Assessment of snow cover dynamics and the effects of environmental drivers in High Mountain ecosystems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F25%3A43910865" target="_blank" >RIV/60076658:12220/25:43910865 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0195925525001660?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0195925525001660?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eiar.2025.107969" target="_blank" >10.1016/j.eiar.2025.107969</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessment of snow cover dynamics and the effects of environmental drivers in High Mountain ecosystems

Popis výsledku v původním jazyce

Remote sensing is crucial for monitoring decadal-scale snow cover dynamics in response to climate change across mountainous ecosystems. This study analyzes spatiotemporal snow cover trends and their driving factors using satellite observations, topographic data, and climate variables. MODIS snow product (MOD10A1) data from 2000 to 2022 were used to assess snow cover changes across 69 High Mountain Ecosystems (HME) in the Middle East. We analyzed data from January to April and October to December to capture seasonal snow cover dynamics. A linear regression method detected significant trends in the Normalized Difference Snow Index (NDSI), while a pixel-based Random Forest (RF) regression model assessed environmental drivers influencing NDSI variability. Results show that northern and northwestern humid regions generally experienced increasing NDSI, whereas central and southern arid regions exhibited a decline, highlighting spatial and temporal heterogeneity in snow cover trends. Temperature and precipitation change significantly influenced NDSI patterns, suggesting climate variability plays a critical role in snow cover disturbances. RF analysis identified mean annual temperature, precipitation changes, and mean annual precipitation as the top three drivers of NDSI variability. Future research should focus on the impact of extreme weather events on snow cover. Additionally, refining the methodology with higher-resolution data across diverse climate zones could enhance predictive accuracy.

Název v anglickém jazyce

Assessment of snow cover dynamics and the effects of environmental drivers in High Mountain ecosystems

Popis výsledku anglicky

Remote sensing is crucial for monitoring decadal-scale snow cover dynamics in response to climate change across mountainous ecosystems. This study analyzes spatiotemporal snow cover trends and their driving factors using satellite observations, topographic data, and climate variables. MODIS snow product (MOD10A1) data from 2000 to 2022 were used to assess snow cover changes across 69 High Mountain Ecosystems (HME) in the Middle East. We analyzed data from January to April and October to December to capture seasonal snow cover dynamics. A linear regression method detected significant trends in the Normalized Difference Snow Index (NDSI), while a pixel-based Random Forest (RF) regression model assessed environmental drivers influencing NDSI variability. Results show that northern and northwestern humid regions generally experienced increasing NDSI, whereas central and southern arid regions exhibited a decline, highlighting spatial and temporal heterogeneity in snow cover trends. Temperature and precipitation change significantly influenced NDSI patterns, suggesting climate variability plays a critical role in snow cover disturbances. RF analysis identified mean annual temperature, precipitation changes, and mean annual precipitation as the top three drivers of NDSI variability. Future research should focus on the impact of extreme weather events on snow cover. Additionally, refining the methodology with higher-resolution data across diverse climate zones could enhance predictive accuracy.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Environmental Impact Assessment Review

ISSN

0195-9255

e-ISSN

1873-6432

Svazek periodika

114

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001487481600001

EID výsledku v databázi Scopus

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