IWMI RESEARCH REPORT The Impacts of Water 142 Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin

Luna Bharati, Guillaume Lacombe, Pabitra Gurung, Priyantha Jayakody, Chu Thai Hoanh and Vladimir Smakhtin

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The Impacts of Water Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin

Luna Bharati, Guillaume Lacombe, Pabitra Gurung, Priyantha Jayakody, Chu Thai Hoanh and Vladimir Smakhtin

International Water Management Institute P O Box 2075, Colombo, Sri Lanka i The authors: Luna Bharati is a Senior Researcher and Head of the Nepal office of the International Water Management Institute (IWMI) in Kathmandu, Nepal ([email protected]); Guillaume Lacombe is a Researcher based at the Laos office of IWMI in Vientiane, Lao PDR ([email protected]); Pabitra Gurung is a Research Officer at the Nepal office of IWMI in Kathmandu, Nepal ([email protected]); Priyantha Jayakody was a Research Officer based at the headquarters of IWMI in Colombo, Sri Lanka, and is now a PhD student at the Mississippi State University, Agriculture and Biological Department in Starkville, Mississippi, United States ([email protected]); Chu Thai Hoanh is a Principal Researcher and Acting Head of the Laos office of IWMI in Vientiane, Lao PDR ([email protected]); and Vladimir Smakhtin is Theme Leader - Water Availability and Access at the headquarters of IWMI in Colombo, Sri Lanka ([email protected]).

Bharati, L.; Lacombe, G.; Gurung, P.; Jayakody, P.; Hoanh, C. T.; Smakhtin, V. 2011. The impacts of water infrastructure and climate change on the hydrology of the Upper Ganges River Basin. Colombo, Sri Lanka: International Water Management Institute. 36p. (IWMI Research Report 142). doi: 10.5337/2011.210

/ water resources / river basins / climate change / hydrology / simulation models / precipitation / evapotranspiration / statistical methods / water balance / water yield / irrigation water / India / Ganges River /

ISSN 1026-0862 ISBN 978-92-9090-744-2

Copyright © 2011, by IWMI. All rights reserved. IWMI encourages the use of its material provided that the organization is acknowledged and kept informed in all such instances.

Cover picture shows Ganga River upstream of Rishikesh, Uttarakhand, India (Photo credit: Vladimir Smakhtin, IWMI)

Please send inquiries and comments to: [email protected]

A free copy of this publication can be downloaded at www.iwmi.org/Publications/IWMI_Research_Reports/index.aspx Acknowledgements The authors would like to thank the Indian Institute of Tropical Meteorology (IITM) for providing regional climate change projection data (Providing REgional Climates for Impacts Studies (PRECIS)) for our analysis. We would also like to thank Nitin Kaushal, Senior Manager, World Wide Fund for Nature (WWF)-India, for helping us with data acquisition and WWF-India for funding this study.

Project This research study was initiated as part of WWF-India’s ‘Living Ganga Programme’.

Collaborators The following organizations collaborated in the research conducted for this report.

International International Water Management Institute (IWMI) WaterManagement Institute

World Wide Fund for Nature (WWF)-India

Donors

Funding for this research was provided by the following organization.

World Wide Fund for Nature (WWF)-India

Contents

Summary vii

Introduction 1

Methods and Data 4

Results and Discussion 14

Conclusions 25

References 27

v

v vi Summary

Provision of detailed continuous long-term and mountainous upper areas of the UGB. On hydrological time series data for any river basin is an annual average, present-day flows throughout critical for estimation, planning and management the UGB are about 2-8% lower than in naturalized of its current and future water resources. Most of conditions. The percentage of flow reduction is the the river basins in India are data poor, including highest during the dry months as water is being its iconic river – Ganga (Ganges). This study withdrawn for irrigation. Dry and wet season flows assessed the variability of flows under present under CC scenario A2 (scenario corresponding and ‘naturalized’ basin conditions in the Upper to high population growth with slower per capita Ganges Basin (UGB) (area of over 87,000 square economic growth and technological change) are 2 kilometers (km )). The naturalized basin conditions lower than those in present climate conditions are those that existed prior to the development of at upstream locations, but higher at downstream multiple water regulation structures, and hence locations of the UGB. Flows under CC scenario B2 may be seen as a reference condition, a starting (corresponding to moderate population growth and point, against which to evaluate the impacts economic development with less rapid and more of planned basin development, as well as the diverse technological change) are systematically impacts of future climate change (CC) on basin higher and lower than those under CC scenario water resources. The later impacts are also part A2 during dry and wet seasons, respectively. The of the study: the PRECIS regional climate model dates of minimum daily discharges are highly (RCM) was used to generate climate projections variable among stations and between different CC for the UGB, with subsequent simulations of scenarios, while the dates of maximum flow are future river flows. Results show that the annual delayed downstream as a result of the delay in average precipitation, actual evapotranspiration the onset of the monsoon in the lower parts of the (ET) and net water yields of the whole basin basin. The report also provides actual simulated were 1,192 millimeters (mm), 416 mm and 615 discharge time series data for all simulated mm, respectively. However, there were large scenarios, in the overall attempt to augment the variations in both temporal and spatial distribution river flow data for this important river basin and to of these components. Precipitation, ET and water facilitate the use of these data by any interested yields were found to be higher in the forested party.

vii

The Impacts of Water Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin

Luna Bharati, Guillaume Lacombe, Pabitra Gurung, Priyantha Jayakody, Chu Thai Hoanh and Vladimir Smakhtin

Introduction

The Ganges River System originates in the models can be driven with the output from the Central Himalayas, and extends into the alluvial general circulation model (GCM) (IPCC 1996; Gangetic Plains and drains into the Indian Ocean Akhtar et al. 2009). However, the resolutions of at the Bay of Bengal. Its basin area (1.09 million GCMs are currently constrained by computational km2) spreads across India (79%), Nepal (13%), and physical reasons to 200 kilometers (km) Bangladesh (4%) and China (4%). The river is for climate change predictions and are too of high importance to riparian countries with an course for hydrological modeling at basin scale. estimated 410 million people directly or indirectly In order to increase the spatial resolution of depending on it (Verghese and Iyer 1993). In these predictions, one method that is used is the upstream mountainous regions, hydropower statistical downscaling techniques, which have is the main focus of development with mega been developed in the last decades (e.g., Wilby and micro projects either under construction or et al. 1999; Bergström et al. 2001; Pilling and being planned in both Nepal and India. After Jones 2002; Guo et al. 2002; Arnell 2003; Booij the main river channel reaches the plains, it 2005; Benestad et al. 2008). A second option is highly regulated with dams, barrages and is the use of dynamical downscaling (e.g., Hay associated irrigation canals. All this infrastructure et al. 2002; Hay and Clark 2003; Fowler and development and abstractions affects the river’s Kilsby 2007; Leander and Buishand 2007). flow regime and reduces flows, which, in turn, Dynamical downscaling fits output from GCMs impacts downstream water availability, water into regional meteorological models. It involves quality and riverine ecosystems. Furthermore, using numerical meteorological modeling to there are concerns that CC is likely to exacerbate reflect how global patterns affect local weather the water scarcity problem in the Ganges Basin. conditions. The high horizontal resolution of a Therefore, modeling the hydrology of the basin is RCM (about 10-50 km) is more appropriate for critical for estimation, planning and management resolving the small-scale features of topography of current and future water resources. and land use. Furthermore, the high resolution To operate a hydrological model, reliable data of RCM is ideal to capture the spatial variability on climatological variables such as temperature, of precipitation as input into hydrological models precipitation, evaporation, etc., over space and (Gutowski et al. 2003; Akhtar et al. 2009), time are necessary. For analysis of the past, such and provide better representation of mountain information can be derived from observational areas affected by the amount of rainfall and the data sets. However, for assessment of the future location of windward rainy areas and downwind with the possible impacts of CC, the hydrological rain shadow areas (Jones et al. 2004).

1 Hydrological simulations using RCM output in water use in the basin were not fully assessed. data-sparse basins such as the Ganges involves Some other modeling studies examined, in detail, several problems, including uncertainties in inputs, the hydrological regime of individual glaciers model parameters and model structure (Akhtar in the UGB (Singh et al. 2008), rather than the et al. 2009). The main disadvantages of RCMs impacts of water use and CC on basin-wide water are that they inherit the large-scale errors of their resources. Similarly, Seidel et al. (2000) modeled driving GCM model and require large amounts of the runoff regime of the Ganges and Brahmaputra boundary data previously archived from relevant basins, accounting for precipitation, remotely GCM experiments. Additional uncertainties can be sensed snow covered areas and temperatures linked to the local scale patterns in downscaling of using the Snowmelt Runoff Model (SRM). They temperature, precipitation and evapotranspiration found that the already high risk of floods during in a specific basin (Bergström et al. 2001; Guo et the period July to September is slightly increased al. 2002; Akhtar et al. 2009). with CC. Numerous papers can be found on Although modeling studies that analyzed the impact of water resources development and the impacts of water infrastructure development CC on downstream areas of Bangladesh (e.g., on the hydrology and water resources in other Ahmad et al. 2001; Jian et al. 2009; Mirza 2004; parts of the world are available, studies focusing Rahaman 2009). These studies, however, do not on the Ganges Basin are limited. The National assess the dynamics of water availability and use Communication (NATCOM) project by the Ministry in upstream areas within Nepal and India. of Environment and Forests, Government of Apart from the general paucity of hydrological India, quantified the impact of CC on water and water resources studies with fine spatial resources of all major Indian river systems resolution in the Ganges Basin, the inherent (Gosain et al. 2006). This study used the Hadley problem of this basin is the availability of Centre Regional Climate Model 2 (HadRM2) daily observed discharge data, against which models weather data as input to run the Soil and Water can be calibrated and validated. Discharge data in Assessment Tool (SWAT) hydrological model to the Himalayan part of the basin are scarce due to determine the spatio-temporal water availability lack of measurement stations. In the downstream in the river systems and to calculate basin water plains, although discharge data from gauging balances. This study suggests that precipitation, stations exist, these data are not accessible to evapotranspiration and runoff will increase by the public due to national security laws in India. approximately 10% in the Ganges Basin. The This leaves most of the hydrology studies of the NATCOM study, however, did not consider the Ganges, which are carried out by the government effect of water infrastructure development in the agencies, being classified and not accessible in basin and modeled the Ganges without water the public domain. In addition, simulated data are abstraction and use. Furthermore, the simulations also not widely shared, hence impeding their use were not validated against observed flow data, in subsequent water resource applications. making the model results uncertain. The UGB (upstream of Kanpur Barrage; The Water Evaluation And Planning (WEAP) catchment area 87,787 km2) (Figure 1) model was used by de Condappa (2009) encompasses various physiographic conditions, for large-scale assessment of surface water is of great cultural and spiritual importance for resources in the Indus and Ganges basins, the country, yet, it is already highly regulated with with a special focus on the contribution of snow multiple water structures (with more plans on the and glaciers to flow. Several, relatively simple way), and will most likely experience significant scenarios of changes in glaciated area were changes in hydrology (with significant economic simulated. Results suggest that glaciers play the and social implications) due to CC. role of buffers against interannual variability in This study is part of WWF-India’s “Living precipitation, in particular, during years with a Ganga Programme.” The main objective of this weak monsoon. However, the impacts of CC and programme is to develop and promote approaches

2 for sustainable water resources management, characterize the likely impacts of CC at both present including environmental flows which conserve and ‘naturalized’ flow regimes. For purposes of this biodiversity and support livelihoods under present study, naturalized flows are defined as ‘free flowing and changing climate scenarios (WWF 2011). flows in the mainstream without dams and barrages, In the present study, the flow variability of the and irrigation diversions’. These assessments catchment is characterized for four scenarios are the prerequisites for the subsequent detailed corresponding to pairwise combinations of present water allocation modeling under present and future and future water infrastructure development and flow regimes in the UGB. In addition, the aim of climate conditions, as detailed in the section, the simulation modeling here is to provide freely Methods and Data. Results are presented in two available flow time series for the UGB, in order to parts: 1) water balances for subcatchments for enhance data availability and initiate hydrological present and ‘naturalized’ conditions (prior to the data sharing. All simulated data are also available development of multiple regulation structures), and via IWMI’s water data portal (http://waterdata.iwmi. 2) several indicators of hydrological variability that org/).

FIGURE 1. Upper Ganges Basin (UGB) with locations of barrages, reservoirs, hydrometeorological stations and ‘environmental flow (EF) sites’, where environmental flow assessment was undertaken under the “Living Ganga Programme.”

3 Methods and Data

The analytical framework of the study is presented radiation, wind speed and relative humidity) in Figure 2 and detailed in the following sections. measured inside and around the basin from The SWAT hydrological model is used to simulate 1971 to 2005 were collected. flows. 3. Calibration and validation of SWAT model The steps of the analytical framework are against observed flow data over the period detailed below. 2000-2005. 1. SWAT model setup for the UGB: a model 4. Selection of a RCM for simulation of future with 21 subbasins was setup using a Digital climate conditions: output time series Elevation Model (DEM), soil and land use/land simulated by PRECIS over the periods 1961- cover (LULC) maps and flow data. 1990 and 2071-2100 were provided by IITM. 2. Collection of observed climate data: daily 5. Adjustment of climate change data: data for five climate variables (precipitation, PRECIS time series data for the grid maximum and minimum temperature, solar cells encompassing climatic stations were

Spatial Maps (DEM, Soil and LULC) Model Setup Precipitation Observed Climate Data (1971 - 2005) Temperature Relative humidity Wind speed Solar radiation Model Calibration and Validation Observed Flow Data (2000 - 2005) BL [1961 - 1990] Regional PRECIS CC Data A2 [2071 - 2100] B2 [2071 - 2100]

Adjustment of CC Data Reset Model (Observed versus Baseline)

(i) Present Scenario without CC (1971 - 2005) Running Model for Different Scenarios (ii) Naturalized Scenario without CC (1971 - 2005) (iii) Present Scenario with CC-A2 (2071 - 2100) (iv) Present Scenario with CC-B2 (2071 - 2100)

Analysis of Model Results and Reporting

FIGURE 2. Analytical framework. Note: BL = baseline scenario; A2 = Climate conditions as projected by PRECIS under A2 scenarios; B2 = Climate conditions as projected by PRECIS under B2 scenarios

4 compared to actual records over the period infrastructure developments on flow regimes 1961-1990 to statistically determine the at the four ‘EF sites’ were characterized required bias adjustments. These adjustments using indicators of hydrological alterations. were then applied to the projection period This report presents the main results of this 2071-2100 based on the assumption that analytical framework. the same bias occurs in both simulation and projection periods. 6. Setting study scenarios: based on the SWAT Model Description and Setup objectives of the study, four scenarios with different land and water use, and climate SWAT is a process-based continuous hydrological conditions were established. These are model that predicts the impact of land (i) present condition scenario (water management practices on water, sediment and infrastructure development as of 2005 and agricultural chemical yields in complex basins present climate as measured from 1971 to with varying soils, land use and management 2005); (ii) ‘naturalized’ scenario, assuming conditions (Arnold et al. 1998; Srinivasan et al. that no water infrastructure were built under 1998). The main components of the model include present climate conditions (1971-2005); (iii) climate, hydrology, erosion, soil temperature, plant climate change scenarios, assuming that growth, nutrients, pesticides, land management, water infrastructure is that of 2005, under channel and reservoir routing. SWAT divides a 2071-2100 climate conditions as projected basin into subbasins. Subbasins are connected by PRECIS under A2 scenarios; and (iv) B2 through a stream channel. Each subbasin is scenarios. further divided into Hydrological Response Units (HRU). A HRU is a unique combination of soil 7. Scenario simulation using SWAT model. and vegetation types. SWAT simulates hydrology, 8. Analysis of simulated results: simulated vegetation growth and management practices at water balances were compared between the HRU level. present and ‘naturalized’ conditions. The The hydrological cycle is simulated by SWAT impacts of climate change and water using the following water balance equation (1):

n

SWt = SWo + (Rday - Qsurf - Ea - wseep - Qgw ) (1) Σi = 1

where: SWt: Final soil water content (mm); SWo: Initial soil water content (mm); t: Time in days; Rday: pre- cipitation on day i (mm); Qsurf : surface runoff on day i (mm); Ea: actual evapotranspiration on day i (mm); wseep: percolation on day i (mm); and Qgw: return flow on day i (mm).

Basin subdivision allows differences in because it can be used in large agricultural river evapotranspiration for various crops and soils basin scales to also simulate crop water use. to be simulated. Runoff is predicted separately As actual data for irrigation distribution was not for each subbasin and routed to obtain the total available, the calculated crop water use helped runoff for the basin. This increases the accuracy determine the irrigation water requirements in the and gives a much better physical description of basin. The SWAT model has been extensively the water balance. More detailed descriptions of used in the USA and elsewhere for calculating the model can be found in Arnold et al. (1998), water balances of primarily agricultural catchments Srinivasan et al. (1998) and Neitsch et al. 2005. (e.g., Jha 2011; Bharati and Jayakody 2011; Garg The SWAT model was chosen for this study et al. 2011).

5 The SWAT model requires three basic files for 106 m3, respectively. There are three main canal delineating the basin into subbasins and HRUs: systems. The Upper Ganga canal takes off from Digital Elevation Model (DEM), soil map and Land the right flank of the Bhimgoda barrage with a Use/Land Cover (LULC) map. Figure 3(a) shows head discharge of 190 m3/s, and irrigates an area the DEM for the basin using 90 m Shuttle Radar of 2 million hectares (Mha). The Madhya Ganga Topography Mission (SRTM) data. canal provides annual irrigation water to 178,000 The elevation in the UGB ranges from 100 m hectares (ha). The Lower Ganga canal from the in the lower plains to 7,500 m in the upper Narora weir irrigates 0.5 Mha. mountain region. Some mountain peaks in the During the winter season, a part of the runoff headwater basin are permanently covered by in the basin is generated through contributions snow. Figure 3(b) shows the land use map which from snowmelt and glacier melt. Therefore, for was developed using the LandSat image from this study, snowmelt was computed using the 2003. According to this map, around 65% of SWAT model. In the model, when the mean the basin is occupied by agriculture. The main daily air temperature is less than the snowfall crop types (identified from the land use map temperature, the precipitation within a HRU and district statistics) are wheat, maize, rice, is classified as snow and the liquid water sugarcane, pearl millet and potato. About 25% equivalent of the snow precipitation is added of the area of the UGB is covered by forests, to the snowpack. The snowpack increases with mostly in the upper mountains. For the naturalized additional snowfall, but decreases with snowmelt scenarios, as there are no water provisions for or sublimation. In the model, snowmelt is irrigation, the irrigated crops such as rice and controlled by the air and snowpack temperature, sugarcane are replaced by rainfed crops from the the melting rate and the areal coverage of snow. region, i.e., lentils and wheat. Snowmelt is then included with rainfall in the Figure 3(c) shows the soil map of the basin. calculations of runoff and percolation. Further There are nine soil types in the UGB. Lithosols information on snowmelt calculations can be dominate the upper, steep mountainous areas found in Neitsch et al. 2005. Wang and Melesse and are very shallow and erodible. Cambisols and (2005) evaluated the performance of snowmelt luvisols are found in the lower areas. Cambisols hydrology of the SWAT model by simulating are developed in medium- and fine-textured streamflows for the Wild Rice River watershed material derived from alluvial, colluvial and (located in the USA), and found that the SWAT aeolian deposits. Most of these soils make good model had a good performance on simulating the agricultural land. Luvisols are tropical soils mostly monthly, seasonal and annual mean discharges used by farmers because of its ease of cultivation, and a satisfactory performance on predicting but they are greatly affected by water erosion and the daily discharges. When analyzed alone, the loss in fertility. Annual average precipitation in the daily streamflows during the spring, which were UGB ranges from 550 to 2,500 mm (Figure 3(d)). A predominantly generated from melting snow, major part of the rainfall is due to the southwestern could be predicted with an acceptable accuracy, monsoon from July to October. Wet season and the corresponding monthly and seasonal corresponds to the period July to November and mean discharges could be simulated very well. the dry season extends from December to June. The Gangotri glacier is located within the UGB. The UGB mainstream is highly regulated with Therefore, in order to include the contribution of dams, barrages and corresponding canal systems this glacier in the present model setup, discharge (Figure 1). The two main dams, Ramganga and data collected from a gauging site very close to Tehri, became operational in 1988 and 2008, and the snout of the glacier (Singh et al. 2006) was have total storage capacities of 2,448 and 3,540 added to the relevant subbasin.

6 (a) Digital Elevation Model, SRTM (b) Land use map, Land Sat TM, 2003

(c) Soil Map, FAO (d) Mean Annual Precipitation (1971-2005)

FIGURE 3. The maps of the UGB used for modeling, with numbers and boundaries of subbasins used in hydrological simulations. Note: FAO = Food and Agriculture Organization of the United Nations.

7 Available Observed Time Series Data 1. Due to restrictions on the distribution of Ganges data from the Indian Central Water Commission The SWAT model requires time series of observed (CWC), only a very short time series of data at climate data, including precipitation, minimum some barrages were available. Although daily and maximum temperature, sunshine duration, observed flow data were available from Narora, wind speed and relative humidity. Table 1 lists the climate stations used in the model. The locations only monthly time series data were available for of stations are shown in Figure 1. The SWAT the other sites. As the model works with daily model uses the data of a climate station nearest time steps, simulated daily flow values had to be to the centroid of each subcatchment as an input accumulated into monthly values for calibration for that subcatchment (Figure 3d). and validation. The existing dams, barrages and Table 2 presents details of the flow stations irrigation deliveries were incorporated into the used for calibration and validation of the model. model using available salient features from the Locations of the flow stations are shown in Figure relevant barrage/dam authorities.

TABLE 1. Details of the meteorological data used.

Station code1 Name Available record Available data type R T S W H

42111 Dehradun2 1970-2005 x x x x x 42103 Ambala2 1970-2004 x x x x x 8207 Simla2 1989-2005 x x x x x 42140 Roorkee2 1970-1994; 2002-2005 x x x x x 42182 Delhi2 1970-2005 x x x x x 42366 Kanpur 1970-1974; 1986-1995 x x 42471 Fatehpur 1970-2005 x x 42189 Bareilly2 1970-2005 x x x x x 42260 Agra 1970-2005 x x 42262 Aligarh 1970-2005 x x 42143 Najibad2 1970-2005 x x x x x 42147 Mukteshwar2 1970-2005 x x x x x 42148 Pant Nagar2 1970-2005 x x x x x 42265 Mainpuri 1970-2005 x x x 42665 Shajapur 1970-2005 x 42266 Shahjahanpur 1970-2005 x x x x

Notes: 1 Station codes correspond to locations shown in Figure 1. 2 Station has large data gaps. R = Precipitation; T = Minimum and maximum temperature; S = Sunshine duration; W = Wind speed; H = Relative humidity.

8 TABLE 2. Details of flow stations and data availability for model calibration and validation.

Station code Location Catchment Available Type of data area (km2) record Flow_1 Bhimgoda 23,080 April 2002 - December 2005 Monthly inflow into the barrage

Flow_2 Narora 29,840 January 2000 - June 2005 Daily spill release from the dam Flow_3 Kanpur 87,790 June 2003 - December 2005 Monthly spill release from the dam excluding dry season flows

Model Calibration and Validation developed at the Meteorological Office Hadley Centre, UK, for generating high-resolution The period from January 1, 1970 to December climate change information for many regions of 31, 1971 was used to warm-up the model. The the world (Jones et al. 2004). It has a spatial available data between 2000 and 2005 from each resolution of 0.22° x 0.22°. Climate data from the station were divided into two sets, each of them GCM, Hadley Centre Coupled Model, version 3 including the same number of daily observations. (HadCM3) (Jones et al. 2004), were downscaled The first and second sets were used for model with PRECIS for the UGB under A2 and B2 calibration and validation, respectively. Model Special Report on Emission Scenarios (SRES) parameters were calibrated simultaneously for all scenarios (IPCC 2000) by IITM. A2 and B2 are three flow stations. two climate change SRES scenarios studied The model performance was determined by by the Intergovernmental Panel on Climate calculating the coefficient of determination (R2) Change (IPCC). A2 corresponds to a story line and the Nash-Sutcliffe Efficiency (NSE) criterion. of high population growth with slower per capita R2 and NSE values for each simulation are economic growth and technological change, and B2 presented in Table 3. The model performance corresponds to a story line of moderate population over calibration and validation periods is growth and economic development with less rapid acceptable, according to the model performance and more diverse technological change. PRECIS ratings proposed by Liu and De Smedt (2004). data used in that study were extracted from 15 In addition, comparisons were made between grid cells corresponding to the location of the the measured and simulated annual water flow 15 meteorological stations previously described. volumes. The differences in volume ranged from These time series data cover the periods 1961- 22 to 25% and from 7 to 9% during calibration 1990 and 2071-2100, and include four variables: and validation periods, respectively. precipitation, temperature, wind speed and relative Figure 4 shows observed precipitation, humidity. Although these PRECIS data result from a observed and simulated flows for inflow into the downscaled global climate model which accounts for Bhimgoda barrage, outflow from Narora barrage regional climate and topographic characteristics, they and outflow from Kanpur barrage. Although still exhibit discrepancies with regard to observed flows are regulated at these sites, observed and meteorological data. For instance, the mean simulated hydrographs match very well. This adds absolute relative difference in annual precipitation confidence to the results that are presented in the depths between PRECIS and observed time series following sections. over the baseline period 1970-1990 is about 55%. Therefore, PRECIS data were adjusted in such a Downscaling of Climate Model Data way that, at each of the 15 station locations, the Climate data from PRECIS were used as input main statistical properties of adjusted PRECIS to the SWAT hydrological model in order to output (mean and standard deviation) match those assess future river flow scenarios. PRECIS of the historical data. This statistical downscaling is an atmospheric and land surface model approach is described in Bouwer et al. (2004).

9 8.6% 6.7% -7.2% Difference 754 663 1,090 Validation

694 622 1,017 Observed Simulated Annual water flow volume

24.7% 21.8% 22.1% Difference

923 1,437 1,103 Simulated Calibration

756 905 1,152 Observed

good Good Ratings (> 0.85) Excellent Very Very Very good Very Very good Very Very good Very (0.65 - 0.85) (0.65 - 0.85) (0.60 - 0.64) (0.65 - 0.85) (0.65 - 0.85) 0.85 0.84 0.88 0.60 0.81 0.66 Value Validation Validation

> 0.85 Ratings (> 0.85) (> 0.85) (> 0.85) Excellent Excellent Excellent Excellent Very good Very Very good Very (0.65 - 0.85) (0.65 - 0.85)

0.90 0.86 0.92 0.86 0.67 0.82 Value Calibration

2 2 2

Performance statistics (Fraction) R R R NSE NSE NSE Statistic

June period Jan 1, 2003 - 2004 - 2005 – June 1, 30, 2005 31, 2005 Validation 31, 2005 December December January 1,

and period 2002 - 2003 - 2004 - 2000 – April 1, June 1, June 1, October 31, 2002 31, 2003 31, 2003 Calibration December December January 1, October 31,

Flow_1 (Bhimgoda) (Narora)

Flow_2

Flow_3

(Kanpur)

Station code and location

2004

TABLE 3. A summary of the SWAT model performance evaluation. summary of the SWAT A 3. TABLE

10 (a)(a) 10,000 0

8,000 400

Calibration Validation 6,000 800 ) m /s) 3 2 2 ( m R = 0.86 R = 0.84 n ( m

NSE = 0.67 NSE = 0.81 o t i w o t a l i

F 4,000 1,200 p i c e P r

2,000 1,600

0 2,000 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06

Precipitation Simulated flow Observed flow

(b)(b) 10,000 0

8,000 400

Calibration Validation

6,000 800 ) m /s) 3 2 2 ( m R = 0.92 R = 0.88 n ( m

NSE = 0.90 NSE = 0.85 o t i w o t a l i

F 4,000 1,200 p i c e P r

2,000 1,600

0 2,000 Jan-00 Oct-00 Aug-01 Jun-02 Apr-03 Feb-04 Dec-04 Oct-05

Precipitation Simulated flow Observed flow

(c)(c) 10,000 0

8,000 400

Calibration Validation 6,000 800 )

2 2 m /s)

3 R = 0.86 R = 0.60 ( m

( m n

NSE = 0.82 NSE = 0.66 o w t i o

l 4,000 1,200 t a F i p i c e P r 2,000 1,600

0 2,000 Jan-03 May-03 Oct-03 Mar-04 Aug-04 Jan-05 Jun-05 Nov-05

Precipitation Simulated flow Observed flow

FIGURE 4. Observed and simulated flows at (a) Bhimgoda barrage, (b) Narora barrage, and (c) Kanpur barrage.

11 The period 1961-2008 was used to calculate the is explained by the fact that observed data standard deviation, and average of PRECIS and are point-based while PRECIS data are grid- observed data. For each of the four downscaled based (each cell extends over an area of 0.22 climate variables, specific adjustment rules were x 0.22 square degrees - about 25 x 25 km2). defined and are detailed below. Thus, the probability of having a dry day over this area is much lower than that of having a Precipitation dry day at a point location. It is assumed that, for each month of the year, ii) For each month of the year, a daily the proportion of dry days (daily precipitation precipitation threshold H (mm/day) is = 0 mm) should be identical in both data sets determined in such a way that P% of PRECIS (PRECIS and observation). In order to meet this hypothesis, the distribution of non-rainy days in daily precipitation values are higher than H. the original PRECIS data set was modified before iii) For each month of the year and for both data it was statistically downscaled, as described in sets (all observed values and PRECIS values Bouwer et al. (2004). The successive steps of the higher than H), the means, M and calculations are described below. OBSERVATION MPRECIS, and standard deviations, STOBSERVATION i) The average proportion (P) of rainy days and STPRECIS, respectively, of rainy day depths (> 0 mm) for each month of the year was (> H) were calculated. determined for both PRECIS and observed data sets for each meteorological station. iv) For each month of the year, adjusted PRECIS P was found to be significantly higher for daily values ‘y’ were calculated from the PRECIS data, i.e., PRECIS data include fewer original PRECIS data ‘x’ as shown in Equation dry days than observations. This difference (2) below.

If x < H, then y = 0

STOBSERVATION If x > H, then y = ( x - MPRECIS ) + MOBSERVATION (2) STPRECIS

Temperature Furthermore, the SWAT model required maximum and minimum temperature as an As the PRECIS data set does not include maximum and minimum temperature but only input and the adjusted PRECIS temperatures

daily averages, data adjustment was based on were in daily average (xAVG). Therefore, the mean observed daily temperature calculated from observed daily data were used to calculate observed maximum and minimum values. For monthly averages and standard deviations of each month of the year, average and standard maximum, minimum and average temperature deviation of daily temperatures were calculated for (M , M , M , ST , ST observed and PRECIS data using available daily OBS, MAX OBS, MIN OBS, AVG OBS, MAX OBS, , and ST ), and then the following values for the period 1961-2008. Data adjustment MIN OBS, AVG was performed similarly to precipitation data, equations were used to calculate the maximum

although the first step of precipitation adjustment daily PRECIS temperature (TMAX) (Equation (3))

(threshold definition) was not necessary in the and minimum daily PRECIS temperatures (TMIN) case of temperature. (Equation (4)).

12 STOBS, MAX Maximum daily PRECIS temperature, T MAX = ( x AVG - MOBS,AVG) + MOBS,MAX (3) STOBS, AVG

STOBS, MIN Minimum daily PRECIS temperature, T MIN = ( x AVG - MOBS,AVG) + MOBS,MIN (4) STOBS, AVG

Wind speed observed values, in comparison with those of PRECIS data. The latter, when used for the data In the data set of observations, wind speed values adjustment, could produce daily relative humidity are measured 2 meters above the ground surface. values exceeding 100%. In contrast, PRECIS data correspond to wind speeds 10 meters above the ground surface. This Scenario Setting difference in elevations results in higher PRECIS wind velocities, as wind is slowed down by Four scenarios were simulated. In scenario 1, frictional resistance close to the surface. As this the water infrastructure system up until the year difference is observed for each month of the year, 2005 was included. This scenario includes water a unique reduction coefficient (0.38) was applied abstractions from dams and barrages. The Tehri to all PRECIS daily values. This coefficient was Dam (Figure 1), which became operational in calculated by dividing the mean observed daily 2008, is not included in this scenario. Irrigated wind speed value averaged over the period 1961- crops such as rice, wheat, corn, finger millet, 2008 by that obtained from PRECIS data. Further sugarcane and potato represent the major crop adjustment steps consisted of applying Equation types during present conditions. Scenario 2 (1) to PRECIS wind speed daily values, similarly represents the ‘naturalized’ condition without any to other climate variables. artificial flow regulation. Simulated flows for this scenario were produced after having removed Relative humidity all water infrastructures from the calibrated The adjustment of relative humidity is similar to model. In this scenario, farming areas were that of temperature and includes the same steps. characterized by rainfed crops such as mung A further adjustment, when required, consisted bean and wheat. Most of the non-agricultural of replacing values exceeding 100% by ‘100%’. land is covered by natural forest. Scenarios 3 Such cases (less than 1% of adjusted values), and 4 correspond to the present conditions of was due to the following reasons: in some cases, water infrastructure development, abstraction the range of observed relative humidity values (up until 2005) with CC scenario adjusted from over the baseline period was found to exceed PRECIS time series data under SRES A2 and that of the PRECIS data. This resulted in higher B2, respectively. Table 4 provides a detailed monthly averages and/or standard deviations for description of the scenarios.

TABLE 4. Description of simulated scenarios. No. Water infrastructure development Climate input data

1 Present (as of 2005) Observed data 1971-2005 2 Naturalized conditions Observed data 1971-2005 3 Present (as of 2005) Adjusted PRECIS data over period 2071-2100 under A2 scenario 4 Present (as of 2005) Adjusted PRECIS data over period 2071-2100 under B2 scenario

13 Results and Discussion balances from the lower part of the catchment, containing irrigated areas, are affected by water Water Balance Under Present Conditions regulation through barrages, dams and canals. and Natural Conditions (Scenarios 1 and 2) The large network of canals is transferring water from one subbasin to irrigate crops in another Figure 5 shows the annual average water balance subbasin. Therefore, it is difficult to determine for all 21 subbasins of the UGB, and Figure the accuracy of the runoff calculations from each 6 shows the mean monthly water balance of subbasin. However, the ET and precipitation the whole basin, both under Scenario 1. Four figures are useful in characterizing the water hydrological components are considered, i.e., availability and use in each of the subbasins. The precipitation, actual evapotranspiration (ET), net maximum precipitation of 2,504 mm occurred in water yield (which is a routed runoff from the subbasin 3 and minimum precipitation of 536 mm subbasin) and balance closure. The term ‘balance occurred in subbasins 8 and 11 (see also Figure closure’ includes groundwater recharge, change 3(d)). Furthermore, the maximum ET of 671 mm in soil moisture storage in the vadose zone and occurred in subbasin 7 and the minimum ET of model inaccuracies. 177 mm occurred in subbasin 10. Annual average precipitation, actual ET and The mean monthly results from 1971 to 2005 net water yield of the whole basin were 1,192, (Figure 6) show that there are large temporal 416 and 615 mm, respectively. However, there variations in the water balance components. The was a large variation in the spatial distribution of maximum precipitation of 338 mm occurred during these components. Precipitation, ET and water August and a minimum of 7 mm occurred in yield were found to be higher in the forested and November. Similarly, water yields are also much mountainous upper areas. In the upper subbasins, higher during the monsoon months as compared to water yield is higher than ET. However, in some the dry season. ET, however, which is more related to of the lower subbasins dominated by agriculture, land cover, was found to be lowest during the winter ET values were higher than water yield. Water months, i.e., November-January (post-rice harvest).

3,000

1,500

0 (mm)

-1,500 Annual average water balance

-3,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Subbasin number

Precipitation Actual ET Net water yield Balance closure

FIGURE 5. Annual average water balance results of model simulation at the subbasin level (1971-2005). The subbasin numbers are given in Figure 3.

14 400

200

0 (mm)

-200 Mean monthly water balance

-400 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month Precipitation Actual ET Net water yield Balance closure

FIGURE 6. Mean monthly water balance results of model simulation (1971-2005) for the entire UGB - at Kanpur.

Simulation of Natural Flow Conditions for following sections. Although natural flows have the Four EF Sites (Scenario 2) been simulated for the whole basin, these four locations were chosen for this study as they are Simulating non-regulated, pre-development representative of different agroecological zones flow regimes is important to determine the in the river stretch used for this study. These reference hydrology, against which flow locations were also sites for the EF assessment changes in a basin can be measured. Strictly study and are, therefore, referred to as EF sites speaking, this constitutes the assessment in this report. of water actually available for all uses and Simulated daily flow data at the four EF sites development – a starting point in sustainable under scenarios 1 and 2 were summed up to basin planning and management. Natural flow monthly and annual time steps, and are presented simulations are also required to assess EF in the tables and figures below. As already – the flow regimes that are required for the mentioned above, the modeling period only went ecological health of the river. Results from up to 2005, so the effect of the Tehri Dam, which the EF assessment for the UGB are beyond became operational in 2008, was not considered. the scope of this paper and are discussed in Therefore, for site EF1, only natural flows have another report (WWF 2011). been reported because water is neither stored in Natural flows for four locations (sites EF1- dams nor abstracted for agriculture upstream of EF4, coordinates in Table 5) are presented in the this site.

TABLE 5. Location and names of representative sites along the UGB (see also Figure 1).

Site code Name Latitude Longitude

EF1 Kaudiyala Rishikesh 30°04’29” N 78°30’09” E

EF2 Narora 29°22'22” N 78°2'20” E

EF3 Kachla Bridge 27°55’59” N 78°51’42” E

EF4 Bithur (Kanpur) 26°36'59” N 80°16'29” E

15 Figure 7 shows the plots of annual water water is being withdrawn for irrigation. At Narora flow volume and monthly water flow volume at (site EF2), maximum flow reduction is 70% in site EF4, Bithur (Kanpur), which is located near February. Similarly, at site EF3, the maximum the outlet of the UGB. Table 6 shows simulation flow reduction is 35% in February and at site flow results for the four EF sites including EF4, the maximum flow reduction is 58% also simulated present flows for EF sites 2-4. in February (Table 6). In April, and some other Comparison between natural and present flows months, mean flow volume of the present showed that, on average, the present annual conditions exceeds the flow volume of the natural water volume is 7%, 2% and 8% lower than conditions. This is caused by the basin flow in the natural conditions at EF sites 2, 3 and transfer from Ramganga Dam. Although high 4 (Narora, Kachla Bridge and Bithur (Kanpur)), percentages of flow reduction occur in the dry respectively. At all sites, the percentage of flow season, the contribution of flow in this season to reduction is highest during the dry months as annual flow is low (less than 10%).

(a)(a) EF4 - Bithur (Kanpur) 18,000

16,000

14,000

) 12,000 C M M

( 10,000

e m u l 8,000 V o 6,000

4,000

2,000

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Natural monthly flow volume Present monthly flow volume

(b)(b) EF4 - Bithur (Kanpur) 100,000

80,000

60,000 ) C M M (

e 40,000 m u l V o

20,000

0 7 1 7 3 7 5 7 7 9 8 1 8 3 8 5 8 7 8 9 9 1 9 3 9 5 9 7 9 0 1 0 3 0 5 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 1 9 2 0 2 0 2 0

Year Annual natural flow volume Annual present flow volume

FIGURE 7. (a) Annual flow totals, and (b) average monthly flow distribution for Bithur (Kanpur).

16 0 18 18 10 Dec 0 4 19 12 Nov 0 4 2 2 Oct 2 0 5 Sep

0 0 7 -1 Aug 0 -1 11 Jul 16 Jun 7 May 0 -10 30 24 -60 -44 12 31 Apr Percentage of monthly reduction in flows (%) 14 Mar 0 70 44 35 58 35 Feb 0 68 27 44 Jan

0 (0%) 0 (%)) 774 (2%) Annual in flows (volume 2,976 (7%) 2,976 (8%) 4,793 reduction 39,632 42,165 52,268 38,445* Present volume (MCM) Annual simulated flow

38,445 42,608 42,909 57,061 Natural

EF sites

EF1 (Kaudiyala/ Rishikesh) EF2 (Narora) EF3 (Kachla Bridge) (Kanpur)) EF4 (Bithur Note : * No reduction compared with natural conditions, because there was no water use upstream of this site during the study period . TABLE 6. Simulated flow results at EF sites.

17 Figure 8 shows the comparison of water a few subbasins in the lower catchment where yield (runoff from subbasin) distribution at the water yields have increased and this is a result subbasin level for the natural condition as well of water transfers from the Upper and Madhya as the present condition. As can be expected, Ganga canals. water yield or runoff, from the upper forested Figure 9 shows flow duration curves (FDC) subbasins, have not changed. However, there in present and natural conditions at the EF sites. are reduced flows during the present condition These curves indicate that flows are lower in from the lower subbasins, mainly due to water the present condition in comparison with natural withdrawals for agricultural production. There are conditions.

FIGURE 8. Percentage change in mean annual net water yields at present condition in comparison to natural condition of model simulation at subbasin level (i.e., present-natural).

18 (a) (b)

EF1 - Kaudiyala/Rishikesh EF2 - Narora

10,000 10,000

1,000 /s) 3 /s) 3 1,000 Flow (m Flow (m 100

100 10 0 20 40 60 80 100 0 20 40 60 80 100 Percentage of exceedence (%) Percentage of exceedence (%) Natural Present

(c) (d)

EF3 - Kachla Bridge EF4 - Bithur (Kanpur)

10,000 10,000

1,000 /s) 3 /s) 1,000 3 Flow (m Flow (m 100

100 10 0 20 40 60 80 100 0 20 40 60 80 100 Percentage of exceedence (%) Percentage of exceedence (%)

Natural Present Natural Present

FIGURE 9. Flow duration curves (FDC) for (a) Kaudiyala/Rishikesh, (b) Narora, (c) Kachla Bridge, and (d) Bithur (Kanpur) sites.

Table 7 shows the difference in flow rates are designed taking into consideration a design between simulations of natural and present discharge corresponding to 40% to 90%. The conditions for 40% to 90% of percentile of difference in flows affects any future planning exceedence. Usually, water-related infrastructure of water resources development as well as such as hydropower plants or irrigation systems allocations for environmental flows in the river.

TABLE 7. Difference in flows between simulations of the natural and present conditions at EF sites.

Difference in flows (natural-present) (m3/s)

Percentage of exceedence EF2 (Narora) EF3 (Kachla Bridge) EF4 (Bithur (Kanpur))

40% 232 99 156

50% 120 28 115

60% 93 17 48

70% 89 25 54

80% 114 41 68

90% 126 37 88

19 Analysis of Changes in Specific Flow conditions, dry season flows remain stable all Characteristics Under Different Simulation along the river stream, with values ranging from 3 Scenarios Including CC (Scenarios 1-4) 302 to 340 m /s. This flow regime indicates that river flow mostly originates from the melting of Five indicators of hydrological variability were glacier and snow cover while the flow contribution derived from simulated time series: mean daily dry from the water table drainage is negligible. During season flows, mean daily wet season flows, the the wet season, mean daily discharge consistently date of maximum flow, the date of minimum flow increases from upstream to downstream, reflecting and the number of reversals reflecting the rate of the successive flow contributions of the tributaries, change in river discharge. and collecting surface runoff produced by monsoon rainstorms. While the impact of water infrastructure remains moderate during the wet Hydrological Variability and Alteration Caused season (flows under present condition are 6% by Dams Under Present Climate Conditions lower than flows produced in natural conditions), The analysis first focuses on flows simulated from relative flow changes during the dry season are observed precipitation, either under the present of a higher magnitude, especially at EF sites 2, conditions of water infrastructure development 3 and 4, as no dam exists in the EF1 headwater or in naturalized conditions. Figure 10 displays catchment. Between the sites EF1 and EF4, the mean dry and wet season discharges computed dams have induced a 25% flow decrease as a from mean monthly discharge values. In natural result of dry season irrigation.

400 Dry season 4,000 Wet season 350 3,500 /s) /s)

3 300 3 3,000 m m ( (

e 250 e 2,500 g g a r a r h 200 h 2,000 Natural c c i s i s d d

150 1,500 Present a n a n e 100 e 1,000 M M 50 500 0 0 EF1 EF2 EF3 EF4 EF1 EF2 EF3 EF4

FIGURE 10. Mean dry and wet season daily discharge for the period 1971-2005 at four EF sites, under naturalized (natural) and present conditions.

Figure 11 displays the mean Julian date of the 1-day minimum flow does not follow this 1-day minimum and maximum flow at four EF pattern. Dates at sites EF1 and EF3 are similar sites, under natural and present conditions. In (dates = 22) while the 1-day minimum flow natural conditions, 1-day minimum and maximum occurs much earlier at sites EF2 and EF4 (date flow conditions are delayed downstream. This = 1 for EF2; date = 2 for EF4). This alteration of shift in the flow regime results from the difference the natural flow regime results from the operation in the onset of the monsoon between upstream of the dams located upstream of the sites EF2 and downstream parts of the basin. First rains and EF4, storing flows at the beginning of the of the wet season occur in the upper part year. The impact of these dams on the date of of the basin (Figure 12). Under the present the 1-day maximum flow is imperceptible, as the conditions, the downstream shift in the date range of flow variations caused by the operation of the 1-day maximum flow is similar to that of the dams is much lower than the mean river observed under natural conditions. In contrast, discharge during this period of the year.

20 120 Date of minimum flow 240 Date of maximum flow 238 100 236 234 80 232 Natural 60 230 228 Present Julian date 40 Julian date 226

20 224 222 0 220 EF1 EF2 EF3 EF4 EF1 EF2 EF3 EF4

FIGURE 11. Timing of annual extreme water conditions.

250

EF1 EF2

200 EF3 EF4

150

100 Precipitation depths (mm)

50

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 10-day periods

FIGURE 12. Mean observed precipitation (1971-2005) in the subcatchments of four EF sites.

The number of reversals is calculated by caused by the mainstream’s integration of flow dividing hydrological records into ‘rising’ and fluctuations of the tributaries. Under present ‘falling’ periods in which daily changes in flows conditions, the number of reversals at site are either positive or negative, respectively. EF2 is greater than the number of reversals The annual average number of reversals recorded at site EF1. The increase in the flow indicates whether a flow regime is influenced variability between sites EF1 and EF2 reflects only by precipitation input or includes anthropic the great impact of dams located between the alterations. Figure 13 displays the mean annual two stations. As flows are still moderate in the number of reversals at four EF sites, under upper part of the basin, the operation of dams natural and present conditions. In natural can significantly alter the natural river regime, conditions, the number of reversals decreases unlike in the downstream areas where the downstream, reflecting the lower temporal greater river discharge is less impacted by dam variability of the natural flow regime, mostly operation.

21 120 Number of reversals

100

80

Natural 60

Present 40 Number of reversals/year

20

0 EF1 EF2 EF3 EF4

FIGURE 13. Mean annual number of reversals recorded at four EF sites and at the basin outlet, under natural and present conditions.

Hydrological Changes Caused by ‘Climate site EF4 under PRECIS-A2 precipitation, in Change’ Scenarios Under Present Water comparison with flows produced by present Infrastructure Development climate conditions. Similar patterns are observed for precipitation (Figure 15), suggesting that Figure 14 displays the mean dry and wet season spatial variations in flow patterns originates flows at the four EF sites under present climate from the precipitation distribution over the conditions, and climate change projections from subcatchments of the four EF sites. Flows the PRECIS RCM under A2 (PRECIS-A2) and produced by PRECIS-B2 precipitation are B2 (PRECIS-B2) scenarios. Upstream of site systematically higher and lower than those EF3, dry and wet season flows produced by produced by PRECIS-A2 precipitation during PRECIS under the A2 scenario, precipitation is the dry season and wet season, respectively. lower than that in present climate conditions. These flow differences most likely result from This tendency inverts downstream of the site the difference of PRECIS-A2 and PRECIS-B2 EF3, with higher dry and wet season flows at precipitation as displayed in Figure 15.

450 Dry season 4,000 Wet season 400 3,500 /s) /s) 350 3 3 3,000 300 Present 2,500 climate 250 2,000 200 PRECIS-A2 1,500 150 PRECIS-B2 Mean discharge (m Mean discharge (m 100 1,000 50 500 0 0 EF1 EF2 EF3 EF4 EF1 EF2 EF3 EF4

FIGURE 14. Mean dry and wet season daily discharge under present climate conditions (for the period 1971-2005), and for future climate conditions under scenarios A2 and B2 (for the period 2071-2100).

22 Wet season Dry season 1,800 700

1,600 600 1,400 500 1,200 400 1,000 Present climate

800 300 PRECIS-A2 600 200 PRECIS-B2 400 100 200 Mean annual precipitation (mm) Mean annual precipitation (mm) 0 0 EF1 EF2 EF3 EF4 EF1 EF2 EF3 EF4

FIGURE 15. Mean dry and wet season precipitation under present climate conditions (observed precipitation averaged over period 1971-2005), and for future climate conditions under scenarios A2 and B2 (for the period 2071-2100).

Figure 16 displays the average Julian dates explains the delay of the minimum flows under of minimum and maximum 1-day flow at four EF scenarios A2 and B2. Julian dates of maximum site under present and future climate conditions. flows are delayed downstream as a result of The dates of minimum 1-day flow are highly the delay in the onset of the monsoon in the variable among stations and between different lower parts of the basin (Figure 12 and Figure climate scenarios. This variability is caused by the 17). Maximum flows simulated from PRECIS-A2 operation of the dams, as the volume of controlled precipitation occur later than maximum flows outflow is of the same order of magnitude as simulated from PRECIS-B2 precipitation. This time natural flow. As a result, a slight change in the lag is caused by the slight difference in the timing dam outflow results in a significant change in the of the wettest period, which occurs earlier in the date of the minimum flow. The same phenomenon case of PRECIS-B2.

140 Date of minimum flow 240 Date of maximum flow 120 235 100 Present e e t t 230 80 climate D a D a

n n a a i i PRECIS-A2 l 60 l u u 225 J J PRECIS-B2 40 220 20

0 215 EF1 EF2 EF3 EF4 EF1 EF2 EF3 EF4

FIGURE 16. Occurrence of annual extreme flow conditions.

23 200 200 PRECIS-A2 PRECIS-B2 180 EF1 180 EF1 160 160 EF2 EF2 140 140 120 EF3 120 EF3 100 100 EF4 EF4 80 80 60 60 40 40

10-day precipitation depth (mm) 20 10-day precipitation depth (mm) 20 0 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 10-day period 10-day period

FIGURE 17. Mean precipitation in the subcatchments of four EF sites, as predicted by PRECIS A2 and B2 scenarios. Averages computed over the period 2071-2100.

Figure 18 displays the number of reversals The greater number of reversals under climate at four EF sites. At sites EF1, EF2 and EF3, change conditions in the downstream part of the number of reversals under climate change the catchment is caused by the higher variability conditions (PRECIS-A2 and PRECIS-B2) is lower of precipitation. At each EF site, the number than the number of reversals observed under of reversals is greater under PRECIS-B2 in present climate conditions. At site EF4, this comparison with PRECIS-A2. This difference tendency reverses and the number of reversals results from the greater temporal variability of becomes greater under climate change conditions. PRECIS-B2 precipitation.

120 Number of reversals

100

80 e d a t 60 Present climate a n i l u J PRECIS-A2 40

PRECIS-B2 20

0 EF1 EF2 EF3 EF4

FIGURE 18. Mean annual number of reversals recorded at four EF sites, under present and future climate conditions.

24 Conclusions

This report explains the results of the first (known in the basin. In the upper parts of the basin, to authors) attempt to analyze the impacts of especially in the Uttarkhand District, plans for water infrastructure development in the entire further hydropower development are underway. UGB, by comparing flow changes under natural Decrease in precipitation and flows will affect and present conditions. The analysis shows that, water availability in the planned projects. Similarly, on average, annual flows at present are 2-8% change in the timing of the monsoon as well as lower than under naturalized conditions. Higher increased variability in precipitation, especially in flow reduction in the dry season (up to 70% in the lower parts of the basin, will affect the current February) is detected, compared to just a small irrigation water regulation practices. Therefore, percentage change in the wet season. Therefore, some adjustment to agricultural practices such as various dams and barrages constructed to date early sowing might be necessary if the projected have reduced mainly the flows during the dry changes under the B2 scenario become a reality. season – when irrigation water demands are The present modeling study did not consider the highest. Flow regulation through dams and scenarios of future water resources development barrages has also changed the timing of annual under future climate scenarios. As mentioned extreme water conditions such as the date of above, in the UGB, especially in upper parts of minimum and maximum flows. The change in the basin, several hydropower dams are being the timing of the minimum flow date is, however, planned or operationalized. The impacts of the affected more than the maximum flows. Future already constructed Tehri Dam are now coming water resources development plans need to take into effect. The combined impact of future water this into serious consideration in order to avoid infrastructure development and climate on river further detrimental impacts on the river ecology. flow in the UGB and the availability of water for Also, the study simulated the impacts of agriculture and other uses, as well as the impacts CC on water infrastructure development in of CC on operation of infrastructure itself, is a the UGB. The results suggest that both dry subject of a subsequent ongoing study. and wet season flows under CC scenario A2 It could be argued that precipitation scenarios (scenario corresponding to high population used to anticipate hydrological change under CC growth with slower per capita economic growth are not reliable as they originate only from one and technological change) are lower than that climate model: PRECIS RCM forced by HadCM3. under present climate conditions at upstream It is now accepted that climate models are not locations, but higher at downstream locations able to accurately simulate precipitation, mostly and at the basin outlet. Flows simulated under because of their inability to simulate actual climate CC scenario B2 (corresponding to moderate dynamics. For instance, Kingston et al. (2011) population growth and economic development showed that uncertainty in precipitation is the with less rapid and more diverse technological main source of error in hydrological projections. change) are found to be higher during the dry Even the use of averaged precipitations projected season, and lower during the wet season than by several climate models cannot reduce this that under CC scenario A2. Under CC scenario uncertainty as the variability between different B2, the timing of the maximum flow period is climate projections from different models is earlier than that under present conditions. This high. However, Rupa Kumar et al. (2006), who basically means that the monsoon might start assessed the biases in PRECIS simulation over earlier. Furthermore, greater temporal variability of India by comparing simulated and observed precipitation was found in the lower basin under precipitation, found that this RCM is able to both the A2 and B2 scenarios. All these results reasonably predict the climate over India, both in are very relevant to future water management terms of means and extremes. A second source of

25 uncertainty in the PRECIS precipitation projections severely affects model calibration and validation, originates from the SRES scenarios, associated and, in the end - simulations of future scenarios. with specific gas emission conditions that may The authorities responsible for observed result in various precipitation conditions. In the hydrological data management and sharing should present study, two very contrasting scenarios seriously consider opening their archives for water are used to cover the whole range of possible research needs. Without more open policies on precipitation changes. Consequently, even if the observed data access, proper planning of water CC projections used in this study are biased resources development in the Himalayan parts because of the use of only one climate model, it of India is impossible. The uncertainty in climate is moderated by the use of the two contrasting projections is another major issue associated SRES emission scenarios - A2 and B2. with studies like this one. Improvement in CC The main constraint in this study (as well as projections as well as access to more climate in all research carried out on water resources in data would certainly enhance the accuracy of the the Ganga Basin) is the availability of observed simulations, and in the end – planning for the data (on climate, hydrology, etc.). This limitation future.

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28 IWMI Research Reports

142 The Impacts of Water Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin. Luna Bharati, Guillaume Lacombe, Pabitra Gurung, Priyantha Jayakody, Chu Thai Hoanh and Vladimir Smakhtin. 2011.

141 Low-Cost Options for Reducing Consumer Health Risks from Farm to Fork Where Crops are Irrigated with Polluted Water in West Africa. Philip Amoah, %HUQDUG.HUDLWD0D[ZHOO$NSOH3D\'UHFKVHO5REHUW&$EDLGRRDQG)OHPPLQJ Konradsen. 2011.

140 An Assessment of Crop Water Productivity in the Indus and Ganges River Basins: Current Status and Scope for Improvement;XHOLDQJ&DL%KDUDW5 Sharma, Mir Abdul Matin, Devesh Sharma and Sarath Gunasinghe. 2010.

139 Shallow Groundwater in the Atankwidi Catchment of the White Volta Basin: Current Status and Future Sustainability. Boubacar Barry, BenonyKortatsi, *HUDOG)RUNXRU0XUDOL.ULVKQD*XPPD5HJDVVD1DPDUD/LVD0DULD5HEHOR Joost van den Berg and Wolfram Laube. 2010.

138 Bailout with White Revolution or Sink Deeper? Groundwater Depletion and Impacts in the Moga District of Punjab, India. Upali A. Amarasinghe, Vladimir 6PDNKWLQ%KDUDW56KDUPDDQG1LVKDGL(UL\DJDPD

137 Wetlands, Agriculture and Poverty Reduction. Matthew McCartney, Lisa-Maria 5HEHOR6RQDOL6HQDUDWQD6HOODPXWWXDQG6DQMLYGH6LOYD

136 Climate Change, Water and Agriculture in the Greater Mekong Subregion5RE\Q -RKQVWRQ*XLOODXPH/DFRPEH&KX7KDL+RDQK$QGUHZ1REOH3DXO3DYHOLF Vladimir Smakhtin, Diana Suhardiman, Kam Suan Pheng and Choo Poh Sze. 2010.

135 Impacts of Climate Change on Water Resources and Agriculture in Sri Lanka: A Review and Preliminary Vulnerability Mapping1LVKDGL(UL\DJDPD9ODGLPLU 6PDNKWLQ/DOLWK&KDQGUDSDODDQG.DULQ)HUQDQGR

134 Evaluation of Current and Future Water Resources Development in the Lake Tana Basin, Ethiopia. Matthew McCartney, Tadesse Alemayehu, Abeyu Shiferaw and Seleshi Bekele Awulachew. 2010.

133 Mapping Drought Patterns and Impacts: A Global Perspective 1LVKDGL (UL\DJDPD9ODGLPLU6PDNKWLQDQG1LODQWKD*DPDJH

Electronic copies of IWMI's publications are available for free. Visit www.iwmi.org/publications/index.aspx Related Publications

Bartlett, R.; Bharati, L.; Pant, D.; Hosterman, H.; McCornick, P. G. 2010. Climate change impacts and adaptation in Nepal. Colombo, Sri Lanka: International Water Management Institute (IWMI). 35p. (IWMI Working Paper 139) www.iwmi.org/Publications/Working_Papers/working/WOR139.pdf

Cai, X.; Sharma, B. R.; Matin, M. A.; Sharma, D.; Gunasinghe, S. 2010. An assessment of crop water productivity in the Indus and Ganges River Basins: current status and scope for improvement. Colombo, Sri Lanka: International Water Management Institute (IWMI). 30p. (IWMI Research Report 140) www.iwmi.org/Publications/IWMI_Research_Reports/PDF/PUB140/RR140.pdf

Jha, R.; Smakhtin, V. 2008. A review of methods of hydrological estimation at ungauged sites in India. Colombo, Sri Lanka: International Water Management Institute (IWMI). 24p. (IWMI Working Paper 130) www.iwmi.org/Publications/Working_Papers/working/WOR130.pdf

Smakhtin, V.; Gamage, M. S. D. N.; Bharati, L. 2007. Hydrological and environmental issues of interbasin water transfers in India: A case of the Krishna River Basin. Colombo, Sri Lanka: International Water Management Institute (IWMI). 35p. (IWMI Research Report 120) www.iwmi.org/Publications/IWMI_Research_Reports/PDF/PUB120/RR120.pdf

Venot, J. P.; Turral, H.; Samad, M.; Molle, F. 2007. Shifting waterscapes: Explaining basin closure in the Lower Krishna Basin, South India. Colombo, Sri Lanka: International Water Management Institute (IWMI). 60p. (IWMI Research Report 121) www.iwmi.org/Publications/IWMI_Research_Reports/PDF/PUB121/RR121.pdf

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