000147197 001__ 147197 000147197 005__ 20241212141913.0 000147197 0247_ $$2doi$$a10.1002/hyp.15322 000147197 0248_ $$2sideral$$a140987 000147197 037__ $$aART-2024-140987 000147197 041__ $$aeng 000147197 100__ $$aRojas-Heredia, Francisco 000147197 245__ $$aSnow depth distribution in canopy gaps in central Pyrenees 000147197 260__ $$c2024 000147197 5060_ $$aAccess copy available to the general public$$fUnrestricted 000147197 5203_ $$aThis research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small‐size, medium‐size, large‐size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest‐ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains. 000147197 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E02-23R$$9info:eu-repo/grantAgreement/ES/MICINN/CGL2017-82216-K$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-124220OB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130114B-I00 000147197 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000147197 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000147197 700__ $$aRevuelto, Jesús 000147197 700__ $$aDeschamps-Berger, César 000147197 700__ $$aAlonso-González, Esteban 000147197 700__ $$aDomínguez-Aguilar, Pablo 000147197 700__ $$aGarcía, Jorge 000147197 700__ $$0(orcid)0000-0003-4831-4060$$aPérez-Cabello, Fernando$$uUniversidad de Zaragoza 000147197 700__ $$aLópez-Moreno, Juan Ignacio 000147197 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi. 000147197 773__ $$g38, 11 (2024), e15322 [17 pp.]$$pHydrol. process.$$tHYDROLOGICAL PROCESSES$$x0885-6087 000147197 8564_ $$s932987$$uhttps://zaguan.unizar.es/record/147197/files/texto_completo.pdf$$yVersión publicada 000147197 8564_ $$s2396435$$uhttps://zaguan.unizar.es/record/147197/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000147197 909CO $$ooai:zaguan.unizar.es:147197$$particulos$$pdriver 000147197 951__ $$a2024-12-12-12:44:48 000147197 980__ $$aARTICLE