Potential of the Reliability-Resilience-Vulnerability (RRV) Based Drought Management Index (DMI)

Tuesday, 16 December 2014
Rajib Maity1, Kironmala Chanda1, Nagesh Kumar D2, Ashish Sharma3 and Rajeshwar Mehrotra4, (1)Indian Institute of Technology Kharagpur, Kharagpur, India, (2)Indian Institute of Science, Bangalore, India, (3)University of New South Wales, Sydney, NSW, Australia, (4)University of New South Wales, Sydney, Australia
This paper highlights the findings from a couple of recent investigations aimed at characterizing and predicting the long-term drought propensity at a region for effective water management. A probabilistic index, named as Drought Management Index (DMI), was proposed for assessing the drought propensity on a multi-year scale at the chosen study area. The novelty of this index lay in the fact that it employed the Reliability-Resilience-Vulnerability (RRV) rationale, commonly used in water resources systems analysis, with the assumption that depletion of soil moisture across a vertical soil column is analogous to the operation of a water supply reservoir. This was the very first attempt to incorporate into a drought index the resilience of soil moisture series, which denotes the readiness of soil moisture to bounce back from drought to normal state. Further, the predictability of DMI was explored to assess the future drought propensity, which is essential for adopting suitable drought management policies at any location. For computing DMI, the intermediate measures i.e., RRV were obtained using the Permanent Wilting Point (PWP) as the threshold, indicative of transition into water stress. The joint distribution of resilience and vulnerability of soil moisture series was subsequently determined using Plackett copula. The DMI was designed such that it increases with increase in vulnerability as well as with decrease in resilience and vice versa. Thus, it was expressed as the joint probability of exceedence of resilience and non-exceedence of vulnerability of a soil moisture series. An assessment of the sensitivity of the DMI to the length of the data segments indicated that a 5-year temporal scale is optimum to obtain stable estimates of DMI. The ability of the DMI to reflect the spatio-temporal variation of drought propensity was illustrated using India as a test bed. Based on the observed behaviour of DMI series across India, on a climatological time scale, a DMI prediction model comprising of deterministic and stochastic components was developed. It was found to indicate a reasonably good prediction skill at many, if not most, locations over the country thereby establishing the potential of DMI for drought management in the medium term water resources planning horizon.