Spatial Climate Research Articles

Geography and Environment Shape Spatial Genetic Variation and Predict Climate Maladaptation Across Isolated and Disjunct Populations of Pinus muricata.

Assessing the evolutionary potential of rare species with limited migration amidst ongoing climate change requires an understanding of patterns of genetic variation and local adaptation. In contrast to the large distributions and population sizes of most pines, Pinus muricata (bishop pine) occurs in a few isolated populations along coastal western North America and is listed as threatened by the IUCN. To quantify how current genetic variation is influenced by distribution and environment, we generated reduced representation DNA sequencing data for most extant populations of P. muricata (12 locations, 7828 loci). We assessed geographic variation in differentiation and diversity and used genetic-environment association (GEA) analyses to characterise the contribution of environmental variables to local adaptation and genetic structure. Based on these inferences, we quantified genomic offset as a relative estimate of potential maladaptation under mild (SSP1-2.6) and severe (SSP5-8.5) climate change scenarios across 2041-2060 and 2081-2100. Despite occurring in small, isolated populations, genetic diversity was not low in P. muricata. Population differentiation was, however, defined across a hierarchy of spatial scales, with stands generally forming genetically identifiable groups across latitude and environments. GEA analyses implicated temperature- and soil-related variables as most strongly contributing to local adaptation. Estimates of maladaptation to future climate varied non-linearly with latitude, increased with severity of projections and over time, and were predicted by increases in annual temperature. Our results suggest that isolation and local adaptation have shaped genetic variation among disjunct populations and that these factors may shape maladaptation risk under projected climate change.

Climate change risk trap: low-carbon spatial restructuring and disaster risk in petroleum-based economies

Abstract The economic risks from climate change can be separated into two categories: physical risks from increased frequency and magnitude of extreme weather and climate events, and transition risks due to rapid decarbonisation leading to stranded assets and shifts in production and the labour market. On the global level, the two risks are widely recognized as trade-offs by the academic and policy literature: rapid decarbonisation increases transition risks while reducing physical risks, and vice versa for a delayed reduction of greenhouse gas emissions. In contrast, little is known about the interaction between the two risks on smaller geographical scales. This paper investigates the interactions between physical and transition risks on the sub-national level for petroleum-based economies. Their strong dependency on the export of fossil fuels requires a more far-reaching economic restructuring to meet both local and global emission targets than most other economies, exposing them to high transition risks. At the same time, the profound economic changes resulting from a low-carbon transition lead to a spatial redistribution of assets and labour, when brown assets and jobs get stranded and new green assets and jobs are created elsewhere. Building on the literature on spatial economic restructuring and climate disaster risk, we show that, unlike the common conceptualisation as a trade-off, physical and transition risk can increase simultaneously on the sub-national level. We call this dynamic the climate change risk trap. The paper provides an empirical illustration of this trap using the example of flash flood risk in Kuwait, a wealthy petroleum-based economy in the Gulf region. It shows how decisions on urban planning and low-carbon economic restructuring have increased flash flood risk. The analysis highlights the importance of considering climate disaster risk and environmental impact assessments in low-carbon transition planning to avoid falling into the climate change risk trap.

Pliocene Warmth and Patterns of Climate Change Inferred From Paleoclimate Data Assimilation

AbstractAs the last time period when concentrations were near 400 ppm, the Pliocene Epoch (5.33–2.58 Ma) is a useful paleoclimate target for understanding future climate change. Existing estimates of global warming and climate sensitivity during the Pliocene rely mainly on model simulations. To reconstruct Pliocene climate and incorporate paleoclimate observations, we use data assimilation to blend sea‐surface temperature (SST) proxies with model simulations from the Pliocene Modeling Intercomparison Project 2 and the Community Earth System Models. The resulting reconstruction, “plioDA,” suggests that the mid‐Pliocene (3.25 Ma) was warmer than previously thought (on average 4.1°C warmer than preindustrial, 95% CI = 3.0°C–5.3°C), leading to a higher estimate of climate sensitivity (4.8°C per doubling of , 90% CI = 2.6°C–9.9°C). In agreement with previous work, the tropical Pacific zonal SST gradient during the mid‐Pliocene was moderately reduced (°C, 95% CI = –0.4°C). However, this gradient was more reduced during the early Pliocene (4.75 Ma, °C, 95% CI = –°C), a time period that is also warmer than the mid‐Pliocene (4.8°C above preindustrial, 95% CI = 3.6°C–6.2°C). PlioDA reconstructs a fresh North Pacific and salty North Atlantic, supporting Arctic gateway closure and contradicting the presence of Pacific Deep Water formation. Overall, plioDA updates our view of global and spatial climate change during the Pliocene, as well as raising questions about the state of ocean circulation and the drivers of differences between the early and mid‐Pliocene.

Integrating Historical biogeography and Pliocene climate fluctuation to Unraveling the evolution of Tigris-Euphrates drainage basin through widespread freshwater Barbinae (Cypriniformes: Cyprinidae)

The formation history of the Tigris-Euphrates basins (TEB) and Karun River remains mostly unknown. To unravel the evolutionary history of the TEB and Karun River, we integrated the biogeography and climatic niche models of the freshwater fish with the largest natural distribution pattern. This study investigates 16 species of the Barbinae subfamily with wide distributions and high diversity. Mitochondrial Cytb and COI genes were obtained from various locations via GenBank and utilized to generate a dated phylogeny and reconstructed ancestral areas with the BioGeoBEARS model. We used the BAMM tools to estimate rates of diversification through time. Furthermore, by integrating potential geographic distribution with four spatial climate variables including max temperature of warmest month, min temperature of coldest month, temperature annual range and annual precipitation, we reconstruct ancestral predicted niche occupancy (PNO) profiles using ecological niche modeling (ENM) method. Based on the evolution of endemic TEB and Karun River Barbinae, we hypothesize that ancestral clades appeared in the TEB in the late Oligocene/early Miocene (∼23.12 Mya) and dispersed to the basin from the mid-Miocene onward (∼14.33 Mya). Our results suggest that the mid-Miocene uplift of the Zagros Mountains likely impacted Karun River flow in southwestern Iran, contributing to the observed diversification patterns in Barbinae. In addition, niche divergence was dominant in Barbinae diversification, alongside phylogenetic niche conservatism. The study introduces a method combining phylogenetic, biogeographic, and niche modeling data to explore freshwater fish evolution in the Tigris-Euphrates and Karun basins, highlighting geological impacts on biodiversity and diversification processes.

Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration

The intensification of climate change and the implementation of territorial spatial planning policies have jointly increased the complexity of future carbon storage changes. However, the impact of territorial spatial planning on carbon storage under future climate change remains unclear. Therefore, this study aims to reveal the potential impacts of future climate change and territorial spatial planning on carbon storage and sequestration, providing decision support for addressing climate change and optimizing territorial spatial planning. We employed the FLUS model, the InVEST model, and the variance partitioning analysis (VPA) method to simulate carbon storage under 15 different scenarios that combine climate change scenarios and territorial spatial planning for Xiamen in 2035, and to quantify the individual and combined impacts of territorial spatial planning and climate change on ecosystem carbon sequestration. The results showed that (1) by 2035, Xiamen’s carbon storage capacity is expected to range from 32.66 × 106 Mg to 33.00 × 106 Mg under various scenarios, reflecting a decrease from 2020 levels; (2) the implementation of territorial spatial planning is conducive to preserving Xiamen’s carbon storage, with the urban development boundary proving to be the most effective; (3) carbon storage is greatly affected by climate change, with RCP 4.5 more effective than RCP 8.5 in maintaining higher levels of carbon storage; and (4) the influence of territorial spatial planning on carbon sequestration consistently exceeds that of climate change, particularly under high-emission scenarios, where the regulatory effect of planning is especially significant.

Assessing the spatiotemporal precipitation trends from ERA5-Land over Indonesia region

Abstract The availability and accuracy of high-resolution spatial and temporal climate data are required for various climate-related analyses, especially in areas with varying topography and climate. The long-term ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecast (ECMWF) combines climate model and observation data from around the world into a complete and consistent collection with a spatial resolution of 0.1°. This high-resolution climate data is considered to be a reliable gridded dataset spanning various regions. Due to the scarcity of ground-based data in Indonesia, ERA5-Land is expected as an alternative to represent the climate condition. However, a comprehensive assessment prior to usage should be conducted. This study evaluates the proficiency of ERA5-Land based on observation data from the Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) to represent the precipitation variability over Indonesia during 1990-2022. Comprehensive investigations on the spatial-temporal dynamics of precipitation are presented. The ERA5-Land precipitation trend shows a similar pattern in annual total accumulated precipitation and precipitation cycle to CHIRPS observations. In areas with monsoon patterns like south Sumatera, Java, southern Kalimantan, Sulawesi, and part of Papua, low bias is observed often during the June-July-August (JJA) dry season, while high bias is observed often during the December-January-February (DJF) and September-October-November (SON). The similar observational trends and acceptable bias of ERA5-Land precipitation data convince the use of the data in simulating precipitation variability in the Indonesian region, which is essential for further climate studies.

Simulation of wheat water footprint using AquaCrop model under the climate change, case study in Qazvin plain

Simulating crop water consumption has been introduced as a valuable decision tool in food security. Such a tool is typically used to support a better understanding of how to increase water-use efficiency to satisfy optimal water management and sustainability. However, climate change is one of the most important and influential factors that restrain sustainable development, agriculture, and food security. Wheat is one of the most important and strategic products in the world and Iran. Therefore, in this study, the impacts of future climate changes on winter wheat yield, water requirement (WR), evapotranspiration (ET), and water footprint (WF) were evaluated in Qazvin Plain, Iran. As such, the outputs from five general circulation models (EC-EARTH, GFDL-CM3, MPI-ESM-MR, MIROC5, and HADGEM2-ES) were fed into the LARS-WG model to get finer spatial climate data for four future periods (P1:2021–2040, P2:2041–2060, P3:2061–2080, P4:2081–2100) considering three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). Thereafter, the projected climate change data were used in the FAO AquaCrop model to simulate the variability of wheat characteristics. The results proved the superiority of LARS-WG to model the maximum and minimum temperatures and precipitation (P) of the baseline scenario (1986–2015). Moreover, results revealed that the wheat WF will decrease in future periods. The modeling results showed that the average wheat yield and biomass will increase in future periods by 7.67 and 15.98 tons/ha, respectively, as compared to the baseline. The highest increase was recorded by the HadGEM2-ES model with RCP8.5 during 2081–2100. The average WR in the baseline was 127.14 mm, which was projected to decrease in future periods. The results show that ET will potentially increase in the period 2021–2040. As a consequence, the adapted methodology produced significantly superior outcomes and can aid in decision-making for both water managers and development planners.

How to account for spatial trade-offs in planning for urban climate adaptation? Optimizing green and grey infrastructures

Urban Nature-Based Solutions have emerged as sound strategies for urban climate change adaptation, but they lack effective decision-support tools. This paper proposes a decision-support framework that relies on Multi-Objective Optimization (MOO), the consideration of actual space availability, the consideration of the simultaneous objectives of climate change adaptation and water management, the integration of demand for co-benefits, and the participatory exploration of trade-offs for decision-makers. The MOO is applied to the case study of the French city of Bordeaux, to identify the optimal location of Groundwater Recharge Infrastructures (GRI) that include Nature-Based Solutions. It provides decision-makers with spatially explicit solutions for developing GRI, with the objectives of maximizing groundwater recharge, maximizing urban cooling, while minimizing opportunity costs, in the context of urban climate change adaptation. Results indicate an array of solutions between two polar strategies: large scale specialized grey solutions in most effective recharge areas, or diffused Nature-Based Solutions to satisfy citizen demand for multiple benefits. The participatory trade-off analysis proposed in this paper is a novel way to co-design spatial climate change adaptation strategies in urban contexts.

Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta, Egypt

The aim of this research is to develop and compare single, hybrid, and stacking ensemble machine learning models under spatial and temporal climate variations in the Nile Delta regarding the estimation of the blue and green water footprint (BWFP and GWFP) for wheat. Thus, four single machine learning models (XGB, RF, LASSO, and CatBoost) and eight hybrid machine learning models (XGB-RF, XGB-LASSO, XGB-CatBoost, RF-LASSO, CatBoost-LASSO, CatBoost-RF, XGB-RF-LASSO, and XGB-CatBoost-LASSO) were used, along with stacking ensembles, with five scenarios including climate and crop parameters and remote sensing-based indices. The highest R2 value for predicting wheat BWFP was achieved with XGB-LASSO under scenario 4 at 100%, while the minimum was 0.16 with LASSO under scenario 3 (remote sensing indices). To predict wheat GWFP, the highest R2 value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Peeff, Tmax, Tmin, and SA), and scenario 5 (Peeff and Tmax). The lowest value was recorded with LASSO and scenario 3. The use of individual and hybrid machine learning models showed high efficiency in predicting the blue and green water footprint of wheat, with high ratings according to statistical performance standards. However, the hybrid programs, whether binary or triple, outperformed both the single models and stacking ensemble.

Long-Term Spatiotemporal Trends in Precipitation, Temperature, and Evapotranspiration Across Arid Asia and Africa

This study examines trends in precipitation (PRE), maximum temperature (TMAX), minimum temperature (TMIN), and potential evapotranspiration (PET) using the Modified Mann-Kendall test and Sen’s slope estimator between 1901 and 2022 in the arid lands of Central Asia, West Asia and North Africa. The results reveal complex spatial and temporal climate change patterns across the study area. Annual PRE shows a slight negative trend (Z = −0.881, p = 0.378), with significant decreases from 1951–2000 (Z = −3.329, p = 0.001). The temperatures exhibit strong warming trends (TMIN: Z = 9.591, p < 0.001; TMAX: Z = 8.405, p < 0.001). PET increased significantly (Z = 6.041, p < 0.001), with acceleration in recent decades. Spatially, precipitation decreased by 10% in maximum annual values, while PET increased by 10–15% in many areas. Temperature increases of 2–3 °C were observed, with TMAX rising from 36–39 °C to 39–42 °C in some MENA regions. Seasonal analysis shows winter precipitation decreasing significantly in recent years (Z = −1.974, p = 0.048), while summer PET shows the strongest increasing trend (Z = 5.647, p < 0.001). Spatial analysis revealed clear latitudinal gradients in temperature and PET, with higher values in southern regions. PRE patterns were more complex, with coastal and mountainous areas receiving more precipitation. The combination of rising temperatures, increasing PET, and variable PRE trends suggest an overall intensification of aridity in many parts of the region. This analysis provides crucial insights into the climate variability of these water-scarce areas, emphasizing the need for targeted adaptation strategies in water resource management, agriculture, and ecosystem conservation.

Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change.

Vertical and diel niches modulate thermal selection by rainforest frogs.

Thermoregulatory behaviour determines an organism's body temperature and therefore its physiological condition, and may differ for organisms situated across climate gradients. Species' preferred or selected temperatures may be higher in warmer locations-referred to as coadaptation-or lower in warmer temperatures-countergradient variation. Here, we tested if rainforest amphibians exhibited coadaptation or countergradient thermal selection across an underappreciated spatial climate gradient (vertical height from forest floor to canopy) and separating diel activity (diurnal versus nocturnal behaviour). We captured 2534 amphibians over 216 ground-to-canopy surveys, and conducted 282 thermal selection assays for 37 species while pairing microclimate measurements and mechanistic model predictions to understand vertical and daily thermal variation in the field. Amphibians exhibited countergradient thermal selection: species occupying cool nocturnal conditions in canopies selected warmer temperatures than species occupying hot diurnal conditions at the forest floor. Furthermore, amphibians selected warmer temperatures than the average conditions that they were exposed to when active, and this divergence was especially high for nocturnal arboreal species (8.68°C). This suggests that rainforest amphibians dramatically underfill the warm end of their thermal niches, a trend across local thermal gradients that reflects recent findings across elevational and latitudinal gradients. We show that considering multidimensional climate gradients is important to evaluate thermoregulatory behaviour, and its evolutionary underpinnings, for understanding species' niches and community assembly.

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

Evaluation of flood metrics across the Mississippi-Atchafalaya River Basin and their relation to flood damages.

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks.

Unraveling the distribution pattern and driving forces of soil microorganisms under geographic barriers.

The Altai Mountains (ALE) and the Greater Khingan Mountains (GKM) in northern China are forest regions dominated by coniferous trees. These geographically isolated regions provide an ideal setting for studying microbial biogeographic patterns. In this study, we employed high-throughput techniques to obtain DNA sequences of soil myxomycetes, bacteria, and fungi and explored the mechanisms underlying the assembly of both local and cross-regional microbial communities in relation to environmental factors. Our investigation revealed that the environmental heterogeneity in ALE and GKM significantly affected the succession and assembly of soil bacterial communities at cross-regional scales. Specifically, the optimal environmental factors affecting bacterial Bray-Curtis similarity were elevation and temperature seasonality. The spatial factors and climate change impact on bacterial communities under the geographical barriers surpassed that of local soil microenvironments. The assembly pattern of bacterial communities transitions from local drift to cross-regional heterogeneous selection. Environmental factors had a relatively weak influence on myxomycetes and fungi. Both soil myxomycetes and fungi faced considerable dispersal limitation at local and cross-regional scales, ultimately leading to weak geographical distribution patterns.IMPORTANCEThe impact of environmental selection and dispersal on the soil microbial spatial distribution is a key concern in microbial biogeography, particularly in large-scale geographical patterns. However, our current understanding remains limited. Our study found that soil bacteria displayed a distinct cross-regional geographical distribution pattern, primarily influenced by environmental selection. Conversely, the cross-regional geographical distribution patterns of soil myxomycetes and fungi were relatively weak. Their composition exhibited a weak association with the environment at local and cross-regional scales, with assembly primarily driven by dispersal limitation.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Assessing the Impact of Urban Morphologies on Waterlogging Risk Using a Spatial Weight Naive Bayes Model and Local Climate Zones Classification

Rapid urbanization has altered the natural surface properties and spatial patterns, increasing the risk of urban waterlogging. Assessing the probability of urban waterlogging risk is crucial for preventing and mitigating the environmental risks associated with urban waterlogging. This study aims to evaluate the impact of different urban spatial morphologies on the probability of urban waterlogging risk. The proposed assessment framework was demonstrated in Guangzhou, a high-density city in China. Firstly, a spatial weight naive Bayes model was employed to map the probability of waterlogging risk in Guangzhou. Secondly, the World Urban Database and Access Portal Tools (WUDAPT)-based method was used to create a local climate zone (LCZ) map of Guangzhou. Then, the range of waterlogging risk and the proportion of risk levels were analyzed across different LCZs. Finally, the Theil index was used to measure the disparity in waterlogging risk exposure among urban residents. The results indicate that 16.29% of the area in Guangzhou is at risk of waterlogging. Specifically, 13.06% of the area in LCZ 2 is classified as high risk, followed by LCZ 1, LCZ 8, and LCZ 10, with area proportions of 11.42%, 8.37%, and 6.26%, respectively. Liwan District has the highest flood exposure level at 0.975, followed by Haizhu, Yuexiu, and Baiyun. The overall disparity in waterlogging exposure in Guangzhou is 0.30, with the difference between administrative districts (0.13) being smaller than the difference within the administrative districts (0.17). These findings provide valuable insights for future flood risk mitigation and help in adopting effective risk reduction strategies at urban planning level.

Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area

Understanding the spatio-temporal pattern of carbon emission (CE) is prerequisite for formulating carbon reduction policies. Previous studies emphasized quantitative analysis of CE inventory while ignoring sectoral spatial distribution. This study fills this gap by developing a framework for coupling the CE quantitative prediction model with the sectoral CE spatial model based on the Long-range Energy Alternatives Planning (LEAP) model, spatial proxy data and local climate zone (LCZ). The framework's sectoral CE results reveal a great varied landscape within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the leading bay areas in the world with rapid urbanization and emphasis on low-carbon development, under four carbon reduction scenarios. By 2060, the CN3 scenario that considers both energy-supply and consumption sides, predicts a drastic emission cut to 35.76 million tons, just 10 % of the business as usual (BAU) scenario's forecast, mainly from transportation (29.45 million tons) and industry (9.34 million tons) sectors. Besides, compared with the common CE spatial products, the spatial simulation results of sectoral CE in our framework present detailed spatial differences at the jurisdictional level. The findings are conducive for governments to formulate accurate CE reduction and optimization strategies of the cities towards to the 2060 carbon neutrality.

Toward obsolete housing: A complementary explanation of increasing coastal vulnerability

This paper explains the process of increasing coastal vulnerability in response to coastal flooding, from the property market viewpoint. Rooted in knowledge about housing submarket-based neighborhood change, this study adds new understandings of how housing obsolesces influence vulnerability following rapid land use changes, which are currently studied as a new exposure by many researchers. We employ six years of house sales transaction data (n = 1,029) in a spatial data-driven delineation with k-means, through which the submarket shift can be visualized. This approach addresses weaknesses in the existing methods to measure neighborhood change. We find that second-hand houses purchased by low-income people tend to cluster close together in newly formed submarkets in at-risk areas creating submarkets for western-inner city and distressed properties. Our data series also show that the geographical growth of the submarket for distressed properties is expanding, creating further blight in the submarket for western-inner city. Consequently, the submarket for distressed properties tends to transform into an area characterized by substandard housing conditions or slum living. Bearing in mind that the consequences of a flood will cover a wide range of issues, such as the future of public services, and the welfare of citizens in general, disaster risk management is fundamental to socio-economic development of a country. This study can enrich discussions around housing vulnerability and neighborhood change and support the current debate over spatial adaptation and climate change.

Racial/ethnic disparities in the distribution of heatwave frequency and expected economic losses in the US

Previous research on social disparities in heat exposure has not examined heatwave frequency or economic damage at the local or neighborhood level. Additionally, most US studies have focused on specific cities or regions, and few national-scale studies encompassing both urban and rural areas have been conducted. These gaps are addressed here by analyzing racial/ethnic disparities in the distribution of annual heatwave frequency and expected economic losses from heatwave occurrence in the contiguous US. Census tract-level data on annualized heatwave frequency and expected loss from the FEMA’s National Risk Index are linked to relevant variables from the American Community Survey. Results indicate that all racial/ethnic minority groups except non-Hispanic Black are significantly overrepresented in neighborhoods with greater annual heatwave frequency (top 10% nationally), and all minority groups are overrepresented in neighborhoods with greater total expected annual loss from heatwaves, compared to non-Hispanic Whites. Multivariable models that control for spatial clustering, climate zone, and relevant socio-demographic factors reveal similar racial/ethnic disparities, and suggest significantly greater heatwave frequency and economic losses in neighborhoods with higher percentages of Hispanics and American Indians. These findings represent an important starting point for more detailed investigations on the adverse impacts of heatwaves for US minority populations and formulating appropriate policy interventions.