Sea-level rise and mental health among coastal communities: A quantitative survey and conditional process analysis.

This is the first large-scale empirical study examining the impact of sea-level rise induced by climate change on mental health outcomes among coastal communities. The study focuses on Bangladesh, a country severely affected by salinity ingress, flood risks, and agricultural damage due to sea-level changes. Participants (n = 1,200) randomly selected from three coastal regions each having high, moderate, or low vulnerability to sea-level rise were surveyed during the pre-monsoon season in 2021. The cross-sectional survey included validated measures of psychological distress, depression, anxiety, stress, environmental stressors, resource loss, and demographics. The results indicated significantly higher levels of psychological distress, depression, anxiety, and stress in residents of high-vulnerability areas compared to moderate or low-vulnerability regions. Resource loss served as a mediating variable between environmental stressors and mental health outcomes. Furthermore, demographic analyses showed that older adults and women reported higher levels of psychological distress. These findings align with the Sendai Framework for Disaster Risk Reduction, highlighting urgent need for targeted mental health interventions and sustainable models of care in coastal areas increasingly threatened by sea-level rise.

Optimizing health service location in a highly urbanized city: Multi criteria decision making and P-Median problem models for public hospitals in Jeddah City, KSA.

Rapid urbanization and population growth have increased the need for optimizing the location of health services in highly urbanized countries like Kingdom of Saudi Arabia (KSA). This study employs a multiple-criteria decision making (MCDM) approach, e.g., fuzzy overlay technique by combining the P-Median location-allocation model, for optimizing health services. First, a geodatabase, containing public hospitals, road networks and population districts, was prepared. Next, we investigated the location and services of five public hospitals in Jeddah city of KSA, by using a MCDM model that included a fuzzy overlay technique with a location-allocation model. The results showed that the allocated five hospitals served 94 out of 110 districts in the study area. Our results suggested additional hospitals must be added to ensure that the entire city is covered with timely hospital services. To improve the existing situation, we prioritized demand locations using the maximize coverage (MC) location problem model. We then used the P-Median function to find the optimal locations of hospitals, and then combined these two methods to create the MC-P-Median optimizer. This optimizer eliminated any unallocated or redundant information. Health planners can use this model to determine the best locations for public hospitals in Jeddah city and similar settings.

Insights into parents' perceived worry before and during the COVID-19 pandemic in Australia: inequality and heterogeneity of influences.

BACKGROUND: Excessive worry is an invisible disruptive force that has adverse health outcomes and may advance to other forms of disorder, such as anxiety or depression. Addressing worry and its influences is challenging yet crucial for informing public health policy. METHODS: We examined parents' worries, influences, and variability before and during COVID-19 pandemic and across geography. Parents (n = 340) and their primary school-aged children from five Australian states completed an anonymous online survey in mid-2020. After literature review, we conceptualised the influences and performed a series of regression analyses. RESULTS: Worry levels and the variables contributing to parents' worry varied before to during the pandemic. The proportion of parents who were "very worried all the time" increased by 14.6% in the early days of the pandemic. During the pandemic, ethnic background modified parents' worry and parents' history of daily distress symptoms was a significant contributor (p < 0.05). Excessive exposure to news remained significant both before and during the pandemic. The primary predictor of parents' worry before COVID-19 was perceived neighbourhood safety, while the main predictor during COVID-19 was financial risk due to income change. Some variable such as neighbourhood safety and financial risk varied in their contribution to worry across geographical regions. The proportion of worried children was higher among distraught parents. CONCLUSION: Parents' worry during the health pandemic was not triggered by the health risks factors but by the financial risk due to income change. The study depicts inequality in the impact of COVID-19 by ethnic background. Different policies and reported virus case numbers across states may have modified the behaviour of variables contributing to the geography of parents' worry. Exposure to stressors before the COVID-19 pandemic may have helped parents develop coping strategies during stressful events. Parents are encouraged to limit their exposure to stressful news. We advocate for parents-specific tailored policies and emphasise the need for access to appropriate mental health resources for those in need. Advancing research in geographical modelling for mental health may aid in devising much-needed location-targeted interventions and prioritising resources in future events.

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction.

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Developing a high-resolution gridded rainfall product for Bangladesh during 1901-2018.

A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901-2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product's ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of -2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R2) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901-2018 was in the range of -4.5 to 4.3%, NRMSE between 9 and 19% and R2 in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.

Exposure to fine particulate matter (PM2.5) during landscape fire events and the risk of cardiorespiratory emergency department attendances: a time-series study in Perth, Western Australia.

BACKGROUND: Landscape fires (LFs) are the main source of elevated particulate matter (PM2.5) in Australian cities and towns. This study examined the associations between daily exposure to fine PM2.5 during LF events and daily emergency department attendances (EDA) for all causes, respiratory and cardiovascular outcomes. METHODS: Daily PM2.5 was estimated using a model that included PM2.5 measurements on the previous day, remotely sensed aerosols and fires, hand-drawn tracing of smoke plumes from satellite images, fire danger ratings and the atmosphere venting index. Daily PM2.5 was then categorised as high (≥99th percentile), medium (96th-98th percentile) and low (≤95th percentile). Daily EDA for all-cause and cardiorespiratory conditions were obtained from the Western Australian Emergency Department Data Collection. We used population-based cohort time-series multivariate regressions with 95% CIs to assess modelled daily PM2.5 and EDA associations from 2015 to 2017. We estimated the lag-specific associations and cumulative risk ratios (RR) at lags of 0-3 days, adjusted for sociodemographic factors, weather and time. RESULTS: All-cause EDA and overall cardiovascular presentations increased on all lagged days and up to 5% (RR 1.05, 95% CI 1.03 to 1.06) and 7% (RR 1.07, 95% CI 1.01 to 1.12), respectively, at the high level. High-level exposure was also associated with increased acute lower respiratory tract infections at 1 (RR 1.19, 95% CI 1.10 to 1.29) and 3 (RR 1.17, 95% CI 1.10 to 1.23) days lags and transient ischaemic attacks at 1 day (RR 1.25, 95% CI 1.02 to 1.53) and 2 (RR 1.20, 95% CI 1.01 to 1.42) days lag. CONCLUSIONS: Exposure to PM2.5 concentrations during LFs was associated with an increased risk of all-cause EDA, overall EDA cardiovascular diseases, acute respiratory tract infections and transient ischaemic attacks.

Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6.

Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) - home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2-4.5 and 5-8.5. Spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for near (2020-2059) and far (2060-2099) periods were examined in order to understand temporal changes and associated uncertainty. The results indicated large spatial heterogeneity and temporal variability in projected changes of bioclimatic indicators. A higher change was projected for mainland SEA in the far future and less in maritime region during the near future. At the same time, uncertainty in the projected bioclimatic indices was higher for mainland than maritime SEA. Analysis of mean multi-model ensemble revealed a change in mean temperature ranged from - 0.71 to 3.23 °C in near and from 0.00 to 4.07 °C in far futures. The diurnal temperature range was projected to reduce over most of SEA (ranging from - 1.1 to - 2.0 °C), while isothermality is likely to decrease from - 1.1 to - 4.6%. A decrease in isothermality along with narrowing of seasonality indicated a possible shift in climate, particularly in the north of mainland SEA. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.

Vulnerability of Australia to heatwaves: A systematic review on influencing factors, impacts, and mitigation options.

BACKGROUND: Heatwaves have received major attention globally due to their detrimental effects on human health and the environment. The frequency, duration, and severity of heatwaves have increased recently due to changes in climatic conditions, anthropogenic forcing, and rapid urbanization. Australia is highly vulnerable to this hazard. Although there have been an increasing number of studies conducted in Australia related to the heatwave phenomena, a systematic review of heatwave vulnerability has rarely been reported in the literature. OBJECTIVES: This study aims to provide a systematic and overarching review of the different components of heatwave vulnerability (e.g., exposure, sensitivity, and adaptive capacity) in Australia. METHODS: A systematic review was conducted using the PRISMA protocol. Peer-reviewed English language articles published between January 2000 and December 2021 were selected using a combination of search keywords in Web of Science, Scopus, and PubMed. Articles were critically analyzed based on three specific heatwave vulnerability components: exposure, sensitivity, and adaptive capacity. RESULTS AND DISCUSSION: A total of 107 articles meeting all search criteria were chosen. Although there has been an increasing trend of heat-related studies in Australia, most of these studies have concentrated on exposure and adaptive capacity components. Evidence suggests that the frequency, severity, and duration of heatwaves in Australian cities has been increasing, and that this is likely to continue under current climate change scenarios. This study noted that heatwave vulnerability is associated with geographical and climatic factors, space, time, socioeconomic and demographic factors, as well as the physiological condition of people. Various heat mitigation and adaptation measures implemented around the globe have proven to be efficient in reducing the impacts of heatwaves. CONCLUSION: This study provides increased clarity regarding the various drivers of heatwave vulnerability in Australia. Such knowledge is crucial in informing extreme heat adaptation and mitigation planning.

Spatial Pattern and Land Surface Features Associated with Cloud-to-Ground Lightning in Bangladesh: An Exploratory Study.

Severe weather events such as lightning appear to be a significant threat to humans and property in South Asia, an area known for intense convective activity directly related to the tropical climate of these areas. The current study was conducted in Bangladesh and examined the association between cloud-to-ground (CG) lightning and ground surface properties, with the aim of improving existing knowledge regarding this phenomenon. GLD360 data from 2015 to 2020 were used to describe the seasonal lightning climatology. Elevation, land use and land cover, vegetation and surface heat flux data were used to examine all land surface features possibly associated with CG lightning occurrence. Hot and cold spot spatial patterning was calculated using local indicators of spatial association. Results indicated a strong CG lightning seasonality. CG stroke density varied considerably across seasons with the pre-monsoon exhibiting the highest density. This was followed by occurrences in the monsoon season. The March-June period experienced 73% of the total observed. Elevation appeared to influence the post-monsoon CG stroke, however, its role in the other seasons was more difficult to define. The land cover/lightning index indicated that waterbodies and herbaceous wetlands had more influence than other land cover types, both during the day and at night, and it appeared that latent heat flux played a major role. The CG stroke hot and cold spot locations varied diurnally. The findings suggest that large-scale irrigation practices, especially during the pre-monsoon months, can influence the observed spatiotemporal pattern. The production of hotspot maps could be an initial step in the development of a reliable lightning monitoring system and play a part in increasing public awareness of this issue. Supplementary Information: The online version contains supplementary material available at 10.1007/s41748-022-00310-4.

Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city's thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001-2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Impact of landscape pattern changes on hydrological ecosystem services in the Beressa watershed of the Blue Nile Basin in Ethiopia.

Landscape pattern changes are mostly due to human activities, and such changes often affect ecosystem functions and services. This study was conducted to evaluate the response of hydrological ecosystem services (HESs) to structural landscape changes. Spatiotemporal changes in two specific HES indicators, water yield (WY) and sediment export (SE), were quantified by analyzing historic (1972-2017) and projected land use/land cover changes (2017-2047). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Model was used for this purpose. Results indicated that WY and SE changed significantly (p ˂ 0.01) during the study period. The total WY and SE increased by 30.29% and 98.69%, respectively, between 1972 and 2017. Analysis of the projections for the next three decades (2017-2047) suggested an increase in WY and SE by 4.8% and 93.11%, respectively. Furthermore, results revealed that WY and SE are strongly influenced by landscape composition, and metrics such as percentage of landscape (PLAND), mean patch size (MPS), and large patch index (LPI) of farmland and plantations were found to be key factors affecting HESs degradation in the Beressa watershed. PLAND (VIP = 1.34; w = 0.55; and VIP = 1.32; w = 0.56) and MPS (VIP = 1.32; w = 0.50 and VIP = 1.31; w = 0.56)) of farmland cover contributed most to the changes in WY and SE, respectively. Similarly, PLAND (VIP = 1.33; w = 0.54 and VIP = 1.28; w = 0.52), LPI (VIP = 1.27; w = 0.52 and VIP = 1.30; w = 0.54) and MPS (VIP = 1.29; w = 0.52) of plantation cover also contributed more to the change in WY and SE. Besides that, of anthropogenic factors, compositions of natural vegetation and grassland cover were found to heavily influence HESs in the watershed studied. The findings of the study suggest that soil and water conservation interventions are vital to minimize and control water-related problems and enhance ESs.

Is there a correlation between children's outdoor active mobility behaviour and neighbourhood safety? A systematic review of the evidence.

OBJECTIVE: To identify, summarise and evaluate evidence on the correlation between perceived and actual neighbourhood safety (personal and road danger) and diverse forms of outdoor active mobility behaviour (ie, active play, exercise, and travel) among primary-school-aged children. DESIGN: A systematic review of evidence from observational studies exploring children's active mobility behaviour and safety. DATA SOURCES: Six electronic databases were searched: Google Scholar, PubMed, Scopus, Science Direct, ProQuest and Web of Science from study inception until July 2020. DATA EXTRACTION AND SYNTHESIS: Study selection and quality assessment were conducted independently by two reviewers. We expanded on a quality assessment tool and adopted a vote-counting technique to determine strength of evidence. The outcomes were categorised by individual, family and neighbourhood levels. RESULTS: A total of 29 studies were included, with a majority of cross-sectional design. Higher parental perceived personal safety correlated with increased children's active mobility behaviour, but most commonly in active travel (eg, independent walking or cycling to a local destination). Increased concerns regarding road danger correlated with a decrease in each type of children's active behaviour; active travel, play and exercise. However, these correlations were influenced by child's sex/gender, age, car ownership, neighbourhood types, across time, and proximity to destination. Limited or inconclusive evidence was found on correlate of children's outdoor active mobility behaviour to 'stranger danger', children's perceived personal safety, race/ethnicity, socioeconomic status or measured safety. CONCLUSION: Children are restricted by perception of safety. Encouraging children's active travel may require future strategies to address characteristics relevant to types of the neighbourhood that promote a high sense of personal safety. Children and parents may embrace other types of active mobility behaviour if road danger is mitigated. Sex/gender and age-specific interventions and redesign of public places could lead to child-friendly cities. Future studies may benefit from adopting validated measurement methods and fill existing research gaps.

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh.

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

Spatio-temporal changes in habitat quality and linkage with landscape characteristics in the Beressa watershed, Blue Nile basin of Ethiopian highlands.

An increase in human population generally exerts pressure on natural habitats and leads to a decline in biodiversity resources. As a proxy for biodiversity study, an evaluation of habitat quality (HQ) change caused by land use/land cover (LULC) and associated landscape structural changes may provide a scientific basis for ecological protection and landscape management. This study analyzed spatio-temporal changes in HQ over the last four decades and predicted the trends over the next three decades. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was employed to evaluate the state of HQ. Criteria of habitat naturalness, habitat complexity and a soil degradation index were used to classify habitat types. Results showed that, between 1972 and 2017, areas with high HQ indicators declined by about 20% while areas with poor HQ increased by 11%. An unprecedented expansion of anthropogenic LULC changes related to the growth of human settlements and artificial plantations and a decline in natural and semi-natural habitats resulted in the total loss of HQ by about 35%. The mean value of HQ decreased from 0.60 to 0.45 during the study period. The distribution of moderate levels of HQ, primarily in farmlands, remained essentially unchanged. Predicted HQ values are expected to follow a similar trend to past decades with 41.5% of the areas continuing to decline, although with a slight HQ improvement in some areas. The spatial distribution of HQ is negatively correlated with habitat degradation (R2 = 0.95 at p < 0.01) and slope (R2 = 0.84 at p < 0.05). HQ change also appears more strongly influenced by landscape composition than by configuration in the watershed. The most important landscape structure variables accounted for HQ change were LPI, PLAND and MPS of anthropogenic habitats, suggesting reducing habitat modifications and restoring degraded natural habitats is crucial to maintain biodiversity in the study area.

Children's outdoor active mobility behaviour and neighbourhood safety: a systematic review in measurement methods and future research directions.

BACKGROUND: Numerous studies have examined the association between safety and primary school-aged children's forms of active mobility. However, variations in studies' measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers' knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children's outdoor active mobility behaviour and used this analysis to propose future research directions. METHOD: A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies' inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. RESULTS: Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children's neighbourhoods varied within 10 studies' objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies' measurement methods assisted in outlining a three-dimensional relationship between 'what' (determinants), 'where' (spatial) and 'when' (time) within a methodological conceptual framework. CONCLUSIONS: The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children's health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children's active mobility and safety.

Changes in landscape composition and configuration in the Beressa watershed, Blue Nile basin of Ethiopian Highlands: historical and future exploration.

Analyzing long-term dynamics of landscape patterns can provide important insights into the changes in landscape functions, that are necessary for optimizing resource management strategies. This study primarily aimed at quantifying landscape structural change. The Land use/land cover (LULC) layers of 1972, 1987, 2002, and 2017 were mapped from Landsat images, and projected to 2032 and 2047. Factor analysis was then employed to select independent core metrics of landscape composition and configuration to characterize the landscape. A post-classification comparison indicated that, between 1972 and 2017, natural vegetation, grassland, barren land and waterbody covers declined by 89.9%, 67.9%, 67.8 and 15.9%, respectively. On the other hand, plantation increased by 692.1% followed by human settlement (138%) and farmland (21.8%). A similar trend is likely to continue in 2032 and 2047 with a slight decline in the plantation category in 2047. Analysis of landscape metrics revealed that between 1972 and 2017, the number of patches increased. Specifically, plantation, barren land, settlement and grassland increased by 171.4%, 69.7%, 65.8% and 28.6%, respectively. In contrast, natural vegetation, farmland and waterbody declined by 53.1%, 46.3% and 33.9%, respectively. Future predictions showed a declining trend of the number of patches for all LULC types. An increasing trend in the largest patch index and patch size for farmland, plantation, and settlement categories was observed across all years, suggesting intensified human activities in the landscape. Consequently, natural habitat category has declined and become fragmented. Landscape pattern has changed considerably and become more fragmented over the last 45 years. Nevertheless, the future projections suggest a decline in fragmentation and potentially increased assemblage of patches forming simple patterns with fewer number of large size class patches. The results of this study could perhaps be applied in designing strategies for landscape management planning and resource conservation decision-making.

Predicting hydrologic responses to climate changes in highly glacierized and mountainous region Upper Indus Basin.

The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation and snowmelt could be reasons for further escalation of the problem. Therefore, estimation of hydrological processes is critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using soil and water assessment tool (SWAT). The methodology includes: (i) development of SWAT model using land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from 1978 to 1993; (iii) model validation for the time 1994-2003; (iv) bias correction of regional climate model (RCM), and (v) utilization of bias-corrected RCM for future assessment under representative concentration pathways RCP4.5 and RCP8.5 for mid (2041-2070) and late century (2071-2100). The results of the study revealed a strong correlation between simulated and observed flow with R 2 and Nash-Sutcliff efficiency (NSE) equal to 0.85 each for daily flow. For validation, R 2 and NSE were found to be 0.84 and 0.80, respectively. Compared to baseline period (1976-2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of the twenty-first century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP4.5 and RCP8.5, respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century, respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century, respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model could be used as effective tool for climate change valuation and for sustainable management of water resources in future.

Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh.

The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide, particularly in densely populated countries such as Bangladesh with inadequate health care facilities. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not have been pragmatically implementable in developing countries due to social and economic reasons (i.e., poor education, less public awareness, massive unemployment). Hence, to elucidate COVID-19 transmission dynamics with respect to the NPI status-e.g., social distancing-this study conducted spatio-temporal analysis using the prospective scanning statistic at district and sub-district levels in Bangladesh and its capital, Dhaka city, respectively. Dhaka megacity has remained the highest-risk "active" cluster since early April. Lately, the central and south eastern regions in Bangladesh have been exhibiting a high risk of COVID-19 transmission. The detected space-time progression of COVID-19 infection suggests that Bangladesh has experienced a community-level transmission at the early phase (i.e., March, 2020), primarily introduced by Bangladeshi citizens returning from coronavirus epicenters in Europe and the Middle East. Potential linkages exist between the violation of NPIs and the emergence of new higher-risk clusters over the post-incubation periods around Bangladesh. Novel insights into the COVID-19 transmission dynamics derived in this study on Bangladesh provide important policy guidelines for early preparations and pragmatic NPI measures to effectively deal with infectious diseases in resource-scarce countries worldwide.

Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios.

Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010-2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.

Analyzing spatial and space-time clustering of facility-based deliveries in Bangladesh.

BACKGROUND: A spatial and temporal study of the distribution of facility-based deliveries can identify areas of low and high facility usage and help devise more targeted interventions to improve delivery outcomes. Developing countries like Bangladesh face considerable challenges in reducing the maternal mortality ratio to the targets set by the Sustainable Development Goals. Recent studies have already identified that the progress of reducing maternal mortality has stalled. Giving birth in a health facility is one way to reduce maternal mortality. METHODS: Facility delivery data from a demographic surveillance site was analyzed at both village and Bari (comprising several households with same paternal origins) level to understand spatial and temporal heterogeneity. Global spatial autocorrelation was detected using Moran's I index while local spatial clusters were detected using the local Getis G i * statistics. In addition, space-time scanning using a discrete Poisson approach facilitated the identification of space-time clusters. The likelihood of delivering at a facility when located inside a cluster was calculated using log-likelihood ratios. RESULTS: The three cluster detection approaches detected significant spatial and temporal heterogeneity in the distribution of facility deliveries in the study area. The hot and cold spots indicated contiguous and relocation type diffusion and increased in number over the years. Space-time scanning revealed that when a parturient woman is located in a Bari inside the cluster, the likelihood of delivering at a health facility increases by twenty-seven times. CONCLUSIONS: Spatiotemporal studies to understand delivery patterns are quite rare. However, in resource constraint countries like Bangladesh, detecting hot and cold spot areas can aid in the detection of diffusion centers, which can be targeted to expand regions with high facility deliveries. Places and periods with reduced health facility usages can be identified using various cluster detection techniques, to assess the barriers and facilitators in promoting health facility deliveries.