Integrated climate change risk assessment and evaluation of adaptation perspective in southern Punjab, Pakistan

Climate change is posing stresses on water resources, food security, population, environment and economy of the southern Punjab. Integrated climate change risk assessment is carried out using assessed likelihood approach for defined mean, hot & dry, central, warm & wet climate models over selected time slices and adaptation plans. Climate models are based on the 5th, 50th & 95th percentiles of PRECIS RCM projections of temperature & precipitation under IPCC A2 & A1B scenarios. Four time slices 2015, 2035, 2065 and 2085 are selected to assess the temporal climate change risk and to evaluate the performance of selected adaptations to reduce climate threats over considered assets. Results are presented in terms of risk indices and risk reduction units (RRUs). In first half of the 21st century, climate change risk will continue to increase from current level and is high (>10) in most of the selected time slices. Maximum ensembles of climate models, time slices and adaptation plans observe moderate (37-40 RRUs) and high (40-55 RRUs) risk class. Cumulative risk has been calculated through integration of sectoral sensitivity e.g. population density, land use, food security and multidimensional poverty to climate change risk class using AHP and overlaying in GIS environment. About 90% and 83% area of southern Punjab is falling in high cumulative risk. About 13% area, comprising Muzaffargarh and Rajanpur district is under very high cumulative risk. Water induced adaptations like development of water resources, dam & flood control protection, temporary flood barriers and water resource acquisition arc the preferred and suitable adaptations as these observed >100 RRUs for most of the ensembles. Assessing baseline vulnerability and sectoral sensitivity to climate stimuli are the hot spots requiring priority attention and firm decision making by disaster management authorities and communities residing in southern Punjab. (C) 2018 Elsevier B.V. All rights reserved.