Rainwater harvesting (RWH), the small-scale collection and storage of runoff
for irrigated agriculture, is recognized as a sustainable strategy for
ensuring food security, especially in monsoonal landscapes in the developing
world. In south India, these strategies have been used for millennia to
mitigate problems of water scarcity. However, in the past 100 years many
traditional RWH systems have fallen into disrepair due to increasing
dependence on groundwater. This dependence has contributed to accelerated
decline in groundwater resources, which has in turn led to increased efforts
at the state and national levels to revive older RWH systems. Critical to the
success of such efforts is an improved understanding of how these ancient
systems function in contemporary landscapes with extensive groundwater
pumping and shifted climatic regimes. Knowledge is especially lacking
regarding the water-exchange dynamics of these RWH tanks at tank and
catchment scales, and how these exchanges regulate tank performance and
catchment water balances. Here, we use fine-scale, water-level variation to
quantify daily fluxes of groundwater, evapotranspiration (ET), and sluice
outflows in four tanks over the 2013 northeast monsoon season in a tank
cascade that covers a catchment area of 28 km
Issues of water stress are now estimated to impact more than one-third of
the global population, and it is predicted that this fraction will nearly
double as the world reaches peak population (Wada et al., 2014). Such
increases in water stress are driven not only by a growing population,
changing patterns of food consumption and climate-driven changes in water
availability (Wiltshire et al., 2013) but also by spatial and temporal
mismatches between water availability and water demand (Oki, 2006). From a
spatial perspective, regional per capita water availability can vary
drastically from more than 50 000 m
Both spatial and temporal mismatches in water stress and availability characterize the climatic regime of India. The monsoon-driven climate common to semi-arid areas of India results in remarkable temporal variation where it is common for half of the year's total rainfall to fall over a period of only 20 h (Keller et al., 2000). With such limited annual water availability and the extreme intra-annual rainfall variability, there have been ongoing efforts in India to increase storage capacity and additional water supplies for agricultural production and economic development (Grey and Sadoff, 2007). Over the last century, such efforts have focused primarily on large-scale projects designed to ensure higher levels of water storage and availability, such as the building of large dams and canal systems (Cullet and Gupta, 2009; Mehta, 2001). For millennia, however, India has met the demand for seasonal water storage and increased water availability at the local level via the building of village-scale rainwater harvesting (RWH) structures, often referred to as tanks (Van Meter et al., 2014).
It is estimated that more than 39 000 of these RWH tanks are present in the
southern Indian state of Tamil Nadu, which is the focus of the present study
(Van Meter et al., 2014). These RWH tanks, which commonly take the form of
earthen impoundments, 20–40 ha in size (Gunnell and Krishnamurthy, 2003),
are built up from natural depressions in the landscape and have historically
been designed to meet the water needs of subsistence-level farmers for rice
production via managed sluice channels for irrigation (Farmer, 1977). Tanks
are often linked in a cascade with overflow from the upstream tanks spilling
into surplus channels that lead to downstream tanks. The tank systems have
fallen into decline in recent decades, primarily as a result of increasing
reliance on groundwater pumping, and cheap access to electricity. This has
led to declining groundwater levels, which, coupled with a growing demand for
increased agricultural production, have led to renewed interest in these
traditional systems (Kumar et al., 2008; Shah, 2004). Although the majority
of existing RWH tanks still remain in a state of disrepair (Anbumozhi et al.,
2001), it is estimated that reviving RWH systems at an all-India scale could
potentially add as much as 125 km
With the renewed and large-scale interest in the use of RWH structures (Garg et al., 2013; Rockstrom et al., 2002; van der Zaag and Gupta, 2008), it is critically important to ask whether these ancient structures perform their intended purpose of significantly improving water availability in a basin (Batchelor et al., 2003; Bouma et al., 2011; Calder et al., 2008a, b; Garg et al., 2013). To do so requires quantifying the dominant tank inflows and outflows, specifically evapotranspiration (ET), groundwater recharge, and sluice outflows to irrigated fields. These water fluxes determine relative water allocation to aquifer supplies, irrigation needs, and atmospheric losses, and are influenced by a wide range of both natural and management controls, from climate and geology to the more direct anthropogenic controls (e.g., sluice outflow regulation). As such, a better understanding of tank fluxes and drivers of these fluxes is necessary when managing individual and cascades of tanks to meet both societal (irrigation demand) and environmental (increasing rates of groundwater recharge) needs (Glendenning et al., 2012; Neumann et al., 2004; Ngigi, 2003).
Unfortunately, there is a lack of empirical studies that quantify tank hydrologic fluxes, especially at the scale of watersheds comprising of multiple tanks (Glendenning et al., 2012; Mialhe et al., 2008; Pandey et al., 2011, 2013). One reason for the lack of information is that both groundwater recharge and ET are highly spatially variable, and thus difficult to accurately measure at the field scale (Glendenning et al., 2012). Most previous studies of RWH tanks estimate recharge as a residual term in the water-balance method (Glendenning et al., 2012); in arid environments, however, recharge magnitude is small compared to other fluxes (Bond, 1998), making estimates from water balance residuals vulnerable to errors in other measured components. Furthermore, water-balance methods used in RWH tanks estimate recharge using modeled values of tank evapotranspiration, another rarely measured but critically important water flux in these arid environments (Sharda et al., 2006). While there is consensus regarding the value of direct measurements of temporal variations in recharge and evapotranspiration fluxes from RWH structures, such data are difficult to obtain due to the inherent complexities in making these measurements, especially under resource constraints (Glendenning et al., 2012).
Population and land-use data for the study cascade.
Here, we propose an innovative use of the White (1932) method as a
cost-effective means of obtaining spatially integrated, direct measurements
of both ET and groundwater exchange in flooded RWH tanks. The White method,
which was originally developed to estimate the magnitude of groundwater
consumption by phreatophytes (Loheide, 2008; Loheide et al., 2005), has
since been used to estimate ET and groundwater exchange in small, surface
water systems (Carlson Mazur et al., 2014; Hill and Durchholz, 2015;
McLaughlin and Cohen, 2013). In these systems, diurnal variations in
high-resolution surface water level data are used to decouple ET dynamics
from groundwater exchange. In this paper, we demonstrate an application of
this method to RWH structures, which are more complex than the systems
studied thus far in that they have additional outflows (overflow and sluice
outflow), and are much larger in spatial extent ( At the local scale, how do tanks partition water, and what is the spatial variability in this partitioning behavior along a tank cascade? At the catchment scale, how do tanks alter the water balance in a basin? What percentage of the irrigation requirements do tanks meet, and can they be managed more efficiently to increase this fraction?
The study site is located in the south Indian state of Tamil Nadu, in the foothills of the Western Ghats mountain range (Fig. 1a). The region surrounding the tank cascade is semi-arid, receiving a mean annual rainfall of 850 mm, with 50 % of that falling during the northeast monsoon season (October–December) (Government of Tamil Nadu, 2011; Vose et al., 1992). ET is greater than rainfall from January through July, while it is less than rainfall during the monsoon months (Fig. 1b). For the year in which the field study was done (2013), rainfall over the northeast monsoon season (October–December) was 355 mm, which is close to the 70-year average of 363 mm.
The focus of the study is the Thirumal Samudram (TS) tank cascade, a
hydrologically connected group of four rainwater harvesting tanks that
encompass an overall catchment area of 28 km
The landscape surrounding the tank cascade has a gentle slope, ranging 0.5–1.0 %, and is characterized by Alfisol and Vertisol soils underlain by fractured rock of granitic origin (CGWB, 2012; ICRISAT, 1987; Palaniappan et al., 2009). Land use for the study area is primarily agricultural. Within the study cascade, 81 % of the land is devoted to agricultural use, with 42 % of this total being irrigated (Table 1) (DHAN, 2010). During the northeast monsoon season (October–January), paddy (rice) is the primary crop in the region, while during other periods of the year, a variety of other crops are cultivated, including cotton, groundnuts, and pulses (Government of Tamil Nadu, 2011).
Tanks in south India are created through the construction of an earthen dam (bund) across depressional areas in the landscape as a means of storing surface runoff (Van Meter et al., 2014) (Fig. 2). During elevated water levels, flooding extends beyond the main depressional area and into flatter, often farmed areas (i.e., tank water spread area). The bunds are constructed using locally available materials, usually a combination of amassed earth and stones, supported by the roots of trees and bushes growing along the bunds (Weiz, 2005). Sluices (typically sliding gates) are constructed within the tank bund and are used to control the release of water into irrigation channels, which then transport the stored water to agricultural fields in the downstream tank command area (i.e., fields irrigated by tank water. During heavy monsoon rains, water may spill over the tank's overflow weir into surplus channels leading to downstream tanks or to nearby waterways (Van Meter et al., 2014). Tanks are often linked through these surplus channels in chains or cascades that can range in size from several to more than a hundred tanks, forming a dense hydrological network across this intensively managed agricultural landscape.
Summary of tank attributes based on historical tank data and the current study. Current tank capacity is based on our measurements, while historical tank capacity is based on Public Works Department data (DHAN, 2010).
Tank storage capacities vary across sites and time, with the latter due to siltation and desiltation cycles (Weiz, 2005). Historical data regarding maximum tank area and storage volumes for the four study tanks, obtained by the Public Works Department in India in approximately 1900, are summarized in Table 2 (DHAN, 2010). Information regarding the tank irrigated area, also known as the command area or “ayacut” (Weiz, 2005), is also provided. Although the maximum water depths of the four tanks are similar, ranging from 3–4 m at maximum fill, the historical data show that the tank areas vary significantly, ranging from 19.3 ha (Tank 3) to 58.7 ha (Tank 2). The ratio of command area to tank area historically ranged between 0.77 and 1.25 (Table 2), which is characteristic of tank systems found in this area (von Oppen and Subba Rao, 1987; Weiz, 2005). Table 2 also includes measurements made in the present study for comparison (discussed later).
Tank water levels were continuously measured during and in the months
immediately following the 2013 northeast monsoon season (October
2013–February 2014) using total pressure transducers (Solinst Levelogger
Edge, accuracy of
Bathymetric surveys were conducted using a combination of measured water depths in flooded areas (i.e., ground elevations relative to water surface) and a Trimble ProXRT2 GPS receiver paired with a Juno handheld computer for absolute ground elevations in exposed areas. Since Tank 4 had a large number of acacia trees that interfered with the accuracy of the Trimble, a Sokkia total station was used for ground elevation surveys. A total of 16–24 transects at a grid spacing of 40 m were taken in each tank, and all surveyed elevations were converted to ground elevations relative to the tank base (lowest point), which was defined as zero. The bathymetric data were used to create stage–volume and area–volume relationships for each tank, and estimate current tank capacities. The capacities estimated by this method led to reasonable values, with current capacities ranging between 62 and 92 % of the historical capacities (Table 2).
There are six sluices in the study area, two in Tank 1, two in Tank 2, and
one each in Tanks 3 and 4. Water release from the sluices is controlled by a
sluice gate that can be opened to different degrees by a sluice rod. For our
study tanks, the degree of sluice openness remained primarily unchanged
during the period of study, and thus the major factor that controlled sluice
discharge was the tank water level. To understand this relationship, sluice
discharge was estimated at different tank water levels. Discharge was
estimated by measuring the velocity and cross-sectional area over a chosen
section of each outflow channel just downstream from the sluice outlet. This
section was selected based on width uniformity and channel straightness.
Approximately 20–40 measurements were made during each discharge measurement
to obtain a reliable velocity estimate. Stage–discharge relationships
developed for each sluice were used to estimate volumetric daily sluice
outflow rates; these rates were then converted to area-normalized rates
(
As described in Sect. 2.2, in addition to water loss via sluice outflow, water may also flow out of the tank by spillage through the overflow weir into surplus channels during large storm events. Overflow was observed during the study period only in the case of Tank 4 on 20 October, during the first major rains of the monsoon season. For this event, the surplus flow volume was estimated based on the observed drop in water levels between 20 and 21 October.
The White (1932) method was used to calculate daily ET and net groundwater
exchange from high-resolution stage data on days with no rainfall (Fig. 3).
The White method is based on two central assumptions: (1) ET (cm d
The White method for estimating ET and groundwater exchange using diurnal water level fluctuations. Grey bars denote nighttime.
ET and GE (cm d
In our study,
Volumetric water balance calculations were carried out at both the individual
tank and the tank catchment scales across the northeast monsoon season to
answer questions regarding the partitioning of rainfall into the various
outflow components (e.g.,
Water balances were also calculated at the catchment scale using a nested catchment design for four catchments (Fig. 4): (1) Catchment 1 (C1): Tank 1 (T1) and its contributing catchment; (2) Catchment 2 (C2): Tank 2 (T2) and its contributing catchment, which includes Tank 1 and its catchment area and command area; (3) Catchment 3 (C3): Tank 3 (T3) and its contributing catchment, which includes Tanks 1 and 2, and their catchment and command areas; and (4) Catchment 4 (C4): Tank 4 (T4) and its contributing catchment, which includes Tanks 1, 2, and 3 and their catchment and command areas. This nested catchment design enabled us to explore the effect of varying catchment sizes and tank-to-catchment ratios on the water partitioning.
Further, in order to understand the impact of the tanks at the catchment
scale, we explored two scenarios for each of the four catchments scales
(i.e., C1–C4): (1) a with-tank (WT) scenario to represent current conditions
within the catchment (i.e., four existing tanks); and (2) a no-tank (NT)
scenario, with all other conditions (e.g., rainfall, ET on the catchment
area) being the same. For the NT case, catchment-scale runoff was calculated
using the Strange method (Shanmugham and Kanagavalli, 2013) and daily
rainfall over the monsoon season. Remaining rainfall was assumed to exit the
system through ET and groundwater recharge. For the WT case, we assumed the
sluice outflow from the most downstream tank in the catchment (T1 for C1, T2
for C2, T3 for C3, and T4 for C4) to represent the
Schematic for the catchment-scale water balance calculations. The dotted lines represent the boundaries, and points Q1–Q4 represent the outlet points for the four nested catchments C1–C4.
To understand the effect of tanks on catchment-scale groundwater recharge, we assumed the mean recharge to be 17 % of the mean annual rainfall for the NT case following Anurag et al. (2006). For the WT case, the landscape was assumed to include three different domains, with separate recharge fractions being assumed for each domain: (1) tank bed area: GE (Sect. 3.2) was used; (2) tank command area: 50 % of the sum of rainfall and sluice outflow (based on typical values for paddy fields (Hundertmark and Facon, 2003); and (3) the rest of the watershed: 17 % of rainfall (Anurag et al., 2006). The command area and the tank bed area estimates for the four tanks are provided in Table 2.
The current section is divided into two broad subsections. In the first, we report measurements of tank water levels and fluxes (ET and GE), and use these data as a basis for discussing tank water level dynamics across the monsoon season. In the second, we provide analysis of these and complementary data to answer questions regarding controls on the tank and catchment water balances and the ability of tank rainwater harvesting systems to meet irrigation water demand.
Tank water level and daily rainfall for the four tanks over the northeast monsoon season. Initial water levels are zero, reflecting dry tanks at the start of the monsoon season. Tank water level is measured from the deepest point of the tank. In Tanks 3 and 4, sensors were placed at the deepest points, while in Tanks 1 and 2, sensor wells were offset somewhat from the deepest points due to vegetation in the tank beds. As a result, the time series for Tanks 1 and 2 end earlier than those for the other two tanks.
The temporal variation in daily ET over the monsoon season, shown as green bars. There are data gaps in the figure since estimates were made using the White method only on nonrainfall days. ET increases towards the later part of the season, coincident with decreases in tank surface area (shown as the grey shaded area). ET rates are reasonable for the region and season when the inundated area is greater than 25 % of maximum area, as indicated by the dashed line.
Water levels in the tanks rose sharply in mid-October following the monsoon
rains, and then dropped over the next 3 months as water left the tanks
through ET, sluice outflow, and groundwater recharge (Fig. 5). Note that
although the northeast monsoon rains began in early September, the tanks
started filling only in mid-October. This time lag is likely due to a
threshold effect, where runoff to the tanks occurs after cumulative rain
volumes begin to exceed catchment infiltration capacity. Two distinct fill
events can be observed, one on 16 October and the second on 17 November for
all tanks except Tank 1, for which the second fill event is not as apparent.
Between 16 October and 17 November, the trajectories of Tanks 1 and 3
parallel each other, while those of Tanks 2 and 4 are similar to each other.
Towards the latter part of the season, the water-level trajectories of the
four tanks approximately parallel each other. Tank 1 loses its water the
earliest and is mostly dry by January, while the other three tanks retain
some water till February. This difference possibly occurs because Tank 1
overlies Alfisol soils (Table 2), which have higher
ET fluxes estimated with Eq. (1) for the four tanks are
shown in Fig. 6. ET rates derived with the White method are reasonable for
the region and season potential ET (PET) ca. 3–12 mm d
It may also be that an overestimation of the
The temporal pattern of net GE, estimated using Eq. (2), is presented in Fig. 7 together with trends in tank water levels and daily precipitation. GE rates across the monsoon season appear to be driven by a combination of both tank water levels and the occurrence and magnitude of rainfall events. Tank 2, for example, has relatively lower recharge rates (positive values in Fig. 7) in the earlier part of the season, with values decreasing with the occurrence of each major rainfall event, and then increasing incrementally over time until the next rainfall. The last period of significant rainfall occurs in mid-December, and shortly after this time, recharge magnitudes for Tank 2 reach a peak, and then slowly decrease with decreasing tank water levels. A similar pattern can be seen for Tank 4, where the peak recharge value occurs during the mid-December period, followed by a steady decline in recharge magnitudes as tank water levels decrease. In contrast, Tanks 1 and 3 appear to be less impacted by rainfall events; for these tanks, recharge magnitudes begin to decrease with decreases in tank water levels much earlier in the season, after the last major rainfall (64 mm) on 17 November. In the last few weeks of the monsoon season, Tanks 2–4 all switch over to a groundwater inflow regime (negative GE values). Lower recharge rates as well as these switches to groundwater inflow towards the end of the season may be due to tank water levels consistently having greater declines compared to the surrounding aquifer, resulting in decreases and potential reversals of hydraulic head gradients. This period is also, however, punctuated by some distinct, very high groundwater outflow events that may correspond to observed groundwater pumping in the vicinity, highlighting a potential direct human influence to tank recharge rates.
Daily groundwater exchange (mm d
To better characterize the dominant drivers for the magnitude and direction of GE, with the overall goal of generalizing these observations to larger scales, we plotted GE as a function of days since last rainfall for all four tanks (Fig. 8a). For Tanks 2 and 4, there is a threshold value of days since rain (14 days for Tank 2, and 16 days for Tank 4) that separates rainfall–GE relationships. That is, there is significant scatter in the rainfall–GE relationship at values less than this threshold, but strong negative relationships emerge between the two variables at higher values of day since rain (Fig. 8a). In contrast, Tanks 1 and 3 have much lower threshold values of only 1 and 3 days, respectively. This pattern of decreasing recharge with days since last rainfall is reasonable, as water levels in the tank steadily decrease over time, leading to decreased hydraulic head and thus lower rates of recharge. In contrast, immediately following a rain event, the system becomes more dynamic, and recharge is a function of not only tank water levels but also the short-term response of the local surrounding aquifer. When plotted for all tanks, GE was also found to respond linearly to tank water levels for most days throughout the monsoon season, except in the hydrologically dynamic periods after rain events, when the behavior was more erratic (Fig. 8b).
In addition to these patterns of groundwater exchange across the monsoon season, differences can also be seen along the tank cascade, from top (Tank 1) to bottom (Tank 4). First, while recharge dominates the exchange dynamics of Tanks 1–3, Tank 4 is more discharge driven. As shown in Fig. 9a, close to 90 % of all days throughout the monsoon show net recharge behavior for Tanks 1–3, while Tank 4 is split almost equally between net recharge and net discharge days. From a volume perspective, the discharge-to-recharge ratio for the tanks shows a general trend from smaller (0.3 in Tank 1) to larger (1.2 in Tank 4) across the tank cascade (Fig. 9b), with Tank 4 demonstrating net discharge behavior. Tank 4 is the most downgradient tank, suggesting the possibility that aquifer levels adjacent to Tank 4 are higher (possibly due to upstream tanks' recharge) for a longer period of time than the other three tanks, leading to more frequent groundwater inflow.
Our finding of a distinct spatial pattern in groundwater exchange and sluice outflow dynamics across the tank cascade is a novel contribution of the present study. Most studies that have explored the recharge–discharge functions of tanks (Glendenning et al., 2012) have focused on individual tanks, with no consideration of the position of the tank in a cascade as an important control on its functioning. Our results indicate that in order to upscale tank-scale information to understand catchment and regional-scale impact of tanks, more studies should focus on exploring the spatial arrangement of tanks in the landscape.
Partitioning of tank outflows across the northeast monsoon season.
Three questions were posed in the introduction regarding the partitioning of water within a tank cascade, the ways in which tanks alter the catchment water balance, and the ability of tanks to meet irrigation requirements in the semi-arid landscapes of south India. Below, we use our measured data to provide answers to these questions in the context of a discussion of physical vs. management controls on tank functionality.
The first question we asked was how tanks partition incoming surface water (direct rainfall on tank and surface runoff from tank catchment) into various outflow components, namely ET, groundwater outflow/inflow, and sluice outflow to the fields in the tank command area. The flow volumes corresponding to these components for each tank over the duration of the northeast monsoon season are plotted by week in Fig. 10a and are summarized in Table 3. Notably, recharge to groundwater is a significant component of tank outflows. Although the primary function of tanks in south India has historically been to provide surface water for irrigation, and despite the high clay content of soils in the area, groundwater recharge is the primary outflow mechanism in Tanks 1–3 (46–59 % of total outflows). For Tank 4, however, which is dominated by discharge behavior, the primary outflow mechanism is sluice outflow, which directly provides irrigation water to the tank command area. As seen in Fig. 10a, sluice outflows and recharge are the greatest early in the season, when tank levels are at their highest, and then decrease over time, ceasing entirely by mid-December for all four tanks.
Although the surface water volume lost to ET is substantial
(0.48–1.64 million m
Interestingly, a trend can be seen in the relationship between total tank outflows over the monsoon season and the maximum tank capacity (Fig. 10b). As we move down the cascade of tanks, the outflow-to-capacity ratio increases, from 1.06 for Tank 1 to as high as 2.25 for Tank 4. The outflow-to-capacity ratio is an indication of how many times a tank fills up during the season, and the increase in values along the cascade of tanks is a function of increasing return flows from upstream command areas entering the downstream tanks. For Tank 4 in particular, groundwater discharge provides a significant input of water into the tank (Fig. 9). Accordingly, Tank 4 has relatively greater amounts of water available for surface water irrigation throughout the season, with sluice outflow alone accounting for 1.2 times the total tank capacity. This increase in the outflow-to-capacity ratio along the cascade of tanks is an important feature of the tank cascade system, and highlights the need to study the tanks not in isolation, but in relation to their position along the cascade. Biophysical controls (e.g., weeds or sediments in tank beds of upgradient tanks) or management choices (e.g., planting crops with lower or high water requirements in upgradient tanks) can completely alter the water availability in a downstream tank. Thus, rehabilitation efforts and tank management should focus on maximizing benefits at the cascade scale instead of only at the individual tank scale.
The second question we asked was how tanks alter the partitioning of rainfall
into runoff at the catchment outlet (
Water balance summary at the tank catchment scale.
Sluice outflows and irrigation water demand (IWD).
Our results show a dramatic difference between the with-tank and no-tank
scenarios, and a distinct spatial pattern of response in the four nested
catchments. We found a significant decrease in
Despite this strong link between the presence of tanks and groundwater
recharge, tank maintenance has declined across south India as farmers have
become increasingly reliant on groundwater irrigation sources
(Balasubramanian and Selvaraj, 2003). With tank-irrigated area across Tamil
Nadu having decreased from 940 000 ha in 1960 to approximately 503 000 ha
in 2010, some suggest that current tanks are operating at only 30 % of
their potential capacity (Amarasinghe et al., 2009; Government of Tamil Nadu,
2011; Palanisami and Meinzen-Dick, 2001). This degradation of tank
functionality is eliminating or significantly degrading the primary mechanism
for aquifer recharge in an area where, without rainwater harvesting, the
majority of monsoon rainfall will leave a catchment as runoff within hours of
falling. Our water balance calculations show that tanks, with adequate
maintenance, provide a mean groundwater recharge benefit of 5600 m
Water supply-and-demand portraits in our tank cascade. The grey area represents the irrigation water demand (IWD), calculated as the difference between crop water requirements and rainfall (Brouwer et al., 1989). Planting dates were 17 October, 17 October, 25 September, and 13 September, for Tanks 1, 2, 3, and 4, respectively. The darker red area corresponds to the portion of sluice outflow that is utilized to meet the irrigation water demand, while the light red area corresponds to the portion of sluice outflow that is not directly utilized by crops in the tank's command area.
It should be noted that the recharge benefit suggested by the results in our
tank cascade is significantly larger than that reported for a watershed in
Gujarat, a state in western India, where it was shown that the construction of
new rainwater harvesting structures would lead to a 60 % decrease in
catchment runoff, but only a 5 % increase in recharge (Sharma and Thakur,
2007). In the Gujarat catchment, however, annual rainfall is approximately
half that in our south India catchment, and ET rates are estimated at more
than 50 mm d
While the first two questions focused on the physical controls on tank water
dynamics, our third question focused on understanding how tank water
management affects water balances and, in doing so, contributes to meeting
the irrigation requirements of the tank command areas. To answer this
question we have plotted supply-and-demand curves over the growing season
(Fig. 11). The supply curves are the sluice outflow volumes from the four
tanks. The demand curve in this case is the crop water requirement in
mm d
The supply-and-demand curves assess the ability of the tanks to meet paddy water demand by comparing IWDs to sluice outflows. The darker red areas in Fig. 11 denote sluice water used to meet the IWD, while the lighter red areas represent sluice water that is flowing out at a time when crops are not requiring that water. The grey areas in the figure represent the IWD unmet by sluice outflow. Notably, large quantities of surplus sluice water leave the tank soon after it fills. These surplus sluice outflows are not needed by the crops at the time they leave the tank and will either leave the catchment as evaporation or downstream runoff, or will recharge groundwater over the course of the outflow channel and become available to downstream users. Because the sluices are for the most part not actively managed or appropriately maintained in our study area, the sluices remaining perpetually open and outflows are purely a function of water levels in the tank rather than a timed need for irrigation water. As reported in Table 5, it was found that anywhere from 31–79 % of IWD within the study cascade remains unmet, while approximately 15–50 % of available sluice outflows leave the tank unutilized by crops in the tank command areas. This remaining irrigation water demand in many cases must be met by farmers using groundwater pumping to supplement tank water, and in other cases remains unmet, leading to reduced yields or crop failure. In the case of groundwater pumping, it should be noted that a significant portion of the tank water does leave the tanks as groundwater outflow, and is subsequently extracted by groundwater wells for irrigation, thus helping to meet the crop water requirements by an indirect route. The magnitude of this contribution of tank outflows to the crop water budget, however, is difficult to ascertain, and thus has not been included herein.
The timing of planting also has a significant impact on the ability of the tanks to meet crop water requirements (Fig. 11), with the later planting dates in Tanks 1 and 2 leading to more than 70 % of the IWD being unmet by sluice outflows (Table 5). Conversely, Tank 4, with its much earlier planting time (13 September), more effectively meets crop water requirements with sluice outflow. First, the early planting time leads to the lowest total IWD of all the tanks (752 mm), as more of the crop water requirements can be met by rainfall. In addition, there is a better temporal match for Tank 4 between the unregulated sluice outflows at high tank water levels (Fig. 11) and the crop water needs of the plants. Accordingly, more than 500 mm of the IWD is met by sluice outflows, and only 31 % of the overall demand remains unmet. These results suggest that, to optimize tank operations and to maximize the water-provisioning capabilities of the tanks, earlier planting times could be adopted by farmers, with supplemental irrigation from groundwater being utilized until the tanks fill. Such a change in management, however, would be dependent on both groundwater availability and the economics of groundwater pumping. Indeed, interactions with the villagers revealed that the earlier planting dates in the downgradient tanks could be attributed to the greater availability of groundwater in that region, which enables the farmers to plant before the monsoons have arrived.
In recent decades, there has been growing interest in the revival and expanded use of rainwater harvesting tanks across the agricultural landscapes of India and other semi-arid regions to address issues of water scarcity and aquifer depletion. While it is well established that these tanks can increase local water availability, leading to higher crop yields and direct socioeconomic benefits (Palanisami et al., 2010), the impact of widespread use of small, distributed storage reservoirs on the catchment-scale partitioning of water resources is still an open question. Furthermore, while significant resources are being used to rehabilitate tanks, there is a lack of understanding regarding how these ancient structures function in a modern landscape, under current socioeconomic and environmental pressures. The hydrology of these tanks is so intricately tied with the social system in which they are embedded that only a system approach, accounting for interactions between natural and human systems, can allow us to fully understand and manage these systems. Accordingly, any full analysis of tank water dynamics must be carried out within the domain of the emerging science of sociohydrology (Sivapalan et al., 2012).
In this paper we have used high-resolution monitoring of tank water levels to help quantify daily fluxes of evapotranspiration, groundwater recharge, and sluice outflows from the tanks, and have coupled this information with village-level data on planting dates and irrigated areas, to further our understanding of natural and human controls on water partitioning at both tank and catchment scales. At the tank scale, groundwater recharge and sluice outflow were observed to be the largest components of the tank water budget, with ET accounting for only 13–22 % of the outflows, including open water evaporation and ET of plants transpiring in the tank bed. At the catchment scale, our results demonstrate that the presence of tanks within the catchment decreases runoff by approximately 70 %, increases recharge by 40 %, and directly satisfies approximately 40 % of crop water requirements across the northeast monsoon season via surface water irrigation. These findings suggest that village-scale rainwater harvesting tanks can dramatically increase water availability at a local or village scale, but also may have negative impacts on downstream users due to large decreases in catchment runoff. Our results also highlight that, despite ongoing the efforts toward tank rehabilitation in our study cascade, a lack of sluice maintenance leads to a large fraction of tank water not being available for use in the tank command area. Thus, a more efficient management of sluice outflows, and better maintenance of the sluices themselves, could lead to the tanks meeting a higher fraction of crop water requirements.
An interesting and novel attribute of our study is the exploration of biophysical and social controls on tank water dynamics as a function of the location of the tank along a cascade, in a four-tank cascade system. We observe a distinct spatial pattern in groundwater-exchange dynamics with the most downgradient tank being mostly driven by groundwater inflow, while the other tanks are more outflow driven. Consequently the most downgradient tank has a much greater outflow-to-capacity ratio, and is able to provide a much larger volume of sluice outflow compared to its capacity. The ability of the most downgradient tank to provide more irrigation water is a function of the return flow from the command areas of the upstream tanks and highlights the need to study tanks, not in isolation, but as a part of a cascade. There is also a distinct pattern in the crop planting dates in the four tanks, with the more downgradient tanks having earlier planting dates, due to greater availability of groundwater, thus leading to a more efficient use of tank water. This dynamic highlights the feedbacks between the natural and human systems, where a greater availability of water at the catchment outlet leads to farmers deciding on earlier planting dates, which in turn leads to a more efficient use of the available water.
In conclusion, our results demonstrate the significant role that tanks can play in addressing challenges of limited water availability, by both increasing groundwater recharge as well as the water available for irrigation. However, they also draw attention to the potentially negative environmental impacts of tanks with respect to reducing downstream flows. These findings highlight the need to understand the spatiotemporal patterns in tank water dynamics at the basin scale, especially within the framework of a coupled natural and human systems approach that allows a more complete understanding of how tanks alter the sociohydrological dynamics of water-stressed landscapes. Thus, ongoing rehabilitation efforts of tanks must be complemented by more studies that quantify the functioning of these rehabilitated tanks and their impacts in altering basin-scale water dynamics, with the overall goal of appropriately managing tradeoffs between socioeconomic benefits and environmental costs.
The field study was carried out by Michael Steiff under the guidance of Daniel L. McLaughlin. Data analysis was carried out by Kimberly J. Van Meter and Michael Steiff. Drafting of the manuscript was led by Kimberly J. Van Meter and Nandita B. Basu, with contributions by Daniel L. McLaughlin. Nandita B. Basu conceived of the project and was instrumental to the basic experimental design.
This research is financially supported by the US National Science Foundation (1211968), Dynamics of Coupled Natural-Human Systems and by the University of Waterloo start-up funds. We thank the DHAN Foundation for generously sharing their experiences, data, and hospitality during the two field visits that allowed us to carry out this project. We would also like to thank K. Palanisami for his help with establishing connections for the research team within India and with his guidance regarding field site selection.Edited by: A. Mejia