Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

Qiao, Dejing; Li, Z.; Wang, N.; Zhou, Jianmin; Zhang, P.; Gao, Shouting

doi:10.5194/isprs-archives-xlii-3-1401-2018

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The different performances are largely determined by the uncertainties of the snow depth datasets. Influenced by various factors such as forest and grain size of snow, the PMW has overestimated and underestimated the snow depth in some areas of the Tibetan Plateau (Dai et al ., 2017), performing better on the QP (Qiao et al ., 2018). Figure S7 shows the spatial distribution of correlation coefficients of station observation snow depth and PMW snow depth on the corresponding grids, with most stations showing significant ( p < .05) positive correlations in annually and winter and nonsignificant ( p > .05) correlations in spring.…”

Section: Discussionmentioning

confidence: 99%

Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

Zhang

Mao

et al. 2022

Intl Journal of Climatology

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As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades−1, p < .10) and spring (−0.184 cm·decades−1, p < .05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas.

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Section: Discussionmentioning

confidence: 99%

Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

Zhang

Mao

et al. 2022

Intl Journal of Climatology

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Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

Liang

Zheng

et al. 2020

Remote Sensing

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Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

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Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

Cited by 2 publications

References 24 publications

Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

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