The Scale‐Dependency in Freshwater Habitat Regionalisation Analyses

Ecohydrology, Год журнала: 2025, Номер 18(3)

Опубликована: Апрель 4, 2025

ABSTRACT Freshwater ecosystems need efficient protection which requires detailed information regarding the spatial distribution of its environmental characteristics, allows simple habitat suitability assessments for freshwater species. Such characteristics can be assessed with regionalisation analyses, where are spatially clustered to highlight similarities or disparities across a given study area. While large drainage basins useful large‐scale estimates, it is equally important address small streams contribute most stream network length. The question however remains, what relative impact scale and choice variables on analyses? We tested scale‐ variable‐contingent effects in clusters using three analysis designs. used Hydrography90m high‐resolution dataset aggregated land cover, hydro‐geomorphological climatic sub‐catchments six distributed continents zones. then employed k‐means cluster analyses effect (i) scale, (ii) (iii) combination resulting regionalisation. Our results show that similar broad patterns emerged regardless design, whereas basin‐specific uncovered new smaller clusters. Land cover stood out as influential variable design. findings importance addressing assessing unique basin, could provide guidance an improved mapping globally.

Язык: Английский

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A Quantitative Property‐Based Layer and Profile Numerical Soil Classification System for Australia

European Journal of Soil Science, Год журнала: 2025, Номер 76(2)

Опубликована: Март 1, 2025

ABSTRACT Most soil classification systems rely on the identification of genetic horizons, delineated through visual observations guided by theories development. However, these often differ across countries, creating challenges for information transfer and comparison. In this study, we explore application numerical as a means establishing more universally applicable system. Using comprehensive set relevant properties—such available water capacity, bulk density, cation exchange capacity (CEC), effective CEC, pH (in both calcium chloride), organic carbon content texture (sand, silt clay percentages)—clustering analysis was performed using k‐means algorithm. This method generated 40 layer classes 100 profile classes, offering an innovative perspective variation. The spatial distribution exhibited depth‐dependent variation, although it less pronounced than east‐to‐west variation Australia. Notably, aligned well with existing Australian maps. approach marks significant step toward developing fully quantitative system classification, not only within Australia but also global applications, enhancing consistency comparability in science.

Язык: Английский

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Classification of soil layers in Deep Cement Mixing using optimized random forest integrated with AB-SMOTE for imbalance data

Computers and Geotechnics, Год журнала: 2024, Номер 179, С. 106976 - 106976

Опубликована: Дек. 11, 2024

Язык: Английский

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