Environmental variables affecting an arid coastal nebkha

Sanromualdo-Collado, Abel; Gallego‐Fernández, Juan B.; Hesp, Patrick A.; Martínez, M. Luisa; O'Keeffe, Nicholas; Ferrer-Valero, Nicolás; Hernández‐Calvento, Luis

doi:10.1016/j.scitotenv.2021.152868

Cited by 5 publications

(4 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have suggested that the formation and development of nebkhas depend greatly on vegetation growth, sand transportation, and accumulation, as well as their nonlinear interactions in a qualitative manner (Cramer & Midgley, 2015; Du et al, 2010a; Li et al, 2021; Sanromualdo‐Collado et al, 2022; Wang et al, 2008). Various climatic factors influence plant growth on nebkhas, while other local environmental variables such as soil properties and topography may critically impact sand availability, transportation and accumulation.…”

Section: Discussionmentioning

confidence: 99%

Section: How Different Environmental Variables Shape the Distribution...mentioning

confidence: 99%

“…The formation and development of nebkhas can be understood as the result of the intricate interaction and co‐evolution of biotic and abiotic factors. Previous studies have demonstrated the importance of vegetation, sediment supply, wind regime and, in some instances, human interference as pivotal variables in the formation of nebkhas (Goudie, 2022; Hesp, Hernández‐Calvento, Gallego‐Fernández, et al, 2021; Langford, 2000; Nickling & Wolfe, 1994; Nield & Baas, 2008; Sanromualdo‐Collado et al, 2022; Wang et al, 2008). These variables exhibit positive feedbacks, enabling nebkhas to persistently grow under favourable conditions until they reach a maximum height (Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The formation and development of nebkhas can be understood as the result of the intricate interaction and co-evolution of biotic and abiotic factors. Previous studies have demonstrated the importance of vegetation, sediment supply, wind regime and, in some instances, human interference as pivotal variables in the formation of nebkhas (Goudie, 2022;Langford, 2000;Nickling & Wolfe, 1994;Nield & Baas, 2008;Sanromualdo-Collado et al, 2022;Wang et al, 2008).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Biogeomorphological niche of a landform: Machine learning approaches reveal controls on the geographical distribution of Nitraria tangutorum nebkhas

Zhang,

Li,

Mason

et al. 2024

Earth Surf Processes Landf

View full text Add to dashboard Cite

Nebkhas are distinctive biogeomorphological landforms prevalent in global drylands and coastal environments. They play a crucial role in supporting local biodiversity and preventing land desertification and often serve as an indicator of local environmental change. Despite their significance, the environmental factors that affect their geographical distribution and how they respond to climate change have not been fully explored. This study represents a novel application of machine learning models to quantifying the biogeomorphological niche of Nitraria nebkhas in northern China and simulating their geographical distribution under future climate change conditions. Findings underscore that climatic variables influence the growth of formative shrub species on nebkhas, whereas climate, soil and geomorphological conditions, along with their spatial configuration, determine the probability of nebkha occurrence. Predictions under medium and high greenhouse gas emission scenarios indicate a northward shift in the potential distribution of nebkhas in northern China by the end of the century, accompanied by a decrease in the south due to rising temperatures. Given the potential impact of nebkha field degradation on biodiversity and soil hydrological conditions, adaptive land use strategies should be designed to protect nebkhas and mitigate the impact of climate change. Our study not only provides valuable insights for informing policy‐making and conservation initiatives but also serves as an example for quantifying the niche of biogeomorphological landforms and simulating their dynamics by integrating machine learning approaches into empirical geomorphological studies.

show abstract