No abstract

The growing frequency and intensity of extreme weather events have placed cities at the forefront of the human, social, economic, and ecological impacts of climate change. Extreme heat, extended freeze, excessive precipitation, and/or prolong drought impacts neighborhoods disproportionately across heterogenous urban geographies. Underserved, underrepresented, and marginalized communities are more likely to bear the burden of increased exposure to adverse climate impacts while simultaneously facing power asymmetries in access to the policy and knowledge production process. Knowledge co-production is one framework that seeks to address this convergence of disproportionate climate impact exposure and disenfranchised communities. Co-production is increasingly used in sustainability and resilience research to ask questions and develop solutions with, by, and for those communities that are most impacted. By weaving research, planning, evaluation, and policy in an iterative cycle, knowledge and action can be more closely coupled. However, the practice of co-production often lacks reflexivity in ways that can transform the science and policy of urban resilience to address equity more directly. With this, we ask what kind of co-production mechanism encourage academic and non-academic partners to reflect and scrutinize their underlying assumptions, existing institutional arrangements, and practices? How can these efforts identify and acknowledge the contradictions of co-production to reduce climate impacts in vulnerable communities? This paper presents a framework for reflexive co-production and assesses three modes of co-production for urban resilience in Austin, Texas, USA. These include a multi-hazard risk mapping initiative, a resident-driven community indicator system for adaptive capacity, and a neighborhood household preparedness guide. We establish a set of functional and transformational criteria from which to evaluate co-production and assess each initiative across the criteria. We conclude with some recommendations that can advance reflexive co-production for urban resilience.

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.