Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model

2009 ◽  
Vol 23 (7) ◽  
pp. 1076-1089 ◽  
Author(s):  
Noah P. Molotch
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Rio Grande ◽  
Remotely Sensed ◽  
Spatially Distributed ◽  
Snow Water ◽  
Snowmelt Model

scholarly journals Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover

2016 ◽  
Vol 20 (1) ◽  
pp. 411-430 ◽  
Author(s):  
E. Cornwell ◽  
N. P. Molotch ◽  
J. McPhee
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Snow Accumulation ◽  
Model Assessment ◽  
Continental Divide ◽  
Average Maximum ◽  
Snow Water ◽  
Andes Cordillera

Abstract. Seasonal snow cover is the primary water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) snow water equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000–5000 m a.s.l. elevation band is significant for snow accumulation, despite having a smaller surface area than the 3000–4000 m a.s.l. band. On average, maximum snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

scholarly journals Climate change impacts on maritime mountain snowpack in the Oregon Cascades

2013 ◽  
Vol 17 (7) ◽  
pp. 2581-2597 ◽  
Author(s):  
E. A. Sproles ◽  
A. W. Nolin ◽  
K. Rittger ◽  
T. H. Painter
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Climate Change Impacts ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Primary Driver ◽  
Snow Water ◽  
Distributed Process ◽  
Oregon Cascades

Abstract. This study investigates the effect of projected temperature increases on maritime mountain snowpack in the McKenzie River Basin (MRB; 3041 km2) in the Cascades Mountains of Oregon, USA. We simulated the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with SnowModel, a spatially-distributed, process-based model (Liston and Elder, 2006b). Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model then evaluated the inter- and intra-year sensitivity of basin wide snowpack to projected temperature increases (2 °C) and variability in precipitation (±10%). Results show that a 2 °C increase in temperature would shift the average date of peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 2000 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. The results of this study are regionally relevant as melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. While this research focused on one watershed, it serves as a case study examining the effects of climate change on maritime snow, which comprises 10% of the Earth's seasonal snow cover.

scholarly journals Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

2021 ◽  
Vol 18 ◽  
pp. 7-20
Author(s):  
Rebecca Gugerli ◽  
Matteo Guidicelli ◽  
Marco Gabella ◽  
Matthias Huss ◽  
Nadine Salzmann
Keyword(s):  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
High Elevation ◽  
High Mountain ◽  
Data Set ◽  
Novel Approach ◽  
Spatially Distributed ◽  
Precipitation Estimates ◽  
Snow Water

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

scholarly journals Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

2017 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier
Keyword(s):  
Machine Learning ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Microwave Brightness Temperature ◽  
Time Estimates ◽  
Covered Area ◽  
Spatially Distributed ◽  
Snow Water ◽  
Feed Forward Neural Networks

Abstract. In many mountains, snowmelt provides most of the runoff. In Afghanistan, few ground-based measurements of the snow resource exist. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance, but reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE early in the snowmelt season, we consider physiographic and remotely-sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the methods can accurately estimate SWE during the snow season in remote mountains.

scholarly journals Spatio-temporal variability of snow water equivalent in the extra-tropical Andes cordillera from a distributed energy balance modeling and remotely sensed snow cover

2015 ◽  
Vol 12 (9) ◽  
pp. 8927-8976
Author(s):  
E. Cornwell ◽  
N. P. Molotch ◽  
J. McPhee
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Water Resource ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Snow Accumulation ◽  
Model Domain ◽  
Continental Divide ◽  
Snow Water ◽  
Andes Cordillera

Abstract. Seasonal snow cover is the primary water resource precursor for human use and environmental sustain along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically-based energy balance model in order to obtain melt-season peak SWE. Estimates show an overall coefficient of determination R2 of 0.61 compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. Average peak SWE increases by nearly 500 mm for every 1000 m in elevation gain for the central and southern sub-regions of the model domain, but this effect is much less pronounced in the northern reaches. The 3000–4000 m a.s.l. elevation band is the most significant accumulation area for most of the northern and central reaches of the domain, although the 4000–5000 m a.s.l. band, despite a smaller contributing area, almost doubles the accumulation amounts estimated for the lower adjacent subdomain. Snow accumulation reaches an earlier peak in the western Andes, and the eastern side of the continental divide shows lower snow accumulation at all elevations except for the southern region represented by the Neuquén River Basin. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

scholarly journals Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

2018 ◽  
Vol 12 (5) ◽  
pp. 1579-1594 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier
Keyword(s):  
Machine Learning ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Microwave Brightness Temperature ◽  
Time Estimates ◽  
Covered Area ◽  
Spatially Distributed ◽  
Snow Water

Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Nash–Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

scholarly journals The Spatiotemporal Patterns and Interrelationships of Snow Cover and Climate Change in Tianshan Mountains

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 404
Author(s):  
Tong Heng ◽  
Xinlin He ◽  
Lili Yang ◽  
Jiawen Yu ◽  
Yulin Yang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Warming Trend ◽  
Tianshan Mountains ◽  
Spatiotemporal Patterns ◽  
Tianshan Mountain ◽  
Mountainous Areas ◽  
The Future ◽  
Snow Water ◽  
Nighttime Warming

To reveal the spatiotemporal patterns of the asymmetry in the Tianshan mountains’ climatic warming, in this study, we analyzed climate and MODIS snow cover data (2001–2019). The change trends of asymmetrical warming, snow depth (SD), snow coverage percentage (SCP), snow cover days (SCD) and snow water equivalent (SWE) in the Tianshan mountains were quantitatively determined, and the influence of asymmetrical warming on the snow cover activity of the Tianshan mountains were discussed. The results showed that the nighttime warming rate (0.10 °C per decade) was greater than the daytime, and that the asymmetrical warming trend may accelerate in the future. The SCP of Tianshan mountain has reduced by 0.9%. This means that for each 0.1 °C increase in temperature, the area of snow cover will reduce by 5.9 km2. About 60% of the region’s daytime warming was positively related to SD and SWE, and about 48% of the region’s nighttime warming was negatively related to SD and SWE. Temperature increases were concentrated mainly in the Pamir Plateau southwest of Tianshan at high altitudes and in the Turpan and Hami basins in the east. In the future, the western and eastern mountainous areas of the Tianshan will continue to show a warming trend, while the central mountainous areas of the Tianshan mountains will mainly show a cooling trend.

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