Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change

Time-lag and accumulation responses of vegetation growth to average and extreme precipitation and temperature events in China between 2001 and 2020

Climate change is often closely related to vegetation dynamics; time lag (Tlag) and accumulative effects (Tacc) are non-negligible phenomena when studying the interaction between climate and vegetation. But, amidst the escalating frequency of extreme climatic events, the quantification of temporal effects (Teffects) of such extremes on vegetation remains scarce. This research quantifies the Tlag and Tacc responses of China's vegetation to episodes of extreme temperature and precipitation since the early 2000s, utilizing daily meteorological data series. Overall, the precipitation in China has become wetter, and nighttime temperatures have risen significantly. The proportion of areas with Teffects ranged from 1.15 % to 15.95 %, and the correlation coefficient between the climate indices and the Normalized Difference Vegetation Index (NDVI) increased by 0.05 to 0.38 when considering the Teffects, compared to not considering it. The Tacc of vegetation had the strongest response (70.74-88.01 %) to extreme events among all the tested climate indices. Moreover, the Tacc of consecutive climate events had a greater impact on vegetation growth than individual climate event. The average Tacc for extreme temperature and extreme precipitation was 1.7-3.09 months and 2.17-3.25 months, respectively. Events like the over 95 % (R95p) and 99 % (R99p) percentile heavy precipitation and the maximum precipitation amount in one day (Rx1day) caused significant Teffects on NDVI. In addition, 90 % of grasslands exhibit Tacc, mainly contributed by the extreme precipitation indices (55.7 %), while the Teffects of forests were stronger than those of extreme temperature. Furthermore, NDVI was more affected by annual precipitation than by extreme precipitation, but the opposite was true for temperature. The results of this study highlight the importance of considering the Tlag and Tacc when predicting the effects of climate change on vegetation dynamics.

Spatiotemporal variation of hydrological connectivity and its threshold effects on flood dynamics: An examination in the arid and semi-arid regions, China

Hydrological connectivity, a critical indicator of underlying surface changes, plays a pivotal role in the generation and evolution of floods. This study focuses on examining hydrological connectivity and its significant impact on flood dynamics. The Hekou-Longmen section (HL) is used as a case area because of its frequent flooding, which is typical of arid and semi-arid zone. By employing the modified hydrological connectivity index (IC), this study evaluated the hydrological connectivity and examined its spatiotemporal variation of the HL. Based on 1131 Annual Maximum Instantaneous Streamflow (AMS) data of 21 sub-basins in the HL, a panel threshold regression model was used to reveal threshold effect of IC on flood dynamics. The results showed that the annual mean IC showed a decreasing trend, with spatial variation dominated by significant decreases and no change. Furthermore, it was found that the magnitude of the effect of extreme precipitation (EP) on AMS increased with increasing IC thresholds. The threshold effect of EP on AMS were found to exist during the 1990s, 2000s, and 2010s, with thresholds of 2.84, 3.27, and 3.37, respectively. This research established a quantitative framework for comprehensively evaluating the impact of underlying surface changes on flood, providing important reference for the study of flood mechanisms in similar arid and semi-arid regions globally.

Influence of climate variability and land cover dynamics on the spatio-temporal NDVI patterns in western hydrological regions of Bangladesh

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

Monitoring soil moisture in winter wheat with crop water stress index based on canopy-air temperature time lag effect

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT-theoretical, CWSIE-empirical, CWSIH-hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models.

Trends and contribution of different grassland types in restoring the Three River Headwater Region, China, 1988-2012

Rapid greening of the Qinghai-Tibet Plateau had been confirmed, but the contributions to the overall change and its causes in various grassland types has been less studied. Previous research has focused on exogenous factors such as climate change and human activities, rather than on endogenous factors, such as grassland types. Using net primary productivity (NPP), precipitation and temperature data, we applied trend, contribution and pull contribution analysis to understand the spatiotemporal evolution and driving factors of six different grassland types at a pixel scale in the Three River Headwater Region (TRHR) of China from 1988 to 2012. The results showed that grassland NPP in the TRHR increased at an average growth amount of 3.46 gC m-2 yr-1 and an average growth rate of 2.26 %. The average growth amount of alpine desert and alpine steppe (0.42 gC m-2 yr-1, 1.74 gC m-2 yr-1, respectively) showed great potential improvement. The average growth rate (1.27 %, 1.87 %) of montane meadow and alpine meadow, respectively, presented a high potential to increase (P < 0.05). Alpine meadow, montane meadow and temperate steppe were positive pullers to the average growth amount. Alpine steppe and alpine desert were positive pullers to the average growth rate. In general, alpine meadow had the highest growth amount contribution (84.86 %), while alpine meadow and alpine steppe had the highest contribution to the growth rate (62.16 %, 34.24 %, respectively). The study implied that, in addition to external factors, differences in internal factors such as the community composition and structure of different grassland types could also affected the grassland recovery process. These results contribute to understanding the specific differences in the contribution of regional grassland restoration processes from vegetation composition. Assessing grasslands with the potential to increase productivity, we can provide scientific reference for implementing more precise and efficient measures in future grassland management restoration.

Vegetation cover change and its response to climate extremes in the Yellow River Basin

Extreme climate events have increased in frequency and severity under the background of climate change, with vegetation growth exhibiting a sensitive response to them. By assimilating GIMMS NDVI and MODIS NDVI using the Residual Network, we obtained a long time series and high resolution NDVI dataset of the Yellow River Basin (YRB). The dataset was utilized for examining the spatiotemporal variability of NDVI and analyzing the response of vegetation cover to climate extremes with meteorological data. Our findings reveal the following: (1) A significant rise in NDVI was seen in the YRB, displaying a mean growth rate of 0.019/10a (p < 0.001). However, seasonal differences exist. The mean NDVI of multi-year declines from southeast to northwest, while the overall trend of vegetation cover improves. (2) The NDVI response to extreme temperature exhibits noticeable spatiotemporal differences. Daytime extreme high temperature in the northern YRB is negatively correlated with NDVI, while they are positively correlated in the lower YRB and the southern part of the middle YRB. Nighttime extreme high temperature exhibits a positive correlation with NDVI. Overall, NDVI displays a stronger response to extreme precipitation than to extreme temperature, with a negative correlation with CWD and a positive correlation with PRCPTOT. (3) The NDVI demonstrates a lagged response to climate extremes in the YRB, with a greater lag in response to extreme temperature than extreme precipitation. The research findings can provide scientific support for the future management and planning of vegetation in the YRB, as well as contribute to the promotion of ecological environment regulation and sustainable development.

Dark septate endophyte inoculation enhances antioxidant activity in Astragalus membranaceus var. mongholicus under heat stress

The influence of dark septate endophytic (DSE) on the antioxidant activity of Astragalus membranaceus var. mongholicus under heat stress was investigated. A. membranaceus plants, with or without DSE inoculation, were grown at 28°C for 8 weeks in a greenhouse and subsequently subjected to heat stress conditions (42°C) in an artificial climate chamber. DSE inoculation significantly decreased the malondialdehyde (MDA) content during the initial three days of heat stress. The activities of superoxide dismutase (SOD) and peroxidase (POD) of A. membranaceus leaves were significantly enhanced by DSE inoculation under heat stress, with SOD activities being 63-81% higher than in other treatments. The glutathione (GSH) and putrescine (Put) contents accumulated significantly on the third day under heat stress with DSE inoculation. Additionally, the contents of soluble sugars and proline (Pro) exhibited significant increases on the seventh day of heat stress and were 33-55% and 81-83% higher than in other treatments, respectively. Three-way ANOVA shows that DSE inoculation under heat stress exerted a significant impact on MDA. Multivariate linear regression and structural equality modelling (SEM) further show that the interaction among these antioxidants significantly decreased MDA content and maintained the normal function of cell membranes. In conclusion, DSE inoculation enhanced the heat tolerance of A. membranaceus by boosting its antioxidant capacity and reducing MDA production. This study highlights the potential of utilizing DSE as a strategy to enhance plant heat tolerance.

Investigating non-point pollution mitigation strategies in response to changing environments: A cross-regional study in China and Germany

Climate change and urbanization have altered regional hydro-environments. Yet, the impact of future changes on the pollution risk and associated mitigation strategies requires further exploration. This study proposed a hydraulic and water-quality modeling framework, to investigate the spatiotemporal characteristics of pollution risk mitigation by low impact development (LID) strategies under future Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP) scenarios. Results demonstrated that the LID strategies exhibited an effective performance of pollutant removal in the current hydro-environment, with the removal rates ranging from 33% to 56%. In future climate and urbanization scenarios, the LID performance declined and turned to be uncertain as the greenhouse gas (GHG) emissions increased, with the removal rates ranging from 12% to 59%. Scenario analysis suggested that the LID performance was enhanced by a maximum of 73% through the diversified implementation of LID practices, and the performance uncertainty was reduced by a maximum of 67% through the increased LID deployment. In addition, comparative analysis revealed that the LID strategies in a well-developed region (Dresden, Germany) were more resilient in response to changing environments, while the LID strategy in a high-growth region (Chaohu, China) exhibited a better pollutant removal performance under low-GHG scenarios. The methods and findings in this study could provide additional insights into sustainable water quality management in response to climate change and urbanization.

Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015-2044), mid (2045-2074), and far (2075-2100) futures in comparison to the historical period (1985-2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Spatial-Temporal Characteristics of Spring Maize Drought in Songnen Plain, Northeast China

With the intensification of global warming, food production will face serious drought risk. In view of the insufficient applicability of the existing crop drought index, a standardized crop water deficit index (SCWDI) was constructed based on the construction idea of the standardized precipitation evapotranspiration index (SPEI) and the crop water deficit index (CWDI) in this study. On this basis, the spatial and temporal characteristics of spring maize drought in Songnen Plain were explored by the slope trend analysis and Morlet wavelet analysis methods. The results show the following: (1) Compared with the existing drought index, the SCWDI shows obvious advantages in drought monitoring of spring maize. (2) In the whole growth stage of spring maize, the change trend of SCWDI was small in the temporal series (−0.012/10a). Spatially, the drought trend of spring maize was mainly decreasing (−0.14~0/10a). The drought frequency of spring maize in each growth stage was mainly light drought in most regions. (3) The three main drought cycles of spring maize in Songnen Plain were 29 years, 10 years, and 4 years. In the next few years, the drought of spring maize in Songnen Plain was controlled by the first main cycle, and the drought years may increase, which should be prevented. The research was expected to provide technical support for crop drought monitoring and agricultural disaster prevention.

Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects

Quantifying the contributions of climate change (CC) and human activities (HA) to vegetation change is crucial for making a sustainable vegetation restoration scheme. However, the effects of extreme climate and time-lag and -accumulation effects on vegetation are often ignored, thus underestimating the impact of CC on vegetation change. In this study, the spatiotemporal variation of fractional vegetation cover (FVC) from 2000 to 2019 in northern China (NC) as well as the time-lag and -accumulation effects of 15 monthly climatic indices, including extreme indices, on the FVC, were analyzed. Subsequently, a modified residual analysis considering the influence of extreme climate and time-lag and -accumulation effects was proposed and used to attribute the change in the FVC contributed by CC and HA. Given the multicollinearity of climatic variables, partial least squares regression was used to construct the multiple linear regression between climatic indices and the FVC. The results show that: (1) the annual FVC significantly increased at a rate of 0.0268/10a from 2000 to 2019 in all vegetated areas of NC. Spatially, the annual FVC increased in most vegetated areas (∼81.6 %) of NC, and the increase was significant in ∼54.6 % of the areas; (2) except for the temperature duration (DTR), climatic indices had no significant time-lag effects but significant time-accumulation effects on the FVC change. The DTR had both significant time-lag and -accumulation effects on the FVC change. Except for potential evapotranspiration and DTR, the main temporal effects of climatic indices on the FVC were a 0-month lag and 1-2-month accumulation; and (3) the contributions of CC and HA to FVC change were 0.0081/10a and 0.0187/10a in NC, respectively, accounting for 30.2 % and 69.8 %, respectively. HA dominated the increase in the FVC in most provinces of NC, except for the Qinghai and Neimenggu provinces.

The effect of greenness on allergic rhinitis outcomes in children and adolescents: A systematic review and meta-analysis

BACKGROUND

The relationship between greenness and health emerges as new public health concern. More published studies from multiple areas have explored the relationship between greenness and allergic rhinitis (AR) in children and adolescents. This study aims to determine the association between greenness and allergic rhinitis by systematic review and meta-analysis, in order to provide a more comprehensive assessment of the impact of greenness on AR in children and adolescents.

METHODS

The relative literature was systematically searched in PubMed, Embase, and Web of science lastly on September 25, 2022. Terms related to greenness and allergic rhinitis were used for searching. Summary effect estimates of greenness on AR in children and adolescents were calculated for per 10 % increase of greenness exposure with different buffer sizes by random-effects model.

RESULTS

A total of 579 studies were screened, and fourteen studies from Europe, Asia and North America were finally included. Most greenness exposure were measured by normalized difference vegetation index (NDVI). Enhanced vegetation index, outdoor-green environmental score and existed to measuring different greenness types. Greenness surrounding residences and schools were assessed. The overall effect of greenness on primary outcome was 1.00 (95%CI = 0.99-1.00). Most effect estimates of greenness were included in the NDVI-500 m group, and the pooled OR was 0.99 (95%CI = 0.97-1.01). No significant pooled estimates were found in analyses with study locations.

CONCLUSION

This study indicates no significant association between greenness exposure and AR in children and adolescents. Various exposure measures and conversion of data may affect the results of this meta-analysis. More precise assessment of personal greenness exposure in well-designed prospective studies are vital for drawing a definite association in future. Furthermore, greenness exposure surrounding schools should be paid considerable attention for its effect on AR in school-aged children and adolescents.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Zoning of precipitation regimes on the Qinghai-Tibet Plateau and its surrounding areas responded by the vegetation distribution

Compared with other factors influencing vegetation patterns, such as light and temperature, precipitation has relatively large variability, especially on the Qinghai-Tibet Plateau (QTP), where the natural environment is extremely fragile and sensitive. However, the impact of precipitation regimes, rather than precipitation amount, on vegetation has seldom been revealed. This study characterised the precipitation regimes by both the amount and temporal distribution of precipitation and zoned the QTP as different precipitation regimes accordingly. The response of vegetation to such precipitation regimes was then investigated. The results indicate that the vegetation patterns are quite consistent with zoning, that is, there is a certain type or a few dominant types of vegetation in each sub-region divided by the precipitation regimes. The areas where the precipitation became more uniform within a year were concentrated in grassland and bare land, which benefits the restoration and improvement of the ecological environment of the plateau. The increase in precipitation variability in the south-eastern part of the plateau may lead to natural disasters such as floods and mudslides. This study provides a novel perspective to understand the distribution of vegetation patterns.

Vegetation Greenness Dynamics in the Western Greater Khingan Range of Northeast China Based on Dendrochronology

Understanding the vegetation greenness dynamics in the forest–steppe transition zone is essential for ecosystem management, and in order to study ecological changes in the region. This study provides a valuable record of the vegetation greenness dynamics in the western Greater Khingan Range over the past 193 years (1826–2018) based on tree-ring data represented by the normalized difference vegetation index (NDVI). The reconstructed vegetation greenness dynamics record contains a total of 32 years of high vegetation greenness and 37 years of low vegetation greenness, together occupying 35.8% of the entire reconstructed period (193 years). Climate (precipitation) is the main influence on the vegetation greenness dynamics at this site, but human activities have also had a significant impact over the last few decades. The magnitude, frequency, and duration of extreme changes in vegetation greenness dynamics have increased significantly, with progressively shorter intervals. Analyses targeting human behavior have shown that the density of livestock, agricultural land area, and total population have gradually increased, encroaching on forests and grasslands and reducing the inter-annual variability. After 2002, the government implemented projects to return farmland to its original ecosystems, and for the implementation of new land management practices (which are more ecologically related); as such, the vegetation conditions began to improve. These findings will help us to understand the relationship between climate change and inter- and intra- annual dynamics in northeastern China, and to better understand the impact of human activities on vegetation greenness dynamics.

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

The frequency of extreme climate events has increased resulting in major changes to vegetation in arid and semi-arid areas. We selected 12 extreme climate indices and used trend analysis and multiple linear regression models to analyze extreme climate trends in steppe areas of Inner Mongolia and their impact on the normalized difference vegetation index (NDVI). From 1998 to 2017, the NDVI of the Inner Mongolia steppe increased overall; however, there was a small area of decrease. Extreme climate indices related to warming exhibited increasing trends, particularly in the desert steppe. Although the extreme precipitation index did not change significantly overall, it increased in the northeastern and southwestern regions of the study area and decreased in the central region. The established model showed that the extreme climate explained the highest NDVI variation in desert steppe (R2 = 0.413), followed by typical steppe (R2 = 0.229), and meadow steppe (R2 = 0.109). In desert steppe, TX90P (warm days index) had the greatest impact; in typical steppe, R10 (number of heavy precipitation days index) had the greatest impact; in meadow steppe, R95P (very wet days index) had the greatest impact. This study offered new insights into dynamic vegetation changes in steppe areas of Inner Mongolia and provided a scientific basis for implementing environmental protection strategies.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the T_min in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Climate change, monsoon failures and inequality of impacts in South India

This article examines the structural aspects of climate vulnerabilities in the context of monsoon failures. The paper is based on a unique household survey, conducted in Tamil Nadu, India. The study uses a rural differentiation framework to interrogate unequal vulnerabilities to monsoon failures, based on measures such as Gini coefficients and Lorenz curves of monetary losses. Results show that negative consequences of climate change in general, and monsoon failures in particular, intensify pre-existing socio-economic disparities. When the rural differentiation theory is applied in a broader sense, the analysis shows that landed and farming households have greater exposure and losses. When we move beyond these aggregate categories, the revelation is that households with pre-existing disadvantages such as marginal landholders, subsistence farmers and agricultural workers are more vulnerable.

Appraising the historical and projected spatiotemporal changes in the heat index in Bangladesh

Climate change-derived extreme heat phenomena are one of the major concerns across the globe, including Bangladesh. The appraisal of historical spatiotemporal changes and possible future changes in heat index (HI) is essential for developing heat stress mitigation strategies. However, the climate-health nexus studies in Bangladesh are very limited. This study was intended to appraise the historical and projected changes in HI in Bangladesh. The HI was computed from daily dry bulb temperature and relative humidity. The modified Mann-Kendal (MMK) test and linear regression were used to detect trends in HI for the observed period (1985-2015). The future change in HI was projected for the mid-century (2041-2070) for three Representative Concentration Pathway (RCP) scenarios, RCP 2.6, 4.5, and 8.5 using the Canadian Earth System Model Second Generation (CanESM2). The results revealed a monotonic rise in the HI and extreme caution conditions, especially in the humid summer season for most parts of Bangladesh for the observed period (1985-2015). Future projections revealed a continuous rise in HI in the forthcoming period (2041-2070). A higher and remarkable increase in the HI was projected in the northern, northeastern, and south-central regions. Among the three scenarios, the RCP 8.5 showed a higher projection of HI both in hot and humid summer compared to the other scenarios. Therefore, Bangladesh should take region-specific adaptation strategies to mitigate the impacts of HI.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00704-021-03705-x.

A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia

The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990–2000, 2000–2018, and 1990–2018 were computed using delta (Δ) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson’s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990–2028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions.