This study focuses on evolving an integrated water management plan (IWMP) for Shimla City (erstwhile summer capital of British India). Presently it is the state capital of Himachal Pradesh. Total water demand (2014) is 58.46 million litres per day (MLD) against system capacity of 54.54 MLD. The present deficit of 3.92 MLD (2014) may amplify to 59.01 MLD in 2051. Resource assessment in the related watersheds namely, Ashwani, Nauti, Giri and Pabbar accomplished using remote sensing techniques and geographic information system (GIS) based Arc-SWAT hydrological model. Average annual precipitation in all watersheds for 26 years (1984–2010) is about 1,005 mm, out of which about 34% flows as runoff, 8% as groundwater and about 58% as evapotranspiration. Being ungauged watersheds, water balance equation considered as the validation criteria, coefficient of correlation ‘R’ between observed rainfall and simulated runoff varies from 0.94–0.96. Results further validated through actual measurement of inflow in lean period in one of the major sources, i.e. Giri River, which has shown very good correlation (R = 0.95) between simulated and observed stream flow. The study establishes that existing sources are not sustainable. IWMP suggests, source augmentation coupled with rainwater harvesting and reuse of wastewater as long-term strategic measures.

INTRODUCTION

Integrated water management plan (IWMP) takes into account future predictable and non-predictable issues. A study conducted in India to meet future water demands of Bangalore city, suggesting short-term and long-term strategies using sustainable solutions for efficient management of water cycle combined with reuse of treated wastewater (Narayana et al. 2012).

The Soil and Water Assessment Tool (SWAT), having an interface with Arc-View geographic information system (GIS) software, was used successfully for estimation of runoff and sediment yield from an area of Suni to Kasol, an intermediate watershed of Satluj River located in the Western Himalayan region, in a study ‘Simulation of Runoff and Sediment Yield for a Himalayan Watershed Using SWAT Model’ (Jain et al. 2010), which is an adjoining watershed.

A water balance study ‘Application of semi-distributed hydrological model for basin level water balance of the Ken basin of Central India’ has been done successfully with Arc-SWAT hydrological model. In this study it has been established that average annual rainfall of Ken basin for 25 years (1985–2009) is about 1,132 mm, out of which about 23% flows as surface runoff, 4% as groundwater and about 73% as evapotranspiration (Murty et al. 2014).

In another study ‘Resource assessment and strategic planning for improvement of water supply to Shimla City in India, using geospatial techniques’, short-term strategic planning has been suggested by drawing water from Giri River. Resource assessment has been accomplished using remote sensing techniques and GIS based Arc-SWAT hydrological model. It has been established that average annual precipitation in Giri watershed is about 1,011.44 mm for 26 years (1984–2010), out of which about 32% flow as runoff, 12% as groundwater and about 56% as evapotranspiration (Sharma et al. 2015).

Inadequacy of sources is the most critical issue which has been addressed in this study. IWMP for Shimla City envisaged for ensuring sustainable water supply as per future water demands and aspirations of the people.

STUDY AREA

Shimla Planning Area (Shimla City) is the study area (Figure 1). It comprises Shimla Municipal Corporation area, Dhalli, New Shimla. Tutu, Kufri, Shoghi and Ghanahatti, which is the only Class I city in the state of Himachal Pradesh. It is the capital city of the state of Himachal Pradesh with a permanent and a floating population (Census of India 2011) of 205,405 and 128,307 persons, respectively. Geographical area is 100 km2 and population density is 236 persons/km2. Due to uneven topography, altitude of the city varies from 1,507–2,454 m above mean sea level. The average annual rainfall is 1,577 mm with an average of 84 rainy days.
Figure 1

Map of study area (Shimla City).

Figure 1

Map of study area (Shimla City).

Combined watersheds and digital elevation model (DEM) of watersheds under Shimla water system is shown in Figures 2 and 3.
Figure 2

Combined watersheds under the Shimla water supply system.

Figure 2

Combined watersheds under the Shimla water supply system.

Figure 3

DEM of all watersheds under the Shimla water supply system.

Figure 3

DEM of all watersheds under the Shimla water supply system.

Water supply

Water supply for Shimla was initially planned and installed by the British in 1875. With the passage of time city the grew both geographically and demographically and water demand increased manifold resulting into a vast expansion of Shimla water supply system. Operating cost of the existing system is about $12.72 million (INR 763.5 million) per annum. Authority is supplying water costing $0.89 (INR 53.44) per kilolitre (kL), whereas only $0.25 (INR 15.24) per kL (average) is being charged from the consumers. The general layout plan of Shimla water supply scheme up to the main storage tanks is shown in Figure 4.
Figure 4

Existing water supply network.

Figure 4

Existing water supply network.

Prospective water demand and supply

Future population of the study area projected as per standard mathematical projection methods is based on census data (1971–2011). The growing gap between future demand and supply of water, worked out as per demand norms prescribed in IS 1172–1993, CPHEEO (1999), is shown in Table 1.

Table 1

Gaps between demand and supply under Shimla planning area

  Permanent Floating Demand Supply Deficit 
Year population population MLD MLD MLD 
2001 174,789 56,000 32.93 30.00 2.93 
2011 205,405 128,307 41.92 54.54 0.00 
2014 217,544 150,000 58.46 54.54 3.92 
2018 234,303 164,494 63.09 54.54 8.55 
2021 247,310 186,187 67.27 54.54 12.73 
2031 293,532 258,497 81.62 54.54 27.08 
2033 303,345 272,959 84.56 54.54 30.02 
2041 344,748 330,807 96.97 54.54 42.43 
2048 384,221 381,424 108.38 54.54 53.84 
2051 402,223 403,117 113.55 54.54 59.01 
2061 467,918 475,427 131.73 54.54 77.19 
2071 544,544 547,737 152.09 54.54 97.55 
  Permanent Floating Demand Supply Deficit 
Year population population MLD MLD MLD 
2001 174,789 56,000 32.93 30.00 2.93 
2011 205,405 128,307 41.92 54.54 0.00 
2014 217,544 150,000 58.46 54.54 3.92 
2018 234,303 164,494 63.09 54.54 8.55 
2021 247,310 186,187 67.27 54.54 12.73 
2031 293,532 258,497 81.62 54.54 27.08 
2033 303,345 272,959 84.56 54.54 30.02 
2041 344,748 330,807 96.97 54.54 42.43 
2048 384,221 381,424 108.38 54.54 53.84 
2051 402,223 403,117 113.55 54.54 59.01 
2061 467,918 475,427 131.73 54.54 77.19 
2071 544,544 547,737 152.09 54.54 97.55 

HYDROLOGICAL MODELING

Hydrological modelling is a simplified and conceptual representation of the part of the hydrologic cycle. In the second half of the 19th century different mathematical models were developed primarily to determine the peak flow of the design of various hydraulic structures, bridges, flood protection works, drainage systems, etc. (Gosain et al. 2009). Mulvaney (1851) developed the simplest hydrologic model using rational formula. Later on, for large and homogeneous basins, the method was modified to include the effect of non-uniform rainfall distribution and spatial variation of watershed characteristics. Sherman (1932) developed the Unit Hydrograph Model based on the principle of linearity and superposition. The superposition principle is based on assumptions that catchment behaves like a linear, dynamic and time-variant contributing system with respect to the rainfall–runoff conversion. During the 1950s, the system approach was used for analysis of complex dynamic systems. The response function was obtained from the analysis of input and output data with representative mathematical expressions.

Continuous hydrologic simulations were developed during the 1960s through conceptual models. Unlike event models these continuous hydrologic models account for soil moisture balance in a watershed over a long-term period and are capable of simulating daily, monthly, and seasonal stream flow (Ponce 1989). Pereira et al. (1962) developed an early application of continuous hydrologic models. Later on many models were developed like Dawdy & O’ Donnell (1965), Stanford Watershed Model, Hydrologic Simulation Package-Fortran IV (Crawford & Linsley 1966; Bicknell et al. 1997), Sacramento (Burnash et al. 1973), the Precipitation-Runoff Modeling System (Leavesley et al. 1983), etc. The performance of these models is controlled by the processes of drainage system. The parameters are estimated by the optimization procedure. These models are quite useful but not suitable for ungauged catchments in the absence of long-term data for calibration (Gosain et al. 2009).

For continuous simulation of runoff from watershed taking into account spatially varying physical factors, distributed hydrological models have been introduced. Distributed models are structured to take into account the spatial variations of catchment characteristics represented by network of grid point data (Refsgaard 1997). The very first outline of distributed physically based model was developed by Freeze & Harlan (1969). Thereafter several models came into existence, out of which the most commonly used is the Soil and Water Assessment Tool (Arnold et al. 1998).

The SWAT model, developed by the United States Department of Agriculture, Agricultural Research Service, in the early 1990s, is able to simulate the land phase of the hydrologic cycle in daily, monthly and yearly time steps. Due to non-availability of requisite input data and heterogeneity of the system, stream flow assessment in ungauged watershed is one of the most challenging tasks in surface hydrology. SWAT is a distributed and continuous time simulation model that can be used for stream flow assessment even in ungauged watersheds as it does not require much calibration like other conventional models (Gosain et al. 2011).

METHODOLOGY

SWAT is a data driven physical model and an efficient tool to predict the runoff, erosion, sediment load and nutrient transport in a watershed using data comprising of topography, soil and climate, etc. In this study, SWAT has been used to predict runoff in Ashwani, Nauti, Giri and Pabbar River keeping in view its efficiency, accuracy and universal acceptability. Resource assessment is accomplished using the Arc-SWAT hydrological model which is very useful for ungauged mountainous watersheds.

Input data

The SWAT input database comprises climatic, soil, topographic and land use land cover database. Input/output files documented as per SWAT Documentation Manual (Neitsch et al. 2010). The input database for SWAT simulation with its source of procurement in respect of study area is tabulated in Table 2.

Table 2

Input database for SWAT simulation and source of procurement

Database Parameters and source of procurement 
Climatic database Daily rainfall and metrological data 
(rainfall, temperature, wind speed, relative humidity, solar radiation) (1984–2010) IMD, NASA, NICRA, SWAT Global Data. http://www.indiawaterportal.org/data/metdata, http://www.nicra-icar.in/, http://eosweb.larc.nasa.gov./sse, http://globalweather.tamu.edu/home/view/ 
Soil database Soil physical properties 
(texture, bulk density, AWC, saturated conductivity, soil albedo & organic carbon, etc.) National Bureau of Soil Survey and Land Use Planning, Nagpur, India 
(Soils of Himachal Pradesh) NBSS Publication 57 (B) +2 Sheets of soil map (1:500,000 scale) 
Topography Advanced Space-borne Thermal Emission and Reflection-Global Digital Elevation Model (ASTER-GDEM) Elevation & Slope, 30 metre resolution. http://www.gdem.aster.ersdac.or.jp/ 
Land use Land use, land cover map/ satellite imageries (LANDSAT-TM) 30 m resolution 
Land cover database http://www.usgs.glovis/ 
Database Parameters and source of procurement 
Climatic database Daily rainfall and metrological data 
(rainfall, temperature, wind speed, relative humidity, solar radiation) (1984–2010) IMD, NASA, NICRA, SWAT Global Data. http://www.indiawaterportal.org/data/metdata, http://www.nicra-icar.in/, http://eosweb.larc.nasa.gov./sse, http://globalweather.tamu.edu/home/view/ 
Soil database Soil physical properties 
(texture, bulk density, AWC, saturated conductivity, soil albedo & organic carbon, etc.) National Bureau of Soil Survey and Land Use Planning, Nagpur, India 
(Soils of Himachal Pradesh) NBSS Publication 57 (B) +2 Sheets of soil map (1:500,000 scale) 
Topography Advanced Space-borne Thermal Emission and Reflection-Global Digital Elevation Model (ASTER-GDEM) Elevation & Slope, 30 metre resolution. http://www.gdem.aster.ersdac.or.jp/ 
Land use Land use, land cover map/ satellite imageries (LANDSAT-TM) 30 m resolution 
Land cover database http://www.usgs.glovis/ 

Existing watersheds, land use land cover map and soil map of the study area drawn using ERDAS IMAGINE and Arc-GIS, shown in Figures 5 and 6.
Figure 5

Land use land cover map of all watersheds.

Figure 5

Land use land cover map of all watersheds.

Figure 6

Soil map of the Shimla district.

Figure 6

Soil map of the Shimla district.

DEM of combined watersheds (Figure 3) indicates that elevation within the study area varies from 683–5,221 m above mean sea level. Land use land cover map of combined watersheds (Figure 5) indicates major area under forest cover (51%) followed by barren land (34%), settlement (5%), vegetation (7%), snow (2%) and water bodies (only 1%). The area falls under lesser Himalayan region. The major constraints of the area are rock outcrops on steep slopes, shallow soil depth, low available water capacity (AWC) and severe water erosion. The predominant soil classification area under study area is fine loamy (30%) followed by sandy skeletal (21%), loamy skeletal (18%), sandy (11%), coarse loamy (10%), fine loamy calcareous (7%) and loamy skeletal calcareous (3%) (Figure 6).

On the basis of daily data of weather parameters in respect of the study area for the period 1984 to 2010, it is found that: (i) average annual precipitation is 1,005 mm; (ii) temperature varies from −4.4 to 39.4 °C; (iii) average wind speed is 3.15 m/sec; (iv) average solar radiation is 19.13 MJ/m2; and (v) average relative humidity is 0.56.

RESULTS AND VALIDATION

The SWAT model run successfully with available spatial/non-spatial data as mentioned above and stream flow in the reach and water yield in each sub-basin is simulated. The SWAT model does not require elaborate calibration if the basic characteristics of the basin regarding soil, topography, climate and land use-land cover are incorporated correctly in the model (Gosain et al. 2005). However, in this study calibration is done for key sensitive parameters based on sensitivity analysis, for validation of output in the lean period in the case of the Giri watershed (Table 3).

Table 3

Key sensitive parameters

Parameters Units Default value Calibrated value 
CN (curve number) NA varies 5% decrease 
SOL_AWC (soil water available capacity) mm H2O/ mm soil 0.09–0.19 5% decrease 
ESCO (soil evaporation compensation factor) NA 0.01–1.0 0.95 
GW-REVAP (rate of transfer from shallow aquifer to root zone) NA 0.02–0.2 0.15 
REVAPMN (threshold water depth in shallow aquifer for percolation to deep aquifer to occur) mm 0–300 100 
GWQMN (threshold water depth in shallow aquifer reqd. for base flow to occur) mm 0–300 100 
CH_N2 (Manning's ‘n’ value for the main channel) NA 0.014 0.025 
Parameters Units Default value Calibrated value 
CN (curve number) NA varies 5% decrease 
SOL_AWC (soil water available capacity) mm H2O/ mm soil 0.09–0.19 5% decrease 
ESCO (soil evaporation compensation factor) NA 0.01–1.0 0.95 
GW-REVAP (rate of transfer from shallow aquifer to root zone) NA 0.02–0.2 0.15 
REVAPMN (threshold water depth in shallow aquifer for percolation to deep aquifer to occur) mm 0–300 100 
GWQMN (threshold water depth in shallow aquifer reqd. for base flow to occur) mm 0–300 100 
CH_N2 (Manning's ‘n’ value for the main channel) NA 0.014 0.025 

NA: not applicable.

Simulated annual average model outputs (1984–2010) in respect of Ashwani, Giri, Nauti and Pabbar watersheds taking outlet at existing headworks (pumping station); Outlet O1, except Pabbar is shown in Table 4.

Table 4

Annual average model outputs of watersheds at outlet O1 (1984–2010)

Variable name Ashwani watershed Giri watershed Nauti watershed Pabbar watershed 
Outlet (existing headworks) O1 O1 O1 O1 (intake) 
Sub basin area (km212.47 494.6 151 150.88 
Precipitation (mm H2O) 1,003.67 1,011.44 998.07 1,006.44 
Snowmelt (mm H2O) 14.54 14.88 14.55 14.90 
Actual evapotranspiration (mm) 559.99 538.43 554.72 675.43 
Soil water (mm) 31.77 27.66 31.61 38.33 
Percolation (mm) 237.98 247.32 235.10 75.54 
Surface runoff (mm H2O) 30.90 8.07 8.48 15.50 
GW-contribution (mm) 112.51 118.44 112.35 19.30 
Water yield (mm H2O) 314.22 342.43 319.02 402.60 
Stream flow (Cumec) 0.12 5.37 1.52 1.92 
Variable name Ashwani watershed Giri watershed Nauti watershed Pabbar watershed 
Outlet (existing headworks) O1 O1 O1 O1 (intake) 
Sub basin area (km212.47 494.6 151 150.88 
Precipitation (mm H2O) 1,003.67 1,011.44 998.07 1,006.44 
Snowmelt (mm H2O) 14.54 14.88 14.55 14.90 
Actual evapotranspiration (mm) 559.99 538.43 554.72 675.43 
Soil water (mm) 31.77 27.66 31.61 38.33 
Percolation (mm) 237.98 247.32 235.10 75.54 
Surface runoff (mm H2O) 30.90 8.07 8.48 15.50 
GW-contribution (mm) 112.51 118.44 112.35 19.30 
Water yield (mm H2O) 314.22 342.43 319.02 402.60 
Stream flow (Cumec) 0.12 5.37 1.52 1.92 

This study has established that average annual precipitation in all watersheds (study area) for 26 years (1984–2010) is about 1,005 mm, out of which about 34% flows as runoff, 8% as groundwater and about 58% as evapotranspiration.

At present, water is being lifted from respective river/tributary with different pumping capacity as per availability of stream flow in the lean period (15 May–15 June). Monthly simulation of stream flow viz-á-viz rate of withdrawal in respect of existing watersheds at the existing headworks (pumping station) is shown in Figures 79.
Figure 7

Monthly stream flow and withdrawal at Ashwani headworks.

Figure 7

Monthly stream flow and withdrawal at Ashwani headworks.

Figure 8

Monthly stream flow and withdrawal at Giri headworks.

Figure 8

Monthly stream flow and withdrawal at Giri headworks.

Figure 9

Monthly stream flow and withdrawal at Nauti headworks.

Figure 9

Monthly stream flow and withdrawal at Nauti headworks.

Figures 79 reveal that there is no scope of lifting additional water at existing headworks keeping in view the riparian right of downstream population and environmental flow. The situation is extremely critical in the case of Ashwani headworks. In the case of Nauti watershed, there is a deficit in the months of May–June and November–December. In the case of Giri watershed, water deficit in the source is present in the months of May, June and November. In all sources there is surplus flow during the monsoon period (July–October) but it is not being used as the existing system is non-storage type. The monthly inflow at outlet #1, intake weir (sub basin area 150.9 km2) of Pabbar watershed is shown in Figure 10, which indicates that stream flow in the month of April, May, October and November may not fulfill the additional demand of 59.01 million litres per day (MLD) (0.68 Cumec) up to 2051.
Figure 10

Monthly stream flow and proposed withdrawal at intake weir Outlet #1, Pabbar.

Figure 10

Monthly stream flow and proposed withdrawal at intake weir Outlet #1, Pabbar.

Validation

All watersheds are ungauged as such no statistical data of observed flow is available for validation of results. However, considering the water balance equation as the validation criteria, a rainfall–runoff model has been developed for all watersheds which are linear equations as shown in Table 5.

Table 5

Simulated rainfall–runoff models

Watershed Rainfall–runoff model Coefficient of determination ‘R2’ Correlation coeff. ‘R’ 
Ashwani Y=0.668X-356.9 0.883 0.939 
Giri Y=0.814X-310.5 0.926 0.962 
Nauti Y=0.660X-340.0 0.884 0.940 
Pabbar Y=0.627X-229.6 0.919 0.958 
 Where Y = runoff in mm and X = rainfall in mm 
Watershed Rainfall–runoff model Coefficient of determination ‘R2’ Correlation coeff. ‘R’ 
Ashwani Y=0.668X-356.9 0.883 0.939 
Giri Y=0.814X-310.5 0.926 0.962 
Nauti Y=0.660X-340.0 0.884 0.940 
Pabbar Y=0.627X-229.6 0.919 0.958 
 Where Y = runoff in mm and X = rainfall in mm 

Values of coefficient of determination (R2) (0.883–0.926) and correlation coefficient (R) (0.939–0.962) indicates very good correlation and validates the model output. Further, simulated average stream flow in lean period (January–May, period 2001–2010) is reasonably acceptable with respect to observed flow in the case of Giri watershed having similar physiographic features as in the case other watersheds with coefficient of determination (R2) as 0.916 and correlation coefficient (R) as 0.95 at Giri headworks as shown in Figure 11. Thus input data in case of Giri watershed can be replicated in the remaining watersheds for simulation.
Figure 11

Observed vs simulated lean period flow in Giri River (2001–2010).

Figure 11

Observed vs simulated lean period flow in Giri River (2001–2010).

AUGMENTATION STRATEGIES

Results of SWAT simulation reveals that existing sources of Nauti and Ashwani watersheds have been fully utilized. In Giri watershed additional extraction is possible by converting it into storage based scheme, but it has socio-environmental ramifications. Keeping in view the growing gap between future demand and supply from existing sources, three options have been identified: (i) to draw additional water from River Pabbar; (ii) to pump additional demand from River Satluj; and (iii) rainwater harvesting (RWH), partial reuse of wastewater coupled with balance water from River Satluj or Pabbar which are snow fed perennial rivers.

Option-I envisages gravitating additional water from the upper reach of River Pabbar at a distance of about 138 km from Shimla having an altitude of 4,010 m and above, costing exorbitantly $196.99 million (INR 11,819.4 million, 2014 price level) for additional water of 51 MLD (Figure 12). Augmentation cost per MLD was calculated to be $3.86 million (INR 231.6 million) The contributory catchment area of Pabbar River at proposed diversion weir at an altitude of 2,872 m above mean sea level is 150 km2 spread between 2,872–4,010 m elevation above mean sea level. The alignment passes through dense protected forest areas, steep slopes and hill peaks receiving heavy snowfall during winter. Life cycle cost (LCC) of the project over next 34 years including four years construction period (1948) works out to be $245.94 million (2014 price level).
Figure 12

Layout plan of Pabbar proposal (Option I).

Figure 12

Layout plan of Pabbar proposal (Option I).

Option-II envisages lifting additional water from River Satluj (Kol Dam Reservoir) from proposed intake structures at Sunni at a distance of 22 km from Shimla, involving a lift of about 1,600 m up to existing storage reservoirs at Ridge and near State Museum (Figure 13). Project cost in this case is $55.96 million (INR 3,357.6 million, 2014 price level) for additional water of 51 MLD. Cost per MLD is $1.10 million in comparison with $3.86 million (Option-I). LCC of the project over next 34 years including four years construction period (1948) works out to be $191.52 million (2014 price level). Option-II is also viable on socioenvironmental front as no cultivable and forest land is required for acquisition. The terrain is comparatively easier with no heavy snow bound peaks.
Figure 13

Layout plan of Satluj proposal (Kol Dam Reservoir).

Figure 13

Layout plan of Satluj proposal (Kol Dam Reservoir).

Option-III envisages potential RWH and partial reuse of wastewater in uncongested areas for non-potable consumption coupled with lifting the balance requirement from River Satluj (Kol Dam Reservoir) as per Option-II. Lifting the entire additional demand without resorting to water conservation measures, social awareness and regulatory mechanism is non-viable on techno-socio economic considerations.

RWH and partial reuse of wastewater should be made mandatory with proper legislation support. The water tariff structure needs to be regulated in consonance to operating cost of the system. With the above proposed water conservation measures, sources shall be supplemented significantly and there will be a substantial drop in the additional water demand, thus making this option the most viable and economical one. Growing gap between demand and supply shall reduce significantly as tabulated in Table 6.

Table 6

Gaps between demand and supply with RWH and reuse of wastewater

  Demand Supply RWH* Reuse** Total supply Deficit 
Year MLD MLD MLD MLD MLD MLD 
6(3 + 4 + 5) 7 (2-6) 
2021 67.27 54.54 4.82 1.78 61.14 6.13 
2031 81.62 54.54 9.64 3.56 67.74 13.88 
2041 96.97 54.54 14.46 5.34 74.34 22.63 
2051 113.55 54.54 19.28 7.13 80.95 32.60 
2061 131.73 54.54 19.28 7.13 80.95 50.78 
2071 152.09 54.54 19.28 7.13 80.95 71.14 
  Demand Supply RWH* Reuse** Total supply Deficit 
Year MLD MLD MLD MLD MLD MLD 
6(3 + 4 + 5) 7 (2-6) 
2021 67.27 54.54 4.82 1.78 61.14 6.13 
2031 81.62 54.54 9.64 3.56 67.74 13.88 
2041 96.97 54.54 14.46 5.34 74.34 22.63 
2051 113.55 54.54 19.28 7.13 80.95 32.60 
2061 131.73 54.54 19.28 7.13 80.95 50.78 
2071 152.09 54.54 19.28 7.13 80.95 71.14 

*5% built up area (2231.36 hect.) proposed for RWH up to 2021, 10% by 2031, 15% by 2041 and thereafter 20%. Annual average rainfall is 1,577 mm.

**5% recycling of wastewater (existing sewage treatment capacity: 35.63 MLD) proposed up to 2021, 10% by 2031, 15% by 2041 and thereafter 20%. Quantum of wastewater will increase with water consumption making more recycled water available in future with increased treatment capacity.

Thus prospective water deficit between 2021 and 2071 decreases by 6.6 to 26.41 MLD, if we resort to water conservation measures thus causing economic gain of $2.15–8.59 million per annum which will provide a great relief to the water system.

Option-III is the most viable one on socio-technoeconomic and environmental considerations as the gap between demand and supply decreases significantly (52 to 27%) between 2021 and 2071. LCC in the case of Kol Dam and Pabbar River proposal is shown in Figure 14.
Figure 14

Demand vs LCC.

Figure 14

Demand vs LCC.

Augmentation cost of the water system under all options as per 2014 price index is given in Figure 15. Keeping in view the LCC compounded at 8% interest and present augmentation cost (2014), Option-III is the best option. Anticipated water deficit decreases by 6.6–26.41 MLD between 2021 and 2071, causing great relief to the water system keeping in view the production cost of water (2014) of $0.89 per kL (INR 53.44/kL).
Figure 15

Augmentation cost for different options.

Figure 15

Augmentation cost for different options.

Comparison of available strategic options for augmentation of the existing water system is shown in Table 7.

Table 7

Comparison of available options

Description Option- I Option- II Option- III 
Source Pabbar River Satluj River Satluj River + RWH + treated wastewater 
Cost per MLD (2014) $3.86 million $1.10 million $1.10 million 
Water deficit (2051) 59.01 MLD 59.01 MLD 32.60 MLD 
Augmentation cost (2014) $227.78 million $64.91 million $35.86 million 
Type of scheme Gravity Lift Lift 
Distance of source 138 km 22 km 22 km 
Alignment route Steep slopes, snowy hill peaks, dense protected forest areas and cultivable land Normal slopes, no cultivable land and protected forest area Normal slopes, no cultivable land and protected forest area 
Environmental issues Major socioenvironmental issues involved No socioenvironmental issue Environmentally friendly 
Description Option- I Option- II Option- III 
Source Pabbar River Satluj River Satluj River + RWH + treated wastewater 
Cost per MLD (2014) $3.86 million $1.10 million $1.10 million 
Water deficit (2051) 59.01 MLD 59.01 MLD 32.60 MLD 
Augmentation cost (2014) $227.78 million $64.91 million $35.86 million 
Type of scheme Gravity Lift Lift 
Distance of source 138 km 22 km 22 km 
Alignment route Steep slopes, snowy hill peaks, dense protected forest areas and cultivable land Normal slopes, no cultivable land and protected forest area Normal slopes, no cultivable land and protected forest area 
Environmental issues Major socioenvironmental issues involved No socioenvironmental issue Environmentally friendly 

CONCLUSIONS

Water supply is a major impediment for the sustainable growth of Shimla. At present, the Shimla water system is a non-storage type and has been designed only on the basis of lean period stream flow. Inadequacy of sources is responsible for water scarcity, hampering developmental and tourists’ activities. The system can be designed for pumping additional demand as per Option-III, comprising of lifting additional water from Satluj River (Kol Dam) coupled with water conservation measures adopted in a phased manner. RWH and recycling of wastewater as proposed (2021–2071) will save the precious water to the extent of 6.6–26.41 MLD causing great economic gain for the state. The government of Himachal Pradesh should come forward with time bound IWMP, as a topmost priority, so that residents and tourists no longer suffer from water scarcity. This will go a long way towards the sustainable development of a heritage city like Shimla in the days to come.

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