Fine Scale Research Articles

A US heat disaster? Intersection of social vulnerability and temperature extremes exacerbated by mid-century climate change and population shifts

Abstract Future impacts of climate change-driven increases in extreme heat exposure on population health will be driven both by the frequency and intensity of future heat, as well as changes in the size and vulnerability of affected communities. Given indications of spatial persistence of social vulnerability across the U.S., we explore the implications for heat exposures of the most vulnerable populations due to mid-century climate change. Population heat exposure is estimated by combining heat hazard with population growth projections. Specifically, median projected acute (95% maximum temperature), extreme (hot days, daily maximum heat index >40C), and chronic (cooling degree days) heat hazard are assessed in contemporary (1995-2014) and projected (2041-2060) epochs across 25+ climate models under three climate scenarios against 2020 social vulnerability index (SVI). By mid-century, increases in both chronic and extreme heat will be concentrated in the South region. Population is expected to grow faster in census tracts at the high and low extrema of the social vulnerability index. The intersection of these projected future trends suggests that the intensity and exacerbation of heat exposures due to climate change will not be evenly distributed across the population. Individuals who are most vulnerable today will face disproportionately larger heat exposures circa mid-century. Populations in high SVI areas will both increase in size and experience the largest increases in extreme heat exposure. Spatial aggregation masks fine scale changes. While cities and counties with concentrations of vulnerable populations will face rightward shifts in the tails of their temperature across the U.S., extreme and especially chronic population heat exposures will be concentrated in a swath stretching across southern U.S. states. The magnitude of population heat exposure changes and their intersection with underlying vulnerability require action to focus adaptation resources on resilience to extreme heat.

Geographical variation and the role of climate and soil on phenotypic traits of Calamus rhabdocladus across provenances in China

BackgroundPhenotypic traits are indicative of a plant’s resource utilization and survival strategies. Understanding the geographical differentiation of Calamus rhabdocladus phenotypes holds significant theoretical and practical value for genetic breeding and the selection of superior geographical provenances. This study analyzed the geographic variation in 15 phenotypic traits of leaves, stems, roots, flagellums, and other parts from 10 provenances of C. rhabdocladus in China. The phenotypic characters of C. rhabdocladus were measured in the field, while soil factors were collected in the field and analyzed in the laboratory. Climatic data were obtained through online sources. Pearson’s correlation was used to analyze the relationships between the 15 phenotypic traits. Redundancy analysis and variance decomposition analysis were used to quantify the effects of climatic and soil factors on the geographical variation in phenotypic traits of C. rhabdocladus, and the key influencing factors were obtained by comparing the explanatory rates of soil and climatic factors on phenotypic traits of C. rhabdocladus.ResultsThe results showed that the Coefficient of variation (CV) of phenotypic traits among provenances ranges from 7.48 to 182.16%, with root dry weight exhibiting the largest variation. The plasticity indices varied between 0.16 and 0.82, with the flagellum volume showing the highest value. Significant correlations were observed among leaf, stem, flagellum, and root traits. Average air temperature was identified as the crucial climatic factor influencing the phenotypic traits of C. rhabdocladus, while effective nitrogen and organic matter content were the key soil factors influencing these traits. Within the scope of this study, climatic factors provided a better explanation of phenotype variation than soil factors.ConclusionsThese results highlight the importance of climatic adaptation in shaping phenotypic diversity and suggest that future research should explore the genetic mechanisms underlying these traits and their interactions with environmental factors at finer spatial scales.

Trends and variability of marine heatwaves in Portuguese coastal waters.

Trends and variability of marine heatwaves in Portuguese coastal waters.

Diversity and environmental distribution of Asgard archaea in shallow saline sediments

In recent years, our understanding of archaeal diversity has greatly expanded, especially with the discovery of new groups like the Asgard archaea. These archaea show diverse phylogenetic and genomic traits, enabling them to thrive in various environments. Due to their close relationship to eukaryotes, a large number of metagenomic studies have been performed on Asgard archaea. Research on the fine scale distribution, diversity and quantification in saline aquatic sediments where they mostly occur, has, however, remained scarce. In this study, we investigated depths of shallow saline sediment cores from three distinct European environments: the Baltic Sea near Hiddensee, the coastal Lake Techirghiol in Romania, and an estuarine canal in Piran, Slovenia. Based on 16S rDNA amplicon sequencing, we observe variation in the relative abundance and occurrence of at least seven different Asgard groups that are distinct between the three environments and in their depth distribution. Lokiarchaeia and Thorarchaeia emerge as dominant Asgard groups across all sites, reaching maximal relative abundances of 2.28 and 2.68% of the total microbial communities respectively, with a maximal abundance of all Asgard reaching approx. 5.21% in Hiddensee. Quantitative PCR assays provide insights into the absolute abundance of Lokiarchaeia, supporting distinct patterns of distribution across depths in different sediments. Co-occurrence network analysis indicates distinct potential microbial partners across different Asgard groups. Overall, our study shows that Asgard archaea are found as a stable component in shallow sediment layers and have considerably diversified on macro- and microscales.

Synthetic population generation with public health characteristics for spatial agent-based models.

Agent-based models (ABMs) simulate the behaviors, interactions, and disease transmission between individual "agents" within their environment, enabling the investigation of the underlying processes driving disease dynamics and how these processes may be influenced by policy interventions. Despite the critical role that characteristics such as health attitudes and vaccination status play in disease outcomes, the initialization of agent populations with these variables is often oversimplified, overlooking statistical relationships between attitudes and other characteristics or lacking spatial heterogeneity. Leveraging population synthesis methods to create populations with realistic health attitudes and protective behaviors for spatial ABMs has yet to be fully explored. Therefore, this study introduces a novel application for generating synthetic populations with protective behaviors and associated attitudes using public health surveys instead of traditional individual-level survey datasets from the census. We test our approach using two different public health surveys to create two synthetic populations representing individuals aged 18 and over in Virginia, U.S., and their COVID-19 vaccine attitudes and uptake as of December 2021. Results show that integrating public health surveys into synthetic population generation processes preserves the statistical relationships between vaccine uptake and attitudes in different demographic groups while capturing spatial heterogeneity at fine scales. This approach can support disease simulations that aim to explore how real populations might respond to interventions and how these responses may lead to demographic or geographic health disparities. Our study also demonstrates the potential for initializing agents with variables relevant to public health domains that extend beyond infectious diseases, ultimately advancing data-driven ABMs for geographically targeted decision-making.

Unlocking the geography of Azobé timber (Lophira alata): revealing spatial genetic structure beyond species boundaries

BackgroundThe illegal trade of tropical timber constitutes a major and persistent environmental problem. Since the detection of fraud in trade documents remains challenging, forensic tools that can independently trace timber origin are needed. In this study, we evaluated the potential of the chloroplast genome (plastome) as a genetic tool to verify the claimed species and geographic origin of timber from Azobé (Lophira alata), an intensively exploited and threatened tropical tree species.ResultsWe sampled 480 trees from Lophira alata and the congeneric species L. lanceolata across nine countries in Central and West Africa. Sampling included L. alata trees from 15 logging concessions in Cameroon, Gabon and the Republic of the Congo. DNA was isolated from the cambium or leaf tissue, and complete plastid genomes were assembled. A total of 228 SNPs from 436 trees were retained, which formed 35 pDNA haplotypes (with a length of 179 SNPs). The two Lophira species shared one plastid haplotype and contained several closely related plastid haplotypes. For the exploited L. alata, we detected a moderately strong correlation between genetic and spatial distances. Two haplotypes were widely spread across the core of Central Africa, while several others were more spatially constrained or endemic, for example, in West Gabon (potentially a L. alata cryptic species) and Northern Congo.ConclusionsThe distribution of haplotypes revealed a clear spatial structure. Some widely spread haplotypes potentially hamper site distinction of Azobé wood samples, but still reveal their wider region of origin. In regions where endemic haplotypes are present, differentiation may be successful at finer scales. Thus, the potential spatial resolution for timber tracing may vary across regions. We assembled the first reference database of plastome-wide SNP datasets for Azobé timber, with a focus on the major logging areas. Our work represents a step towards plastome-based timber tracing for this species, but also reveals limited potential of this method for species differentiation. To validate the potential of the plastid genome for timber tracing, further steps, including assignment and blind sample tests, will be needed.

Nuance in the Narrative of a Brown Poison Frog: Environmental Alkaloids and Specialized Foraging in a Presumed Toxin-Free and Diet-Generalized Species.

In poison frogs (Dendrobatidae), conspicuous colors have evolved repeatedly in tandem with high numbers and quantities of skin toxins (alkaloids). Here, we focus on an inconspicuously-colored species-Silverstoneia flotator-which has long been deemed toxin-free and thought to forage opportunistically on mites and ants. Both assumptions have received some empirical support, but there is also evidence that predators avoid S. flotator. In a Panamanian S. flotator population, we sampled invertebrates in frog diets and the surrounding environment (using Berlese and pitfall traps) and screened for skin, dietary, and environmental alkaloids using untargeted metabolomics. We found that while the frogs are opportunistic consumers of mites and ants, they display preferences at finer taxonomic scales (for symphypleonan springtails and Pheidole ants). We also annotated 64 skin compounds as alkaloids, 38 of which were present in the environment. One alkaloid present in the skin and environment is likely the highly potent epibatidine. While the average biosynthetic (class and superclass) diversity of alkaloids in a dorsal skin sample is higher than that of a ventral skin and environmental-but not dietary-sample, environmental samples diverge more in their alkaloids' biosynthetic diversities than do dietary or skin samples. The frogs consume a consistent set of alkaloids, forage in a variable chemical space, and possess diverse dorsal skin alkaloids. They might use finer-scale diet specialization to modulate the types, quantities, and numbers of alkaloids they ingest. We encourage further examination of inconspicuously-colored taxa to better understand the ecological importance of diet-acquired toxins and specialized diets in these organisms.

DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale

Abstract Air pollution, specifically PM2.5, has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to infer the concentration of air pollutants at a fine scale. In this study, we propose DeepAir, a learning framework for estimating PM2.5 concentrations at a fine scale with sparsely distributed observations. DeepAir integrates a pre-trained convolutional neural network with the LightGBM method. This framework estimates the PM2.5 concentration of a given patch, utilizing a synergy of geographical information, meteorological conditions, and satellite observations. We select California as the focal region and train the model with data from 2014 to 2017 provided by 130 PM2.5 observation stations in the state. Upon training, the model can be applied to estimate the daily PM2.5 concentrations at 1 km resolution across California. Our methodology meticulously incorporates meteorological variables, with a particular emphasis on wildfire propagation, and contemplates the complex interplay of various features. To ascertain the efficacy of our model, we employ the 10-fold cross-validation technique, which confirms that our model surpasses traditional ML and standalone deep learning methods.

Applications of species distribution modeling and future needs to support marine resource management

Abstract Fisheries science agencies are responsible for informing fisheries management and ocean planning worldwide, often requiring scientific analysis and management actions across multiple spatial scales. For example, catch limits are typically defined annually over regional scales, fishery bycatch rules are defined at fine spatial scales on daily to annual time scales, and aquaculture and energy lease areas are defined over decades for subregional permitting at intermediate scales. Similarly, these activities require synthesizing monitoring data and mechanistic knowledge operating across different spatial resolutions and domains. These needs drive a growing role for models that predict animal presence or densities at fine spatial scales, including daily, seasonal, and interannual variation, often called species distribution/density models (SDMs). SDMs can inform many ocean management needs; however, their development and usage are often haphazard. In this paper we discuss various ways SDMs can and have been used in stock, habitat, protected species, and ecosystem management activities as well as marine spatial planning, survey optimization, and as an interface with ecosystem and climate models. We conclude with a discussion of future directions, focusing on information needs and current development, and highlight avenues for furthering the community of practice around SDM development and use.

Forest Structure and Stand Characteristics Influence the Space Use and Fine‐Scale Movements of Fishers (Pekania pennanti)

ABSTRACTUnderstanding animal space use can inform management and conservation, particularly in landscapes where conservation of imperiled species must be balanced with resource use by humans. We used GPS collar data to evaluate fisher (Pekania pennanti) space use and behavior at multiple spatial scales in relation to a multi‐scale vegetation structure in the southern Cascade mountains of Oregon, USA. From 2015 to 2018, we estimated space use of 9 individual fishers collared during 25 deployments. To evaluate whether the vegetation structure affected fisher behavior within use areas, we modeled variation in motion variance and estimated fine‐scale space use using step selection functions, including evaluating where fishers exhibit selection for corridor‐type movements. Fisher use areas exhibited greater canopy cover, heterogeneity, and canopy height than stands available across the study landscape. Fishers exhibited higher motion variance as canopy cover and canopy height decreased, with the strongest effects in areas with < 26% canopy cover or < 23 m canopy height. Fine‐scale habitat selection by fishers was positively associated with higher canopy cover and height, and fisher corridor‐type movements were more likely to occur in symmetrically shaped, larger forest stands. Importantly, we found that fishers used greater canopy cover and height across spatial scales but exhibited asynchronous responses to structural heterogeneity at different spatial scales. At fine scales, fishers selected against heterogeneity in canopy cover and height and areas closer to stand edges and exhibited more variable movement in open or early‐seral areas. Conversely, fisher space use at coarser scales was positively related to heterogeneity in forest cover and the shape and size of forest stands. Through this work, we highlight the importance of scale‐dependent context when considering the effects of forest structure on species' space use and illustrate vegetation and patch characteristics that may promote the spaceuse offishers and other forest‐dependent species in mixed‐use landscapes.

Ecological Factors at Fine Spatial Scale Associated With Habitat Use by Tigers in Western Terai Arc Landscape, Nepal.

Conservation of designated source sites is a fundamental strategy for global tiger recovery. Reliable estimates of tiger Panthera tigris habitat use within these source sites are crucial for informing effective management strategies. In this study, we assessed tiger habitat use within the Bardia-Banke Complex, one of the 42 global source sites, situated in the western Terai Arc Landscape (TAL) of Nepal. We conducted a grid-based detection and non-detection camera trap survey across 719 grid cells, each measuring 2 × 2 km2. To assess tiger habitat use while accounting for imperfect detectability, we applied a single-season occupancy model. We analyzed nine covariates that have the potential to influence tiger habitat use in the Complex, including terrain, co-predators, prey, water availability, and disturbance. We found that fine scale (2 × 2 km2) tiger habitat use in the Complex was 0.43 (SE ± 0.0085, 95% CI: 0.414-0.448). Our analysis demonstrated that tigers used habitats unevenly across the Bardia-Banke Complex. Our results showed that the terrain ruggedness index, prey index, and proximity to waterholes were key determinants of tiger habitat use. Tiger habitat use was positively associated with prey abundance and negatively associated with terrain ruggedness and distance to waterholes. We emphasize the importance of influencing habitat covariates that determine the probability of habitat use for taking appropriate habitat-management decisions for tiger conservation in the TAL. We highlight the importance of periodic assessment of tiger habitat use in this globally significant source site to monitor changes in spatial habitat use patterns, serving as a measure of the effectiveness of wildlife management interventions.

Can “fine scale” data on air pollution be an evaluation tool for public health professionals?

Can “fine scale” data on air pollution be an evaluation tool for public health professionals?

Dynamic Load Balancing Based on Hypergraph Partitioning for Parallel Geospatial Cellular Automata Models

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the spatiotemporal dynamics of LULC during the simulation, leading to an increase in load imbalance among computing units and degradation of the computational performance of a parallel CA. This paper presents a dynamic load balancing method based on hypergraph partitioning for multi-process parallel geospatial CA models. During the simulation, the sub-domains are dynamically reassigned to computing processes through hypergraph partitioning according to the spatial variation in computational workloads to restore load balance. In addition, a novel mechanism called Migrated-SubCellspaces-First (MSCF) is proposed to reduce the cost of workload migration by employing a non-blocking communication technique to further improve computational performance. To demonstrate and evaluate the effectiveness of our method, a parallel geospatial CA model with hypergraph-based dynamic load balancing is developed. Experiments using a dataset from California showed that the proposed dynamic load balancing method achieved a computational performance enhancement of 62.59% by using 16 processes compared with a parallel CA with static load balancing.

Hierarchy in Structuring of Resource Selection: Understanding Elk Selection Across Space, Time, and Movement Strategies.

Movement is a fundamental aspect of animal ecology that varies across space, time, and among individuals or groups within a population. Broad-scale patterns of animal movement are often classified into different movement strategies, such as resident, nomadic, or migratory. While landscape-level environmental patterns can predict the presence of different movement strategies in an area, elucidating how these patterns downscale to fine-scale resource selection behaviors remains a challenge. Partially migratory systems, where both migrants and residents coexist, offer a unique opportunity to address these questions. Using tracking data from four Rocky Mountain elk (Cervus canadensis) herds situated primarily within Colorado, USA, we assessed between-herd, seasonal, and within-herd variation in resource selection behavior. We modeled fine-scale seasonal resource selection and compared strategy-specific behaviors using resource selection functions. Additionally, we used a consistency score to quantify the extent of differentiation in resource selection behavior across strategies, seasons, herds, and groups of covariates. We found variation in strategy frequency within each herd and in selection behavior, highlighting the complexity and context-dependence of strategy-specific selection. Despite herd-specific differences, some consistent trends emerged, with elk avoiding human development and roads at fine scales while selecting areas with higher productivity during summer. Our consistency analysis identified where elk most diverged in their selection behavior, revealing the greatest differences among herds, followed by variation between seasons, and lastly between movement strategies. Elk exhibited more uniform responses to productivity, contrasting with greater differentiation in responses to anthropogenic-related covariates. Overall, our study improves our understanding of elk behavior across space, time, and movement strategies and sheds light on the hierarchical influences of space and time in constraining behavior.

Corrigendum to “Evolution of benthodemersal fish communities in a changing European estuary at fine and broad temporal scales” [Estuar. Coast Shelf Sci. 301 (June 2024), 108738

Corrigendum to “Evolution of benthodemersal fish communities in a changing European estuary at fine and broad temporal scales” [Estuar. Coast Shelf Sci. 301 (June 2024), 108738

Fine Scale Patterns and Drivers of Plant Species Richness on a Sub‐Antarctic Island

ABSTRACTAimsCold environments, such as polar systems, are highly vulnerable to global change drivers such as climate change and invasive species. Therefore, it is essential to assess what drives the diversity of native and invasive species in these systems.LocationWe investigated what drives native and alien plant species richness on sub‐Antarctic Marion Island and determined the scale‐dependence of these drivers.MethodsNative and alien plant species richness was surveyed at “small” (1 m2) and “large” (9 m2) sampling grains. Difference in richness between the two sampling grains was calculated to assess how local turnover contributes to species assemblage. The factors driving richness at both grains, and the differences in richness between the two grains, were analysed using simultaneous auto‐regressive models. Drivers related to energy and environmental heterogeneity were correlated with native richness, whilst drivers related to productivity were related to alien richness.ResultsBiotic interactions with a cushion plant facilitated native richness, but restricted alien richness at low elevations. Further, some drivers of richness depended on spatial grain. Native richness was positively related to northness at large, but not small grain size, suggesting that higher northness increases local turnover at a grain size > 1 m2. On the other hand, topographical wetness index (TWI) boosted native richness at small but not large grains, implying that competition for water limits coexistence at low TWI (i.e., low moisture availability) only at small grain. Differences in native species richness between large and small grain sizes were more pronounced at low elevations, suggesting higher compositional heterogeneity at low altitudes.ConclusionsOur study highlights that drivers of plant species richness in a polar ecosystem differ between native and alien plant species. Additionally, the effects of some drivers on richness differ between sampling grains, and considering these differences provides insight into drivers of local patterns of species assemblage.

Vegetation influences wolf fine-scale habitat selection and movement rate in a logged coastal rainforest

Vegetation and its modification by humans can shape wildlife habitat selection and movement. A better understanding of how wolves select and move through natural and human modified vegetative cover can be used to implement forest management that considers impacts on wolves and their prey. We analyzed fine-scale wolf habitat selection and movement in a coastal temperate rainforest (Prince of Wales Island, Alaska, USA) in relation to: (1) young (≤ 30 years) and old (> 30 years) logged areas, (2) continuous measures of vegetative cover (as estimated via LiDAR), and (3) distance to roads, using integrated step-selection analysis (iSSA). Wolves selected areas with less forest canopy and understory cover at the population level, although they switched to selecting understory when within logged forest stands. The continuous canopy and understory measures vary at a fine spatial scale and thus appear to better explain fine-scale wolf selection and movement than categorical landcover classes representing the age of logged stands. Wolf selection of young (≤ 30 years) and old (> 30 years) successional logged areas, and areas near roads, was mixed across individuals. All individual wolves avoided canopy cover, but varied in their selection of logged stands, understory, and roads. Similarly, there was variability in movement rate response across individual wolves, although at the population level wolves moved faster through old (> 30 years) logged areas and through areas with less understory vegetation. Open vegetation including that present recently after logging is selected by wolves, and facilitates wolf movement, but this effect may be ephemeral as vegetation undergoes succession.

The ecohydrology of a Japanese knotweed invasion

<p>Polygonum cuspidatum (Japanese knotweed) is an herbaceous, clonal invasive plant that can create a monodominant stand that outcompetes native species, particularly throughout riparian zones. To better understand how this species is so successful at invading, and why particular locations in riparian settings are more at risk, we investigated a variably invaded riparian valley site in the northeastern United States. We measured spatial variations in hydrologic and soil attributes (soil texture, depth profiles, volumetric water content) in relation to spatial variations in Japanese knotweed abundance (percent coverage). We also investigated how the arrival of this species alters the hydrologic properties of the soil (e.g., infiltration rate) at a fine spatial scale (< 1 m). Across all variables observed, the strongest linear correlation (<em>p</em> < 0.01, <em>r</em> = 0.70) was found to be a positive relationship between mean percent coverage of Japanese knotweed and soils with a higher clay proportion. To understand causation in this relationship, and to better know how knotweed alters the physical environment it invades, we measured soil infiltration rates in the presence and absence of stems (<em>n</em> = 40). We found that knotweed occurrence can create a significant (<em>p</em> = 0.05) decrease in infiltration rate, but only within finer soils. These results may elucidate new possible ecohydrologic feedback mechanisms between Japanese knotweed and soil properties that could promote a higher competitive advantage in finer soils. This new insight, along with other known mechanisms promoting knotweed growth, can help to better identify and prioritize conservation efforts around locations more at risk of a successful invasion.</p>

To Play or Not to Play: Mapping Unequal Provision of Children’s Playgrounds

Children’s playgrounds are an important component providing opportunities for children’s play. Few studies, however, have explored the unequal geographic provision of these valuable spaces. This work addressed this research gap by identifying the key data and methods required to analyse children’s playground provision at both global and national geographic scales. The aims of the paper were twofold: It firstly explored the potential for mapping children’s playground provision at a global scale and validated such an approach using finer spatial scales. Secondly, the unequal provision of playgrounds was investigated at a national scale in England, UK using open data sources. Relationships with the size of the settlement and deprivation were also explored. The work used a range of secondary datasets through applying quantitative GIS and statistical approaches. The results demonstrated that, due to data quality issues, a world approach to map playground provision was not currently viable. At a national scale, results highlighted substantial inequality in provision, with some settlements in England experiencing five times the number of children per playground, despite being broadly comparable in terms of population. Deprived settlements in England tended to have fewer, smaller, and further-away playgrounds. The patterns were most stark in the largest settlements. London, however, was consistently an exception to these patterns, where deprived areas tended to have more and closer playgrounds. Acknowledging the numerous competing different metrics to measure provision of children’s playgrounds, the research generated a framework for bringing together a wide range of interrelated data into a condensed form for comparison. Thus, the approach facilitated the identification of interventions within different contexts in order to reduce inequalities in playground provision and bolster children’s democratic right to the city for play.

Assessing the Use of Alternative Soil Data in Hydrological and Water Quality Modeling with SWAT+: SSURGO and POLARIS at Sub-Basin and Field Scales

The accuracy of soil databases is essential in hydrological modeling, yet limited studies have evaluated the implications of using emerging soil datasets like POLARIS compared to traditional ones such as SSURGO. This study evaluates the performance of POLARIS soil data for simulating the streamflow and sediment yield at both the sub-basin and field scales within the Big Muddy Watershed (BMW), Illinois, U.S.A., using a soft-calibrated SWAT+ model. The field-scale analysis focused on cropland-dominated HRUs from two sub-basins with contrasting POLARIS-SSURGO similarities at the sub-basin scale, optimizing computational efficiency. POLARIS results were compared to those derived from the widely used SSURGO soil database using a soft-calibrated SWAT+ model. At the sub-basin scale, the two datasets showed strong overall agreement for the streamflow and sediment yield over the 81 BMW sub-basins, with minor discrepancies, especially in sediment yield predictions, which exhibited more variability. At the field scale, the agreement between POLARIS and SSURGO was good for both variables, streamflow and sediment yield, though the sediment yield showed greater variability as shown at the sub-basin level. At both scales, the POLARIS and SSURGO outcomes for the streamflow and sediment yield did not always follow the same trend, with discrepancies observed in some sub-basins and HRUs. This suggested that while POLARIS can replicate SSURGO’s streamflow outcomes, this similarity does not always extend to sediment yield predictions and vice versa. At the sub-basin scale, the POLARIS and SSURGO outcomes showed strong alignment (88.9% in “very good” agreement). However, at the field scale, this alignment decreased to 42.9% and 33.3% in specific sub-basins. This indicates that sub-basin aggregation reduces local variability, while finer scales reveal greater sensitivity to soil and hydrological differences. This study highlights POLARIS as a robust alternative to SSURGO for hydrological modeling. Future research should explore its broader application across diverse conditions.