A study on the effectiveness of percolation ponds as a stormwater harvesting alternative for a semi-urban catchment

One of the challenges in urban stormwater management is to identify a suitable stormwater management method which will be socially, technologically and economically viable. In this paper, a study on the effectiveness of decentralized and interconnected percolation ponds as a stormwater harvesting technology, for a partially urbanized (semi-urban) catchment is presented. When applied to a case study region in Katpadi, Tamil Nadu, the results were encouraging. The investment required for implementing the proposed stormwater harvesting came to be about 555 Million for Option I and 714 Million for Option II. The annual volume of water that can be added to the groundwater system through infiltration from the ponds was found to be 1.22 Mm in the case of Option I and 0.74 Mm in the case of Option II. The percentage area under stormwater harvesting for the entire catchment was found to be 6.14% under Option I and 9.36 under Option II. The hydrologic performance of the proposed stormwater harvesting system indicated that for peak runoff values Option II is more efficient (in terms of minimizing runoff volume) compared to Option I; however, for daily rainfall values, Option I is hydrologically more efficient when compared to Option II.


INTRODUCTION
With growing urban population and urban water demand, stormwater harvesting (SWH) is becoming one of the preferred approaches for stormwater management in urban areas. Urban SWH refers to the practice of collecting, storing/infiltrating and treating runoff from impervious areas such as roof tops, parking lots, roads, etc., for partially meeting water demands of a community ( were found to be the best alternative to reduce the peak runoff but due to load considerations, their implementation on old buildings and commercial regions becomes highly questionable. For developing countries like India, Gogate et al. () and Koh et al. () developed a multiple attribute decision-making process for selecting the most suitable stormwater management alternative for a part of Pune city, and they concluded that leaky wells combined with rain gardens was the most suitable SWH alternative.
In summary, it can be stated that the choice of an appropriate SWH method, in general, is controlled by factors like economy, space availability, ease of implementation, and technology available locally. By taking into account the aforementioned factors in selecting an appropriate SWH technology for a locality, research is being presented in this paper to demonstrate the effectiveness of percolation ponds as a viable alternative for SWH for towns/cities in a developing country like India, where the implementation of LIDs like rain gardens, porous pavements, vegetative swales, bio-filters and infiltration trenches may not be feasible both practically and economically. The inspiration for use of percolation ponds as a SWH system was derived from the ancient temple tank system prevalent in South India. In ancient and medieval times, a temple tank served not only a spiritual purpose but also served as a water harvesting structure (Rao & Han ; Lei et al. ; Arulbalaji & Maya ). These tanks were more common in South Indian temples, since all the rivers in South India were rain-fed, and hence were less dependable for their flow during dry months. To overcome a shortage of water during dry months ponds were constructed which stored excess runoff during rainy seasons and percolated the water into the groundwater system. These ponds were well connected with each other such that excess flow from one pond would flow to the next connected pond.
Hence, the ponds not only served as percolation units but also served as temporary detention for flood water. Other reasons for selecting percolation ponds is that they are economical, can be constructed with locally available technology, can be easily retrofitted into the existing storm sewer system and, more importantly, can facilitate in increasing groundwater levels locally through infiltration (Central Groundwater Board ; Gogate et al. ). In this paper, we present a methodology for SWH by emulating the ancient temple tank systems.
The SWH system is designed by adopting a decentralized approach. In a decentralized approach a larger catchment is divided into smaller units so that it becomes easy to construct and maintain a SWH system. To check the efficacy of the proposed SWH system, the optimization model was applied to a case study area which is a part of Katpadi Town in the state of Tamil Nadu, India. The size of the study area is about 19 km 2 and almost 33% of this area is residential, the remaining area consisting of undeveloped land and vegetated land. The results indicated that a decentralized approach to SWH through a system of percolation ponds and lined channels can improve water availability potential locally. Figure 1 shows the detailed methodology flow chart for the proposed SWH system using percolation ponds. As can be seen from the flow chart, the entire methodology consists of three blocks: rainfall-runoff processing for the catchment (Block 1), optimization model framework (Block 2) and performance evaluation of the SWH system (Block 3). In Block 1, peak runoff resulting from impervious surfaces within the catchment is generated for pre-urbanized and post-urbanized catchment characteristics. The pre-urbanized and post-urbanized land use details for the catchment can be obtained through Google Earth satellite data. The peak runoff is estimated using rational method formula:

METHODOLOGY FOR THE PROPOSED STORMWATER HARVESTING SYSTEM
where Q is the peak runoff from the catchment (m 3 /s), i is the design rainfall intensity for a design return period estimated using intensity distribution function (IDF) relationship (mm/hr), A is the catchment area in hectare, and C is the rational coefficient which is a function of land use type. To determine the runoff volume, the discharge obtained from was determined for each sub-catchment (shown in Table 1) and sub-catchment unit using the Kirpich formula: where, L is the flow path length of the catchment unit (in metres) and S is the slope (m/m).
The pre-and post-urbanized runoff resulting from Block 1 is used as input to one of the constraints in the optimization model. Block 2 consists of the optimization model framework, wherein decision variables, objective function and constraints are discussed. The optimization model is solved using the appropriate optimization tool.
The output of Block 2 will be the optimal dimensions of the percolation ponds required. Finally, the performance of the designed percolation ponds under different rainfall events are determined using pre-defined efficiency measures through the performance evaluation of the SWH system.
A detailed application of the proposed methodology to a case study area is discussed in the next section.

APPLICATION OF METHODOLOGY TO A CASE STUDY AREA
To demonstrate the applicability of the proposed SWH system, the optimization model was applied to a case study region in Vellore, Tamil Nadu. A schematic figure of the case study area can be seen in Most of the residences in the area do not practise any water harvesting methods. The average groundwater level in the

).
In future, as the area becomes more urbanized and the population grows, immense stress on the groundwater source can be expected. It is, therefore, the right time to implement suitable SWH measures which will increase the water level in the groundwater table. To assess the level of decentralization that will be beneficial from a hydrologic and economic perspective, the following two SWH options were considered: • Option I: SWH through a series of interconnected percolation ponds and with decentralization at sub-catchment unit (SCU) level.
• Option II: SWH through a series of interconnected percolation ponds, with decentralization at sub-catchment The catchment here is the entire study area; sub-catchment is the unit of division when the catchment is divided into smaller units; and sub-catchment unit is the unit of division resulting from further division of a sub-catchment.

Arrangement of percolation pond within a SC and SCU
For decentralization of the SWH system at SC level, the entire catchment area was divided into 18 sub-catchments, with each sub-catchment roughly corresponding to a ward as defined by Vellore Municipal Corporation (https:// www.tn.gov.in/dtp). The further division of a sub-catchment into SCUs was accomplished by roughly identifying the area enclosed within the major roads ( Figure 3).
For this purpose, Google Earth VHR satellite image depositories with a spatial resolution ranging from 15 m to 15 cm were used. The final classification of the study area into SCs and SCUs can be seen in Figure 4. One of the primary assumptions in the proposed optimization model is that each SC or SCU will have only one percolation pond such that all the surface runoff from the impervious area within an SC/SCU will flow through gravity towards the pond. This will be possible if the pond is located at the lowest elevation within a SC/SCU.
The lowest elevation point within a SC/SCU was identified using elevation profile mapping of the study area in Google Earth and this lowest point in a SC/SCU is identified as the suitable site for situating a percolation pond (the arrangement of interconnected tanks is shown in Figure 5).
It should be noted that the soil type is similar (sandy loam) throughout the study area and the groundwater table is well below the ground level in all the sub-catchments. catchment level can be envisaged as a cascade of microreservoirs.

Constraints for the design of percolation ponds
Construction of a SWH system like percolation ponds requires a lot of space, which can be very difficult to acquire for semi-urbanized catchments such as the study area presented in this research paper, where land acquisition can be both difficult and costly. To reflect this reality in the optimization model, a constraint was defined to limit the usable space within a SC/SCU for the construction of a pond. Since all the ponds were assumed to be trapezoidal with side slopes of 1/2H:1 V, the constraint on space availability for a pond can be expressed as: and n is the number of divisions within the catchment.
The maximum area available for the construction of a pond in a sub-catchment is shown in Table 3. Apart from capturing the surface runoff and infiltrating it into the groundwater system, the other important function of the pond is to reduce the peak runoff volume generated within a catchment unit. In the present study, this constraint was defined in such a way that the runoff volume resulting from a catchment unit after implementing the proposed SWH system will be greater than or equal to the difference   Table 3. Hence, the constraint on absorbing portion of increasing in runoff due to urbanization can be expressed as: where, V preÀurban i and V postÀurban i are the peak runoff volume (in m 3 ) from impervious area under pre-urban and posturban catchment characteristics, respectively. The third constraint to the optimization model will be the storagecontinuity equation for the pond, which can be mathematically expressed as: where V excess i is the excess volume from pond i; V excess j is the excess volume flow to pond i from pond j situated at a higher elevation; k is the number of ponds at higher elevation connected to pond i.

Decision variables
As the total system cost will directly depend on the size of the pond, the decision variables for the optimization model will be: length of percolation pond in catchment  Table 4. The objective function therefore can be expressed as: where, C capitalÀcost is the capital cost of constructing a percolation pond ( /m 3 ); C landÀcost is the land acquisition cost ( /m 2 ); and PWF is the present worth factor, which is given by: where, r is the inflation rate in percentage and t is design period of analysis in years.

RESULTS AND DISCUSSION
The optimization model discussed in the earlier section was applied to the case study area of Katpadi, a semiurbanized catchment. The optimal dimensions of the pond in the catchment unit were determined for either of the options for a design rainfall intensity of five-year return period. The design rainfall intensity for each catchment unit was determined using the IDF equation shown in Table 5. The IDF curve was developed using daily rainfall data from the years 1950 to 2019. When applied to a catchment unit, the duration of rainfall in the IDF equation was taken equivalent to the time of concentration of the catchment unit (a valid assumption made in the application of rational method (Chow et al.

).
The optimization model was solved using the fmincon  Table 6.
Due to a paucity of space, the optimal pond dimensions for remaining sub-catchments under Option I are provided in the Supplementary material (Tables SCU1 to   SCU18). From the results, it can be inferred that the size of the pond in a catchment unit is majorly influenced by factors such as size of the catchment unit, location of the pond in the series and space availability within the catchment unit. For instance, SC13 has the largest pond dimensions as it receives runoff from many upstream subcatchments; also, the space available is sufficiently large.
For the catchment units of roughly equivalent size (SC15, SC16 and SC17) and not receiving any runoff from upstream ponds, the size of the pond (  Percolation pond volume,

Maintenance cost of percolation pond (in rupees)
Capital cost (C capital-cost ) 2.84% of (C capital-cost )  This indicates a better per-capita water demand can be achieved with Option I when compared to Option II.

Hydrologic performance of percolation ponds
Apart from economic criteria the proposed SWH options were also compared for their performance in utilizing potential rainfall for SWH, replicating pre-urbanized runoff hydrograph and runoff capture efficiency. The efficiency measures for runoff capture and potential rainfall utilization were determined based on the efficiency measures proposed by Sorup et al. (). The efficiency related to volumetric rainfall (E r ) expresses how well a SWH system is able to exploit rainwater as a resource and is defined as: where, V managed is the runoff volume managed by SWH entering from the impervious regions of the catchment and  V total annual rainfall is the total annual rainfall received by the catchment.
E rmax is the spatially independent efficiency measure expressing the ratio between managed volume (V managed ) and the volume of runoff received by the proposed SCM (V theoretical maximum ): In Equation (10), V managed is the volume of runoff retained by the SWH system and V theoretical maximum is the runoff volume from the contributing catchment unit.
Finally, the efficiency of the stormwater harvesting system to infiltrate the runoff from the contributing impervious area (E i ) can be expressed as the ratio of total annual infiltration (V infiltrated ) to V total annual rainfall . This is an efficiency measure defined by us to determine the infiltration potential of the proposed SWH system. This measure is expressed as:  Table 9, E i , indicates the capability of the proposed SWH system in infiltrating the stored water. This is again higher under Option I (5.56%) when compared to under Option II (3.37%).
To understand the functioning of the percolation ponds in controlling peak runoff volume and infiltrating the captured runoff within the pond, the designed optimal SWH system was simulated for a daily rainfall data series (23 days) of year 2015 (the highest rainfall year within the past 25 years). The rationale for selecting this set of data was that it was the duration in which a total depth of about 504.7 mm of rainfall occurred, which was about 38% of the total annual rainfall (1,374 mm) in that year. The rainfall hyetograph for the data used for simulating the SWH system is shown in Figure 6. For the sake of brevity, the hydrographs are shown only for a few subcatchments, i.e., most impervious (Figure 7 Finally, Figure 9 shows the potential depth of monthly infiltration that can be achieved after implementing the pro-

SUMMARY AND CONCLUSION
In this paper, a decentralized SWH system using a series of percolation ponds for a semi-urbanized catchment has been proposed. When the proposed SWH system was evaluated for the highest rainfall month that had occurred within the last 25 years, the resulting runoff hydrographs (Figure 7(a)-7(d)) for the least urbanized, most urbanized, largest and smallest sub-catchments indicated that there was a considerable reduction in peak runoff in both Option I and Option II.
Although, in the present study, the viability of using a series of interconnected percolation ponds for SWH has been discussed, there are however certain limitations in the proposed methodology. The primary limitation is with respect to the use of percolation ponds, which should be adopted only when the groundwater table is well below (preferably 8 m bgl) the ground level and also the underlying soil should be moderately permeable to facilitate faster infiltration of water from the ponds. Also, the methodology discussed will be more suitable for towns/cities where sufficient space is available for the construction of ponds.
Existence of a well-laid out storm sewer network for an area can minimize the cost and effort in diverting water from impervious areas to the ponds. The optimization model was solved using a deterministic approach, whereas, in a more realistic setting, there can be many uncertainties involved, for example, uncertainty in rainfall, uncertainty in estimating infiltration rate from the ponds and uncertainty in land use changes. However, the methodology presented in this research paper can be considered as a step towards designing a more complicated SWH system using percolation ponds which can include the aforementioned uncertainties. To conclude, we feel there are many cities/towns across India, where the proposed SWH system can be implemented beneficially, notwithstanding the limitations mentioned above. There is, however, a lot of scope for further improving the proposed method of SWH using percolation ponds. For example, in the optimization framework, the possibility of supplying the harvested water for satisfying a proportion of daily water demand was not considered. When included in the optimization model, this can affect the sizing of the ponds. Another interesting area for research would be to examine the uncertainties involved in the percolation process through the ponds, and its effect on their recharge potential. It will also be interesting to evaluate the performance of the proposed SWH system under climate change scenario and potential change in urbanization of the area in future.

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