Congress Resources: Papers, posters and presentations

< Return to abstract list

Characterising Drought In Beas River Basin Using Standardised Runoff Index

Congress: 2015
Author(s): Adebayo Adeloye, Soundharajan Bankaru Swamy
Heriot-Watt University1

Keyword(s): Sub-theme 3: Hydrology,
Droughts and water scarcity, due to the natural variability in river runoff have severe socio-economic implications for societies relying on major water supply systems such as reservoirs. Drought is defined as a condition of insufficient availability of water caused by deficit in precipitation over a period (Heim, 2002). According to the IPCC's 5th assessment report, the frequency of meteorological and agricultural droughts is likely to increase due to climate change in dry regions (IPCC, 2014), further exacerbating the water shortage problem. Predicting the temporal, spatial and severity variations of drought is essential for developing appropriate conservation measures and operating policies. Drought indices currently in use are derived from climate variables such as precipitation and temperature, whereas the most direct indicator of water availability is the runoff which integrates the precipitation, temperature and the transformation influence on these by the catchment. This research study thus explores the possibility of assessing the drought severity and time duration using standardised runoff index (SRI) similar to the Standardised Precipitation Index (SPI) proposed by McKee et al., (1993), for the Beas river basin, India. This indicator requires only one input variable (i.e. runoff) to characterise the drought. The analysis of frequency, duration and severity of drought will be useful in developing the reservoir water management policies and implementing appropriate water rationing for various water users.

Materials and Methods
The standardised precipitation index (SPI) concept devised by McKee et al. (1993) for predicting drought severity can be applied to runoff (McKee et al., 1993; Shukla and Wood, 2008). The procedure of calculating the SRI is given in Shukla and Wood (2008) and is given here briefly. A time series of monthly runoff data set is prepared using the historical data and a probability distribution is fitted for the time series. The cumulative probability of the monthly runoff is estimated using the probability distribution. The cumulative probability value is converted to a standard normal variate with zero mean and unit variance (Stedinger et al., 1993). This value (standard normal variate) is the SRI for a particular monthly runoff data. This procedure is repeated for different averaging periods (1month, 3 months, 6 months and 12 months).

Results and Discussion
Beas runoff data at Pong reservoir in India is used to estimate the SRI, to characterise the drought. Catchment area of the basin is 12561 km2, out of which the permanent glacier catchment is 780 km2 (Jain et al., 2007). The multi-purpose Pong reservoir supplies irrigation water to 1.3 Mha in Punjab, Haryana and Rajasthan. The water released from the reservoir passes through the turbines to generate the hydropower. Monsoon rainfall between July and September is a major source of runoff in the river, apart from snow and glacier melt. The historic mean annual runoff at reservoir site is 8485 Mm3 (annual CV is 0.225). For drought analysis, the monthly runoff data from January 1998 to December 2012 were used.

The 3 parameter log-normal distribution is selected for fitting the monthly runoff series, as suggested by the Shukla and Wood (2008) and Adeloye et al. (2010). For using SRI as drought severity indicator, the categorisation of drought is adopted from Abril et al. (2014) and is given in Table 1. SRI for Beas river basin at Pong reservoir for 1, 3, 6 and 12 months averaging period was used to analyse the drought. A drought event is defined as a period in which SRI is below -0.84. When the time periods are shorter (1 and 3 months), the SRI moves frequently above and below zero, compared to the longer time periods (6 and 12 months). When time periods are short, the drought events are many but shorter in duration, however, with the longer time periods, the effect is just the opposite (fewer drought events but longer in duration).

The behaviour of the number of drought events with the duration, for the small durations of 1 and 3 months, each new month runoff value immediately influence the moving average thus making this average to respond quickly and change from wet to drought and vice-versa. For the larger durations (6- and 12 months), each new month has less impact on the average monthly runoff and SRI responds slowly compared to shorter time period, thus producing fewer drought events with longer duration. Table 2 provides the percentage of time periods that the runoff was under different drought categories.

Drought characterisation of Beas river basin was carried out using the SRI for the period from 1998 to 2012 based on monthly runoff data. Analysing the historical monthly river runoff data (1 month) at Pong reservoir in Beas river basin (1998 to 2012) shows that, 87% of the time the river flows are near normal and above near normal and 13% of the time the flows are categorised as moderate to severe drought. When the time scale of analysis is increased to 3- and 6 months, percentage of Time the River flows are below normal (i.e. drought) is increased to 25 and 27% respectively. This shows the need for reservoir storage to meet the average monthly demand (equal to mean monthly runoff) during the droughts, which is not surprising given that the Pong reservoir on the Beas River has now been operated since 1998 to regulate the drought in the river.

Table 1. Drought Categorisation (Abril et al., 2014)

Table 2. Percentage of time Beas runoff under different drought categories
Abril, A.M., Maeso, J.U., Trevino, J.G. and Chilikova-Lubomirova, M. (2014). Water resources and society with respect to water stress and drought. Dooge Nash International Symposium 2014, Dublin, 223-233.
Adeloye, A. J., Pal, S. and O’Neill, M. (2010). Generalised storage-yield-reliability modelling: independent validation of the Vogel-Stedinger (V-S) model using a Monte Carlo simulation approach. Journal of Hydrology, 388: 234-240.
IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. part a: Global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Jain, S.K., Agarwal, P.K. and Singh, V.P.(2007). Hydrology and water resources of India, Springer, The Netherlands.
McKee, T. B., N. J. Doesken, and J. Kleist. (1993). The relationship of drought frequency and duration to timescales, paper presented at 8th Conference on Applied Climatology, Am. Meteorol. Soc., Anaheim, Calif.
Shukla, S. and A. W. Wood (2008), Use of a standardized runoff index for characterizing hydrologic drought, Geophys. Res. Lett., 35(2): 1-7.
Stedinger, J.R., Vogel, R.M. and Foufoula-Georgiou, E. (1993). Frequency analysis of extreme events. In: Maidment, D.R. (Ed.), Handbook of Applied Hydrology, McGraw-Hill, NY, USA (Chapter 19).

© 2011 IWRA - International Water Resources Association - - Admin