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WATER RESOURCES RESEARCH, VOL. ???, XXXX, DOI:10.1029/,
Over-Extraction in the Shallow and the Deep – The1
Sustainability and Reliability of India’s Groundwater2
Irrigation3
Ram Fishman,1
Tobias Siegfried,1
Pradeep Raj,2
Vijay Modi,3
and Upmanu
Lall1
1Columbia Water Center, The Earth
Institute, Columbia University, New York,
NYC, USA
2Groundwater Department, Government
of Andhra Pradesh, Hyderabad, India
3Department of Mechanical Engineering,
Columbia University, New York, NY, USA
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Abstract. The excessive exploitation of aquifers is emerging as a world-4
wide problem, but it is nowhere as dramatic and consequential as it is in In-5
dia, the world’s largest groundwater consumer for irrigation. While the prob-6
lem is usually framed in terms of long-term depletion of fossil aquifers, we7
focus here on the agricultural implications of over-exploitation in aquifer of8
limited storage, such as those that underlie most of peninsular India, by con-9
trasting water table and irrigation dynamics in two irrigation intensive re-10
gions of India that differ in underlying hydrogeology. In the deep alluvial aquifers11
of Punjab of north-western India, water table dynamics are dominated by12
declining trends, while in the hard rock, shallow aquifer region of Telangana13
in southern-central India, dynamics are dominated by short-term fluctua-14
tions. We show that irrigation from the deep aquifers in Punjab is largely15
unaffected by fluctuations in water tables and rainfall, but in the hard rock16
shallow aquifers of Telangana, irrigation is more variable and sensitive to these17
stochastic variables. These findings indicate that energy and land are the bind-18
ing constraints to irrigation in Punjab, but physical water scarcity is the bind-19
ing constraint in Telangana. We argue that over-exploitation of a deep aquifer20
is primarily an issue of long-term sustainability, whereas in a shallow aquifer,21
it leads to increased short-term variability in irrigation and a loss of buffer-22
ing capacity which can be harmful economically.23
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1. Introduction
The excessive exploitation of groundwater aquifers is emerging as a worldwide problem,24
but it is nowhere as dramatic and consequential as it is in India, the world’s largest con-25
sumer of groundwater (250 km3 per year), and a country where up to 70 % of agricultural26
production and 50 % of the population depend on this vital resource [The World Bank27
and Government of India, 1980; Shah, 2008]. An understanding of the consequences of28
excessive exploitation for agricultural production is clearly an important research agenda.29
However, despite the pervasive indications of excessive extraction around the country,30
documentation or analysis of the associated impacts on irrigated agriculture are hard to31
find. This paper takes a first step in this direction.32
Usually, the problem is posed in terms of consistent declines in water table and the33
implications for the long-term sustainability of irrigated agriculture [Moench, 1992; Rodell34
et al., 2009; Tiwari et al., 2009; Wada et al., 2010]. Such a decline is typical in over-35
exploited aquifers of large storage, such as the alluvial aquifers that cover much of northern36
and western India. In this kind of environment, increases in the use of energy for pumping37
can make up for the decline in water tables while guaranteeing constant or increasing water38
use at the same time. This could be one of the reasons for the difficulty of observing the39
impacts of groundwater depletion on irrigated agriculture, especially in light of the fact40
that in much of India, energy use for pumping is determined by political, rather than41
economic rationale: farmers usually face a flat, low (if any) charge on electricity use for42
pumping, and use as much of it as they can, even while lobbying for increases in its supply.43
144
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In India, however, intensive groundwater irrigated agriculture is practiced over a large45
range of hydrogeological conditions. Much of India, in particular, overlays hard rock,46
shallow aquifers of more limited storage. The exploitation of these aquifers has proven47
to be as economically important as that of alluvial aquifers. It has resulted in a similar48
boom in irrigation and agriculture [Shah, 2008], as well as a similar pattern of unregulated49
and excessive extraction (Figure 1). In this paper, however, we argue that the nature,50
dynamics and implications of over-exploitation of these shallower aquifers is fundamentally51
different than it is in deep, thick aquifers.52
Our analysis utilizes data on water tables, rainfall and irrigation over the last two53
decades to perform a comparative analysis of coupled groundwater and agricultural dy-54
namics in two key regional hotspots of depletion. These represent the two prominent55
hydrogeological regimes in the country, i.e. the state of Punjab on the one hand [Kumar56
et al., 2007], which overlies the deep alluvial aquifers of the Gangetic basin, and Telangana57
[Raj , 2004a, 2006], in the state of Andhra Pradesh on the other, which overlies shallow,58
fractured hard rock aquifers much like those that cover much of peninsular India.59
While the two regions have similar agricultural energy economies, and both are labeled60
as over-exploited by India’s central groundwater board (see Central Ground Water Board61
[2007], also Figure 1), we find clear differences in their groundwater usage dynamics that62
are consistent with the appearance of bottom effects in the shallow aquifers of Telangana63
and their absence, so far, in the deep aquifers of Punjab. There, the dynamics of both64
water tables and irrigation are dominated by steady secular declines. Conversely, in Telan-65
gana they are dominated by short-term variability (with large welfare costs). In Punjab,66
water supply for irrigation is insensitive to rainfall or water tables, but in Telangana, it67
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is highly dependent on both. Thus, groundwater access can make up for a bad rainfall in68
Telangana, but only if the water tables are not excessively low under which the buffering69
ability groundwater is limited. In Punjab, land availability and electricity supply are the70
limiting factors on irrigation but in Telangana, it is water availability.71
Our comparative analysis is intended to highlight unique elements of over-extraction72
of shallower aquifers that tend to receive much less attention in both the policy and73
theoretical literature than the steady declines in water tables in aquifers like Punjab’s.74
We believe such an analysis is important for two reasons. First, it is important in itself75
because, as stated above, shallow, hard rock aquifers support significant parts of India’s76
groundwater irrigated areas and are important sources of water supply all over the world77
[Shiklomanov , 2000; UNDP , 2006]. Second, it can provide an indication of where deeper78
aquifers might be heading in the longer run as water levels approach the bottom, and can79
thus inform the policy debate surrounding groundwater depletion.80
Indeed, while increases in the energy supply allow agriculture and farmers to temporar-81
ily escape the impacts of falling water tables, this process cannot continue indefinitely.82
Eventually, the bottom is reached: either the resource is literally depleted (i.e. the bottom83
of the aquifer reached) or the costs of further deepening wells become prohibitive. When84
that happens, water extraction will have to decline to renewable levels, i.e. to the level85
of natural recharge, and increases in energy use will be of no avail (and we shall argue,86
actually harmful). The time it would take to reach the bottom, however, depends on the87
properties of the aquifer in question, and in particular, on its storage capacity (or the88
depth of its bottom). Where aquifers have a large storage and thus are thick, the impacts89
of falling water tables might still be hidden in agricultural energy usage (for which reliable90
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data are very difficult to find) but by looking at low storage, i.e. thinner aquifers, we91
might expect to find more direct evidence of the impacts.92
One insight from our analysis is that while in deep aquifers excessive exploitation (i.e.93
excessive energy usage) is an issue of long-term sustainability, in shallower aquifers it is94
primarily an issue of short-term reliability in water supply. Water tables simply cannot95
decline consistently because they can reach the bottom and can be recovered by abundant96
rains, but they tend to be more variable for these very same reasons. This variability97
translates to lack or reliability in water supply and the associated welfare and economic98
costs. Thus, the classification of a thin aquifer as over-exploited on the basis of short-term99
(annual deficit) data has to be interpreted with caution. Shallow aquifers cannot be over-100
exploited, in terms of a mass deficit, consistently over long periods of time. This is perhaps101
part of the reason they are not detected by the recent satellite gravity measurement based102
methods employed by [Rodell et al., 2009] and [Tiwari et al., 2009], among others, to103
dramatically confirm the extent of depletion over northern India. However, they can be104
over-exploited in years when they are relatively well recharged, and this has the social105
costs we highlight in this paper.106
A second, related, insight has to do with the notion of the buffering service offered107
by groundwater storage against inter-annual fluctuations in rainfall, a major economic108
concern in the semi-arid tropics especially, and in developing countries in particular [Ribot109
et al., 1996]. In shallow aquifers, the initial development of irrigation can provide such110
a buffer, but if excessively developed, can also undermine it. In other words, the inter-111
annual buffer value of groundwater aquifers has a non-monotonic relationship to irrigation112
development. Asymptotically, we will argue it can be completely lost. We will discuss113
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how the degree of social aversion to fluctuations in agricultural productions can determine114
the optimal level of irrigation development (in particular, energy usage) and why it may115
be difficult, from the policy perspective, to achieve this optimal level.116
The rest of the paper is organized as follows. In Section 2 we describe the regions117
of study and the data. Section 3 introduces a simple coupled human-natural modeling118
framework of groundwater use and conducts a comparative analysis of the dynamics of119
water tables in the two regions. Section 4 contains a comparative analysis of the dynamics120
of water supply for irrigation. Section 5 discusses the social welfare and policy implications121
and section 6 concludes.122
2. Regional Overview and Data
2.1. Agricultural Production
When it comes to food production, the state of Punjab has been leading the way in India123
ever since the green revolution started on its soils in the late 1960s. Today, groundwater124
irrigation has taken over from the traditional surface irrigation network of the state as125
the dominant source of irrigation and food production. Despite its relatively small area126
(1.5% of India’s total), Punjab is now the principal provider of cereals (rice and wheat)127
to the rest of the country (53% in terms of total production), an increase attributed to128
the expansion of irrigation to cover virtually the entire cropped area of the state. Its129
seasonal cycle is overwhelmingly dominated by irrigated cultivation of rice during the130
rainy season (i.e. Kharif, June-October) and of wheat during the dry season (i.e. Rabi,131
October-February).132
In Telangana, agriculture was traditionally only feasible in the rainy season (Kharif).133
The cultivation of irrigated crops, predominantly rice and some cotton, was limited to134
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small areas in which shallow, hand dug wells provided sufficient water yield, or where135
topographic conditions allowed the constructions of small tanks. Elsewhere, rain fed136
cultivation was dominated by traditional crops such as Bajra (millets), Jowar (Sorghum)137
and certain pulses. In this hard rock geology, groundwater could substantially accumulate138
only in certain pockets of weathered granite, concentrated along ancient flow paths, and139
it was above these pockets alone that shallow wells could be dug that provided a sufficient140
water yield to irrigate a few hectares of paddy rice.141
The situation began to change in the 1980s with the introduction of bore-wells that142
could tap deeper pockets of groundwater, and the spread of rural electrification that143
could provide the energy needed to pump this groundwater to the surface. The increased144
water yields allowed farmers to expand dramatically the area under irrigation. From a145
mere 23% in 1985, irrigated area has grown to cover 38% of net sown area in 2001 (see also146
Figure 2). Almost this entire increase is due to irrigation by bore-wells (from 0.3% to 10%147
of net sown area) and shallower dug wells (from about 8% to about 13% of net sown area).148
Bore-well irrigated area has expanded consistently and dramatically, but this expansion149
has been partially eroded by the decline in area irrigated by other sources. The decline in150
areas served by tanks is noteworthy and is attributed to lack of maintenance and siltation151
(which is also possibly related to farmers taking up private wells instead). The decline152
in areas irrigated by other wells (dug wells and borecumdug wells, which are bore-wells153
drilled at the bottom of open dug wells) is often also attributed to the expansion of deeper154
tubewells and the resulting lowering of the water table over time.155
The expansion of irrigation is believed to have played a role in the rapid agricultural156
growth in the region in the period of 1970-2001 (see for example Vakulabaharanam [2004]).157
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Irrigation allowed the cultivation of waterintensive crops like rice and cotton, the use of158
high yielding varieties (HYVs) and above all, a second cropping during the dry season159
(called the Rabi season, which last from October to March). By the turn of the century,160
Telangana rice production began to rival that of the canal-laden coastal regions (AP is161
one of the major rice growing regions of India) and Rabi season production began to rival162
that of the Kharif season.163
Both Telangana and Punjab are semi-arid regions that employ an energy intensive, rice164
dominated, productive irrigation system that is nevertheless placing great pressure on the165
energy sector (which is both energy starved and required to cross subsidize the basically166
free electricity provided to farmers). In both of them, rice was not cultivated on a large167
scale prior to the advent of groundwater irrigation because of the inadequacy of local168
precipitation. Both of the regions are now classified by the CGWB as over-exploited (i.e.169
Figure 1), and anecdotal reports from both regions tell of drying up and deepening of170
bore-wells. Table 1 highlights some of the basic commonalities and differences between171
the two areas.172
In Punjab, almost the entire cultivated area is irrigated in both seasons (even though173
wheat, which requires far less water, is grown in the dry season). In Telangana, a smaller174
share of the area is irrigated, especially in the dry season. Despite the similarity in175
horsepower and hours of electricity supply, a well in Punjab is able to irrigate a larger176
area, a reflection of the lower hydraulic conductivity and storage capacity of Telangana’s177
fractured granite, in comparison to alluvial strata2. In Punjab, aquifers are vast and deep,178
and the real constraint on irrigation is availability of energy and land. In Telangana, it179
seems that the supply of energy has a more limited capacity in overcoming physical water180
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scarcity. The analysis to follow will further confirm this suggestion by showing that the181
high degree of variability in irrigated areas in Telangana over time is driven to a large182
extent by fluctuations in the water table.183
2.2. Groundwater situation
Plots of bi-annual regionally averaged drawdown for Punjab and Telangana are pre-184
sented in Figure 3. Drawdown is measured before (June) and after (November) the annual185
monsoonal recharge season (June-September), during which more than 75 % of the total186
annual rainfall in India is concentrated [Singh et al., 2007]. It is clear, from the figure,187
that the level of recharge is correlated with the amount of precipitation, and that there188
is a large degree of inter-annual variability in both.189
Also, while plots in both regions show the characteristic seesaw pattern of rainy season190
rise and dry season decline, and while the magnitude of the drawdown is comparable,191
there is a clear contrast between the two regions. In Punjab, water table dynamics are192
dominated by a declining trend, the familiar symptom of over-extraction. In Telangana,193
they are dominated by a high degree of short-term fluctuations and there is no clear long-194
term trend. While water tables show a net decline on an average year, the trend can be195
reversed by one or two consecutive wet monsoons that can largely recharge the aquifers.196
This picture is also reflected in the summary statistics of Table 3.197
In comparison to Punjab, seasonal changes in water tables are larger in Telangana198
(because of both, the higher rainfall and the lower porosity of the strata), but the annual199
decline and rise in drawdown almost cancel out to leave an smaller and insignificant trend200
and a larger degree of variation in annual net change. Finally, it is also interesting to201
note that mean precipitation is higher in Telangana – in fact, it is almost sufficient for202
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rice cultivation3. It has to – there is no large reservoir of old water to tap, which is how203
Punjabi farmers are able to cultivate rice with the lower amounts of rainfall. In other204
words, Punjabi rice cultivation relies on the mining of an effectively fossil source of water205
– hence the unsustainable and steady decline in drawdown. Telangana’s irrigation is not206
unsustainable.207
2.3. Data
In both Telangana and Punjab, we use district-level data on irrigated areas, water208
tables (we will use the terms water table, drawdown, or depth to water interchangeably)209
and precipitation. Data are available for the nine districts of Telangana, AP, and for210
eleven districts in Punjab (Table 2 lists all datasets and sources). Annual precipitation211
figures were obtained from a national gridded dataset through a process of weighted spatial212
averaging (for details see Siegfried et al. [2010]). Water table figures were obtained through213
district-wise averaging of data from a network of monitoring wells operated by the state214
groundwater boards of Punjab and Telangana available on a bi-annual basis (pre- and post-215
monsoon). In Punjab, data were obtained for the period 1971–2003 and in Telangana,216
we have data from two separate sets of monitoring wells, one for the period 1986–2002217
and another for the period 1998–20064. We take these averaged water tables as indicators218
of fluctuations rather than absolute value of the water tables actually experienced by219
farmers.220
We focus on agricultural seasons in which land is intensively irrigated, mainly for rice221
cultivation. In Punjab, rice is cultivated only during the rainy season (Kharif). The extent222
of area devoted to irrigated rice cultivation were obtained from from the Indian Harvest223
Database from the Center for Monitoring Indian Economy (2008). In Telangana, details224
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of areas irrigated during the rainy Kharif season (net irrigated area) and dry Rabi season225
(area irrigated more than once) were obtained from the Directorate of Economics and226
Statistics, Government of Andhra Pradesh (APDES). In both seasons, a major potion of227
irrigated areas is devoted to rice cultivation. In Telangana, some figures are also available228
on the source-wise decomposition of irrigated areas (only for the rainy season until 1998,229
and for both seasons afterwards). Figure 4 describes the composition of irrigation sources230
in the year 2000.231
3. Water Table Dynamics
The purpose of the model presented here is to understand annual fluctuations in ground-232
water supply in a given district d. For this purpose, we consider the water budget of a233
simple bathtub aquifer of depth B, the entire surface area (normalized to be one unit) of234
which can cultivated by a given crop with a given water requirement w (Figure 5). This235
aquifer can be an extensive uniform aquifer like those in Punjab, or a much smaller local236
aquifer like those that make up the complex hydrogeology of Telangana. We thus look237
at a fixed spatial extent and ignore trends resulting from an expansion of irrigated area.238
The discrete time step t can refer to a season or to an annual cycle.239
The average depth to water at the beginning of period t in a district d is denoted by Dd,t.240
The volume of water stored in the aquifer at t is Vd,t = ρd(Bd−Dd,t), where ρd is the mean241
porosity which assumed to be uniform and Bd−Dd,t is the saturated thickness at time t.242
Let the amount of water extracted for irrigations net of return flow from excess irrigation243
be Wd,t. Since the average crop water requirement is wd, then area Ad = Wd/wd can be244
irrigated in a given period t. Let the amount of net natural discharge from the aquifer245
that is irretrievably lost to the downstream be Ld,t. Finally, let Pd,t be the in-period t246
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precipitation in a particular district. Correspondingly, aquifer recharge from rainfall is247
Rd,t and assumed to be an increasing function of Pd,t.248
The water budget thus becomes249
Vd,t+1 = Vd,t −Wd,t − Ld,t +Rd,t (1)
For the reasons outlined in the previous section, we assume that Wd,t and Ld,t are250
functions of the available storage. Hence, under the assumptions251
W = W (V ) ≤ V, L = L(V ) ≤ V (2)
we get252
Vd,t+1 = Vd,t − f(Vd,t) +Rd,t (3)
where f(Vd,t) = W (Vd,t)+L(Vd,t) is a positive, non-decreasing function (we will verify this253
empirically in section 4) of the initial storage V .254
Precipitation, and therefore recharge, fluctuates stochastically in time. We will assume,255
for simplicity, that Pd,t are a sequence of independent and identically distributed random256
variables. Equation 3 therefore describes a stochastic dynamical process. Such a process257
can converge to a steady state. This steady state is itself a probability distribution of258
water tables that is invariant under the dynamics, and if the process is ergodic, describes259
the long-term distribution of water tables over time (see for example Feller [1966]).260
We estimate a linearized version of this process, i.e.261
Vd,t+1 = Vd,t − wVd,t +Rd,t (4)
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or, by writing it in terms of observable water tables262
Dd,t+1 = (1− w)Dd,t −r
ρPd,t + cd (5)
where we have assumed that recharge is proportional to precipitation, i.e. Rd,t = rPd,t,263
with r a recharge coefficient, and cd are district specifics constant with cd = wBd. Since264
recharge is always positive, for a stochastic process shown in Equation 5 to converge to a265
steady state, we must have 1 ≥ w ≥ 0.266
We estimate the model by using available water table data from the two regions. In267
each region, water table data is available twice a year (pre-and post monsoon) per district.268
We use the full district-year panel to estimate an ‘average’ regional magnitude of the269
parameters w,r and ρ 5. across the entire panel of observations in each region. Because of270
the high degree of correlation across districts, errors are allowed to be correlated spatially271
(clustered by year).272
Table 4 displays regression estimates for Telangana (columns 1-2) and Punjab (column273
3) for pre-monsoon (May) water tables6. The estimation suggest, first, that every addi-274
tional mm of rainfall raises the water table by about 3–4 mm in Telangana and by about275
2 mm in Punjab. In terms of physical properties, these estimates should correspond to276
the product of recharge coefficient and porosities in the two regions.277
Second, as shown in Table 4, estimated coefficients on pre-season water tables in Punjab278
are not much different from 1, i.e. w ≈ 0. This suggests that neither extraction nor279
discharge respond strongly to starting depth to water there. In the shallow aquifers280
of Telangana, in contrast, the coefficients are both estimated to be smaller than 1, i.e.281
0 < w < 1 with large probability. This indicates that annual declines in water tables282
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are lower when the starting depth to water is deeper. Indeed, the presence of a shallow283
‘bottom’ would reflect in just this way: large declines are simply not possible when the284
water table starts off near to it.285
This interpretation of the results suggests that in Telangana, either discharge or ex-286
traction or both are significantly and negatively associated with the pre-season storage287
(groundwater levels). Put differently, water extraction in this shallow aquifer region is288
limited by water scarcity. In contrast, In the alluvial regions of Punjab, our results suggest289
that extraction is little influenced by water table constraints. There is sufficient supply of290
energy and no physical water constraint, so that the declines in water tables can continue,291
being still far from the physical bottom. In the following section we will use data on292
irrigation to check this directly and confirm these predictions.293
4. Irrigation Dynamics
Here, we examine the variation in irrigated area over time and its sensitivity to ground-294
water tables. Since direct measurements of water use are not available we use both total295
irrigated areas (data available in Telangana only) and rice cultivated areas (the principal296
consumer of irrigation water in both regions) as a measure of water use in agriculture.297
Irrigated area provides a good proxy of water availability for irrigation. It is determined298
through cropping choices that are mostly made in the beginning of the agricultural sea-299
son (although it is possible that some of the area is abandoned during the season due300
to either insufficient rainfall, flooding or other factors like pests), and therefore should301
reflect limits on water supply, as perceived by farmers. In fact, it was farmer interviews302
conducted in June 2008 in Nalgonda district (see map Figure 4), that have led us to test303
this relationship. Farmers who wish to maximize the extent of irrigated land will naturally304
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prefer to spread the available water supply over as large an area as possible, contingent305
on pre-season water availability. Of course, in making these choices, they may still take306
into account uncertainties in precipitation during the coming rainy season).307
In contrast, the yield of irrigated crops is subject to numerous stochastic influences,308
including the distribution of precipitation, pest outbreaks and solar radiation, to name a309
few, and would thus be a poorer proxy of water availability. Below, we will actually see310
that rice yields are less sensitive to water tables and rainfall than are rice areas.311
Irrigated areas show a great deal of variability in Telangana, a variability that, as we312
will discuss in the next section, can have painful economic implications for farmers. Our313
goal is to assess to what degree this variability is caused by the large fluctuations in water314
tables and precipitation that we studied in the previous section.315
We estimate a model of the form:
log(Ad,t) = −aDd,t + bPd,t + cdt+ log(Ad) + ed,t (6)
Here, Ad,t is area, Dd,t is depth to water, Pd,t is annual (monsoon) rainfall, and Ad are316
unknown base district specific areas, d is a district index and t is a year index. This317
logarithmic model assumes that changes in water tables or rainfall have a scale effect on318
irrigated area, i.e. that the local effects of these variables on irrigated areas on the farm319
level is rather homogenous, and therefore the size of the effect is proportional to the base320
irrigated area in a district. This form is convenient, because it allows us to use a fixed321
effects approach to remove differences in basic scales of irrigation across districts.322
Also, we include district-specific time trends (i.e. district specific growth rates of irri-323
gated areas) in order to isolate annual fluctuations from steady increases that result from324
geographical expansion of irrigation over time7. We also check the robustness of the re-325
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sults to the inclusion of quadratic time trends, to the clustering of errors across districts,326
and to AR(1) serial correlation.327
While no data on irrigated area is available for Punjab, such data in Telangana are328
available for the rainy (Kharif) and dry (Rabi) season separately. We run the regressions329
separately over the two periods 1986–2002 and 1998–2006 for which two different water330
table time-series are available. Regressions estimates are displayed in Table 5. The effect331
of the pre-season depth to water has a clear and strong effect in both seasons. A one332
meter decline in depth to water leads to a 13 % reduction in irrigated areas in the dry333
season (consistently across both periods, columns 1 and 3) and a 4.5 % reduction in the334
rainy season (column 2). While annual rainfall is important in the rainy season, and a 100335
mm increase raises irrigated area by 3.5 %, it has no significant effect in the dry season.336
This could reflect either cropping decisions that are based on early season rainfall or the337
abandoning of some irrigated area if precipitation does not fulfill its expected share of the338
water requirements and is consistent with the fact, that rainfall is very highly concentrated339
in the rainy season, so its only effect on the dry season should be mediated through the340
pre-season water table. It is the cumulative rainfall over several years that matters for dry341
season irrigation, not only the same year’s rainfall. In that sense, groundwater resources342
provide a buffer, but this buffering capacity is limited.343
Columns 5-7 in Table 5 display regression estimates for source-wise rainy season irrigated344
areas. Areas irrigated by wells, especially the shallower wells, are mostly responsive to345
water tables, whereas areas irrigated by tanks, shallow surface structures, are especially346
responsive to rainfall. This is consistent with our framework: groundwater irrigation is347
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not as sensitive to same year rainfall as small surface storage, but it is sensitive to the348
water table, especially if it comes from shallower wells.349
We also apply similar regressions for the areas cultivated with rice, which dominates350
intensive irrigation in both regions and allows for a comparative analysis. In Punjab, rice351
cultivation is largely restricted to the rainy (Kharif) season, since dry season temperatures352
are unsuitable for it whereas dry season irrigation is devoted to wheat which has an353
approximately 40 % lower irrigation water requirement as compared to rice in the region354
[Bandyopadhyay and Mallick , 2003; Kang et al., 2003]. In Telangana, rice cultivation355
dominates irrigation in both the rainy and dry seasons.356
Regression results, displayed in Table 6, paint a similar pattern as the irrigated area357
regression (Table 5). A one meter decline in the pre-rainy season water table in Telangana358
leads to a 8 % reduction in rice cultivated area. In the dry season, the reduction is larger359
at 14 %. There are no significant effects of either water tables or precipitation in Punjab.360
We also check the effects on rice yields. The effects of rainfall or water tables are sub-361
stantially lower than the corresponding effects on areas, in terms of magnitude, statistical362
significance (except for rainfall during the rainy season in Telangana), and explanatory363
power, consistently with out empirical framework’s focus on areas. In Punjab, rainfall364
actually has a negative effect which is probably attributable to flooding and water logging365
or to reductions in solar radiation, pointing again to the absence of water limitations in366
this region.367
These regression results support the discussion in Section 2 about the relative impor-368
tance of water scarcity and energy in Punjab and Telangana. They are also consistent369
with anecdotal evidence. During the drought years of 1988, 1997 and even 2002, irrigation370
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in Telangana suffered greatly and our interviews in the region have revealed that many371
farmers had to abandon their farms and migrate, whereas in Punjab the effect was hardly372
noticeable. Since water tables in Telangana’s hard rock aquifers are highly variable even373
on small spatial scales, deeper average water tables may mean a greater proportion of wells374
are completely dry at the beginning of the season so their owners have to forgo irrigated375
cultivation. Moreover, the interviews showed that farmers gauge pre-season water tables376
and take them into account in deciding how much area to sow with paddy, especially in377
the dry season, when groundwater is the sole source of irrigation water. The decision in378
the rainy season is more complicated because farmers are facing an uncertain supply from379
rainfall, and it is likely, but not certain that it will both fill their wells and reduce irri-380
gation requirements. Moreover, low or excessive rain might force them to abandon some381
of their cropped areas and this might be reflected in the accounting of irrigated areas.382
Whatever the precise mechanism is, both anecdotal evidence and the above regressions383
paint a similar picture of the important role of physical water scarcity as a limiting factor384
in Telangana and its basically non-existent role in Punjab.385
In summary, we find that in Telangana, irrigation grows at a more variable rate than in386
Punjab, and even despite the limitations of our coarse dataset, we are able to see that a387
significant portion of this variation is explained, in a statistically significant and intuitive388
manner by stochastic changes in water supply (and these explain a large fraction of the389
variation). In the dry season, annual fluctuations are largely driven by pre-season water390
tables, and in the rainy season, by rainfall. In the deep aquifers of Punjab, in contrast,391
such changes do not seem to impact irrigation much.392
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5. Welfare and Policy Dimension
Discussions of the social impacts of groundwater over-extraction, in India and gener-393
ally, usually revolve around the long-term threats to the sustainability of agricultural394
production and incomes as a result of falling water tables. In Section 3, we saw that395
over-extraction of deep alluvial aquifers in north-western India has indeed led to consis-396
tent declines in water tables over several decades. Conversely, in a prominent region of397
central India overlying shallow aquifers, a declining trend does not subsist over long-term398
period. Off course, this is a natural consequence of the limited storage of such aquifers.399
Extraction cannot actually exceed renewable supply consistently in the long-run, and wa-400
ter tables cannot, by definition, keep declining, because they relatively rapidly approach401
the bottom, and also, because they are rapidly recovered by abundant rainfall.402
The classification of such aquifers as over-extracted should therefore be interpreted in403
a different light. In what sense, then, can such aquifers be excessively exploited? If the404
issue is not really an issue of sustainability, what are the associated welfare losses?405
While lacking in trend, water table dynamics in the hard rock region display a large406
degree of inter-annual variability. In Section 4, we showed that these fluctuations translate407
to fluctuations in irrigated areas, which in turn lead to a high degree of variability in food408
production and farmers’ incomes. Here, we will argue that beyond a point, increases in409
the extraction effort (the density of wells, energy usage, etc.) of a thin aquifer lead to410
increased variability in water supply. This variability can carry high social costs, and411
it is in this sense that extraction can be excessive. In other words, we argue that it is412
extraction effort that can be excessive, because it enables unrestrained extraction when413
water is abundant, and this increases the variability in water tables and, as a result, the414
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variability in water extraction and in irrigation. It is this variability that is the main415
source of social welfare losses associated with excessive extraction effort.416
Inter-annual fluctuations in income can carry heavy social costs, especially in a rural, de-417
veloping country context. The development economics literature documents high income418
variability as a prominent characteristic of rural poverty 8 and demonstrates the adverse,419
direct and indirect effects on consumption, welfare, mean income, investment decisions420
and technology adoption [Morduch, 1994; Townsend , 1994]. The primary cause is that the421
poor often lack access to formal financial mechanisms like insurance or credit that enable422
them to maintain stable consumption in the face of income fluctuations. Traditional, in-423
formal mechanisms for consumption smoothing are prevalent in these environments, but424
they seem to only be able to offer partial protection, and are likely to be ineffective in deal-425
ing with non-idiosyncratic income shocks, i.e. shocks that are covariate across households,426
like the spatially coherent rainfall deficiencies we study here. One direct indication of the427
social importance of income variability in rural India is provided by the government’s428
large-scale efforts to provide farmers with alternative sources of income during times of429
low agricultural productivity, a large fraction of which are climate- and water-related.430
Formally, the social welfare loss associated with variability in agricultural production431
means that the (gross) benefits from groundwater exploitation depend not only on the432
mean supply of irrigation water (positively), but also on its variability (negatively). The433
socially optimal choice of the water extraction effort (i.e. well density and depth, pump434
HP, etc.) involves a balance between energy and infrastructure costs and the net benefits435
of irrigation. Below, we illustrate how this optimal level of effort is affected by the presence436
of variability and show in what sense extraction effort can be excessive.437
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We analyze a linear form of the coupled groundwater-agriculture dynamics presented438
in Section 3, in which439
W (V ) = eV (7)
and (assuming natural discharge takes place after extraction)440
L(V ) = n(1− e)V (8)
The assumption of linear discharge (post-human harvesting) is the standard modeling441
assumption in linear reservoir models where n is a response factor. The assumption of442
linear water extraction is also common in the natural resources literature (sometimes443
called the Gordon-Schaffer relationship), where the proportionality constant e is referred444
to as harvesting effort. Here, we understand e as an extraction effort. This parameter is445
constrained to be lower than unity, and reflects the density and depth of wells and the446
energy supply, in terms of both pump horsepower and hours of power supply.447
This way of modeling human harvesting behavior is appropriate for un-regularized com-448
mon property, open-access resources, for which incentives to conserve, or reduce effort449
levels are lacking. It is especially apt in the Indian context, because even the electricity450
for pumping is provided at a flat rate which is independent of actual usage (and even this451
charge has been nullified lately), further reducing the incentives for conservation or reduc-452
tions in use. In this context, the level of extraction effort corresponds to the length of daily453
power distribution by the government, as well as to well density and pump horsepower.454
The irregular supply of power is further reason to fully utilize it when it is available. In-455
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deed, all anecdotal evidence suggests farmers utilize all electricity provided to maximize456
their water extraction.457
With these assumptions, the dynamics of water tables and water extraction obey the458
following stochastic dynamics:459
Dt+1 = (1− e− n(1− e))Dt −r
ρRt + (e+ n(1− e))B (9)
Wt = eρ(B −Dt) (10)
This simple model can describe the deep aquifers of Punjab as well as the shallow460
aquifers of Telangana by varying the parameter B correspondingly. Both regions have461
roughly similar water requirements per area (same cropping pattern), w ≈ Wt, and compa-462
rable water tables D, but they differ in aquifer depths where B(Punjab)>> B(Telangana)463
which means that e(Punjab)<< e(Telangana)9.464
Over time, the stochastic dynamics it describes converge to a steady state probability465
distribution of water tables and water supply, a distribution that describes both the466
risk profile of water supply in a given year (far enough in the future) and the temporal467
distribution of water extraction over long time scales.468
The time scale needed to converge to the steady state depends on the parameters of the469
model, and in particular, on the depth to the bottom. As an illustration, Figure 7 displays470
a simulation of the model for two parameterizations that roughly capture the two regions,471
and differ primarily in their depth to bottom B. It is clear that the shallower aquifers472
(low B) reach steady states faster (give a constant level of effort). The steady state is not473
an appropriate description of the dynamics of Punjab’s groundwater irrigation, if only by474
the fact it is still able to consistently support extraction in excess of natural recharge, but475
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it can be appropriate for the shallow aquifers of Telangana, as is suggested by Figure 3,476
and on which we now focus.477
The steady state corresponding to a given extraction effort level e is characterized by478
a probability distribution for the water table, and hence for water extraction. Increases479
in effort level affect both the expected value and higher moments of this probability480
distribution. In the linear model, we can obtain closed-form solutions for the expected481
value and variance of water extraction, which we will denote by µW (e) = E(W ) and482
σ2W (e) = V(W ), by taking the expectation and variance of the dynamic equation in the483
infinite time limit, and noting that recharge from rainfall is independent of pre-season484
water table 10. Denoting by µR(e) = E(R) and σ2R(e) = V(R) the mean and variance of485
aquifer recharge, we find:486
µW =eµR
e+ n(1− e) (11)
σ2W =
σ2Re
2
1− (e+ n(1− e))2 (12)
From these expressions, it can be readily shown that both the expectation and variance487
of water extraction increase in effort level, i.e.488
dµW
de≥ 0,
dσWde≥ 0 (13)
A higher extraction capacity enables one to capture more of the annual recharge from489
rainfall and lose less to natural discharge. The expected value of water extraction therefore490
increases in e(unless n = 0). The increase in variability is less obvious. It captures the fact491
that high extraction efforts allow the near emptying of the aquifer even after a relatively492
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abundant recharge, which leave no buffer in the aquifer in case a dry year follows. The493
reduction in buffering results in higher variability. In the limit, as effort increases and494
e → 1, all buffering capacity of the aquifer is lost: water extraction is exactly equal to495
recharge, and is as variable as rainfall.496
Figure 8 shows a plot of the mean and the spread of the long-term water extraction for497
two values of n. The benchmark case n = 0 (no natural discharge losses) is instructive,498
and is probably a reasonable approximation in Telangana’s aquifers. In this limit expected499
water extraction µW remains constant at the value of average rainfall recharge µR, even as500
the variability σW keeps increasing. This simply reflects the fact that long-term extraction501
must equal long-term recharge (note that the mean depth does increase with e, but these502
two balance each other in terms of water extraction. Off course, the initial volume of503
water stored in the aquifer can help avoid this long-term mass balance for a while — and504
for quite a long while in a deep aquifer where this initial storage can be very large). At505
this limit, it obviously makes no economic sense to increase extraction effort, even if it506
were costless to do, because it only increases the variability without improving the mean.507
For positive values of n, this is not as obvious, as both the expected level and variance508
of water extraction increase simultaneously. The trade-off between increases in these two509
variables depends on the social welfare function and the degree to which it embodies510
aversion to variability.511
A standard way in economic theory to describe a risk-averse social welfare function512
associated with a randomly distributed water supply, is to use a rising, concave benefit513
function B(W ), and define a gross social welfare function associated with a probability514
distribution of water supply as the expected benefit, GB(e) = Ee(B(W )), where the sub-515
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script e indicates that the expectation is taken with respect to the probability distribution516
associated with the steady state reached as a result of extracting water at the effort level517
e. Again, the use of the steady state mean benefit is appropriate for the thin aquifer of518
Telangana since it is reached quite rapidly. 11519
The gross social welfare function is a function of e, the water extraction effort, which520
is a measure of the extent of (private and government financed) expenditure on pumping521
infrastructure and energy supply, and has a cost, which we will assume can be described522
by a convex cost function C(e). The net social benefit from an effort level e is then:523
NB(e) = GB(e)− C(e) (14)
The optimal level of social effort is524
e = argmax NB(e) (15)
Our goal here is to understand the effect of stochastic variability in recharge on the525
optimal level of social effort. In the deterministic case this would amount to optimizing526
NB(e) = Be(W (e))− c(e). The stochasticity of precipitation complicates things because527
varying the level of effort now affects the entire probability distribution of water supply,528
and therefore affects the expected benefit not only through the change in the expected529
water supply, but also in higher moments of the water supply distribution.530
Our linear model in Equation 7 and 8 allowed us to calculate the first two moments of531
the water extraction probability distribution. To build on these results, we will assume532
that the benefit function is quadratic 12 (so it will be a function of only the first two533
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 27
moments) and concave, and that the gross benefit function is also concave in effort so it534
has a unique maximum:535
Be(w) = aw − bw2
GB(e) = Ee(B(W ))
= B(Ee(W )) + (Ee(B(W ))−B(Ee(W )))
= B(µW )− bσ2W (16)
We have seen in Equation 12 above that σ2W is proportional to σ2
R, the variance of536
aquifer recharge. The optimal level of effort is determined by the first-order condition:537
dB(µW )
de− bdσ
2W
de=dC(e)
de(17)
Suppose now that σR is increased, i.e. that the variability in aquifer recharge is in-538
creased. This decreases the LHS. To rebalance the equation for the new level of σR, e539
should therefore be lowered, because by assumption of concavity that would increase the540
LHS and decrease the RHS. Thus, the new optimal level of effort becomes lower. This541
proves that, in our linearized model, the optimal level of extraction effort e is decreasing542
in the variability of rainfall. In particular, it is lower than the deterministic optimal level.543
This is intuitively pleasing. Once again, a higher extraction effort reduces the losses544
to natural discharge, but reduces the reliability of water extraction. Taking this into545
consideration reduces the socially optimal level of harvesting effort. This is the key insight546
of our model.547
6. Discussion and Conclusion
D R A F T March 2, 2011, 11:49am D R A F T
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By mining the large volumes of stored water, and by continuously enhancing extrac-548
tion capacity through increases in the depth of wells and the power of pumps, Punjabi549
agriculture can temporarily sustain water use levels that exceeds mean recharge, escape550
the long-run mass balance and remain far from steady state. Eventually, this process551
will come to a halt. Extraction capacity will hit a practical (the energy or drilling costs552
become prohibitive) or theoretical limit (maximum effort reached) and water extraction553
will start to decline and the system will converge to a steady state, but this outcome does554
not seem to be very near in Punjab.555
In Telangana, however, the shallowness of the aquifer means convergence to steady556
state is rapid so the steady state can provide a reasonable approximation. Increases in557
extraction capacity can sometimes raise water supply above mean recharge, but this will558
be a short-lived benefit. In the long-run, as our model shows, such increases will improve559
the mean (at a decreasing rate), and they will also reduce the buffering capacity of the560
irrigation system and its reliability. The length of the time-series we have is insufficient561
to establish these predictions empirically, but the general pattern suggested by Figure 2 is562
not inconsistent with them. The boom in bore-well proliferation (an increase in extraction563
capacity) has had some positive returns, but they are not as high as would be hoped and564
they have certainly not made the groundwater irrigated agricultural system more reliable565
or steady.566
The lack of regulation on groundwater pumping and structuring of the costs of energy567
in much of India, including our regions of study in Punjab and Andhra Pradesh, makes568
it unlikely that the socially optimal level of extraction effort, as discussed above, will be569
achieved. First, electricity for pumping is provided at no cost in the two states (elsewhere570
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 29
in India the rate is flat and highly subsidized). Farmers do not face a marginal cost of571
neither pumping energy nor groundwater and have little incentive to use it efficiently,572
especially as they are locked in a tragedy of the commons over the exploitation of this573
common resource. Under these conditions, it is natural for farmers to use as much energy574
as their pumps can use while it is supplied, which is sure to be excessive from the social575
point of view. Second, while farmers do bear the costs of installing wells, the open access576
to the groundwater resource suggests there is an excess of wells being drilled and that577
erodes the water supply and profit per well (for more discussion on that topic, see also578
Athanassoglou et al. [2011]). For these two reasons, it is likely that extraction capacity is579
excessive, even if only from the point of view of average profit.580
Even if the costs of enhanced variability are ignored, there are good reasons to ex-581
pect extraction capacity to be excessive, in other ways, in both Punjab and Telangana.582
Groundwater is a common, free access resource. There is no real limitation or regulation583
on the drilling of wells to access it. Basic economic principles predicts inefficiently ex-584
cessive extraction capacity to be installed in exploiting such an open access resource, in585
the sense that the mean returns per unit of effort (i.e. per well, or per unit of energy)586
will be lower than optimum. To make matters worse, in both the states, Andhra Pradesh587
and Punjab, as in most key groundwater irrigated parts of India, energy costs are heavily588
subsidized by state governments and the low tariffs are also flat, so there is no marginal589
cost on the use of pumping energy, not to speak of water.590
The analysis in this paper suggests there are additional costs to this excessive extraction591
capacity because of the variability it enhances. Not only is extraction capacity inefficiently592
excessive in terms of average profits, but in shallow aquifers like Telangana’s, our model593
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suggests it may lead to an increase in variability that further reduces social welfare. Our594
analysis thus provides additional motivation for reducing energy use in Indian agriculture.595
It may not be unreasonable for a government to find it justified to spend large amounts of596
energy in order to sustain irrigation, support rural livelihood and boost food production.597
However, our analysis shows that even if the government’s energy costs are ignored, a598
large energy supply may actually harm the welfare of the farmers that utilize it in ways599
that have not been appreciated so far.600
Acknowledgments. Support from the Pepsi Co. foundation and the Columbia Uni-601
versity’s Cross-Cutting Initiative at the Earth Institute is gratefully acknowledged. We602
would like to dedicate this paper to the memory of Dr. Pradeep Raj who passed away603
early 2011. Dr. Raj was instrumental in furthering our understanding of the groundwater604
situation in India. We are grateful to him for having been a constant source of inspiration605
and learning. He will be deeply missed. We thank Victor Vazquez. We thank S. P. Tucker606
and the district collector of Nalgonda District. We thank the Groundwater department,607
Government of Andhra Pradesh and Nalgonda district, as well as the Andhra Pradesh608
directorate of Economics and Statistics, for helpful discussions and data. We offer special609
thanks to A. C. Reddy for assistance and insights in the field. We thank the participants610
of the 11th Occasional California Workshop on Environmental and Resource Economics,611
University of California Santa Barbara, the Eleventh Annual Colorado University Environ-612
mental and Resource Economics Workshop, Vail, Colorado, the Sustainable Development613
seminar at Columbia University, New York and sessions in the American Geophysical614
Union meetings in San Francisco, 2009, for helpful discussions.615
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Notes
1. While reliable figures are difficult to come by, it is clear that energy use for pumping has increased steadily over the last
few decades [Morris, 1996; Dubash, 2008] and in many states it is estimated to use more than 40 % of total electricity
consumption and be responsible for more than 40% of the annual budget deficit [Briscoe et al., 2006].616
2. The hydraulic conductivity of granite is in the order of 10−7 ft/day (even though it is highly variable and can locally be
as high as 10 − 103 ft/day) whereas in an alluvium it is in the range 0.1 − 103 ft/day [Raj et al., 1996; Raj , 2004b].
3. Off course, a smaller fraction of the total land area is irrigated in Telangana, but on the other hand, only a fraction of
precipitation over unirrigated land is actually captured for recharging the aquifer.
4. It should be noted that water tables reported by monitoring wells tend to be lower than those reported by farmers in
their irrigation wells, probably due to local cones of depression around active irrigation wells.
5. Each of our two regions is relatively homogenous in its hydro-geologic properties, but the two regions are very different
from one another
6. Note that regressions for post-monsoon water tables (not shown) produce similar results.
7. Growth in irrigated areas can take place on both the extensive and intensive margin. The spread of irrigation wells to
new locations extends irrigation geographically, whereas an increase in the supply of energy can increase the yield of an
existing well. Remarkably, the rise of irrigated areas in Punjab coincides with the steady decline in water tables and the
associated rise in the amount of energy required to lift a given amount of water. The method of paddy cultivation makes
it unlikely that the intensity of irrigation has been improving in any significant manner, so the spread of irrigation is
likely driven by an even steeper increase in energy use for pumping.
In Telangana, where hard rock aquifers are more localized and heterogeneous, the geographical expansion of wells
can exploit new pockets of groundwater so it is probably the more potent driver of the expansion of irrigation. This is
especially true if, as suggested in the introduction, irrigation there is constrained by water scarcity, rather than land or
energy limitations: an increase in the energy supply per well should do little to increase the supply of water beyond a
temporary access to hitherto untapped layers of water through a deeper, more energized well. Figure 2 suggests that is
part of what happened when bore-wells were introduced in Telangana: the rise in areas irrigated by bore-wells was soon
followed by a decline (and larger fluctuations) in areas served by the traditional, shallow, open dug wells.
8. Some of the original evidence comes from the same areas we are analyzing here, e.g. [Walker and Ryan, 1990]
9. Indeed, in this linear model, the estimates of Section 4, mean that in Punjab e + n(1 − e) << 1, whereas in Telangana
e+ n(1 − e) is not negligible. Note also that in this linear model, we have, to first order, Wt+1/Wt ≈ 1 + ∆D/B, so the
coefficients of log irrigated areas on water tables can be interpreted as the inverses of some effective depth of the aquifer:
this is consistent with the comparison of Punjab and Telangana (Table 6) as well as the comparison of different irrigation
sources within Telangana (Table 5).
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10.It should be noted that in this approximation the model does not prevent the aquifer from spilling over which implies
that negative values of D might occur artificially. Hence, e must be at least high enough to guarantee that E(D) > 0 for
the model to be meaningful, i.e. that Be(e+n) > R, so that natural losses plus extraction exceed mean recharge. Lower
values of e lead to rising water tables, i.e. a regime that is far removed from the realities of both of our regions
11.This approach would not be appropriate for thick aquifers where it only describes the long-term distribution and therefore
needs to be balanced with short-term benefits in an inter-temporal optimality framework. We do not engage in such
analysis in this paper. It has been extensively discussed in the groundwater economics literature (see [Rubio and Casino,
2001] for a review), and our focus here is different.
12.As an example of a welfare function that describes one of the harmful effect of variability, consider the market price p of
irrigated crop production F assumed to be proportional to the water supply in any year. This market price is negatively
related to the supply F , and it is standard to assume a linear relationship p = p0 − ρF . The annual profit from irrigated
cultivation is pF = p0F −aF 2. The expected (or mean long-term) profit is reduced when variability is increased (keeping
the expected value constant), since prices and production are negatively correlated E(pF ) ≤ E(p)E(F ) and the difference
is higher the more variable F is.
References
Athanassoglou, S., G. Sheriff, T. Siegfried, and T. Huh, A simple mechanism for a complex617
aquifer, 2011, mimeo.618
Bandyopadhyay, P., and S. Mallick, Actual evapotranspiration and crop coefficients of619
wheat (triticum aestivum) under varying moisture levels of humid tropical canal com-620
mand area, Agricultural Water Management , 59 , 33–47, 2003.621
Briscoe, J., R. Malik, and W. Bank, India’s Water Economy: Bracing for a turbulent622
future, Oxford University Press, 2006.623
Central Ground Water Board, Annual report, 2006-2007, Tech. rep., Ministry of Water624
Resources, Govt. of India, Faridabad, 2007.625
Dubash, N. K., The electricity-groundwater conundrum: Case for a political solution to626
a political problem, Economic and Political Weekly , 2008.627
D R A F T March 2, 2011, 11:49am D R A F T
FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 33
Feller, W., An Introduction to Probability Theory and Its Applications , vol. 2, 3 ed., Wiley,628
1966.629
Kang, S., B. Gu, T. Du, and J. Zhang, Crop coefficient and ratio of transpiration to630
evapotranspiration of winter wheat and maize in a semi-humid region, Agricultural631
Water Management , 59 , 239–254, 2003.632
Kumar, M., K. Kumari, A. Ramanathan, and R. Saxena, A comparative evaluation of633
groundwater suitability for irrigation and drinking purposes in two intensively cultivated634
districts of Punjab, India, Environmental Geology , 53 , 553–574, 2007.635
Moench, M., Drawing down the buffer: Science and politics of ground water management636
in india, Economic and Political Weekly , 27 , 7–14, 1992.637
Morduch, J., Poverty and vulnerability, The American Economic Review , 84 , 221–225,638
1994.639
Morris, S., Political economy of electric power in india, Economic and Political Weekly ,640
31 , pp. 1274–1285, 1996.641
Raj, P., Groundwater Resource, 2004-05, Andhra Pradesh, Tech. rep., Groundwater De-642
partment, Government of Andhra Pradesh, 2004a.643
Raj, P., Classification and interpretation of piezometer well hydrographs in parts of south-644
eastern peninsular india, Environmental Geology , 46 , 808–819, 2004b.645
Raj, P., Status of ground water in Andhra Pradesh: Availability, Use and Strategies for646
Management, Tech. rep., Groundwater Department, Government of Andhra Pradesh,647
India, 2006.648
Raj, P., L. Nandulal, and G. Soni, Nature of aquifer in parts of granitic terrain Mahabub-649
nagar District, Andhra Pradesh, Journal of the Geological Society of India, 49 , 61–74,650
D R A F T March 2, 2011, 11:49am D R A F T
X - 34 FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP
1996.651
Ribot, J., A. Magalhaes, and S. Panagides, Climate variability, climate change and social652
vulnerability in the semi-arid tropics , Cambridge Univ Pr, 1996.653
Rodell, M., I. Velicogna, and J. Famiglietti, Satellite-based estimates of groundwater654
depletion in india, Nature, 460 , 999–1002, 2009.655
Rubio, S. J., and B. Casino, Competitive versus efficient extraction of a common property656
resource: The groundwater case, Journal of Economic Dynamics and Control , 25 , 1117657
– 1137, 2001.658
Shah, T., Taming the Anarchy: Groundwater Governance in South Asia, RFF Press,659
2008.660
Shiklomanov, I. A., Appraisal and Assessment of World Water Resources, Water Inter-661
national , 25 , 11–32, 2000.662
Siegfried, T., S. Sobolowski, P. Raj, R. Fishman, V. Vasquez, K. Narula, U. Lall, and663
V. Modi, Modeling Irrigated Area to Increase Water, Energy and Food Security in664
Semi-Arid India, Weather, Climate, and Society , 2010.665
Singh, K. K., D. R. Reddy, S. Kaushik, L. S. Rathore, J. Hansen, and G. Sreenivas,666
Application of Seasonal Climate Forecasts for Sustainable Agricultural Production in667
Telangana Subdivision of Andhra Pradesh, India, Springer Berlin Heidelberg, 2007.668
The World Bank and Government of India, India – water resources management sector669
review: Groundwater regulation and management report., Tech. rep., World Bank,670
Government of India, Washington, DC, New Delhi, 1980.671
Tiwari, V., J. Wahr, and S. Swenson, Dwindling groundwater resources in northern india,672
from satellite gravity observations, Geophysical Research Letters , 36 , L18,401, 2009.673
D R A F T March 2, 2011, 11:49am D R A F T
FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 35
Townsend, R., Risk and insurance in village india, Econometrica: Journal of the Econo-674
metric Society , 62 , 539–591, 1994.675
UNDP, Human Development Report , UNDP, United Nations Development Programme,676
2006.677
Vakulabaharanam, V., Agricultural Growth and Irrigation in Telangana: A Review of678
Evidence, Economic and Political Weekly , 2004.679
Wada, Y., L. van Beek, C. van Kempen, J. Reckman, S. Vasak, and M. Bierkens, Global680
depletion of groundwater resources, Geophysical Research Letters , 37 , 2010.681
Walker, T., and J. Ryan, Village and household economies in India’s semi-arid tropics ,682
Baltimore, Maryland, USA: Johns Hopkins University Press. 420pp. ISBN 0-8018-3886-683
X, 1990.684
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X - 36 FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP
R << W
R ! W
R " W
R >> W
R = W
!"#"#$
%&'(
)*&#%"+,#-),-!"#"#$
PunjabShallow and deep sedimentary aquifers (renewable), drawdown: ~ 0.5 m/a
GujaratDeep sedimentary aquifers (non-renew-able), drawdown: ~ 4 m/a
TelenganaShallow hard rock aquifers(renewable), drawdown:~ 0.3 m/a
Figure 1. Situation of groundwater exploitation in India. District-level data are shown.
Red colors indicate districts where, on average, more water is extracted from pumping
W than recharged from precipitation R. Note that the coarse district-level resolution
hides potential smaller scale depletion hotspots by spatial averaging. Territory south of
the dotted lines is generally underlain by hard rock. Data Sources: CGWB, Ministry of
Water Resources, Govt. of India and IMD.
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 37
0
200
400
600
800
1000
1200
1400
1600
0
100000
200000
300000
400000
500000
600000
700000
1970 1976 1982 1988 1994 2000
Rai
nfal
l (m
m)
Are
a (H
a)
Precipitation
Canals
Borewells
Open Wells
Tanks
Figure 2. Development of irrigated area (source-wise) in Telangana from 1970–2005.
Data Source: Directorate of Economics and Statistics, Government of Andhra Pradesh
(APDES).
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Punjab Telangana
Average hours of electricity supply 6.2 6.6
Average pump horsepower 5.4 4.7
Costs of electricity for pumping free free
Principal water consumer rice rice
Hydrogeology alluvial hard rock
Precent of area irrigated (from groundwater) 94% (68%) 41% (43%) (AP)
Area irrigated per well, Kharif Season 2.6 (rice +) 0.8 (rice +)
Area irrigated per well, Rabi Season 2.6 (rice +) 0.5 (rice +)
Table 1. Key irrigation characteristics in Punjab and Telangana. Source: 3rd Census
of Minor Irrigation Schemes.
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 39
0
2
4
6
8
10
12
14 0
200
400
600
800
1000
1200
1400
1600
1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003
Dep
th (M
eter
s)
Pre
cipi
tatio
n (m
m)
Telangana, Precipitation
Punjab, Precipitation
Telangana, Depth to Water
Punjab, Depth to Water
Nalgonda, Depth to Water
Figure 3. Groundwater and precipitation time series for Punjab (alluvial, blue) and
Telangana (hard rock, red). Pre and post monsoon depth to water are displayed for each
year. In Telangana, pre-1986 water table figures is only available in Nalgonda district, a
part of Telangana. Post-1986 data is regional average, and pre-1986 data is taken from
Nalgonda. Post 1986 comparison suggests good agreement between the two.
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Punjab Telangana Source
No. of districts 11 9
Water tables 1971–2003 1986–2002, 2000–2006 SGWB
Irrigated area (by season) - 1970–2004 APDES
Irrigated area (by source) - 1970–2004 (R), 1998–2006 (R + K) DES
Rice area (by season) 1971–2006 1970–1999 CMIE
Rice yield (by season) 1971–2006 1970–1999 CMIE
Annual precipitation 1971–2003 1971–2003 Siegfried et al. [2010]
Table 2. Data availability and sources. R: Rabi season, R + K: Rabi and Kharif season.
SGWB: Government of Andhra Pradesh, Groundwater Dept.; APDES: Directorate of
Economics and Statistics, Government of Andhra Pradesh; CMIE: Center for Monitoring
Indian Economy.
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 41
Rabi decline Kharif rise Annual Change
Punjab µ 1.07 0.89 -0.19
σ 0.19 0.70 0.73
total 17.18 14.19 -3.00
Telangana µ 2.8 2.80 0.01
σ 0.92 1.23 1.31
total 44.78 44.86 0.09
Table 3. Water table dynamics, regional averages for Punjab and Telangana. µ is mean
annual/total change, σ is variability (standard deviation), total refers to total change over
the period 1986–2002. For data source, see Table 2.
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!"#$%&
'"#"(%&
!)*+,+((%&
-).&/+((%&
Figure 4. The pie charts show the decomposition of the average net irrigated area (NIA),
by source, for each district. Note that NIA is the total area that is at least irrigated once
during an agricultural year. Source: Directorate of Economics and Statistics, Hyderabad,
Andhra Pradesh.
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Vt = B − Dt
Rt
Lt
B
Dt
At = Wt/w
Wt
Figure 5. Stylized representation of the modeled groundwater budget for a given
district (note: district identifier d is dropped). Rt is recharge, Wt is pumping, Lt are
subsurface losses, Dt is drawdown, At is irrigated area and w is crop water requirement.
Vt is the saturated thickness of an aquifer with mean porosity ρ. The subscript t denotes
time dependent variables.
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Telangana Telangana Punjab
1986-2002 2000-03 1972-2003
Lagged Depth to Water 0.528 0.772 0.984
(Previous Year) (0.059) (0.064) (0.036)
Precipitation -0.003 -0.004 -0.002
(0.000) (0.001) (0.000)
No. of Observations 140 36 263
Adjusted R2 0.696 0.875 0.897
Table 4. Pre-monsoon water table regressions. Dependent variable: Depth to water.
Regressions are run separately with the two water table data sets available in Telangana
(hard rock region, columns 1,2) and in Punjab (alluvial aquifers, column 3)
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 45
1986-2002* 2000-2006** 1986-2002*, Kharif Season
Rabi Kharif Rabi Kharif Borewells Dug wells Tanks
(1) (2) (3) (4) (5) (6) (7)
Pre-season water table -0.13 -0.045 -0.123 -0.006 -0.04 -0.05 -0.08
(0.016) (0.014) (0.01) (0.023) (0.06) (0.01) (0.06)
Rainfall -0.014 0.035 0.03 0.02 0.13
(0.025) (0.009) (0.03) (0.01) (0.03)
Observations 133 142 63 63 141 142 142
Adjusted R2 0.918 0.927 0.833 0.884 0.911 0.973 0.723
Standard errors in parentheses. Errors are clustered by year.
* Regressions contain district specific linear time trends and a global quadratic time trend.
** Regressions contain district specific constants and a global linear time trend.
Table 5. Irrigated Areas, Telangana. Dependent Variable: (Log) Irrigated Area.
Regressions are run for Kharif (rainy season) and Rabi (dry season) separately, using the
two water table data sets from 1986-2002 (columns 1,2) and 2000-2006 (columns 3,4).
Columns 5-7 report regression estimates for source-wise irrigated areas, available only for
the Kahrif (rainy season).
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Region Telangana Punjab
Season Rabi Kharif Kharif
Area Yield Area Yield Area Yield
(1) (2) (3) (4) (5) (6)
Pre-season water table -0.139 -0.041 -0.84 -0.010 0.001 -0.017
(0.018) (0.009) (0.023) (0.13) (0.018) (0.024)
Rainfall 0.059 0.005 0.031 0.028 0.001 -0.018
(0.018) (0.007) (0.016) (0.010) (0.004) (0.007)
Observations 122 122 124 124 145 116
Adjusted R2 0.923 0.549 0.864 0.650 0.991 0.696
Standard errors in parentheses. Errors are clustered by year.
Table 6. Rice areas and yields regressions. Dependent Variable: (Log) Area Cultivated
with Rice, Rice Yields. All regressions contain district specific linear time trends and a
global quadratic time trend. Regressions are estimated separately for the two rice growing
reasons in Telangana, Kharif(rainy season, columns 1,2) and Rabi (dry season, columns
3,4) and the single rice growing season in Punjab (rainy season, Kharif, columns 5,6).
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 47
1970 1975 1980 1985 1990 1995 2000 200510
5
0
Dep
th to
Wat
er (m
)
1970 1975 1980 1985 1990 1995 2000 2005100
200
300
400
Ric
e C
ultiv
ated
Are
a, G
ross
(’00
0 H
a)
Telangana
1970 1975 1980 1985 1990 1995 2000 200510
5
0
1970 1975 1980 1985 1990 1995 2000 2005200
400
600
800
1000Punjab
Figure 6. Plots of gross area cultivated with rice (right axis) and post-monsoon depth
to water (left axis) in Telangana (hard rock aquifer, top) and Punjab (alluvial aquifer,
bottom). The correlation between groundwater levels and irrigation is clear in the hard-
rock region, but is lacking in the alluvial region, where irrigated areas have been rising
even as water tables fall (de-trended fluctuations are also uncorrelated in a statistical
significant way).
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5 10 15 20 25 300
0.5
1
1.5
time [years]
Prec
ipita
tion
[m]
Panel a
PunjabTelangana
5 10 15 20 25 3025
20
15
10
5
0
time [years]Aq
uife
r sto
rage
hei
ght [
m]
Panel b
PunjabTelangana
5 10 15 20 25 300.5
1
1.5
time [years]
pum
ping
[m]
Panel c
PunjabTelangana
Figure 7. Illustrative, sample model run of the dynamic model presented in Equations 9
and 10 for one precipitation realization. Aquifer depth in Punjab is assumed to be B = 100
m as compared to the Telangana region where we set B = 15 m. Panel a: Sample
precipitation realizations for the two regions; Panel b: Depth to water over time; Panel
c: Individual in-period water extraction. Water requirement are set to those a single rice
crop in Punjab and 1.5 (cropping intensity) in Telangana, which are achieved initially
by effort levels set to e = [0.02, 0.4] for Punjab and Telangana respectively. Further
parameter values used: r = [1, 1](-), ρ = [0.5, 0.25](-) (these values are consistent with
the results of the regressions in table 4), and n = [0, 0](1/a). Stochastic precipitation
series generated with µR = [0.5, 0.8](m) and σR = [0.1, 0.2](m). Notice that steady state
fluctuations (around the mean of 0.8) are reached rapidly in Telangana, whereas after 30
years, water extraction is still far from the steady state value of 0.5 in Punjab, but is
remarkably steady.
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FISHMAN ET AL.: EXCESSIVE EXTRACTION IN THE SHALLOW AND THE DEEP X - 49
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Level of e!ort e
µW
Panel a
2!W (n = 0)µW (n = 0)2!W (n = 0.1)µW (n = 0.1)
Figure 8. The Figure shows the steady state mean (dotted line) and spread (standard
deviation, shaded area) of groundwater supply as a function of extraction effort levels e
(see Equations 11 and 12) for the Telangana region, using the parameterization of Figure
7. Two different parameter values for n are shown. n = 0 is the case where water losses
L to the downstream are negligible.
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