Abstract
Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.
Highlights
As an important constituent of the cryosphere and an indispensable variable used in hydrological science research, snow cover has a significant impact on global climate change and the hydrological cycle [1]
Accurate measurement of the dynamic snow depth over mountainous areas such as Qinghai-Tibetan Plateau (QTP) is important to understanding alpine climate dynamics, hydrological cycle, production and life, and ecological changes
Multiple data sets of long-time series of snow depth such as the passive microwave snow depth dataset (PMSD) and snow cover data such as moderate resolution imaging spectroradiometer (MODIS)-SCF were used for an optimized multivariate reconstruction model development and validation
Summary
As an important constituent of the cryosphere and an indispensable variable used in hydrological science research, snow cover has a significant impact on global climate change and the hydrological cycle [1]. In the global hydrological cycle, snow accumulation and melting contribute greatly to water resource redistribution. It provides the most important resources of freshwater in arid and semi-arid regions [3,4]. As the basic physical characteristics of snowpacks, snow depth is broadly applied in climatic and hydrological simulations [5,6,7]. Snow depth plays an important role in weather forecast [8], snowmelt runoff simulation [1], and drought and flood prediction [9,10]. Since the Qinghai-Tibetan Plateau (QTP) forms the target port of the space in Asia, its massive distribution of ice and snow considerably impact the ecosystem of the region and those of Asia and the Northern
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