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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

<a href="/cs/project/GM22-33266M" target="_blank" >GM22-33266M: Vyhodnocení intenzifikace suchozemského hydrologického cyklu</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF HYDROLOGY X

ISSN

2589-9155

e-ISSN

2589-9155

Svazek periodika

23

Číslo periodika v rámci svazku

100177

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

001224709400001

EID výsledku v databázi Scopus

2-s2.0-85189758537