A simple regional snow hydrological process-based snow depth model and its application in the Upper Yangtze River Basin

Suxia Liu,Yanqun Ren

doi:10.2166/nh.2019.079

Abstract

Abstract Snow depth plays a significant role in the regional water balance, for which snowfall is usually determined by a fixed temperature threshold in regional snow research. This study developed a regional hydrological process-based snow depth model in the Upper Yangtze River Basin by using spatially distributed critical temperature data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data and station data. Based on meteorological station and remotely sensed data, daily snow hydrological components from 1 August 2003 to 31 July 2015 were simulated. Results show that the simulated snow depth patterns agreed with those of the observed snow depth. The multi-year average of the starting date and ending date of snow duration was 16 October and 6 June, respectively, and that of maximum annual snowfall was approximately 455 mm, of which canopy interception comprised 10%, with a maximum value of 50 mm. The proportion of snow sublimation was less than 20%, which was contributed by interception sublimation (40%), snow surface sublimation (40%) and sublimation underneath the canopy (20%). The maximum annual snow sublimation was 29 mm. Snow melting was the primary snow consumption pathway, and approximately 70% of snowfall melted. This research is significant for the assessment and management of water resources in this region.

Highlights

Global snow cover, which can influence climate change, energy balance and water cycles, reaches approximately 46 million km2 in winter (Koivusalo & Heikinheimo ; Zhang et al )
The objectives of this study were to: (1) establish a more accurate simulation model of snow depth based on meteorological data (air temperature, land surface temperature (LST), precipitation, wind speed, relative humidity and sunshine duration), land type data (leaf area index (LAI) and normalised difference vegetation index (NDVI)) and earth parameters, which considers the canopy interception of snow and snow blowing; and (2) test the ability of the model built in this study to simulate components of the hydrological cycle and to evaluate the adaption of this model compared with different models or data resources
The results showed that the difference between simulated and observed snow depth was similar to the normal distribution, with approximately 90% of the difference ranging from À2 to 2 mm (Figure 9)

Summary

Introduction

Global snow cover, which can influence climate change, energy balance and water cycles, reaches approximately 46 million km in winter (Koivusalo & Heikinheimo ; Zhang et al ). Yangtze River Basin (UYRB), which account for most of the snow in the world. Studies of snow depth calculation, at point to regional scales and with various ground observation methods, mathematical statistics, and a focus on geodesy and physical mechanisms, have been conducted in the past few decades (Strack et al ; Molotch & Margulis ; Revuelto et al ). Since the early 1960s, remotely sensed data have been used in snow research, providing a new method for resolving spatial problems, and great progress has been made in this field (Hall et al ; Riggs et al ).

Objectives

Results

Conclusion