雪深时间序列生成:多时间尺度的有效模拟

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Hydrology X Pub Date : 2024-04-01 DOI:10.1016/j.hydroa.2024.100177
Hebatallah Mohamed Abdelmoaty , Simon Michael Papalexiou , Sofia Nerantzaki , Giuseppe Mascaro , Abhishek Gaur , Henry Lu , Martyn P. Clark , Yannis Markonis
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引用次数: 0

摘要

雪深(SD)是水、能量和养分循环的一个关键变量,影响着水量和水质、洪水和干旱的发生、与雪有关的灾害以及地表下的生态功能。因此,量化 SD 动态对一些科学和实际应用至关重要。对可持续降雪的地面测量可提供稀疏位置的信息,而物理全球模型模拟可提供相对较粗的空间分辨率信息。补充这些信息的一种方法是利用随机模型生成水文气候变量的时间序列,并以计算有效的方式保留其统计特性。然而,文献中并没有专门用于生成 SD 时间序列的随机生成方法。在此,我们应用随机模型生成由加拿大 448 个站点训练的合成日标度时间序列。结果表明,该模型捕捉到了观测记录的主要统计特性,包括零和非零标度的日分布、时间聚类(即自相关)和季节模式。该模型在捕捉多个时间尺度上的观测高阶 L-moments 方面也表现出色,93.0% 和 98.3%的站点的模拟和观测 L-skewness 和 L-kurtosis 偏差分别在(-0.1,+0.1)以内。本文介绍的随机建模方法推进了自毁时间序列的生成,而自毁时间序列是开发地球系统模型和评估导致严重损失和死亡的融雪洪水风险所必需的。
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Snow depth time series Generation: Effective simulation at multiple time scales

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, snow-related 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.

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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
期刊最新文献
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