Spatiotemporal Extent Research Articles

The potential for fuel reduction to reduce wildfire intensity in a warming California

Abstract Increasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using empirical relationships derived from historical observations with a novel combination of spatiotemporal resolution (0.375 km, instantaneous) and extent (48 million acres, 9 years). We use machine learning to quantify relationships between sixteen environmental conditions (including ten fuel characteristics and four temperature-affected aridity characteristics) and satellite-observed fire radiative power. We use the derived relationships to create fire intensity potential (FIP) maps for sixty historical weather snapshots at a 2 km and hourly resolution. We then place these weather snapshots in differing background climatological temperature and fuel characteristic conditions to quantify their independent and combined influence on FIP. We find that in order to offset the effect of climate warming under the SSP2-4.5 emissions scenario, fuel reduction would need to be maintained perpetually on ∼3 million acres (or 600 000 acres per year, 1% of our domain, at a 5 year return frequency) by 2050 and ∼8 million acres (or 1.6 million acres per year, 3% of our domain, at a 5 year return frequency) by 2090. Overall, we find substantial potential for fuel reduction to negate the effects of climate warming on FIP.

Dynamics of Irrigated Land Expansion in the Ouémé River Basin Using Field and Remote Sensing Data in the Google Earth Engine

The availability of reliable and quantified information on the spatiotemporal distribution of irrigated land at the river basin scale is an essential step towards sustainable management of water resources. This research aims to assess the spatiotemporal extent of irrigated land in the Ouémé River basin using Landsat multi-temporal images and ground truth data. A methodology was built around the use of supervised classification and the application of an algorithm based on the logical expression and thresholding of a combination of surface temperature (Ts) and normalized difference vegetation index (NDVI). The findings of the supervised classification showed that agricultural areas were 16,003 km2, 19,732 km2, and 22,850 km2 for the years 2014, 2018, and 2022, respectively. The irrigated land areas were 755 km2, 1143 km2, and 1883 km2 for the same years, respectively. A significant increase in irrigated areas was recorded throughout the study period. The overall accuracy values of 79%, 82%, and 83% obtained during validation of the irrigated land maps indicate a good performance of the algorithm. The results suggest a promising application of the algorithm to obtain up-to-date information on the distribution of irrigated land in several regions of Africa.

Sentinel-1 data reveals unprecedented reduction of open water extent due to 2023-2024 drought in the central Amazon basin

Abstract In 2023, an intense drought impacted the Amazon basin triggered by climate change and a strong El Niño event, with the Negro River reaching its lowest water level in 120 years. However, the spatiotemporal open water extent (OWE) during this drought remains unclear. This study comprehensively evaluates OWE variability in the central Amazon using Sentinel-1 synthetic aperture radar (SAR) data since 2017. Monthly OWE masks were generated through an empirical threshold classification with accuracy >95%. Overall, the central Amazon experienced a reduction of ∼8% in OWE in the 2023 dry season months (November and December) when compared to monthly-average. However, reductions of up to 80% in OWE were observed in several specific lakes. Our analysis underscores the unprecedented severity of the 2023/2024 drought on rivers and floodplains. Utilizing SAR remote sensing technologies, this study emphasizes the urgent need for proactive conservation measures to safeguard the Amazon’s ecological integrity amid escalating environmental challenges. Monthly water masks from January/2017 to September/2024 are available here: https://doi.org/10.5281/zenodo.12751783.

The Pivotal Role of Evaporation in Lake Water Isotopic Variability Across Space and Time in a High Arctic Periglacial Landscape

AbstractRapidly changing climate is disrupting the High Arctic's water systems. As tracers of hydrological processes, stable water isotopes can be used for high quality monitoring of Arctic waters to better reconstruct past changes and assess future environmental threats. However, logistical challenges typically limit the length and scope of isotopic monitoring in High Arctic landscapes. Here, we present a comprehensive isotopic survey of 535 water samples taken in 2018 and 2019 of the lakes and other surface waters of the periglacial Pituffik Peninsula in far northwest Greenland. The δ18O, δ2H, and deuterium‐excess values of these samples, representing 196 unique sites, grant unprecedented insight into the environmental drivers of the regional hydrology and water isotopic variability. We find that the spatial variability of lake water isotopes can best be explained through evaporation and the hydrological ability of a lake to replace evaporative water losses with precipitation and snowmelt. Temporally, summer‐long evaporation can drive lake water isotopes beyond the isotopic range observed in precipitation, and wide interannual changes in lake water isotopes reflect annual weather differences that influenced evaporation. Following this, water isotope samples taken at individual times or sites in similar periglacial landscapes may have limited regional representativeness, and increasing the spatiotemporal extent of isotopic sampling is critical to producing accurate and informative High Arctic paleoclimate reconstructions. Overall, our survey highlights the diversity of isotopic compositions in Pituffik surface waters, and our complete isotopic and geospatial database provides a strong foundation for future researchers to study hydrological changes at Pituffik and across the Arctic.

Socio-ecological change in bait fisheries for the common sandprawn Kraussillichirus kraussi in Durban Harbour, South Africa

Bait fishing for the common sandprawn Kraussillichirus kraussi in Durban Harbour, on the east coast of South Africa, has a history going back to the early 20th century and has been influenced by port development and political changes over time. Recent controversy has centred on the fishers’ rights to port access and on the interactions between two sectors that harvest sandprawns for bait. The two sectors are a recreational bait fishery and an unlicensed subsistence (or small-scale) fishery that sells its catches to anglers for an income, in contravention of fisheries regulations. To determine the spatiotemporal extent of the fisheries and the impacts of bait fishing on the sandprawn resource, data on the fishing effort, catch and size composition were collected monthly at the sandbank sites Centre Bank and Wilson’s Wharf in Durban Harbour. A generalised linear modelling framework was used to analyse fishing effort and sandprawn size composition. Small-scale fishers dominated the bait fishery (83%), and fishing effort peaked at low tides and on public holidays and weekends, when the market demand for sandprawns as bait was higher. Fishing effort differed significantly between sandbanks and was dynamic, shifting to Centre Bank when sandprawn abundance at Wilson’s Wharf decreased. Small-scale fishers mainly retained large sandprawns, and did not adhere to daily catch limits, such as those regulating the recreational bait fishery. The results are interpreted from a socio-ecological systems perspective, and within the context of accommodating otherwise excluded small-scale fisheries within South African fisheries legislation.

Data Assimilation and Parameter Identification for Water Waves Using the Nonlinear Schrödinger Equation and Physics-Informed Neural Networks

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN’s loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods.

Drivers of variability in surf zone habitat use by sandy beach fish: Unwitting citizen scientists reveal detailed spatiotemporal patterns

Surf zones of sandy beaches are among the most heavily impacted aquatic ecosystems, yet are of critical ecological importance for inshore fish and fisheries. Knowledge of the drivers of fish habitat use in surf zones is needed across broad scales to advise conservation and fisheries management, but sampling capabilities can be limited in spatio-temporal extent and resolution. The lesser weever Echiichthys vipera is a small, benthic, venomous fish that dominates surf zone fish assemblages in Northwest Europe and inflicts painful stings on beachgoers. This study capitalises on an extensive record of E. vipera sting incidents to characterise variations in surf zone habitat use in relation to key physical environmental factors. Sting incidents, standardised by water user numbers, are used as a proxy for E. vipera abundance across 77 beaches throughout Southwest England, with 2 h resolution, from April–November 2018. General Additive Models indicated a clear peak in E. vipera abundance at spring low tides, in the afternoons of summer months, under calmer wave conditions and at higher levels of solar irradiance. Although the order of significance differed, human water users were also driven by the same variables, compounding sting interactions over time. Key physical variables did not explain spatial variation in E. vipera abundance, although there was a weak relationship with sea surface temperature, and some evidence that reflective beaches are unsuitable. Physical factors explained more spatial variation in human water users, who gathered at more dissipative beaches with greater wave heights. This detailed study of an important surf zone fish reveals clear drivers of temporal variation in habitat use, yet infers wide suitability of beaches varying in the key physical drivers of sandy shore ecology.

Males miss and females forgo: Auditory masking from vessel noise impairs foraging efficiency and success in killer whales.

Understanding how the environment mediates an organism's ability to meet basic survival requirements is a fundamental goal of ecology. Vessel noise is a global threat to marine ecosystems and is increasing in intensity and spatiotemporal extent due to growth in shipping coupled with physical changes to ocean soundscapes from ocean warming and acidification. Odontocetes rely on biosonar to forage, yet determining the consequences of vessel noise on foraging has been limited by the challenges of observing underwater foraging outcomes and measuring noise levels received by individuals. To address these challenges, we leveraged a unique acoustic and movement dataset from 25 animal-borne biologging tags temporarily attached to individuals from two populations of fish-eating killer whales (Orcinus orca) in highly transited coastal waters to (1) test for the effects of vessel noise on foraging behaviors-searching (slow-click echolocation), pursuit (buzzes), and capture and (2) investigate the mechanism of interference. For every 1 dB increase in maximum noise level, there was a 4% increase in the odds of searching for prey by both sexes, a 58% decrease in the odds of pursuit by females and a 12.5% decrease in the odds of prey capture by both sexes. Moreover, all but one deep (≥75 m) foraging attempt with noise ≥110 dB re 1 μPa (15-45 kHz band; n = 6 dives by n = 4 whales) resulted in failed prey capture. These responses are consistent with an auditory masking mechanism. Our findings demonstrate the effects of vessel noise across multiple phases of odontocete foraging, underscoring the importance of managing anthropogenic inputs into soundscapes to achieve conservation objectives for acoustically sensitive species. While the timescales for recovering depleted prey species may span decades, these findings suggest that complementary actions to reduce ocean noise in the short term offer a critical pathway for recovering odontocete foraging opportunities.

Optical fibre sensing of turbulent-frequency motions in the oceanic environment

Observations of turbulence in the oceanic environment are sparse, with very few cases of coherent measurements with significant spatio-temporal extent due primarily to limitations of current observational tools. Here we propose submarine cables with embedded optical fibres as a potential solution to fill this observational gap, and utilise a recent 12-h observational optical fibre data set from a fast-flowing tidal channel to demonstrate such potential. Firstly, the presence of turbulent-scale signals driven by flow-topography interaction is shown at frequencies of 1 Hz and higher. These signals are consistent with the timing of the tidal flow as recorded by a nearby conventional sensor. Secondly, we show the presence of surface gravity waves with periods of 10 s, which are tight in frequency space further offshore but leak energy into the turbulent frequency range on parts of the cable closer to shore. This is compatible with shoreward-propagating surface waves that break in shallow water. Finally, we fit a theoretical spectral structure to the observations to show that much of the collected data (i) has a spectral slope that is consistent with the turbulent inertial subrange, and (ii) has a range of spectral energy consistent with that expected from turbulence generation by bottom drag acting on the tidal flow. In combination, these results highlight the potential for optical fibre sensing of turbulence, and call for a targeted experiment to characterise the fibre’s turbulence-sensing capabilities.

Urban recreational green spaces dynamics and implications in the Bonaberi Neighbourhood of Douala IV Municipality, Cameroon

Globally, urban recreational green spaces constitute a major environmental resource of urban landscapes. These greeneries are suffering from shrinkages and transformations due to pressures on more urban land needed for housing, commercial and industrial purposes and uncontrolled urbanization. This study was conducted to assess the spatiotemporal variations and extent of transformations within the urban green spaces and their associated implications in the Bonaberi neighbourhood of Douala IV Municipality. To achieve this, empirical data was collected through field visits, in-depth interviews with urban authorities and mapping of urban green spaces for 2003 and 2022. Two hundred inhabitants were purposively sampled while local council authorities and other stakeholders were interviewed in the Douala IV Municipality. This was complemented by secondary data obtained from the Douala Urban Council. The collected data were analyzed using descriptive and inferential statistical techniques and results revealed that the rapid transformation and decrease of urban green spaces in terms of spatiotemporal nature is a function of expansion and uncontrolled urban settlements. The shrinkage in urban green spaces in the Douala IV municipality has resulted in loss of biodiversity, increase in urban heat islands, atmospheric pollution, unattractiveness of the environment, crimes, mental and physical health issues. This was x-rayed by the poorly arranged green spaces, littered with wastes and having multiple paths used by motorists, pedestrians and vendor activities. This study concludes that greeneries remain a vital environmental resource, thus, recommending effective application of planning policies to restrict their encroachment. Additionally, greeneries should be treated among the top priorities of the development agenda of urban planning authorities as enshrined in the Sustainable Development Goal 11.

First look at the distribution of deactivated dFADs used by the French Indian Ocean tropical tuna purse-seine fishery

Abstract The presence of abandoned, lost or otherwise discarded fishing gears, including drifting fish aggregating devices (dFADs), in marine ecosystems poses significant ecological and socioeconomic concerns. The estimation of the number of dFADs in the marine environment is challenging due to the loss of tracking information when dFAD tracking buoys are remotely deactivated. For the first time, a data set of dFADs buoy positions, including those that had previously been remotely deactivated, has been made available for the period July–August 2020. Data from this period provide valuable insights into the life expectancy, spatial distribution, and status of deactivated dFAD buoys, enabling a more accurate assessment of dFAD presence and impacts. Deactivated buoys represented a 17.2% increase in the total number of tracked objects, and we estimate the in-water half-life of deactivated dFAD tracking buoys to be 101 days. Including deactivated buoys increases the number of strandings during the SP by 23.7%. Nevertheless, the representativity of these results is unknown given the limited spatio-temporal and numerical extent of our data, highlighting the importance of availability of comprehensive data on dFADs to effectively estimate their total numbers and mitigate their environmental impacts.

Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda.

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions

AbstractAimThe scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data.LocationTemperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA).Time Period1960–2018.Major Taxa StudiedCase studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth.MethodsWe compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model (microclimf) in predicting soil temperatures. We then use ERA5, WorldClim and microclimf to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses.ResultsFor predicting soil temperatures, microclimf had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20‐fold, and temporal resolution 30‐fold respectively.Main ConclusionsWe propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems.

Integrating counts from rigorous surveys and participatory science to better understand spatiotemporal variation in population processes

Abstract Knowledge of variation in population processes (e.g. population growth) across broad spatiotemporal scales is fundamental to population ecology and critical for conservation decision‐making. Count data from rigorous surveys (e.g. surveys with probabilistic sampling design and distance sampling information) can inform population processes but are often limited in space and time. Participatory science data cover broader spatiotemporal extents but are prone to bias due to limited to no sampling design and lack of distance sampling information, hindering their capability of informing population processes. Here, we developed an integrated dynamic N‐mixture model that jointly analyses rigorous survey and participatory science data to inform population growth at broad spatiotemporal extents. The model contains a flexible scaling parameter that allows fixed and random effects to account for biases and errors in participatory science data. We conducted simulations to evaluate the inference performance of this model across a broad range of spatial and temporal overlap between rigorous survey and participatory science data. We also conducted a case study of Baird's Sparrow (Centronyx bairdii), a species of conservation concern, to illustrate the application of the integrated model with rigorous survey data from the Integrated Monitoring in Bird Conservation Regions programme and participatory science North American Breeding Bird Survey and eBird data. Simulations showed that the integrated model improved precision without biasing parameter estimates, in comparison with a model informed by rigorous survey data alone. The case study further demonstrated the utility of the integrated model for quantifying range‐wide, long‐term population processes and environmental drivers despite limited spatiotemporal extent of rigorous survey data. In particular, we found that population growth rate peaked under medium temperature, which were only apparent in the integrated model. The integrated model developed in this study is useful for understanding wildlife population processes at broad spatiotemporal scales with count data. The flexible structure of this model, in particular the scaling parameter, makes it highly adaptable to a broad range of ecological systems and survey procedures. These properties make this modelling approach highly relevant for both population ecology and conservation practice.

Phenological mismatch between trees and wildflowers: Reconciling divergent findings in two recent analyses

Abstract Recent evidence suggests that community science and herbarium datasets yield similar estimates of species' phenological sensitivities to temperature. Despite this, two recent studies by Alecrim et al. (2023) and Miller et al. (2022) found very different results when using different data sources (community science and herbarium specimens, respectively) to investigate whether warming threatens wildflowers with phenological mismatch in relation to shading by deciduous trees. Here, we investigated whether differences between the two studies' results could be reconciled by testing four hypotheses related to model design, species, spatiotemporal data extent and phenophase. Hybrid model structures brought results from the two datasets closer together but did not fully reconcile the differences between the studies. Neither the species nor the phenophase selected for analysis seemed to be responsible for differences in results. Cropping the datasets to match spatial and temporal extents appeared to reconcile most differences but only at the cost of much higher uncertainty associated with reduced sample size. Synthesis: Our analysis suggests that although species‐level estimates of phenological sensitivity may be similar between community science and herbarium datasets, inherent differences in the types and extent of data may lead to contradictory inference about complex biotic interactions. We conclude that, until community science data repositories expand to match the range of climate conditions present in herbarium collections or until herbarium collections match the spatial extent and temporal frequency of community science repositories, ecological studies should ideally be evaluated using both datasets to test the possibility of biased results from either.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst ‘Big Data’, prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with ‘Rain.lta’, reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with ‘NDVI.lta’, reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

Seismic Magnitude Estimation Using Low-Frequency Strain Amplitudes Recorded by DAS Arrays at Far-Field Distances

ABSTRACT Downhole distributed acoustic sensing (DAS) data are now routinely acquired on fiber-optic cables deployed in wells for seismic imaging and microseismic monitoring. We develop a semiempirical workflow for estimating scalar seismic moment and moment magnitude of earthquakes using strain data recorded by downhole DAS arrays. At far-field distances, the time integral of axial strain is proportional to the displacement scaled by apparent slowness. Therefore, seismic moment can be directly estimated from the amplitude of the low-frequency plateau of the strain spectra divided by frequency, similar to the methodology commonly employed for far-field displacement spectra. The effect of polarization on strain amplitudes for different types of body waves is accounted for. Benefitting from the large spatial coverage provided by DAS arrays, moment estimates from multiple channels are averaged and an average radiation coefficient is assumed over the focal sphere. We validate the methods using data of microseismic events simultaneously recorded by a surface geophone array and by DAS on fiber deployed in two horizontal wells during a hydraulic fracturing experiment. For 106 microseismic events in the magnitude range ∼ −0.65 to ∼ +0.55, we find the DAS-derived magnitudes to be consistent with the magnitudes derived from the geophone array using traditional methods, with ∼95% of the magnitude estimates differing by less than ∼0.26 units. The workflow can be potentially extended to DAS arrays in vertical wells and to S waves recorded on dark fiber DAS arrays at the surface. This methodology does not require any calibration beyond knowledge of local seismic properties, and the use of the lowest possible frequencies reduces the influence of subsurface heterogeneities and the finite spatiotemporal extent of earthquake ruptures. The capacity to estimate robust seismic magnitudes from downhole DAS arrays allows improved evaluation and management of fracture growth and more effective mitigation of induced seismicity.

Modelling Europe-wide fine resolution daily ambient temperature for 2003–2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003–2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

Extent of saltwater intrusion and freshwater exploitability in the coastal Vietnamese Mekong Delta assessed by gauging records and numerical simulations

Climate change-driven sea level rise has intensified salinity intrusion (SI) in deltas worldwide, posing significant threats to the exploitation of freshwater resources. In the Vietnamese Mekong Delta (VMD), the third largest delta globally, SI is a recurring challenge along the coastline, degrading freshwater resources for agricultural and domestic use and affecting socio-economic development. In this paper, we investigate the spatiotemporal extent of salinity intrusion in the Ben Tre Province, the hotspot of salinity disaster within the VMD. Long-term salinity monitoring data (25 years from 1996 to 2020) has been analyzed, and a 1D (Mike 11) coupled with 3D hydrodynamic model (Mike 3) was developed. Three scenarios were used to investigate the freshwater resources exploitation: (i) the year of investigation (2021), (ii) 2021 to 2030 climate change impacts, considering different annual exceedance probability of the upstream Mekong discharge (i.e., average flow, relatively low, low and very low), and (iii) extreme salinity intrusion (i.e., the 2016 condition). Our results indicated that salinity patterns are well-stratified at the beginning and end of the dry season but well-mixed during the middle period. Furthermore, over the last 25 years, SI has progressively increased and started earlier in the dry season. The modeling scenarios for SI have also revealed a growing complexity in the exploitation of freshwater resources, highlighting challenges related to timing, depth, and geographical location. The exceedance probability scenarios disclosed higher and deeper salinity intrusion along the channel in VMD, ranging from 50 % to 95 %. This poses significant limitations on the feasibility of freshwater exploitation throughout the Ben Tre Province. Under the current trajectory of climate change, the 2030 scenario anticipates salinity intrusion reaching further inland from the 2021 scenario. This is likely to exacerbate the existing challenges in freshwater resource exploitation, even with comprehensive water infrastructure. We, therefore, propose several management strategies to adapt to salinity intrusion: storing freshwater in main rivers, maintaining consistent operation of water infrastructure systems, and encouraging water-saving distribution and exploitation methods. Moreover, we also recommend supporting the development of new drought-tolerant crop patterns.

Analyzing the Landuse Land Cover Change of Sonadia Island From 1990 to 2020

Remote sensing and Geographic Information Systems (GIS) are essential tools in determining the spatiotemporal extent of Landuse, and Land Cover (LULC) changes, as well as a variety of individual concerns, such as annual and seasonal changes in LULC caused by human interferences and interactions between the physical environment, cultural context, and anthropogenic factors. This study focuses on the LULC change of Sonadia Island, and it uses multi-temporal Landsat imagery from 1990 through 2020. The output analysis revealed four sub-features: mudflats, vegetation, open space, and water bodies. Vegetation cover decreased from 1486.71 hectares in 1990 to 986.13 hectares in 2000, and in 2020, the total area covered by vegetation increased significantly, reaching 1186.47 hectares, up from 497.97 hectares in 2010. Open space increased from 317.16 hectares in 1990 to 510.75 hectares in 2000. The net area expanded to 631.98 hectares in 2010 and then lost to 421.29 hectares in 2020. There was a consistent increase in the mudflats section from 1990 to 2010, when the amounts were 491.4 and 1179 hectares, respectively. By 2020, the area extent decreased to 796.41 hectares. From 1990 to 2000, the waterbody declined from 305.46 to 262.17 hectares, then slightly increased to 291.78 hectares in 2010, and then shrank again to 196.56 hectares in 2020. Therefore, this study could help policymakers decide on future landscape planning and evaluate Sonadia Island's current condition for long-term coastal management. J. Asiat. Soc. Bangladesh, Sci. 49(2): 139-148, December 2023