Snow depth time series Generation: Effective simulation at multiple time scales

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F24%3A100811" target="_blank" >RIV/60460709:41330/24:100811 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.hydroa.2024.100177" target="_blank" >https://doi.org/10.1016/j.hydroa.2024.100177</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.hydroa.2024.100177" target="_blank" >10.1016/j.hydroa.2024.100177</a>

Result language

angličtina

Original language name

Snow depth time series Generation: Effective simulation at multiple time scales

Original language description

Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snowrelated hazards, and sub -surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally -effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non -zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher -order L -moments at multiple temporal scales, with biases between simulated and observed L -skewness and L -kurtosis within (-0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth -system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10501 - Hydrology

Project

<a href="/en/project/GM22-33266M" target="_blank" >GM22-33266M: Investigation of the Terrestrial HydrologicAl Cycle Acceleration (ITHACA)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Name of the periodical

JOURNAL OF HYDROLOGY X

ISSN

2589-9155

e-ISSN

2589-9155

Volume of the periodical

23

Issue of the periodical within the volume

100177

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

10

Pages from-to

1-10

UT code for WoS article

001224709400001

EID of the result in the Scopus database

2-s2.0-85189758537