scholarly journals Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote sensing data

2013 ◽  
Vol 10 (9) ◽  
pp. 11711-11753 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
H. Tsutsui ◽  
Y. Xue ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Search Algorithm ◽  
Remote Sensing Data ◽  
Relative Volume ◽  
Shortwave Radiation ◽  
Topographic Effects ◽  
Study Region ◽  
Basin Scale ◽  
Snowmelt Model

Abstract. Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modeling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimizes an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution – University of Arizona automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimized Cfsnow of 0.0007 m−1. For validation purposes, the optimized Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated vs. observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly-gauged basins, where elevation dependence of snowfall amount is strong.

scholarly journals Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data

2014 ◽  
Vol 18 (2) ◽  
pp. 747-761 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
H. Tsutsui ◽  
Y. Xue ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Search Algorithm ◽  
Relative Volume ◽  
Shortwave Radiation ◽  
Topographic Effects ◽  
Hydrologic Modelling ◽  
Study Region ◽  
Basin Scale ◽  
Snowmelt Model

Abstract. Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised Cfsnow of 0.0007 m−1. For validation purposes, the optimised Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.

Topographic effects on longwave and shortwave surface radiation in a kilometre-scale regional climate model

2020 ◽  
Author(s):  
Christian Steger ◽  
Jesus Vergara-Temprado ◽  
Nikolina Ban ◽  
Christoph Schär
Keyword(s):  
Snow Cover ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
View Factor ◽  
Topographic Effects ◽  
Radiation Correction ◽  
Correction Scheme ◽  
Weather And Climate ◽  
The Impact ◽  
Topographic Parameters

<p>Weather and climate in alpine areas are strongly modulated by complex topography. Besides its influence on atmospheric flow and thermodynamics (such as orographic precipitation and foehn winds), topography also affects incoming surface radiation in various ways. Direct shortwave radiation might be blocked due to shading effects from neighbouring terrain. Diffuse shortwave radiation can be altered by a reduced sky view factor and reflectance of radiation from surrounding terrain. Similar, the net longwave radiation is affected by emissions from neighbouring terrain.</p><p>Radiation in virtually all state-of-the-art weather and climate models is only computed in the vertical direction using the column approximation, and the above-mentioned effects are usually not represented. Still, a few models consider topographic effects by correcting incoming radiation fluxes based on topographic parameters like slope aspect and angle, elevation of horizon, and sky view factor. The Consortium for Small-scale Modeling (COSMO) model includes such a scheme, which is currently only used in the Numerical Weather Prediction mode of the model.</p><p>In this study, we apply the surface radiation correction scheme in the climate mode of COSMO. To study its impacts in detail, we force COSMO’s land-surface model (TERRA) offline with output from a COSMO simulation, which was run without radiation correction at a horizontal resolution of 2.2 km and for a domain covering the Alps. A useful proxy to study the impact of the correction scheme is snow cover duration (SCD), because snow cover length is, amongst other factors, strongly controlled by incoming surface radiation that drives ablation. A comparison of SCD simulated by COSMO with satellite-derived snow cover data (MODIS and AVHRR) reveals a distinctive bias, where SCD is overestimated for south-facing grid cells and underestimated for north-facing cells. Applying the radiation correction in the offline TERRA simulation shows only a moderate reduction of the bias. One reason for this minor improvement is the fact that the topographic parameters are computed from a smoothed digital elevation model (DEM) – thus the impact of the radiation correction scheme is damped. If topographic parameters are computed from unsmoothed DEM, biases in SCD are further reduced. Currently, further sensitivity experiments are conducted to investigate the effect of computing the topographic parameters from a sub-grid DEM and to assess the energy conservation of the radiation correction scheme.</p>

scholarly journals Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass-balance distribution of an alpine glacier

2012 ◽  
Vol 58 (207) ◽  
pp. 151-164 ◽  
Author(s):  
Marie Dumont ◽  
Yves Durand ◽  
Yves Arnaud ◽  
Delphine Six
Keyword(s):  
Spatial Distribution ◽  
Data Assimilation ◽  
Mass Balance ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Variational Data Assimilation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Glacier Mass Balance ◽  
Potential Contribution

AbstractAccurate knowledge of the spatial distribution of the mass balance of temperate glaciers is essential for a better understanding of the physical processes controlling the mass balance and for the monitoring of water resources. In relation to albedo variations, the shortwave radiation budget is a controlling variable of the surface energy balance of glaciers. Remotely sensed albedo observations are here assimilated in a snowpack model to improve the modeling of the spatial distribution of the glacier mass balance. The albedo observations are integrated in the snowpack simulation using a variational data assimilation scheme that modifies the surface grain conditions. The study shows that mesoscale meteorological variables and MODIS-derived albedo maps can be used to obtain a good reconstruction of the annual mass balance on Glacier de Saint-Sorlin, French Alps, on a 100 m × 100m grid. Five hydrological years within the 2000-10 decade are tested. The accuracy of the method is estimated from comparison with field measurements. Sensitivity to roughness lengths and winter precipitation fields is investigated. Results demonstrate the potential contribution of remote-sensing data and variational data assimilation to further improve the understanding and monitoring of the mass balance of snowpacks and temperate glaciers.

scholarly journals A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

2021 ◽  
Vol 13 (4) ◽  
pp. 655
Author(s):  
Animesh Choudhury ◽  
Avinash Chand Yadav ◽  
Stefania Bonafoni
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Significant Negative Correlation ◽  
Shuttle Radar Topography Mission ◽  
Vital Role ◽  
Snow Cover Area ◽  
Study Region ◽  
Northwest Himalaya ◽  
The Past ◽  
Average Annual Temperature

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

Spatial Distribution of Snow Storage and Snowmelt Dynamics in Central Yenisei Siberia

2021 ◽  
pp. 82-92
Author(s):  
I. V. Danilova ◽  
◽  
A. A. Onuchin ◽  
◽  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Sea Level ◽  
Thermal Regime ◽  
Regression Models ◽  
Weather Station ◽  
Digital Maps ◽  
Independent Variables ◽  
Snow Storage

In this paper the spatial distribution of water reserves in the snow cover and the dynamics of snow cover melting due to the peculiarity of the thermal regime were analyzed for the central part of Yenisei Siberia. To create digital maps of water reserves in the snow cover, regression models were developed. The geographic coordinates, elevation above sea level and the distance from the orographic boundaries were used as independent variables in regression models. Based on the created maps, the dynamics of snow cover melting was obtained in the study area, taking into account the thermal regime at a key weather station.

scholarly journals Characterizing the spatial variations and correlations of large rainstorms for landslide study

2017 ◽  
Vol 21 (9) ◽  
pp. 4573-4589 ◽  
Author(s):  
Liang Gao ◽  
Limin Zhang ◽  
Mengqian Lu
Keyword(s):  
Spatial Distribution ◽  
Hong Kong ◽  
Spatial Correlation ◽  
Hazard Analysis ◽  
Primary Objective ◽  
Landslide Hazard ◽  
Distribution Model ◽  
Hazard Evaluation ◽  
Topographic Effects ◽  
The Impact

Abstract. Rainfall is the primary trigger of landslides in Hong Kong; hence, rainstorm spatial distribution is an important piece of information in landslide hazard analysis. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a rotated ellipsoid trend surface and a random field of residuals. The maximum rolling 4, 12, 24, and 36 h rainfall amounts of these storms are assessed via surface trend fitting, and the spatial correlation of the detrended residuals is determined through studying the scales of fluctuation along eight directions. The principal directions of the surface trend are between 19 and 43°, and the major and minor axis lengths are 83–386 and 55–79 km, respectively. The scales of fluctuation of the residuals are found between 5 and 30 km. The spatial distribution parameters for the three large rainstorms are found to be similar to those for four ordinary rainfall events. The proposed rainfall spatial distribution model and parameters help define the impact area, rainfall intensity and local topographic effects for landslide hazard evaluation in the future.

scholarly journals Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

2021 ◽  
Author(s):  
Rui Zhang ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Chunguang Ban
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Significant Negative Correlation ◽  
Spatial And Temporal Variation ◽  
Snow Cover Area ◽  
Northern Edge ◽  
Slight Upward Trend

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

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