Potential contributions of climate change and urbanization to precipitation trends across China at national, regional and local scales

Abstract

Climate change and urbanization collectively influence precipitation changes. However, their separate potential contributions to precipitation changes are not well understood due to their complex interactions. Hence, a “trajectory”‐based method was used to separate their potential contributions across national, regional and local scales in China. Precipitation changes in non‐urban regions can be regarded as representing the influence of climate change and can serve as a reference for isolating precipitation changes due to urbanization in urban areas. Our results revealed that climate change was the dominant factor for precipitation trends, while urbanization exhibited a relatively weak influence, especially for extreme precipitation at the national scale. At the regional scale, the impacts of urbanization on precipitation became more significant. About 20.2 and −30.6% of positive and negative trends in total precipitation originated from urbanization. At the local scale, the potential contribution of urbanization was strongly correlated with local environmental characteristics. Although there were differences in the potential contributions of climate change and urbanization at national, regional and local scales, climate change was the dominant factor for precipitation trends and urbanization acted as a regulator to drying or wetting due to precipitation under climate change. In general, urbanization causes a greater impact on total precipitation than on precipitation extremes. Due to this, attribution approach is static and broad‐based and does not render the level of confidence that is needed for scale‐aware attribution, future studies are needed to understand the physical mechanisms of impacts of local environment changes on precipitation trends at different geographical locations over China.

Asymmetrical Shift Toward Less Light and More Heavy Precipitation in an Urban Agglomeration of East China: Intensification by Urbanization

“…To reduce the impact of any regional confounding factors, we pair rural and urban stations by selecting urban stations that are located within a 100-km buffer of rural stations (Gu et al, 2019b;Luo & Lau, 2018). The above analyses are repeated based on the paired rural-urban stations (Figures S12-S14 and Text S3 in Supporting Information S1).…”

Nonstationary Flood Hazard Analysis in Response to Climate Change and Population Growth

“…For Dahuangjiangkou station, it was found that the nonstationary flood hazard calculated using time covariates was always larger than the stationary flood hazard, since the future exceedance probabilities p t in Equation (4) were getting monotonically larger with time covariates, while the cases were complicated for the nonstationary flood hazard calculated using pop and rain covariates because p t grew in fluctuation with pop and rain covariates ( Figure 11). In addition, the nonstationary flood hazard calculated using pop and rain covariates was larger than that calculated using time covariate for m ∈ [2,30]. For Huaxian station, the nonstationary flood hazard estimated by time or physical covariates were all smaller than stationary flood hazard because the AMFS were decreasing and the exceedance probabilities p t in Equation (4) were getting smaller (Figure 12), indicating that the flood hazard in the Huaxian basin will decrease in future periods.…”

“…The fundamental assumption of the conventional flood frequency analysis is that the annual maximum flood series (AMFS) is independent and identically distributed (iid), also known as the stationary assumption. However, this assumption has been challenged by climate change and human activities [1][2][3][4][5][6][7][8][9][10][11][12]. Thus, the conventional stationary flood frequency analysis should be adapted to nonstationary conditions in changing environments.…”

Influence of urbanization on hourly extreme precipitation over China

“…We used a 1 • × 1 • grid spacing as the basic analysis unit, as in Burke and Stott (2017) and Gu et al (2019). We considered the grid with all its surrounding grids (out of eight possible) as a 3 • × 3 • window (figure S3).…”