Accessing vulnerability of land‐cover types to climate change using physical scaling downscaling model

Gaur, Abhishek; Simonović́, Slobodan P.

doi:10.1002/joc.4887

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…Several studies have found significant impact of land use and global climate change on hydroclimatic variables across the globe, e.g., in India (Singh et al, 2016); USA (Mallakpour & Villarini, 2015; Villarini et al, 2009) and Canada (Buttle, 2011; Gaur & Simonovic, 2017; Kerkhoven & Gan, 2013; Tan & Gan, 2015), highlighted the importance of nonstationary frequency analysis. Following that several studies have examined the validity of the stationary assumption in AM flood extremes and performed nonstationary flood frequency analyses across Canada (Cunderlik & Burn, 2003; Gado & Nguyen, 2016; Leclerc & Ouarda, 2007; Tan & Gan, 2015).…”

Section: Methodsmentioning

confidence: 99%

Identification of flood seasonality and drivers across Canada

Singh

Ghosh

Simonović́

et al. 2021

Hydrological Processes

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Floods are the most frequently occurring natural hazard in Canada. An in-depth understanding of flood seasonality and its drivers at a national scale is essential. Here, a circular, statistics-based approach is implemented to understand the seasonality of annual-maximum floods (streamflow) and to identify their responsible drivers across Canada. Nearly 80% and 70% of flood events were found to occur during spring and summer in eastern and western watersheds across Canada, respectively. Flooding in

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Section: Methodsmentioning

confidence: 99%

Identification of flood seasonality and drivers across Canada

Singh

Ghosh

Simonović́

et al. 2021

Hydrological Processes

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“…Currently, we do not have global subkilometer in situ or satellite observational capabilities from which to derive these forcing variables. Therefore, physical, dynamic, and statistical downscaling approaches have been developed that interpolate the required high-resolution fields from coarser-resolution data incorporating the interactions between the atmosphere and terrestrial surface (Cosgrove et al 2003;Haylock et al 2006;Liston and Elder 2006;Girotto et al 2014;Sunyer et al 2015;Gaur and Simonovic 2017). For precipitation downscaling, Venugopal et al (1999) proposed dynamic space-time scaling of rainfall along with a spatial disaggregation scheme at subgrid scales.…”

Section: Introductionmentioning

confidence: 99%

A Physically Based Atmospheric Variables Downscaling Technique

Rouf

Mei

Maggioni

et al. 2020

Journal of Hydrometeorology

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This study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been implemented over Oklahoma, where high-resolution, high-quality observations are available for validation purposes. Hourly North America Land Data Assimilation System version 2 (NLDAS-2) meteorological data (i.e., near-surface air temperature, pressure, humidity, wind speed, and incident longwave and shortwave radiation) have been spatially downscaled from their original 1/8° resolution to a 500-m grid over the study area during 2015. Results show that correlation coefficients between the downscaled products and ground observations are consistently higher than the ones between the native resolution NLDAS-2 data and ground observations. Furthermore, the downscaled variables present smaller biases than the original ones with respect to ground observations. Results are therefore encouraging toward the use of the 500-m dataset for land surface and hydrological modeling. This would be especially useful in regions where ground-based observations are sparse or not available altogether, and where downscaled global reanalysis products may be the only option for model inputs at scales that are useful for decision-making.

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“…Before estimating future urban temperatures, the Global Climate Model (GCM) simulation under Shared Socioeconomic Pathways (SSPs) needed to be scaled down to a finer resolution to provide accurate and localized projections. The dynamic downscaling methods have been commonly used to explore future urban-rural temperature differences [30][31][32][33][34]. However, previous studies using such a method only focused on representative Global Climate Model (GCM) projections and selected cities for projecting UHI effects instead of UCI [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Evolution Patterns of Cooling Island Effect in Blue–Green Space under Different Shared Socioeconomic Pathways Scenarios

et al. 2023

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Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai’an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment.

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Accessing vulnerability of land‐cover types to climate change using physical scaling downscaling model

Cited by 6 publications

References 45 publications

Identification of flood seasonality and drivers across Canada

Identification of flood seasonality and drivers across Canada

A Physically Based Atmospheric Variables Downscaling Technique

Evolution Patterns of Cooling Island Effect in Blue–Green Space under Different Shared Socioeconomic Pathways Scenarios

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