Climate Indices Research Articles

Climate controls on longshore sediment transport and coastal morphology adjacent to engineered inlets

Coastal jetties are commonly used throughout the world to stabilize channels and improve navigation through inlets. These engineered structures form artificial boundaries to littoral cells by reducing wave-driven longshore sediment transport across inlet entrances. Consequently, beaches adjacent to engineered inlets are subject to large gradients in longshore transport rates and are highly sensitive to changes in wave climate. Here, we quantify annual beach and nearshore sediment volume changes over a 9-yr time period along 80 km of wave-dominated coastlines in the U.S. Pacific Northwest. Beach and nearshore monitoring during the study period (2014–2023) reveal spatially coherent, multi-annual patterns of erosion and deposition on opposing sides of two engineered inlets, indicating a regional reversal of longshore-transport direction. A numerical wave model coupled with a longshore transport predictor was calibrated and validated to explore the causes for the observed spatial and temporal patterns of erosion and deposition adjacent to the inlets. The model results indicate that subtle but important changes in wave direction on seasonal to multi-annual time scales were responsible for the reversal in the net longshore sediment transport direction and opposing patterns of morphology change. Changes in longshore transport direction coincided with a reversal in the Pacific Decadal Oscillation (PDO) climate index, suggesting large-scale, multi-decadal climate variability may influence patterns of waves and sediment dynamics at other sites throughout the Pacific basin.

Spatial and temporal assessment of China's skiing climate resources.

This study introduces an improved Ski Climate Index (SCI) designed to assess skiing suitability in China by applying fuzzy logic. Using daily meteorological data from 733 weather stations for the periods 1961-1990 and 1991-2020, the study identifies significant changes in SCI distribution over time. Additionally, a coupled analysis is performed, integrating the SCI results with the distribution and spatial vitality of 389 ski resorts in China. This analysis provides a comprehensive understanding of the interplay between actual ski resources and the ongoing evolution of the skiing industry in China and three significant results:1) The snow module has a major impact on SCI distribution, while other non-snow natural elements, such as sunshine duration, wind speed, and thermal comfort, influence the overall SCI assessment less; 2) High SCI values are concentrated in Northwestern and Northeastern China, with increased ski climate resources being observed in Shaanxi-Gansu-Ningxia, Southwest Tibet, and Sichuan due to climate change and noticeable declines in the Southern regions of Northeast China.; 3) In terms of the distribution and vitality of ski resorts, the SCI also partially reflects the development of ski resorts. This skiing suitability model uses climate resources to offer valuable insights for key decision-making in resort development and operation, thereby supporting advancement of the ice-snow economy.

Evaluating the impact of improved filter-wrapper input variable selection on long-term runoff forecasting using local and global climate information

Long-term runoff forecasting (LRF) is great important for water resources management. Employing accurate local and global climate information can significantly enhance the accuracy of LRF. The filter-wrapper input variables selection (FWIVS) method can efficiently select local and global climate indices for LRF. However, binary metaheuristic algorithms (BMAs) in wrapper input variable selection (WIVS), such as golden jackal optimization (GJO) and gray wolf optimizer (GWO), frequently encounter local optima, which significantly impact LRF accuracy and remain unexplored. Additionally, previous FWIVS methods overlooked physical relationship between local and global climate information. Consequently, an improved GJO (IGJO) that considers the complementarity of local weather and global climate indices and prevents local optima was proposed. Four machine learning models and an integrated model (IM) were used as learning models for WIVS. Finally, eight BMAs, including IGJO, improved GWO and so on, were constructed and integrated with five learning models to develop forty wrappers for evaluating impact of various BMAs and models on WIVS and LRF. The Jinsha River was served as a case study. Results demonstrated that the impact of the improved BMAs on wrapper fitness and variables selection was higher than learning model. IGJO retained critical variables for WIVS, especially local weather variables. Moreover, IGJO effectively reduced redundant information and extracted more robust complementary global climate indices for WIVS. In addition, LSTM-IGJO exhibited the greatest enhancement in wrapper fitness and LRF accuracy, achieving a NSE of 0.92 and a MAE of 575.83 m3/s. Specifically, this finding indicated that selecting the key variables and selecting the stronger complementary global climate indices instead the redundant local weather variables for retaining key information, while eliminating redundant information, can efficiently improve the LRF accuracy.

Wet creatures in a warming world: How climate change will impact the future distribution of anuran amphibians from Brazilian semiarid region?

The world has been going through a climate crisis that has led to a substantial increase in global temperatures. Consequently, amphibians, ectothermic animals that depend on water to survive/reproduce, are under increasing threat. In this research, we compared the influence of climate change on the geographic distribution between anuran species with occurrence restricted to the Brazilian semi-arid region (Caatinga domain) and widely distributed congeneric species, with occurrence in multiple biomes. We hypothesized that climate change would be more harmful for species of restricted distribution than for those widely distributed. For this purpose, using ecological niche modeling, we designed potential distribution models for current and four future climate scenarios, and compared two metrics (climatic suitability and index of extent of occurrence) between restricted and widely distributed species. The results revealed that the climatic suitability did not differ among current climatic conditions and those expected for 2050 and 2090, nor differed between species with distribution restricted to the Caatinga and widely distributed ones. On the other hand, contrary to our predictions, while species with wide distributions would tend to retract their extent of occurrence in future times, species with restricted distributions would expand their ranges. These results suggest that species with current distribution restricted to Caatinga biome might be relatively favored by the climate changes predicted for the next 80 years. However, we discuss that this result should be interpreted with very caution, because many ecological and conservation requirements need to be guaranteed so that these predictions can actually occur. Our findings contributed to the understanding of the effects of climate change on closely related taxa, with different distribution patterns, revealing particularities of the anuran amphibians that occur in semi-arid environments in response to climate changes predicted for this century.

Spatiotemporal changes in tourism climate comfort in Sichuan Province from 1961 to 2022

AbstractBased on the daily temperature, wind speed, relative humidity, sunshine hours and precipitation in Sichuan Province from 1961 to 2022, the spatiotemporal variation characteristics of tourism climate comfort in Sichuan Province were analysed using the temperature–humidity index (THI), precipitation index (P), sunshine duration index (SSD), wind chill index (WCI) and tourism climate index (TCI). The results show that the average annual TCI in Sichuan Province over the past 62 years is “acceptable” or above and shows a significant increasing trend at a rate of 0.018 per decade. On average, the best travel comfort period in Sichuan Province is from March to May and from October to November. The TCI increased significantly in cold seasons such as February and November with rates of 0.064 and 0.053 per decade, respectively. The increase in TCI is mainly controlled by the increase in THI and WCI, both of which are related to the increase in temperature, with a greater effect in cold seasons and at higher elevations. Most of China's 5A‐level tourist attractions in Sichuan Province (14 of 17) have an annual TCI of “acceptable” or better. Similarly, the top 3 months for climate comfort at these attractions tend to be March to May and October, with ratings reaching “good” or even “very good.” For the other three destinations, all characterized by glaciers and mountains, the top 3 months for climate comfort are concentrated in the warm months. The overall tourism climate comfort of Sichuan Province is increasing, and the climate is changing in a positive way for the tourism industry.

Response of remote sensing-based climatic indicators to drought conditions during El Niño events and implications for water–food–energy nexus

ABSTRACT Hydrometeorological extremes, such as droughts, are a major threat to society and can have extensive damaging effects. In this study, daily rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) quasi-global rainfall dataset were used to calculate the Standardised Precipitation Index (SPI) for the assessment of meteorological drought in Southern Province, Zambia. Normalised Difference Vegetation Index (NDVI) imagery (250 m resolution) from MODIS-Terra, for the period 2000–2021, were used to derive the Standardised Vegetation Index (SVI) in order to assess agricultural drought. The Mann–Kendall trend test and Sen's slope were used to determine the spatial-temporal trends and their magnitudes. This study demonstrated that the droughts of the Southern Province of Zambia can be classified into two categories: regressive and aggressive droughts. Regressive droughts are associated with moderate to strong El Niño events. Although El Niño events undermine water security, regressive droughts tend to result in resilient vegetation owing to residue soil moisture. In contrast, aggressive droughts are characterised by an increase in drought intensity as the season progresses. Water security prospects in the region should focus on climate-smart approaches, such as managed aquifer recharge, to ensure water availability even under extreme drought conditions.

Future Climate Projections and Uncertainty Evaluations for Frost Decay Exposure Index in Norway

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models.

Evaluating thermal comfort indices for outdoor spaces on a university campus

This study evaluates the applicability of three thermal comfort indices—Physiologically Equivalent Temperature (PET), Standard Effective Temperature (SET), and Universal Thermal Climate Index (UTCI)—in various outdoor environments on the campus of Xi’an University, China. Meteorological data were collected on sunny days using a portable weather station at a height of 1.5 m, and subjective questionnaires were administered to 25 healthy university students over three months to gather Thermal Sensation Votes (TSV) and Thermal Comfort Votes (TCV). The study was conducted at four distinct outdoor locations: a lakeside area (Location 1), a shaded path (Location 2), a sports field (Location 3), and a plaza (Location 4). PET, SET, and UTCI values were calculated from the collected data using Rayman software. The analysis revealed significant differences in thermal comfort across the four locations, with the highest proportion of subjects feeling hot at the sports field (54.4%) and the highest proportion feeling cold at the lakeside (39%). The shaded path had the highest proportion of subjects feeling comfortable (79.4%), while the lakeside had the lowest (60.1%). The results indicated that SET underestimated thermal sensation at Locations 1, 3, and 4, necessitating calibration. PET was suitable for Locations 2, 3, and 4 but failed to reflect the thermal sensation at Location 1 due to prolonged sun exposure. In contrast, UTCI demonstrated applicability across all locations. To enhance accuracy, revised indices SET’ and PET’ were formulated using the mean-median method, providing more precise thermal comfort assessments. These findings underscore the limitations of SET and PET under specific conditions and highlight the robustness of UTCI, offering valuable insights for urban planning and design aimed at improving outdoor thermal comfort and well-being.

BUSINESS CLIMATE INDEX. CALCULATION OF BUSINESS CLIMATE INDEX FOR SMALL AND MEDIUM ENTERPRISES AND LARGE BUSINESSES

The article discusses the use of business activity indicators, based on data from the monthly monitoring of enterprises conducted by the Bank of Russia. It considers the indicators of the business climate, divided into large enterprises and small and medium-sized businesses, as these play an important role in the market economy and are more sensitive to crises but also more adaptive than larger businesses. Small and medium-sized business activity indices are in high demand among the business community, so the article focuses on this group of enterprises. General formulas for calculating the business climate indicator are provided by the Bank of Russia. Seasonally smoothed indicators of the business climate were analyzed and compared separately for large businesses and a group including small and medium-sized enterprises. Indicators were compared separately for small and medium enterprises, as well as within industries of small and medium businesses. The response of different groups and industries to economic changes, such as crises and structural transformations, was revealed. The business climate indicator for small and medium-sized companies was compared with the RSBI Support of Russia index. An alternative index was also created using the RSBI methodology, using monitoring data from the Bank of Russia. All calculated indices were compared, the level of correlation was checked, and they were compared with statistical data on small and medium enterprise turnover growth. The article explored the potential of using enterprise data to monitor and analyze the economy.

A Population-Based Linked Cohort Study of Maternal Exposure to Bioclimatic Stress and Hypertensive Disorders of Pregnancy in Australia.

ObjectiveTo identify critical susceptible periods for the associations between bioclimatic exposure (Universal Thermal Climate Index, UTCI) and the risks of gestational hypertension and preeclampsia. ApproachWe linked 415,091 singleton pregnancies of 20–42 weeks’ gestation between 1st January 2000 and 31st December 2015 to UTCI exposure in Western Australia. Distributed lag nonlinear logistic regressions were used to estimate weekly-specific adjusted odds of gestational hypertension and preeclampsia for UTCI exposure from twelve weeks of preconception through pregnancy. ResultsThe analysed pregnancies included 3.7% and 2.8% cases of gestational hypertension and preeclampsia, respectively. Exposures from early pregnancy up to 30th gestational weeks were associated with greater odds of hypertensive disorders of pregnancy (HDPs) with critical susceptible periods at all exposure thresholds, particularly elevated at the 1st (10.2 °C) and 99th (26.0 °C) exposure centiles as compared to the median (14.2 °C). The most elevated weekly OR and 95% CI estimated at 99th exposure centile was 1.07 (95% CI 1.06, 1.08) in gestational weeks 8–18 for gestational hypertension and 1.10 (95% CI 1.08, 1.11) in gestational weeks 11–16 for preeclampsia. Cumulative exposures associated with HDPs with relatively higher ORs, but less precise than weekly exposures. The effects of high exposure were disproportionately elevated in both HDPs for Caucasians, non-smokers, multiparous, and mothers in moderate/low SES residences. Conclusions and ImplicationsMaternal bioclimatic exposure increases the risk of HDPs, suggesting potentially susceptible periods and populations for climate-related targeted interventions. It highlights the need for advising women on environmental risks in early pregnancy.

A comparison of climate drivers’ impacts on silage maize yield shock in Germany

Extreme weather events have become more frequent and severe with ongoing climate change, with a huge implication for the agricultural sector and detrimental effects on crop yield. In this study, we compare several combinations of climate indices and utilized the Least Absolute Shrinkage and Selection Operator (LASSO) to explain the probabilities of substantial drops in silage maize yield (here defined as “yield shock” by using a 15th percentile as threshold) in Germany between 1999 and 2020. We compare the variable importance and the predictability skill of six combinations of climate indices using the Matthews Correlation Coefficient (MCC). Finally, we delve into year-to-year predictions by comparing them against the historical series and examining the variables contributing to high and low predicted yield shock probabilities. We find that cold conditions during April and hot and/or dry conditions during July increase the chance of silage maize yield shock. Moreover, a combination of simple variables (e.g. total precipitation) and complex variables (e.g. cumulative cold under cold nights) enhances predictive accuracy. Lastly, we find that the years with higher predicted yield shock probabilities are characterized mainly by relatively hotter and drier conditions during July compared to years with lower yield shock probabilities. Our findings enhance our understanding of how weather impacts maize crop yield shocks and underscore the importance of considering complex variables and using effective selection methods, particularly when addressing climate-related events.

Climatic controls of fire activity in the red pine forests of eastern North America

Large-scale modes of climate variability influence forest fire activity and may modulate the future patterns of natural disturbances. We studied the effects of long-term changes in climate upon the fire regime in the red pine forests of eastern North America using (a) a network of sites with dendrochronological reconstructions of fire histories over 1700–1900 A.D., (b) reconstructed chronologies of climate indices (1700–1900), and (c) 20th century observational records of climate indices, local surface climate and fire (1950s-2021). We hypothesized that (H1) there are states of atmospheric circulation that are consistently associated with increased fire activity, (H2) these states mark periods of increased climatological fire hazard, and (H3) the observed decline in fire activity in the 20th century is associated with a long-term decline in the frequency of fire-prone states.At the annual scale, years with significantly higher fire activity in the reconstructed and modern fire records were consistently associated with the positive phases of the Pacific North American pattern (PNA), either independently or in combination with the positive phase of the El Niño-Southern Oscillation index (ENSO). During years with both ENSO and PNA in their positive state, the region experienced positive mid-tropospheric heights and temperature anomalies resulting in drought conditions. The fire-prone climate states identified in the reconstructed records became less frequent in 1850 but re-emerged in the 20th century. While our study did not demonstrate a direct influence of climate on the observed decrease in fire activity in the 20th century, it does reveal a clear climate signal embedded within the fire history reconstruction of the region over the past centuries. This study underscores the importance of considering large-scale climatic patterns in understanding historical fire regimes and highlights their role for future fire dynamics in the region and shaping ecological effects of future fires.

Contribution of Climatic Change and Human Activities to Vegetation Dynamics over Southwest China during 2000–2020

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems.

From Rangelands to Cropland, Land-Use Change and Its Impact on Soil Organic Carbon Variables in a Peruvian Andean Highlands: A Machine Learning Modeling Approach

Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (δ13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (“bofedales”) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and δ13CSOC prediction, whereas ANN was the best to model RSOC. “Bofedales” showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted δ13CSOC (− 27.0 ± 0.44 ‰) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of “bofedales” as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and δ13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.

Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future

Study regionThe three headwaters of Tarim river, south of the Xinjiang Province, China. Study focusTemporal runoff concentration variations are crucial for water resources utilization and management, particularly in arid regions. In this study, the runoff concentration in the Tarim River Basin is studied based on analysis of the trend, abrupt changes, and periodicity, identifying key extreme climate indices influencing the Gini-runoff coefficient and predicting its future changes under three Shared Socioeconomic Pathways (SSPs). New hydrological insights for the regionFrom 1962–2020, the Gini-runoff coefficient decreased by 8.3 %, while annual runoff increased by 14.2 %. This is attributed to temperature changes, escalating hydrological risk uncertainty. The anomaly of low geopotential high fields and Central Asian anticyclones have caused an increase in total cloud cover (TCC) and precipitation, and a decrease in the Daily Temperature Range (DTR). Increasing of Warm nights (TN90), Maximum Tmax (TXx), and Hot nights (TR) have enhanced the warm-season runoff. However, the climatic impact on warm-season runoff is less pronounced than that in the cold season, which led to the runoff concentration decline. Changes in hydro-junction regulation led to decrease of Gini-runoff coefficient by 12.11 % from 2001 to 2020 along the Hotan river. Under the three SSP scenarios, the average Gini-runoff coefficient for the Aksu and Hotan rivers are expected to decrease by 3.3 % and 2.6 %, respectively.

Improving thermal comfort using biomimicry in the urban residential districts in New Aswan city, Egypt

AbstractEgypt tends to construct new cities to provide social housing for its citizens. However, the planning of urban residential districts (URDs) in these cities lacks provisions for natural ventilation (NV), and indoor-outdoor thermal comfort, resulting in increased energy consumption during the summer months. This research aims to create a sustainable design through biomimicry to reduce the universal thermal climate index (UTCI) values in the URD in New Aswan thereby enhancing outdoor thermal comfort and reducing energy consumption inside buildings at the level of URD. In this research, the approach of prairie dogs in building their burrows was followed to rearrange and reorient the buildings. Autodesk CFD, Rhino7, Grasshopper, Ladybug, Honeybee, and Dragonfly software was used in the simulation. The rearrangement of the buildings, specifically the oriented design in the east-west direction, caused a difference in pressure between the buildings, leading to improved NV between the buildings. The improvement of NV resulted in a 4.2 °C reduction in UTCI values during specific hours over the six different days in the six summer months, resulting in significant energy savings of 10407.28946 KWh in the URD. The occurrence of a pressure difference between buildings not only improved NV but also enhanced outdoor and indoor thermal comfort, promoting energy conservation. Therefore, careful consideration of the arrangement and orientation of buildings is essential to establish sustainable URDs. Although the difference between the results is slight, it creates substantial differences in the long term for UTCI values, thus the energy conservation in the URDs.

Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness

Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a neural network algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (4615 km3, 37%), SIV trends (46 km3, 17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT estimates, in addition to the already assimilated PWM sea ice concentration.

Invasive-Weed-Optimization-Based Extreme Learning Machine for Prediction of Lake Water Level Using Major Atmospheric–Oceanic Climate Scenarios

Fresh water lakes are vulnerable assets that need to be protected against manmade/natural challenges like climate change and anthropogenesis activities. This study addresses the predictability of the lake water level changes based on the knowledge acquired directly from the climate data. Two fresh water lakes named Lake Iznik and Uluabat, located in Turkey, are addressed. Time series of the lake water levels during October 1990–September 2019 at a monthly scale, along with the corresponding anomalies of 24 Large-Scale Atmospheric–Oceanic Oscillations (LSAOOs) from around the globe, are used in the analysis. The relationship between variables and the structure of the models are initially acquired based on the significance of the dependence between climate indices and lake water levels with consideration of the significance of the Spearman rank-order coefficient. Then, the time series are divided into training (80%) and testing (20%) sets. The Extreme Learning Method (ELM), enhanced with the genetic algorithm (ELM-GA) and Invasive Weed Optimization (ELM-IWO), is then used in the predictive models. Based on the results, Lake Uluabat showed a stronger teleconnection with LSAOOs, while the ELM-GA for Lake Iznik and ELM-IWA for Lake Uluabat depicted the best performance in the prediction of lake water levels. Comparison of the enhanced ELM-IWO to the corresponding ELM-GA illustrates that the ELM-IWO reveals more acceptable results owing to its flexible nature.

A sidewalk-level urban heat risk assessment framework using pedestrian mobility and urban microclimate modeling

Climate change and the associated increase in heat-related hazards pose a pressing threat to urban residents’ health and well-being. People, when walking in particular, are at risk of experiencing heat stress as they navigate urban environments. This study proposes a novel heat risk assessment framework combining pedestrian mobility modeling with urban microclimate modeling. Using this framework, we assessed pedestrian heat-related exposure and risk in urban areas by integrating the Universal Thermal Climate Index (UTCI) as the hazard and pedestrian trips to critical destinations as exposure. We considered temporal variation, in both hazard and exposure, by examining different time periods during the day—morning peak, midday, and evening peak. In addition to hazard and exposure, we also considered vulnerability by focusing on young children and older adults. We contribute to improving the spatial resolution of heat risk assessment by analyzing the hazard for pedestrian trips between home locations and five critical destinations—bus stops, rail stations, parks, schools, and commercial amenities—at the address-point level and using a pedestrian network comprising sidewalks and crosswalks. Our framework helps identify sidewalks with high heat exposure levels as well as home locations with high cumulative heat risk, accounting for walking trips to critical destinations along feasible routes. We demonstrated the effectiveness of this framework by applying it to a 36-square-kilometer area of central Los Angeles, CA. Our findings offer valuable information to urban planners and policy-makers, supporting evidence-based prioritization of intervention sites, climate adaptation strategies, and policy decisions essential for climate-proof planning. By implementing targeted interventions in areas where heat hazard is expected to affect the most vulnerable pedestrians, planners can create heat-resilient, pedestrian-friendly environments while prioritizing the health and well-being of vulnerable groups. This study contributes to the growing knowledge of robust risk assessment methodologies for climate-proof planning, specifically with regard to addressing outdoor heat-related risks during extreme heat events.

Wildfire Loss Modeling: A Flexible Semiparametric Approach

Wildfires have emerged as one of the most devastating natural disasters worldwide, which, in addition to a loss of life, results in billions of dollars in annual losses. This study aims to identify factors that contribute to the frequency and severity of wildfire losses. We propose a semiparametric modeling approach to analyze wildfire property losses in the United States from 1995 to 2020. Our methodology incorporates flexible generalized additive models for location, scale, and shape (GAMLSS), which integrate climate, socioeconomic, and geographical variables into the modeling process. Through empirical analysis, we demonstrate the critical role of spatial effects in modeling wildfire-related property losses. Additionally, we identify key variables, including house index, state population, wildfire duration, and climate index, that significantly contribute to explaining the average property loss. These findings offer valuable insights for enhancing catastrophe risk management for insurance companies and government agencies. Furthermore, the proposed approach can be easily applied to diverse regions grappling with spatially varying catastrophe risks.