Context specific adaptation grammars for climate adaptation in urban areas Mohanasundar Radhakrishnan a, b, * , Tushith Islam c , Richard M. Ashley a, b , Assela Pathirana a, b , Nguyen Hong Quan d , Berry Gersonius a, b , Chris Zevenbergen a, b a IHE Delft Institute for Water Education, 2611 AX, Delft, The Netherlands b CRC for Water Sensitive Cities, Clayton, Melbourne, Australia c Faculty of Technology and Policy Management, Technical University Delft, Jafflaan 5, 2628 BX, Delft, The Netherlands d Vietnam National University - Ho Chi Minh City (VNU-HCM), Center of Water Management and Climate Change (WACC), 01 Street 6, VNU Campus, Quarter 6, Linh Trung, Thu Duc District, Ho Chi Minh City, Viet Nam article info Article history: Received 10 April 2017 Received in revised form 22 October 2017 Accepted 20 December 2017 Keywords: Adaptation pathways Climate adaptation Flexibility Precedent grammar Context specific adaptation abstract In the context of climate adaptation planning there are relationships between adaptation drivers and adaptation measures, which makes the selection and implementation of the adaptation measures a challenging task. This challenge may be addressed by: structuring the adaptation problem using a multiple perspective adaptation framework; and applying a context specific precedence grammar logic for selecting and evaluating adaptation measures. Precedence grammar logic is a set of rule based al- gorithms (grammar) that are based on the relationships in a local adaptation context. This paper dem- onstrates the application of a context specific precedence grammar logic in an adaptation context in Can Tho, Vietnam. Adaptation pathways comprising flood adaptation measures (i.e. dike heightening) for this case were generated using rule based algorithms based on the relationships between the drivers and the adaptation measures. The results show that complex adaptation issues that are structured, can be resolved using a context specific adaptation grammar approach. © 2018 Elsevier Ltd. All rights reserved. 1. Introduction Urban areas, which are home to more than half the world's population and composed of complex interdependent systems are a major challenge for climate change adaptation planning (Revi et al., 2014). The complexity is due to the interactions between social, economic and environmental stressors; where all or any can exacerbate risk to individual and to the households wellbeing (Radhakrishnan et al., 2017). - The economic capacity and ability to make comprehensive decisions in deploying adaptation measures are seen as the key factors in determining the sustainability of Deltas, where the urbanisation and economic activities are concentrated (Tessler et al., 2015). The current frameworks on risk assessment and adaptation call for accounting of all significant natural and anthropogenic drivers in adaptation related decision making (IPCC, 2014; UN, 2015). This can improve the long term resilience of cities against climate change. Decision making at a programme or project level is beset with uncertainties associated with the multiple drivers (Buurman and Babovic, 2016). Also there are uncertainties related to system performance in the range of scenarios anticipated in the future, and uncertainty regarding the ability of any strategy to adapt to future scenarios (Maier et al., 2016). Hence it can be concluded that adaptation related decision making in urban areas should take into account: (i) the complexity of adapting urban systems to climate change; (ii) the need for the consideration of multiple drivers, especially socio-economic (e.g. population, urbanisation, gross domestic product e GDP etc.); (iii) uncertainties associated with the drivers and; (iv) approaches set out in extant enabling frameworks for carrying out risk assessment and development of adaptation plans (Dittrich et al., 2016; Maier et al., 2016; Matteo et al., 2016; Young and Hall, 2015). Expertise on climate change, socio economic drivers that in- crease vulnerability and impacts, integrated assessment modelling for assessing impacts and vulnerability, is becoming increasingly sophisticated (Hallegatte et al., 2011; IPCC, 2013; O'Neill et al., 2015). However, at the municipality level e the level which * Corresponding author. IHE Delft Institute for Water Education, 2611 AX, Delft, The Netherlands. E-mail address: m.radhakrishnan@un-ihe.org (M. Radhakrishnan). Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft https://doi.org/10.1016/j.envsoft.2017.12.016 1364-8152/© 2018 Elsevier Ltd. All rights reserved. Environmental Modelling & Software 102 (2018) 73e83