Climate Downscaling for Urban Adaptation
Why focus on Urban Systems?
Urban agglomerations are considered to be an economic hub of a country supporting massive human settlements having higher population density than surrounding areas which is sustained through the built environment. Due to inadequate urban planning, higher population growth rates and vast informal settlements, urban areas become vulnerable to various natural hazards. In addition to the loss of human lives, any disturbances to urban systems can have negative impacts on the nation’s economy (Malakar and Mishra, 2016). Economic loss can be attributed to the decreased labour efficiency, disruptions in logistics and supply chains, damaged public utilities like water supply and wastewater systems, health care facilities and energy systems.
Are our cities equipped enough to tackle various hazards caused by climate change? What are the city-specific challenges? Where and how to incorporate cost-effective and reliable adaptation policies? These are some of those pressing questions which are needed to be addressed to mitigate the impact of climate change on our urban systems. In addition to the impact hotspots of climate change-related hazards, cities across the globe are significant contributors to climate change (CDP, 2019).
Decision-makers often employ coping and incremental adaptation measures as short and medium term solutions to adapt to the climate change. Coping measures are reactionary in nature that are designed based on past experiences. While incremental measures are taken step by step accounting for the recent trends in local climate and near term socio-economic evolution of the city. In some cases, the solutions adapted through incremental adaptation may become ineffective in the long run, example, we can’t keep expanding the capacity of cities drainage system due to the space constraints and one can’t assure its functionality under the uncertain nature of the climate change (Smid and Costa, 2018). Thus, the long term holistic approach towards climate change adaptation is needed. Inclusion of long term adaptation measures into the planning process is being limited due to several gaps as highlighted below.
The role of climate experts is still unclear in the city’s planning process. It can be due to the knowledge gap in assessing the reliable projections of future climate. Often climate experts fail to convey simple narrative explaining the local relevance of climate change. Also, owing to the limited financial resources at disposal, some city authorities are more concerned with the urgent issues rather than focussing on the investment intensive climate adaptation measures which could yield returns in century’s time scale. Another hindrance to design and implement efficient adaptation policies can be attributed to the lack of synergy between city authorities, citizens, institutes, and non-governmental organizations.
Significance of downscaling techniques in the urban context:
Impact and vulnerability assessment for various parts of the city provides a rational basis to formulate adaptation policies. It can help city authorities to take special adaptation measures to safeguard locations such as schools, hospitals, old age homes, and low-income neighbourhoods, that are occupied by vulnerable sections of the city’s community. Thus, the experience of past extreme climatic events, location-specific current climate information, vulnerability assessment, and evolution of local climate and socio-economic situation are essential to formulate city-specific climate change adaptation policy. The finest resolution of future climate information produced by recent state-of-the-art RCMs varies from 12.5 and 50 km (CORDEX, 2019). Climate models demand high computation power to simulate climate at a higher resolution. As yet, being equipped with the enormous computing power for high-resolution simulations is not the luxury of all climate modelling centres. Therefore, in the context of Urban Climate Change adaptation, the usability of climate models is limited because of these coarse-scale climate projections and parameterization of the sub-grid scale processes. This gap compels the climate modelling community to rely on climate downscaling techniques.
Overall state of the future urban climate will be dictated by the local climate along with the city’s socio-economic evolution. Due to the complex nature of the socio-economic state of the city, it is challenging to predict its evolution. It is being considered that future changes into land use and land cover can capture the signals of socio-economic evolution (Smid and Costa, 2018). City-specific land use and land cover management are being done at much finer scales than the typical grid size of the GCM/RCM. Generally, the climate model runs don’t consider the variation of land use and land cover while simulating the future climate. It means that the projected climate is being influenced by the evolution of atmospheric variables while downplaying the feedback from the land surface processes. Thus, to relate future climate behaviour with the fine-scale socio-economic state of a city, it becomes imperative to downscale the climate outputs generated by the climate model.
Challenges in the utility of downscaled information for urban planning:
Climate modelling community has done a significant amount of work in developing various climate downscaling techniques. But, still, there is a long way to go in mainstreaming these downscaling techniques into the impact assessment for the urban planning process. Here we try to highlight some constraints, as mentioned in the literature, which is limiting the extensive use of these techniques. These constraints are in addition to the scale mismatch between the data availability and data needs as highlighted in the literature (Smid and Costa, 2018).
Climate model outputs exhibit huge uncertainty resulting from several sources such as large grid sizes, parameterization of sub-grid scale processes, assumptions and model structure errors (Goore Bi et al., 2017). Therefore, even after the availability of city-level climate projections decision-makers would favour the more flexible and low-cost adaptation solutions, for example, promoting green infrastructure which can also enhance the quality of life. It becomes challenging to manage a large amount of data generated by climate models and to handle them with suitable software supporting the specific data format and structure. Thus, climate change impact studies also require a certain level of expertise in the IT domain (Smid and Costa, 2018). The demand for high computational power limits the wider deployment of dynamical downscaling compared with the statistical downscaling techniques. Thus, the approach of statistical downscaling sounds more appealing to the general climate modelling community. Although the computational cost is low for statistical downscaling, the selection of appropriate techniques, demand on the available point scale data, and the assumption of stationarity limit the wider incorporation of it into the urban adaptation strategy. The selection of physically meaningful predictor variable which can be accurately simulated by the climate models and that will reflect the climate change signal pose another challenge for researchers using statistical downscaling techniques. Also, the expertise required to employ a statistical downscaling technique for local impact studies is not available with all urban setups.
REFERENCES:
CDP Worldwide Group. Citywide Emissions Map. Retrieved December 18, 2019. https://data.cdp.net/Emissions/2016-Citywide-Emissions-Map/iqbu-zjaj/data
CORDEX (Coordinated Regional Downscaling Experiments). (2019). Domain Reports. World Climate Research Program (WCRP). Retrieved December 20, 2019. http://www.cordex.org/publications/report-and-document-archives/
Gooré Bi, E., Gachon, P., Vrac, M., and Monette, F. Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends. Theoretical and Applied Climatology. 127, 685–699 (2017). https://doi.org/10.1007/s00704-015-1656-y
Malakar, K., and Mishra, T. (2016). Assessing socio-economic vulnerability to climate change: a city-level index-based approach. Climate and Development. Vol. 9 (2017), Issue 4. 348-363. https://doi.org/10.1080/17565529.2016.1154449
Smid, M., and Costa, A. (2018). Climate projections and downscaling techniques: a discussion for impact studies in urban systems, International Journal of Urban Sciences, 22:3, 277-307. https://doi.org/10.1080/12265934.2017.1409132
