown. shown. Numbers Numbers represent represent the thegrid- grid-IDs.IDs. Sustainability 2018, 10, 26 4 of 22 Sustainability 2018, 10, 26 4 of 22

Figure 2. Digital Elevation Model (DEM) of Bengaluru city. Numbers represent the grid IDs. Figure 2. Digital Elevation Model (DEM) of Bengaluru city. Numbers represent the grid IDs. Except for a few central areas in the old city core, the population growth between 2001 and Except2011 has for been a few positive central with areas some inouter the areas old cityreaching core, a thegrowth population of more than growth 300% between [26]. The 2001water and 2011 hasneeds been of positivethe city withare mainly some outermet areasby surfac reachinge water a growthsupply offrom more Cauv thanery300% River, [26 and]. The from water needsgroundwater. of the city are Surface mainly water met byimported surface from water the supply Cauvery from River, Cauvery currently River, at close and fromto 1400 groundwater. million Surfaceliters water per imported day (MLD), from is theapproximately Cauvery River, 75% currentlyof the average at close rainfall to 1400 over million the city, liters signaling per day (MLD),the is approximatelysubstantial anthropogenic 75% of the average influence rainfall on the urban over thewater city, budget signaling [26]. Non-re the substantialvenue water, anthropogenic made up largely of leakage losses, was almost 50% in 2011 [24]. Although surface water supply has increased influence on the urban water budget [26]. Non-revenue water, made up largely of leakage losses, over time, it has been unable to catch up with the rapid growth and expansion of the city [26]. As a was almostresult, 50%groundwater in 2011 provides [24]. Although a large proportion surface water of the supply current haswater increased consumption, over and time, is likely it has to been unablecontinue to catch toup do withso in theseveral rapid wards growth of the and municipality, expansion which of the is city called [26 ].called As aBruhath result, Bengaluru groundwater providesMahanagara a large proportionPalike (BBMP) of the (see current Figure water 3). Th consumption,ere is no effective and isregulation likely to continueof the use to of do so in severalgroundwater wards of in thethese municipality, wards for domestic, which iscommercia called calledl, industrial Bruhath or government Bengaluru agency Mahanagara purposes. Palike (BBMP)Free (see and Figure unrestricted3). There use isis nomade effective of this regulation resource by of households, the use of groundwaterbusinesses, institutions in these and wards for domestic,government commercial, agencies, industrialincluding the or governmentBengaluru Water agency Supply purposes. and Sewerage Free and board unrestricted (BWSSB). use is madeConsequently, of this resource there by households,is practically businesses,no reliable institutionsdata on the andrate governmentand distribution agencies, of groundwater including the Bengaluruwithdrawals. Water Supply Monitoring and Sewerageof the aquifers board from (BWSSB). which groundwater Consequently, is withdrawn there is practically is conducted no reliable by the Department of Mines and Geology (DMG) and the Central Ground Water Board (CGWB). Both data on the rate and distribution of groundwater withdrawals. Monitoring of the aquifers from which these organizations conduct regional exploration and investigation programs. Although many wells groundwaterare used isfor withdrawn pumping groundwater is conducted in bythe thecity, Department the density of of the Mines existing and monitoring Geology (DMG)networks and is the Centralvery Ground low [27]. Water Furthermore, Board (CGWB). the temporal Both freque thesency organizations of monitoring conduct by CGWB regional is once exploration every few and investigationmonths. programs.Hence, even Althoughthough this many dataset wells might are reveal used forsome pumping larger, regional groundwater scale groundwater in the city, the densitybehavior of the existingover many monitoring years, it is networksnot useful isfor very assessing low [ 27the]. state Furthermore, of the urban the groundwater temporal frequency system; of monitoring by CGWB is once every few months. Hence, even though this dataset might reveal some larger, regional scale groundwater behavior over many years, it is not useful for assessing the state of the urban groundwater system; its resilience to important stressors such as anthropogenic forcings and drought conditions; and its response to extreme rain events. Sustainability 2018, 10, 26 5 of 22

Sustainabilityits resilience2018 to, 10 important, 26 stressors such as anthropogenic forcings and drought conditions; and5 of 22its response to extreme rain events.

(a) Percentage of people using groundwater

(b) Water pipeline network

Figure 3. Percentage of people using groundwater in 2011 (a); and water pipeline network in Figure 3. Percentage of people using groundwater in 2011 (a); and water pipeline network in Bengaluru Bengaluru from 2015 (b). from 2015 (b). Sustainability 2018, 10, 26 6 of 22

2.2. Groundwater Monitoring 2.2. Groundwater Monitoring The first step was to create a comprehensive database for the city. Groundwater table mapping was Thecarried first out step between was to createDecember a comprehensive 2015 and Se databaseptember for2017. the Groundwater city. Groundwater levels table (GWL) mapping were wasmeasured carried using out a between monitoring December network 2015 of 158 and wells September in an area 2017. of 700 Groundwater km2. Out of levelsthese 158 (GWL) wells, were 154 2 wellsmeasured had an using unbroken a monitoring record through network the of 158study wells peri inod anand area were of selected 700 km .for Out the of analysis these 158 reported wells, 154here. wells The hadmonitoring an unbroken network record was through established the study usin periodg a novel and approach were selected in which for the the analysis existing reported wells (predominantlyhere. The monitoring municipal network bore was wells) established were used using as piezometers a novel approach where in groundwater which the existing levels wellswere (predominantlymeasured. To achieve municipal a uniform bore wells)spatial weredistribution, used as the piezometers study area where was divided groundwater into grids levels of 5 were km ×measured. 5 km, resulting To achieve in 28 agrids uniform for the spatial city. distribution,Monitoring wells the study should area be wasfree dividedfrom pumping into grids to be of 5used km as× 5piezometers. km, resulting Those in 28municipal grids for wells the city.that were Monitoring not in use wells were should carefully be free selected from pumpingin each grid. to beIn eachused grid, as piezometers. 5–6 monitoring Those wells municipal were established. wells that Groundwater were not in use levels were were carefully measured selected each inmonth each usinggrid. Ina manual each grid, water 5–6 level monitoring sensor wells(Heron were Instruments established. Inc.). Groundwater Figure 4 shows levels the were location measured of the each 154 groundwatermonth using amonitoring manual water loca leveltions, sensor along (Heron with land-cover. Instruments The Inc.). inne Figurer part4 showsof the thecity location has relatively of the 154higher groundwater built-up area. monitoring Several locations,secondary along datasets, with as land-cover.sembled at The the innergrid scale, part of are the presented city has relatively in Table 1.higher built-up area. Several secondary datasets, assembled at the grid scale, are presented in Table1. Maps werewere interpolatedinterpolated from from the the depth depth to GWL to GWL measurements measurements using ordinaryusing ordinary kriging. kriging. Kriging Kriginghas been has found been to performfound to better perform than better inverse than distance inverse weighting distance [ 28weighting,29], and has[28,29], been and used has in otherbeen usedstudies in inother hard-rock studies aquifers in hard-rock in southern aquifers India in southern [15,21,22 ,India30]. Kriging [15,21,22,30]. was performed Kriging was using performed the geoR usinglibrary the of RgeoR(https://cran.r-project.org/package=geoR library of R (https://cran.r-project.org/package=ge). VariogramsoR). for eachVariograms month werefor each fitted month using werenumerical fitted optimization.using numerical Exponential optimization. variograms Exponentia werel foundvariograms to fit thewere data found well, to and fit usedthe data to create well, andinterpolated used to create maps interpolated of depth to groundwater maps of depth (DGW) to groundwater at a spatial (DGW) grid of 100at a m.spatial grid of 100 m.

Figure 4. SpatialSpatial distribution of the land cover and monitoring network. Numbers represent the 5 km grid IDs. Landcover data are from 2011 [[31].31].

Sustainability 2018, 10, 26 7 of 22

Table 1. Different variables for each 5 km grid (Relative density of water supply pipeline is computed. using heatmap plugin of QGIS; 1 means highest density and 0 means lowest density).

Percentage Percentage of Relative Density Elevation Grid ID Grid Name Population of Built-Up People Using of Water Supply (m) Area Groundwater Pipeline 1 Ganganahalli 76,442 15 901 15 0.000 2 143,937 28 907 28 0.063 3 New Town 222,281 35 905 35 0.088 4 Agrahara 117,935 28 919 28 0.016 5 382,500 53 875 53 0.130 6 363,359 67 914 67 0.892 7 Hebbal 506,535 71 892 71 0.565 8 Hennur 406,705 52 884 52 0.323 9 131,351 26 877 26 0.015 10 553,028 63 900 63 0.065 11 984,649 94 934 94 1.000 12 Bangalore 525,245 89 937 89 0.643 13 572,268 81 920 81 0.318 14 377,085 70 902 70 0.087 15 192,238 43 875 43 0.043 16 332,918 38 851 38 0.008 17 Chamarajpet 1,048,839 92 849 92 0.608 18 Shantinagar 529,064 92 895 92 0.675 19 395,813 69 905 69 0.488 20 HAL Airport 284,738 50 893 50 0.178 21 Whitefield 158,578 35 879 35 0.075 22 152,050 28 824 28 0.033 23 Chikkasandra 514,428 66 864 66 0.205 24 JP Nagar 691,729 87 906 87 0.146 25 HSR Layout 579,786 74 887 74 0.126 26 Doddakannelli 161,190 40 886 40 0.002 27 301,144 35 912 35 0.047 28 Begur 229,851 38 911 38 0.003

2.3. Rainfall Data Annual rainfall for more than 100 years (1901–2012) is shown in Figure5. During this period, the maximum, minimum and average annual rainfall were 1527, 393 and 880 mm, respectively. Daily rainfall data covering the study period were collected from about 150 stations maintained by the State Natural Disaster Monitoring Cell (KSNDMC) for the period January 2015–October 2017. The spatial distribution of these KSNDMC rainfall gauges is shown in Figure1. A plot of monthly rainfall averaged over these stations is shown in Figure6. Total rainfall (January–December) for the years 2015, 2016 and 2017 (up to October 2017) was 1153, 716 and 1435 mm. The intra-year rainfall distribution during these three years has strong variability, and each year was quite different. In all three years, the southwest monsoon rainfall was much lower than normal. However, during 2015 and 2017, there were extreme rainfall events, while 2016 was a drought year. In 2015, the months of September–November produced about 50% of the annual rainfall. In 2016, the months of May–July produced about 70% of the annual rainfall. In 2017, the months of August–October produced about 75% of the annual rainfall. The 2017 rainfall was close to the highest received rainfall in more than 100 years. These patterns of drought conditions and extreme rain events over the study period provided a natural opportunity to study the response and resilience of Bengaluru’s groundwater system. Sustainability 2018, 10, 26 8 of 22 Sustainability 2018, 10, 26 8 of 22 Sustainability 2018, 10, 26 8 of 22

Figure 5. Annual rainfall in Bengaluru during 1901–2012. FigureFigure 5. 5. AnnualAnnual rainfall rainfall in in Bengaluru Bengaluru during during 1901–2012. 1901–2012.

Figure 6. Monthly rainfall patterns in Bengaluru city during January 2015–October 2017. FigureFigure 6. 6. MonthlyMonthly rainfall rainfall patterns patterns in in Bengaluru Bengaluru city city during during January January 2015–October 2015–October 2017. 2017. 2.4. Cluster Analysis 2.4.2.4. Cluster Cluster Analysis Analysis In order to aid the interpretation of observed groundwater patterns, a clustering analysis was In order to aid the interpretation of observed groundwater patterns, a clustering analysis was performedIn order using to aid the the secondary interpretation data in of Table observed 1. Hierarchical groundwater cluster patterns, analysis a clusteringwas applied analysis to explore was performed using the secondary data in Table 1. Hierarchical cluster analysis was applied to explore performedthe number using of distinct the secondary clusters emerging data in Table from1. th Hierarchicale secondary cluster data. Ward’s analysis linkage was applied algorithm to explore is used the number of distinct clusters emerging from the secondary data. Ward’s linkage algorithm is used thefor numberhierarchical of distinct clustering clusters by minimizing emerging from the the increase secondary in total data. within-cluster Ward’s linkage sum algorithm of squared is usederror for hierarchical clustering by minimizing the increase in total within-cluster sum of squared error for[32]. hierarchical The optimal clustering number by minimizingof clusters thewere increase identified in total by within-cluster visual inspection sum of squaredof the errorresulting [32]. [32]. The optimal number of clusters were identified by visual inspection of the resulting Thedendr optimalogram. number of clusters were identified by visual inspection of the resulting dendrogram. dendrogram.

Sustainability 2018, 10, 26 9 of 22

2.5. Groundwater Analysis The Water Table Fluctuation (WTF) method was used to estimate the storage change, natural recharge and net outflow for distinct periods of observation. The advantage of the WTF method, described comprehensively in Healy and Cook [33], is that it is a simple method that relies on dense groundwater level measurements, has only one parameter (the specific yield), and no assumptions are made on the mechanisms by which water recharges the aquifer. The WTF method is used in India for operational annual groundwater recharge assessment by government agencies [34]. Nationally, it is performed by delineating the land area into watersheds or catchments with an area of about 600–1000 km2. This scale is tied to the density of long term observation network which is approximately one observation well per 100 km2. Recharge assessment for a year is performed for each of these catchments, treating them as one unit or cell. Several researchers have used variations of the WTF method in southern Indian hard-rock aquifers [15,21,22,35]. We use WTF in two ways in this paper, described below.

2.5.1. Natural Recharge and Net Outflow First, we use the “double water table fluctuation method” [15], which involves splitting the groundwater budget into two distinct periods—dry and wet. These authors used the double WTF method in combination with water balance models and detailed information on several water balance components in a 50 km2, unconfined hard rock aquifer research site in southern India, to estimate both recharge and the effective specific yield [15]. In this paper, we apply the double WTF method to estimate rainfall recharge and total net outflow in the same manner as Sekhar et al. [21] did for the town of Mulbagal (mentioned in the Introduction). Because many components of the budget are not known quantitatively for Bengaluru (pumping, and water and wastewater leakage), we use the double WTF to estimate only the rainfall recharge factor (from the wet period), and the net outflow (from the dry period). For the same reason, we cannot independently estimate specific yield as Maréchal et al. [15] did. Instead, we specify specific yield based on the literature and recent experience, and also show the sensitivity of some of our results to this parameter. The double WTF method is expressed as follows: ∆h Sy = R − Qnet (1) ∆t where Sy is the specific yield (-), h is the groundwater level (L), t is time (T), R is recharge due to rainfall −1 (LT ), and Qnet is the net outflow, Qnet = Qout − Qin (2) where Qout is the total outflow including baseflow, lateral flow and draft. Qin is the recharge to groundwater from anthropogenic flows, i.e., leaking water suppy and wastewater systems (LT−1). Given the value of Sy and measurements of ∆h, Equation (1) has two unknowns. The equation was written for dry (no or insignificant rainfall) and wet (significant rainfall) periods separately, with the assumption that there was no rainfall recharge during the dry period. After solving both equations, net outflow and rainfall recharge can be computed as,

−hy∆hdry Qnet = (3) ∆tdry and S ∆h + Q ∆t R = v wet net wet (4) ∆twet

Equation (3) is solved first to estimate the Qnet by applying it over the dry period where ∆tdry is the length of dry period (T). Then, Equation (4) is solved over the wet period where ∆twet is the length of wet period, to estimate R. Sustainability 2018, 10, 26 10 of 22

Sustainability 2018, 10, 26 10 of 22 2.5.2. Changes in Groundwater Storage For the net change in groundwater storage in 2016, and from the extreme rainfall months in 2.5.2. Changes in Groundwater Storage 2017, the simple form of the WTF is used. For the net change in groundwater storage in 2016, and from the extreme rainfall months in 2017, ∆ = ∆ℎ (5) the simple form of the WTF is used. where ∆V is the net storage change (L) and∆V ∆=h Sisy∆ theh change in groundwater level (L) over(5) a wherespecified∆V periodis the net (∆t storage) (T). change (L) and ∆h is the change in groundwater level (L) over a specified period (∆t)(T). 3. Results and Discussion 3. Results and Discussion 3.1. Clustering Results 3.1. Clustering Results Five variables, as presented in Table 1, were used for the cluster analysis. To account for the location,Five the variables, distance as of presented each grid in from Table the1, werecentra usedl grid for (#12) the was cluster also analysis. used in the To accountcluster analysis. for the location,Details of the the distance clusters of eachformed grid are from presented the central in gridFigure (#12) 7 wasand alsoTable used 2. inFigure the cluster 7a shows analysis. the Detailsdendrogram of the clustersobtained formed from the are six presented variables, in Figurewhich7 could and Table organize2. Figure the 7citya shows into four the dendrogram clusters quite obtainedsensibly. fromThe thespatial six variables,distribution which of these could four organize clusters the is city shown into fourin Figure clusters 7b. quite Cluster sensibly. 1 is Thecharacterized spatial distribution by the outer of these periphery, four clusters which is shownhas significant in Figure open7b. Cluster space, 1less is characterized water supply by from the outerBWSSB periphery, and significant which hasgroundwater significant usage. open Detailed space, less water water supply supply analysis from BWSSBin [26,36] and illustrate significant that groundwaterthe fast-growing usage. outer Detailed areas waterreceive supply much analysisless water in [supply26,36] illustratethan the thatcore theareas. fast-growing Cluster 2 outeris the areasinner receive periphery much characterized less water supply by moderate than the coregrou areas.ndwater Cluster usage, 2 is themoderate inner periphery supply of characterized water from byBWSSB moderate and groundwatermoderate open usage, space. moderate Cluster supply3 contains of water grids from lying BWSSB in the andcentral, moderate older openparts space.of the Clustercity, which 3 contains has better grids water lying supply in the central, from the older BWSSB parts an ofd the is city,densely which built-up. has better Cluster water 4 supplyis similar from to theCluster BWSSB 3, except and is denselythat it has built-up. high population Cluster 4 is de similarnsity and to Cluster hence 3,water except demand that it hasis high high and population not met densityby the water and hence supply water from demand BWSSB. is high and not met by the water supply from BWSSB.

(a) Dendrogram

Figure 7. Cont. Sustainability 2018, 10, 26 11 of 22 Sustainability 2018, 10, 26 11 of 22

(b) Spatial distribution of clusters

Figure 7. Dendrogram and spatial distribution of the clusters obtained from Ward’s Linkage Figure 7. Dendrogram and spatial distribution of the clusters obtained from Ward’s Linkage algorithm. algorithm. Table 2. Details of the clusters formed. Table 2. Details of the clusters formed.

ClusterCluster ID ID Grids Description Description OuterOuter periphery, periphery, lower lower populati populationon density, density, significant significant open 11 1, 1, 2,2, 3,3, 4,4, 9, 15, 16, 21, 21, 22, 22, 26, 26, 27 27 and and 28 28 space,open less space, water less supply water from supply BWSSB from BWSSB Inner periphery, moderate population density, moderate 2 5, 8, 10, 14, 20, 23, 24 and 25 Inner periphery, moderate population density, moderate 2 5, 8, 10, 14, 20, 23, 24 and 25 openopen space, space, moderate moderate water water supply supply from from BWSSB BWSSB Older city core, moderate population density, lower open 3 6, 7, 12, 13, 18 and 19 Older city core, moderate population density, lower 3 6, 7, 12, 13, 18 and 19 space, better water supply from BWSSB Highestopen space, population better density, water supplylower open from space, BWSSB better water 4 11 and 17 supplyHighest from population BWSSB density, lower open space, better 4 11 and 17 water supply from BWSSB 3.2. Groundwater Levels and Dynamics 3.2. Groundwater Levels and Dynamics All groundwater level data collected in various monitoring wells during the period of the studyAll are groundwater publicly availablelevel data (see collected Supplementary in various monitoringMaterials). wellsFor the during 154 thewells period over of 22 the months study are(December publicly available2015–September (see Supplementary 2017), DGW Materials).varied from For 1 theto 15472 m, wells with over a 22mean months of 25.74 (December m and 2015–Septemberstandard deviation 2017), of DGW13.98 variedm. Figure from 8 1shows to 72 m,the with box aand mean whisker of 25.74 plot m andof all standard 154 wells deviation over time. of 13.98The start m. Figure in the8 showsrise in the groundwater box and whisker level plotmatc ofhes all with 154 wellsthe onset over time.of monsoon The start season in the and rise inthe groundwatershallowest groundwater level matches levels with thewere onset observed of monsoon in the season peak and rainfall the shallowest month. In groundwater 2016, shallowest levels weregroundwater observed is in observed the peak in rainfall August, month. while, In in 2016, 2017, shallowest it is observed groundwater in September. is observed The groundwater in August, while,table is in deepest 2017, it isinobserved peak summer, in September. in the month The groundwater of April 2016. table It is is also deepest deep in in peak April summer, 2017, but in thethe monthlateness of of April the 2016.2017 Itmonsoon is also deep extended in April the 2017, dry butperi theod, latenessresulting of in the the 2017 groundwater monsoon extended table in July the dry2017 period, being resulting slightly indeeper the groundwater than that in table April in July2017. 2017 Extremely being slightly high rainfall deeper thanoccurred that inimmediately April 2017. Extremelyafter the dry high spell, rainfall in occurredAugust (221 immediately mm) and after September the dry spell,(520 inmm) August 2017. (221 The mm) boxplot and Septembershows the groundwater table recovering quite dramatically in response to this extreme rainfall. This extreme rainfall response is investigated further in Section 3.4. Sustainability 2018, 10, 26 12 of 22

(520 mm) 2017. The boxplot shows the groundwater table recovering quite dramatically in response to this extreme rainfall. This extreme rainfall response is investigated further in Section 3.4. Sustainability 2018, 10, 26 12 of 22 Sustainability 2018, 10, 26 12 of 22

Figure 8. Box and whisker plot of the DGW of all 154 wells during December 2015–September 2017. Figure 8. Box and whisker plot of the DGW of all 154 wells during December 2015–September 2017. The horizontal line within the box indicates the median and boundaries of the box indicate the 25th The horizontaland 75th percentile. line within The the upper box whisker indicates extends the medianfrom the andhinge boundaries to the largestof value the and box the indicate lower the 25th and Figure 8. Box and whisker plot of the DGW of all 154 wells during December 2015–September 2017. 75th percentile.whisker extends The upperfrom the whisker hinge to the extends smallest from value. the hinge to the largest value and the lower whisker The horizontal line within the box indicates the median and boundaries of the box indicate the 25th extends from the hinge to the smallest value. Figureand 75th9 shows percentile. the box The andupper whisker whisker plot extends for from154 wellsthe hinge grouped to the intolargest the value four and clusters. the lower Groundwaterwhisker levels extends showed from thesubstantial hinge to spatialthe smallest and temporalvalue. variability in all clusters. A relatively Figurelower9 variabilityshows the was box observed and whisker in Cluster plot 4 fordue 154 to less wells number grouped of grid into and the data four points clusters. in this Groundwater Figure 9 shows the box and whisker plot for 154 wells grouped into the four clusters. levels showedcluster. Mean substantial depths to spatial groundwater and temporaltable of 29.27 variability m, 26.22 m, in 17.30 all clusters.m and 23.14 A m relatively were observed lower variability in GroundwaterClusters 1–4, respectively. levels showed All substantialclusters showed spatial similar and temporaltemporal variabilityvariation, as in describedall clusters. above. A relatively was observed in Cluster 4 due to less number of grid and data points in this cluster. Mean depths to Clusterlower 3 showsvariability the shallowest was observed groundwater in Cluster level, 4 whic dueh tolies less in the number inner partof gridof the and city. data This pointscould in this groundwaterbe cluster.due tableto relativelyMean of 29.27depths higher m, to 26.22legroundwaterakage m, from 17.30 thetable msewerage andof 29.27 23.14 and m, m26.22wate werer m,supply observed17.30 lines. m and Cluster in 23.14 Clusters 4m showed were 1–4, observed respectively. All clustersrelativelyin Clusters showed deeper 1–4, similar groundwater respectively. temporal level All compared variation,clusters showedto Cluster as described similar 3, due temporal to above. higher variation,water Cluster demand 3as shows described (higher the above. shallowest groundwaterpopulation).Cluster level, 3 showsCluster which the 1 showed shallowest lies inthe thedeepest groundwater inner ground part waterlevel, of the levelwhic city. hdue lies to Thisin less the water could inner supply part be dueof from the to BWSSBcity. relatively This could higher and high groundwater draft. leakage frombe due the to sewerage relatively higher and water leakage supply from the lines. sewerage Cluster and 4 showedwater supply relatively lines. Cluster deeper 4 groundwatershowed relatively deeper groundwater level compared to Cluster 3, due to higher water demand (higher level comparedpopulation). to Cluster Cluster 3,1 showed due to the higher deepest water ground demandwater level (higher due population).to less water supply Cluster from 1 BWSSB showed the deepest groundwaterand high groundwater level due draft. to less water supply from BWSSB and high groundwater draft.

Figure 9. Cont. Sustainability 2018, 10, 26 13 of 22 Sustainability 2018, 10, 26 13 of 22

Figure 9. Box and whisker plots of the depth to groundwater (DGW) of all 154 wells during Figure 9. Box and whisker plots of the depth to groundwater (DGW) of all 154 wells during December December 2015–September 2017. The horizontal line within the box indicates the median and 2015–Septemberboundaries 2017. of the The box horizontal indicate the line 25th within and 75t theh boxpercentile. indicates The upper the median whisker and extends boundaries from the of the box indicatehinge the to 25ththe largest and value 75th percentile.no further than The 1.5 upper× IQR (Inter-Quartile whisker extends Range) from from thethe hinge. hinge The to thelower largest value no furtherwhisker thanextends 1.5 from× IQR the (Inter-Quartilehinge to the smallest Range) value from at most the 1.5 hinge. × IQR The of the lower hinge. whisker Data beyond extends the from the hingeend to the of the smallest whiskers value are called at most “outlying” 1.5 × IQR pointsof theand hinge.are plotted Data individually beyond the by endfilledof circle. the whiskers are called “outlying” points and are plotted individually by filled circle. Kriged maps of the groundwater table were produced for each month from December 2015 to September 2017. For the monthly variograms used in creating the interpolated maps, the nugget Krigedvaried maps from of 31.7 the m groundwater to 127.9 m, partial table sill were varied produced from 63.2 form2 eachto 132.0 month m2 and from range December varied from 2015 to September1.65 2017. km Forto 5.44 the km. monthly As an example, variograms Figure used 10 inshows creating the variograms the interpolated for two maps,months the of 2016. nugget The varied from 31.7nugget m to 127.9quantifies m, partial the small sill scale varied variability from 63.2present m2 into the 132.0 data m at2 lagand below range the varied average from minimum 1.65 km to distance among observations. The presence of nugget indicates that a higher small scale spatial 5.44 km. As an example, Figure 10 shows the variograms for two months of 2016. The nugget quantifies variability is present in groundwater level compared to the monitoring network. Studies performed the smallin scale similar variability lithology, climate present context in the and data scale at show laged below the presence the average of nugget minimum in an urban distance catchment among observations.(in Mulbagal) The presence [21] and of an nugget absence indicatesof nugget in that agricultural a higher catchments small scale [37]. spatial As suggested variability by Sekhar is present in groundwateret al. [21], level the comparedpossible reason to the for monitoring the higher network.small scale Studiesvariability performed could be the in similarinfluence lithology, of climate contextanthropogenic and scale effects showed in urban the catchments. presence ofThese nugget variograms in an urbansuggestcatchment that a denser (in monitoring Mulbagal) could [21 ] and an absencebe oftested nugget in the in agriculturalfuture, to investigate catchments if, at leas [37].t in As some suggested areas of by theSekhar city where et al. the [21 nugget], the possible is particularly high, an even denser monitoring network is needed. The sill in the variogram indicates the reason for the higher small scale variability could be the influence of anthropogenic effects in urban variance in the data. These variograms shows a higher sill when deeper groundwater levels are catchments.observed These (see variogramsFigure 10a) and suggest lower sill that when a shallo denserwest monitoringgroundwater levels could are be observed tested (see in Figure the future, to investigate10b). if, at least in some areas of the city where the nugget is particularly high, an even denser monitoringMaps of network the groundwater is needed. table The for silla few in months the variogram are presented indicates in Figure the 11. varianceThe color legend in the data. These variogramsshown for showsgroundwater a higher level sill is whensame across deeper the groundwatermaps to facilitate levels comparison. are observed Significant (see spatial Figure 10a) and lowervariability sill when in shallowest the water groundwaterlevels drawdown levels and are rises observed are observed (see Figure in different 10b). dry and wet months. Typically, groundwater levels are deeper in April (see DGW for April 2016 in Figure 11) Mapsfollowing of the groundwaterrecession from tablethe previous for a fewOctober months (i.e., post-monsoon are presented period). in Figure However, 11. The in 2017, color the legend shown fordeepest groundwater groundwater level levels is samein any acrosspart of the city maps were to recorded facilitate as comparison.late as July 2017, Significant because 2016 spatial variabilitywas in a the drought water year levels and the drawdown monsoon, which and risestypically are begins observed in June, in was different delayed dry in 2017. and wet months. Typically, groundwater levels are deeper in April (see DGW for April 2016 in Figure 11) following recession from the previous October (i.e., post-monsoon period). However, in 2017, the deepest groundwater levels in any part of the city were recorded as late as July 2017, because 2016 was a drought year and the monsoon, which typically begins in June, was delayed in 2017. Sustainability 2018, 10, 26 14 of 22 Sustainability 2018, 10, 26 14 of 22

(a) April 2016

(b) August 2016

Figure 10. Experimental and fitted exponential variogram for April and August in year 2016. Figure 10. Experimental and fitted exponential variogram for April and August in year 2016. Most of the grids of Cluster 1 showed relatively deeper groundwater levels, while most of the Mostgrids of Cluster the grids 3 showed of Cluster a relatively 1 showed shallow relatively groundwater deeper level. groundwater The shallow levels, levels whilein these most grids of the gridsare of Clusterunaffected 3 showed even by a relativelydrought year shallow of 2016 groundwater (see April 2016 level. and The July shallow 2016 maps levels in in Figure these grids11). are unaffectedShallow even groundwater by drought levels year in ofthese 2016 older, (see Aprilcore areas 2016 of and the July city 2016were maps also observed in Figure in 11 a). 2009 Shallow sampling of 472 wells in central Bengaluru [22]. The secondary data-informed clustering supports groundwater levels in these older, core areas of the city were also observed in a 2009 sampling of the narrative that the shallow groundwater levels in the inner part of the city (Cluster 3) are due to 472 wells in central Bengaluru [22]. The secondary data-informed clustering supports the narrative that the shallow groundwater levels in the inner part of the city (Cluster 3) are due to lower groundwater use combined with greater recharge leakage from water and waste water systems in the city. On the Sustainability 2018, 10, 26 15 of 22

Sustainability 2018, 10, 26 15 of 22 contrary, the reason for deeper groundwater levels in the outer areas of the city is due to higher lower groundwater use combined with greater recharge leakage from water and waste water groundwater extraction in the absence of water supply from the water utility agency, the BWSSB. systems in the city. On the contrary, the reason for deeper groundwater levels in the outer areas of The topographythe city isof due the to city higher is such groundwater that the cityextraction center in sits the at absence higher of elevation, water supply while from the the newly water growing peripheriesutility are agency, at lower the elevation.BWSSB. The Figure topography 11 therefore of the city suggests is such thatthat thethe city lower-lying center sits peripheries at higher have deeper groundwaterelevation, while table the newly than growing the higher-lying, peripheries olderare at lower city core. elevation. This Figure suggests 11 therefore that ata suggests local scale the groundwaterthat the behavior lower-lying is non-classical, peripheries have i.e., deeper the groundwater groundwater istable relatively than the deeper higher-lying, in the older valleys city than the higher topographiccore. This suggests relief. Thisthat at non-classical a local scale behavior the groundwater of groundwater behavior levelsis non-classical, in the valleys i.e., pointsthe to a groundwater is relatively deeper in the valleys than the higher topographic relief. This non-classical deepeningbehavior of the of hydraulic groundwater gradient, levels in which the valleys could points result to in greatera deepening lateral of flowsthe hydraulic from the gradient, city center to peripherywhich areas. could result in greater lateral flows from the city center to periphery areas.

Figure 11. Interpolated depth to ground water table for selected months during December 2015–SeptemberFigure 11.2017. Interpolated depth to ground water table for selected months during December 2015–September 2017. 3.3. Rainfall Recharge and Net Outflow Groundwater balance was estimated using Equations (3) and (4) over 154 monitoring wells. There was insignificant rainfall during January–February (see Figure6) and it was assumed as dry period. The remaining 10 months (March–December) of 2016 were taken as the wet period. A sensitivity analysis of specific yield to rainfall recharge was performed by varying specific yield from 0.001 to 0.1. The range Sustainability 2018, 10, 26 16 of 22

3.3. Rainfall Recharge and Net Outflow Groundwater balance was estimated using Equations (3) and (4) over 154 monitoring wells.

ThereSustainability was 2018insignificant, 10, 26 rainfall during January–February (see Figure 6) and it was assumed as16 dry of 22 period. The remaining 10 months (March–December) of 2016 were taken as the wet period. A sensitivity analysis of specific yield to rainfall recharge was performed by varying specific yield fromof specific 0.001 yieldto 0.1. was The taken range based of specific on the experimentsyield was taken in similar based hardon the rocks experiments (e.g., [21,30 in]). similar The natural hard rocksrainfall (e.g., recharge [21,30]). factor The (annual natural recharge/annual rainfall recharge rainfall) factor was(annual computed recharge/annual using Equations rainfall) (3) was and computed(4) for each using value Equations of specific (3) yield and and (4) isfor plotted each valu in Figuree of specific 12 along yield with and the is spatial plotted variability in Figure over 12 along154 monitoring with the spatial wells. variability The spatial over variability 154 monitoring in the rainfall wells. recharge The spatial factor variability (RF) increases in the with rainfall the rechargeincrease infactor Sy, as(RF) expected increases (refer with Equations the increase (4) in and Sy, (5)). as expected At a Sy of(refer 0.005, Equations the RF is (4) 13.5%, and (5)). which At isa Syclose of 0.005, to the the range RF observedis 13.5%, inwhich the literatureis close to in the hard range rock, observed e.g., 15.6% in the by literature Maréchal in et al.hard [30 rock,], 13–19% e.g., 15.6%by Mar byéchal Maréchal et al. [ 15et] al.and [30], 8.6% 13–19% by Sekhar by Maréchal et al. [21 ].et Theal. [15] RF valueand 8.6% used by by Sekhar the CGWB et al. for[21]. hard The rock RF valueaquifers used is 12%by the [15 CGWB]. The RFfor valueshard rock used aquifers and estimated is 12% [15]. by theseThe RF researchers values used provide and estimated a reasonable by theseconstraint researchers to the veryprovide wide a rangereasonable illustrated constraint in Figure to the 12 .very A value wide of range 0.005 illustrated was taken in as Figure a reasonable 12. A valueassumption of 0.005 of was effective taken specific as a reasonable yield for assumption further analysis. of effective specific yield for further analysis.

FigureFigure 12. Sensitivity analysis analysis of of rainfall rainfall recharge recharge factor factor ( (RF)) to to specific specific yield. Shaded Shaded region region shows shows thethe spatial spatial variability variability over over 154 154 monitoring monitoring wells. wells.

Using Equation (3), Qnet was estimated to be 123.8 mm with a standard deviation of 82.6 mm Using Equation (3), Q was estimated to be 123.8 mm with a standard deviation of 82.6 mm and, and, using Equation (4), Rnet was estimated to be 96.9 mm with a standard deviation of 67.4 mm. The using Equation (4), R was estimated to be 96.9 mm with a standard deviation of 67.4 mm. The standard standard deviation was computed over 154 monitoring wells. Table 3 shows the mean and standard deviation was computed over 154 monitoring wells. Table3 shows the mean and standard deviation deviation of computed Qnet and R over 154 wells for different clusters. Clusters 1 and 2 shows the of computed Qnet and R over 154 wells for different clusters. Clusters 1 and 2 shows the highest mean highest mean values of Qnet and R, and a similar mean value of net storage change. These are values of Q and R, and a similar mean value of net storage change. These are periphery areas, with periphery areas,net with relatively higher open space conducive to more infiltration and rainfall relatively higher open space conducive to more infiltration and rainfall recharge. Cluster 3, covering recharge. Cluster 3, covering the core central areas, shows the lowest mean values of Qnet and R. Q R Thesethe core areas central are densely areas, shows built-up, the lowestexplaining mean the values low R of. Onenet hypothesisand . These for areas the contrasting are densely behavior built-up, explaining the low R. One hypothesis for the contrasting behavior of Qnet in central versus periphery of Qnet in central versus periphery areas, as articulated in [25,36] and also suggested by the clustering,areas, as articulated is the differential in [25,36 forcings] and also of anthropogenic suggested by recharge the clustering, from leaking is the differentialwater and wastewater forcings of anthropogenic recharge from leaking water and wastewater versus pumping. In periphery Clusters 1 versus pumping. In periphery Clusters 1 and 2, Qnet is likely high because of more groundwater use Q comparedand 2, net tois the likely central high Cluster because 3, of which more groundwater is better connected use compared to surface to water the central suppl Clustery. 3, which is better connected to surface water supply. The difference between the net outflow and recharge listed in Table3 gives the net change in groundwater storage. As can be seen, in 2016, using the 154 well observations, the net change in groundwater storage is negative in all clusters, i.e., groundwater depletion occurred, as is also apparent Sustainability 2018, 10, 26 17 of 22

The difference between the net outflow and recharge listed in Table 3 gives the net change in groundwaterSustainability 2018 storage., 10, 26 As can be seen, in 2016, using the 154 well observations, the net change17 ofin 22 groundwater storage is negative in all clusters, i.e., groundwater depletion occurred, as is also apparent in the boxplots presented in Figures 8 and 9. The net storage change is most negative in in the boxplots presented in Figures8 and9. The net storage change is most negative in Cluster 4, Cluster 4, indicating more groundwater usage and outflow compared to the recharge, as this indicating more groundwater usage and outflow compared to the recharge, as this cluster has lowest cluster has lowest open space and highest water demand (highest population density). Cluster 4 open space and highest water demand (highest population density). Cluster 4 shows the lowest spatial shows the lowest spatial variability due to fewer grids falling in this cluster. variability due to fewer grids falling in this cluster. Annual groundwater storage change was computed using the 154 well data and interpolated Annual groundwater storage change was computed using the 154 well data and interpolated GWD maps for January 2016 and January 2017 in Equation (5). The change in the groundwater level GWD maps for January 2016 and January 2017 in Equation (5). The change in the groundwater level over this period is shown in Figure 13. Since 2016 was a drought year for Bengaluru city, a over this period is shown in Figure 13. Since 2016 was a drought year for Bengaluru city, a significant significant depletion in the groundwater was observed in entire area except some parts of central depletion in the groundwater was observed in entire area except some parts of central Bengaluru (close Bengaluru (close to grid 12) where a rise of up to 2 m was observed. This area is also highlighted in to grid 12) where a rise of up to 2 m was observed. This area is also highlighted in Mehta et al. [26] Mehta et al. [26] who used data from a relatively long-term CGWB monitoring in this area to show who used data from a relatively long-term CGWB monitoring in this area to show the steady rise in the steady rise in GWL over time, and attributed it to the dominance of anthropogenic recharge GWL over time, and attributed it to the dominance of anthropogenic recharge over pumping. In all over pumping. In all other areas, the figure indicates that, due to poor rainfall in 2016, the other areas, the figure indicates that, due to poor rainfall in 2016, the groundwater level declined groundwater level declined by about approximately 12 m in some of the grids, mostly in the outer by about approximately 12 m in some of the grids, mostly in the outer parts of the city. Estimated parts of the city. Estimated mean water storage depleted in the 700 km2 area of the city for 2016 was mean water storage depleted in the 700 km2 area of the city for 2016 was about 26.9 mm (18.8 million about 26.9 mm (18.8 million cubic meters) based on the data of 154 wells and 26.2 mm (18.3 million cubic meters) based on the data of 154 wells and 26.2 mm (18.3 million cubic meters) based on the cubic meters) based on the interpolated data. The standard deviation over space was found to be interpolated data. The standard deviation over space was found to be about 18 mm based on the data about 18 mm based on the data of 154 wells and 11 mm based on the interpolated data. The lower of 154 wells and 11 mm based on the interpolated data. The lower estimate of standard deviation from estimate of standard deviation from kriged data could be due to the presence of nugget in the kriged data could be due to the presence of nugget in the variogram, which smoothens the data. variogram, which smoothens the data.

FigureFigure 13. 13. DifferenceDifference in inthe the groundwater groundwater level level betw betweeneen January January 2017 2017 and and January January 2016. 2016. Positive Positive numbersnumbers indicate indicate rise, rise, and and negative negative numbers numbers indicate indicate fall. fall. Sustainability 2018, 10, 26 18 of 22

Table 3. Mean and standard deviation of the R, Qnet and net storage change (R − Qnet) over 154 wells Sustainability 2018, 10, 26 18 of 22 for all the clusters. Numbers in brackets represent the standard deviation.

Table 3. Mean and standard deviation of the R, Qnet and net storage change (R − Qnet) over 154 wells for all theCluster clusters. ID Numbers in bracketsQnet (mm) represent the standardR (mm) deviation. R − Qnet R (mm) 1 128.89 (84.26) 100.79 (69.22) −28.10 Cluster ID Qnet (mm) R (mm) R − Qnet R (mm) 2 138.33 (89.12) 108.79 (73.05) −29.54 1 128.89 (84.26) 100.79 (69.22) −28.10 3 86.00 (69.08) 72.69 (59.89) −13.31 42 118.85 138.33 (46.31)(89.12) 108.79 84.46 (73.05) (34.78) −29.54 −34.39 3 86.00 (69.08) 72.69 (59.89) −13.31 4 118.85 (46.31) 84.46 (34.78) −34.39 3.4. Extreme Rainfall Response 3.4.A Extreme total of Rainfall 740.7 R mmesponse rainfall was received during August–September, 2017. The recharge from these extremeA total rainfallof 740.7 eventsmm rainfall over thesewas received two months during was August–September, estimated using 2017. Equation The recharge (5). Figure from 14 shows thethese differenced extreme rainfall groundwater events over level these map two between months Julywas 2017estimated and Septemberusing Equation 2017. (5). Interestingly, Figure 14 the highestshows rise the is observeddifferenced in outerground gridswater (Cluster level map 1) and between lowest riseJuly is 2017 observed and inSeptember inner grids 2017. (Cluster 3). TheseInterestingly, outer clusters the highest are in low-lyingrise is observed areas, in whereas outer grids the central (Cluster areas 1) and are lowest at higher riseelevation is observed and more in inner grids (Cluster 3). These outer clusters are in low-lying areas, whereas the central areas densely built-up. A higher fraction of extreme rainfall likely runs off towards the outer areas, where are at higher elevation and more densely built-up. A higher fraction of extreme rainfall likely runs relatively more recharge from the extreme rainfall occurs. off towards the outer areas, where relatively more recharge from the extreme rainfall occurs.

Figure 14. Rise in the groundwater level from July 2017 to September 2017. Figure 14. Rise in the groundwater level from July 2017 to September 2017. The boxplots (Figure 8) show that GWLs were falling in 2017 through June and July, when monsoonThe boxplots rains were (Figure less 8than) show normal. that Interestingly, GWLs were the falling data also in 2017 show through that the Juneextremely and July,high when monsoonrainfall rainsthat immediately were less than followed, normal. during Interestingly, August–September the data also 2017 show (see thatFigure the 6), extremely resulted highin a rainfall thatquick immediately recharge of followed, groundwater during levels August–September in all the stations spread 2017 (see over Figure the city.6), resultedAverage inrise a quickin GWL recharge of groundwaterin September 2017 levels from in that all the of July stations 2017 spreadwas 13.3 over m across the city. 154 Averagemonitoring rise wells, in GWL while in the September lowest 2017 from that of July 2017 was 13.3 m across 154 monitoring wells, while the lowest recorded was 2.6 m Sustainability 2018, 10, 26 19 of 22

(in grid 19) and highest recorded was 24.2 m (in grid 28). The mean rise of 13.3 m due to the rainfall during August–September 2017 had a relatively low standard deviation (over space) of about 4.0 m. During this period, a mean recharge of 66.5 mm for the entire city and a peak recharge of 121 mm (in Grid 28) in some areas were observed. The mean recharge estimate based on the kriged map data also showed nearly the same mean estimate (65.4 mm). However, there was significant difference in the standard deviation over space, 20 mm over 154 wells versus 8.29 mm from interpolated data, owing to the presence of nugget in the data. These two months resulted in a net recharge (increase in net groundwater storage) of about 46.6 million m3 (or 46.6 billion liters) in the 700 km2 of the city. The recharge generated by these extreme events (rainfall of August–September 2017) is similar to the annual recharge in some agricultural areas in semiarid tropical conditions [15,30]. This suggests that groundwater recharge from extreme rainfall is quite significant in Bengaluru, despite being predominantly built-up. In the literature, significant recharge events from extreme precipitation have been reported [38–40] in agricultural regions. In urban areas, however, the conventional narrative is that groundwater recharge is impeded by the dominance of built-up, impervious areas (e.g., [27]). Our observations suggest that, also in urban regions, extreme events can play a major role in groundwater recharge. We hypothesize that these recharge conditions are significantly different from those in agricultural areas, where the recharge is often as a non-point source recharge, i.e., recharge distributed across large areas, such as fields. In urban areas, such non-point source recharge is not feasible due to altered land surfaces in the form of paved surfaces and buildings. However, increased runoff from these altered land use conditions caused localized flooding conditions, during extreme rainfall events. Compounded by the poor storm water drainage conditions in urban cities, the flood waters have higher residence times, which result in recharge through pathways such as construction pits, ponds, storm water drains, and rainwater harvesting structures—the “indirect recharge pathways” that Lerner [4] alludes to as one of the impacts of urbanization. Hence, the recharge would be substantial, and quick, as observed here.

4. Conclusions and Future Improvements In this paper, we have described the monitoring network and groundwater dynamics across a city, highlighting some of the key findings. First, estimates of groundwater storage changes were made using a simplified, vertical balance. The monitoring will be continued until December 2017. This will provide an opportunity to make groundwater level observations for two monsoon and two non-monsoon seasons during the study period. One limitation of this study is that only a vertical and simplified estimate of net outflow was made. This net outflow term aggregates both natural and anthropogenic components. To understand the urban groundwater budget more completely, a more elaborate modeling framework would need to include the magnitude of lateral flow and baseflow, for example, as well as the socioeconomic and infrastructural variables that inform the anthropogenic forcings of recharge from leaking pipes and return flows, and draft from pumping. We are working on such a framework, which is briefly described below. Park and Parker [41] developed a physically based lumped model to simulate groundwater fluctuations in response to precipitation. This model is applicable to estimate groundwater recharge from the observed groundwater level fluctuations as well as groundwater discharge or underflow. One of the limitations of the Park and Parker model is that no external sources or sinks other than uniformly distributed precipitation was considered. Hence, if groundwater pumping exists, then the model needs a suitable adaptation. To use this model for regions with groundwater pumping conditions, Kumar [42] improved the model by taking groundwater pumping into account, which was tested for various conditions by Subash et al. [16]. This improved model (ambhasGW) is available in R (https://cran.rproject.org/package=ambhasGW) for ease of use and to estimate parameters of recharge, discharge, and specific yield from the time series of groundwater level observations. The model provides balance terms of recharge, underflow (or groundwater discharge) and pumping as Sustainability 2018, 10, 26 20 of 22 output. We are currently working on applying this model, and collecting additional socioeconomic and infrastructure data to inform it. To account for the considerable uncertainty in the anthropogenic forcings, we will be applying a formal uncertainty framework, the Generalized Likelihood Uncertainty Framework (GLUE), with the model [43]. More work will be needed to establish the evidence base for these future model outputs. For example, the source of the recharge to groundwater in urban areas could be identified through water chemistry and solute mass balance methods [44,45] Sustainable groundwater management in Bengaluru must account for substantial spatial socio-hydrological heterogeneity. Continuous monitoring at high spatial density will be needed to inform evidence-based policy. Variables in addition to groundwater levels will need to be monitored for identifying sources and flow paths, and linked to their possible anthropogenic drivers.

Supplementary Materials: The data used in the analysis is available at http://bangalore.urbanmetabolism.asia/ geoportal/. Acknowledgments: Research funding from the Cities Aliance Catalytic Fund is gratefully acknowledged. Funds for open access publishing were provided by the corresponding author’s organizational resources. We also thank Manish Gautam, Deepak Malghan and Krishnachandran Balakrishnan for access to some of the secondary data. Author Contributions: M. Sekhar and Vishal K. Mehta conceived and designed the experiments; M. Sekhar led the groundwater measurements campaign; M. Sekhar, Sat Kumar Tomer and Vishal K. Mehta analyzed the data and wrote the paper; S. Thiyaku and Sanjeeva Murthy performed the GIS processing and mapping; P. Giriraj and Sanjeeva Murthy measured the groundwater levels; and Vishal K. Mehta was the principal investigator of the research project. Conflicts of Interest: The authors declare no conflict of interest.

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