Spatial and Temporal Variations of Snow Cover in the Karoon River Basin, Iran, 2003–2015

abstract: The Karoon River Basin, with an area of about 67,000 km2, is located in the southern part of Iran and has a complex mountainous terrain. No comprehensive study has been done on the spatial and temporal variations of snow cover in this region to date. In this paper, daily snow data of Moderate Resolution Imaging Spectroradiometer MODIS Terra (MOD10A1) and MODIS Aqua (MYD10A1) were examined from 1 January 2003 to 31 December 2015, to analyze snow cover variations. Due to difficulties created by cloud cover effects, it was crucial to reduce cloud contamination in the daily time series. Therefore, two common cloud removal methods were applied on the daily data. The results suggested that in winter nearly 43% of the Basin’s area experienced a negative trend, while only 1.4% of the Basin had a positive trend for snow-covered days (SCD); trends in fall and spring were less evident in the data. Using a digital elevation model of the Basin, the trends of SCD in 100 m elevation intervals were calculated, indicating a significant positive trend in SCD during the fall season above 3500 m

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado

ENSO and light-absorbing impurities and their impact on snow albedo in the Sierra Nevada de Santa Marta, Colombia

Snow albedo is an important variable in the coupled atmosphere-earth system at the global level. Moreover, studying its behavior allows us to know the state of the cryosphere. The Sierra Nevada de Santa Marta (SNSM) is a glacier area and the northernmost tropical (10.82◦ N, 73.75◦ W) region in South America. It has a height of up to 5775 m.a.sl., which is the second highest mountain in the world near the marine coast. We analyzed variations in snow albedo related to snow cover, snowfall, temperature, light-absorbing impurities such as blank carbon (BC), organic carbon (OC) and dust, and El Niño—Southern Oscillation (ENSO) phenomenon through 20 years (2000–2020). We mainly use daily data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua NASA satellites. Results showed through correlations that snow albedo has decreased due to Land Surface Temperature (55%, p < 0.001), a positive phase of ENSO (42%, p < 0.001) and dust (37%, p < 0.01) in the SNSM. Additionally, a dust negative effect was more evident on the southern side (up to 49%, p < 0.001) of the SNSM. Backward trajectories by the NOAA HYSPLIT model suggest that dust sources would be soil erosion in the surrounding region. Results can help recognize the influence of ENSO and dust in the glacier decrease of the SNSM.Fil: Bolaño Ortiz, Tomas Rafael. Universidad Técnica Federico Santa María; Chile. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Diaz Gutiérrez, Viverlys L.. Universidad del Magdalena; ColombiaFil: Camargo Caicedo, Yiniva. Universidad del Magdalena; Colombi

Measurements meet human observations : integrating distinctive ways of knowing in the Pamir Mountains of Tajikistan to assess local climate change

&lt;jats:title&gt;Abstract&lt;/jats:title&gt;&lt;jats:p&gt;In mountain environments dimensions of climate change are unclear because of limited availability of meteorological stations. However, there is a necessity to assess the scope of local climate change, as the livelihood and food systems of subsistence-based communities are already getting impacted. To provide more clarity about local climate trends in the Pamir Mountains of Tajikistan, this study integrates measured climate data with community observations in the villages of Savnob and Roshorv. Taking a transdisciplinary approach, both knowledge systems were considered as equally pertinent and mutually informed the research process. Statistical trends of temperature and snow cover were retrieved using downscaled ERA5 temperature data and the snow cover product MOD10A1. Local knowledge was gathered through community workshops and structured interviews and analysed using a consensus index. Results showed, that local communities perceived increasing temperatures in autumn and winter and decreasing amounts of snow and rain. Instrumental data records indicated an increase in summer temperatures and a shortening of the snow season in Savnob. As both knowledge systems entail their own strengths and limitations, an integrative assessment can broaden the understanding of local climate trends by (i) reducing existing uncertainties, (ii) providing new information, and (iii) introducing unforeseen perspectives. The presented study represents a time-efficient and global applicable approach for assessing local dimensions of climate change in data-deficient regions.&lt;/jats:p&gt

IMPLICATIONS OF MODULATING GLACIERS AND SNOW COVER IN MONGOLIA

Mongolia’s cryosphere (glaciers and snow cover) drives ecosystem services and in turn, supports emerging economies in the water-restricted country. However, as Mongolia experiences long-term drought conditions and an increase in annual air temperatures at twice the global rate, the potential adverse effects of the changing cryosphere during a period of climate uncertainty will have cascading implications to water availability and economic development. Using several data sources and methods, I partitioned my dissertation into two components to determine the hydrologic and economic implications of modulations in Mongolia’s cryosphere. The first component is an examination of glacier recession in Mongolia’s Altai Mountains, where I identified the major drivers of glacier recession and the role of glaciers in the regional hydrology. In the second component we created novel techniques to detect snowmelt events and to determine their role in large annual livestock mortality across Mongolia. In chapter 2 we identified a rate of glacier recession of 6.4 ± 0.4 km2 yr-1 from 1990-2016, resulting in an overall decrease in glacier area of 43%, which were comparable to rates of recession in mountain ranges across Central Asia. In chapter 3 we found that glaciers contributed up to 22% of the regional hydrology in the glaciated Upper Khovd River Basin (UKRB) and glacier melt contributions began to decrease after 2016, suggesting an overall depletion of accumulation zones. In chapter 4, we developed a novel approach to detect snow melt events in Alaska, USA – due to its high satellite coverage, climate monitoring network, and previous existing studies – and produced a gridded geospatial data product. In chapter 5, we expanded on the novel methods developed in chapter 4 to determine the spatio-temporal role of snowmelt events on large annual livestock mortality in Mongolia. Results showed strong correlations between snowmelt events and mortality in the southern Gobi during the fall and the central and western regions during the spring. As Mongolia continues to develop climatically vulnerable economic industries, future modulations in Mongolia’s cryosphere will likely decrease regional water-availability and amplify annual livestock mortality

How are Interannual Variations of Land Surface Phenology in the Highland Pastures of Kyrgyzstan Modulated by Terrain, Snow Cover Seasonality, and Climate Oscillations? An Investigation Using Multi-Source Remote Sensing Data

In the semiarid, continental climates of montane Central Asia, with its constant moisture deficit and low relative humidity, agropastoralism constitutes the foundation of the rural economy. In Kyrgyzstan, an impoverished, landlocked republic in Central Asia, herders of the highlands practice vertical transhumance—the annual movement of livestock to higher elevation pastures to take advantage of seasonally available forage resources. Dependency on pasture resource availability during the short mountain growing season makes herds and herders susceptible to changing weather and climate patterns. This dissertation focuses on using remote sensing observations over the highland pastures in Kyrgyzstan to address five interrelated topics: (i) changes in snow cover and its seasonality from 2002 through 2016; (ii) pasture phenology from the perspective of land surface phenology using multi-scale data from 2001 through 2017; (iii) relationships between snow cover seasonality and subsequent land surface phenology; (iv) terrain effects on the snow-phenology interrelations; and (v) the influence of atmospheric teleconnections on modulating the relationships between snow cover seasonality, growing season duration, and pasture phenology. Results indicate that more territory has been experiencing earlier snow onset than earlier snowmelt, and around equivalent areas with longer and shorter duration of snow seasons. Significant shifts toward earlier snow onset (snowmelt) occurred in western and central (eastern) Kyrgyzstan, and significant duration of the snow season shortening (extension) across western and eastern (northern and southwestern) Kyrgyzstan. Below 3400 m, there was a general trend of significantly earlier snowmelt, and this area of earlier snowmelt was 15 times greater in eastern than western rayons. In terms of land surface phenology, there was a predominant and significant trend of increasing peak greenness, and a significant positive relationship between snow covered dates and modeled peak greenness. While there were more negative correlations between snow cover onset and peak greenness, there were more positive correlations between snowmelt timing and peak greenness, meaning that a longer snow cover season increased the amplitude of peak greenness. The amount of thermal time (measured in accumulated growing degree-days) to reach peak greenness was significantly negatively correlated both with the number of snow covered dates and the snowmelt date. Thus, more snow covered dates translated into fewer growing degree-days accumulated to reach peak greenness in the subsequent growing season. Terrain features influenced the timing of snowmelt more strongly than the number of snow covered dates. Slope was more important than aspect, but the strongest effect appeared from the interaction of aspect and the steepest slopes. The influence of atmospheric teleconnection arising from climate oscillation modes was marginal at the spatial resolutions of this study. Thermal time accumulation could be largely explained with Partial Least Squares (PLS) regression models by elevation and snow cover metrics. However, explanation of peak greenness was less susceptible to terrain and snow cover variables. This research study provides a comprehensive evaluation of the spatial variation of interannual phenology in the highland pastures that serve as the foundation of rural Kyrgyz economy. Finally, it contributes to a better understanding of recent environmental changes in remote highland Central Asia

Effects of Climate Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed

Snow-dominated, karst watersheds present particular challenges to accurately modeling streamflow in response to differing climate conditions. This is due to the uneven distribution of snow within a basin, the varied melting rates due to terrain and climate, and the difficulty determining flow paths through karst conduits below ground. One possible solution to these challenges is to model snow at a fine scale, but climate variables are not available at these smaller spatial scales. The choices about which climate dataset to use, and how to downscale the data to a fine scale, will likely affect the accuracy of streamflow simulations. This comprises the primary goal of the thesis. For this project we simulated fine resolution snow processes using two climate datasets downscaled in two different ways and used the simulated snowmelt to feed a deep learning model that can learn patterns between the simulated snowmelt and observed streamflow to then simulate streamflow. The climate datasets differ significantly and resulted in highly different patterns of snow accumulation and melt. However, the deep learning model was able to learn the patterns with the different datasets and accurately generate streamflow for all climate datasets and downscaling methods