Future urban rainfall projections considering the impacts of climate change and urbanization with statistical–dynamical integrated approach

Abstract

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical-dynamical approach.

Urban effects on precipitation: Do the diversity of research strategies and urban characteristics preclude general conclusions?

“…Climatological study Temperate (Hand & Shepherd, 2009) Models Climatological study Temperate (Kusaka et al, 2019;Rose et al, 2008;Sarangi et al, 2018;Seino et al, 2018;Shafir & Alpert, 1990;Shepherd, 2003;Shepherd et al, 2002;Shimadera et al, 2015;Song et al, 2016) Desertic (Luong et al, 2020) Savanna (Shastri et al, 2019) Equatorial (Argüeso et al, 2016;Doan et al, 2021) Case study Temperate (Bélair et al, 2018;Carraça & Collier, 2007;Debbage & Shepherd, 2019;Hosannah & Gonzalez, 2014;Matheson & Ashie, 2008;Ntelekos et al, 2008;Pathirana et al, 2014;Shafir & Alpert, 1990;Shem & Shepherd, 2009;Souma et al, 2013;Xiao et al, 2020) Continental (Miao et al, 2011;Yu et al, 2017;Yu & Liu, 2015;Zhong & Yang, 2015) Desertic (Luong et al, 2020;Yang et al, 2019) Savanna (Simpson et al, 2008)…”

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

“…It has suggested to use variables with a Cramer's V value ≥ 0.15, while if the Cramer's V value of a factor is ≥ 0.4, then that factor can be the best to utilize [70]. With reference to earlier studies [26,27,71], mainly four variables were selected as a factor: elevation, slope, distance from transportation network, and distance from the existing urban area. For all these factors, the Cramer's V value was >0.15.…”

Urbanization Exacerbated Rainfall Over European Suburbs Under a Warming Climate

“…Due to the increasing evidence of urban impacts on rainfall, there is a growing interest in characterizing future rainfall changes by considering both climate change and urban expansion. There are a couple of rainfall projections at continental or national scales, e.g., United States (Georgescu et al., 2021) and India (Shastri et al., 2019). Their results demonstrate that the interplay of climate warming and urban expansion can enhance and surpass regional‐scale rainfall.…”