Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of \u3c0 \u3e°C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisi

Changing Snow Seasonality in the Highlands of Kyrgyzstan

Few studies have examined changing snow seasonality in Central Asia. Here, we analyzed changes in the seasonality of snow cover across Kyrgyzstan (KGZ) over 14 years from 2002/03–2015/16 using the most recent version (v006) of MODIS Terra and Aqua 8 day snow cover composites (MOD10A2/MYD10A2). We focused on three metrics of snow seasonality—first date of snow, last date of snow, and duration of snow season—and used nonparametric trends tests to assess the significance and direction of trends. We evaluated trends at three administration scales and across elevation. We used two techniques to assure that our identification of significant trends was not resulting from random spatial variation. First, we report only significant trends (positive or negative) that are at least twice as prevalent as the converse trends. Second, we use a two-stage analysis at the national scale to identify asymmetric directional changes in snow seasonality. Results show that more territory has been experiencing earlier onset of snow than earlier snowmelt, and roughly equivalent areas have been experiencing longer and shorter duration of snow seasons in the past 14 years. The changes are not uniform across KGZ, with significant shifts toward earlier snow arrival in western and central KGZ and significant shifts toward earlier snowmelt in eastern KGZ. The duration of the snow season has significantly shortened in western and eastern KGZ and significantly lengthened in northern and southwestern KGZ. Duration is significantly longer where the snow onset was significantly earlier or the snowmelt significantly later. There is a general trend of significantly earlier snowmelt below 3400 m and the area of earlier snowmelt is 15 times greater in eastern than western districts. Significant trends in the Aqua product were less prevalent than in the Terra product, but the general trend toward earlier snowmelt was also evident in Aqua data

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

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

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Chapter 8 The Status and Role of the alpine Cryosphere in Central Asia

The alpine cryosphere including snow, glaciers and permafrost are critical to water management in the Aral Sea Basin (ASB) and larger Central Asia (CA) under changing climate: as they store large amounts of water in its solid forms. Most cryospheric components in the Aral Sea Basin are close to melting point, and hence very vulnerable to a slight increase in air temperature with significant consequences to long-term water availability and to water resources variability and extremes. Current knowledge about different components of cryosphere and their connection to climate in the Basin and in the entire Central Asia, varies. While it is advanced in the topics of snow and glaciers, knowledge on permafrost it rather limited. Observed trends in runoff point in the direction of increasing water availability in July and August at least until mid-century and increasing possibility for water storage in reservoirs and aquifers. However, eventually this will change as glaciers waste away. Future runoff may change considerably after mid-century and start to decline if not compensated by increasing precipitation. Cryosphere monitoring systems are the basis for sound estimates of water availability and water-related hazards associated with snow, glaciers and permafrost. They require a well-distributed observational network for all cryospheric variables. Such systems need to be re-established in the Basin after the breakup of the Soviet Union in the early 1990s. This process is slowly emerging in the region. Collaboration between local operational hydro-meteorological services and academic sector, and with international research networks may improving the observing capabilities in high mountain regions of CA Asia in general and the ASB specifically

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

<jats:title>Abstract</jats:title><jats:p>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.</jats:p&gt

Debris-covered glacier systems and associated glacial lake outburst flood hazards:Challenges and prospects

Glaciers respond sensitively to climate variability and change, with associated impacts on meltwater production, sea-level rise and geomorphological hazards. There is a strong societal interest in understanding the current response of all types of glacier systems to climate change and how they will continue to evolve in the context of the whole glacierized landscape. In particular, understanding the current and future behaviour of debris-covered glaciers is a 'hot topic' in glaciological research because of concerns for water resources and glacier-related hazards. The state of these glaciers is closely related to various hazardous geomorphological processes which are relatively poorly understood. Understanding the implications of debris-covered glacier evolution requires a systems approach. This includes the interplay of various factors such as local geomorphology, ice ablation patterns, debris characteristics and glacier lake growth and development. Such a broader, contextualized understanding is prerequisite to identifying and monitoring the geohazards and hydrologic implications associated with changes in the debris-covered glacier system under future climate scenarios. This paper presents a comprehensive review of current knowledge of the debris-covered glacier landsystem. Specifically, we review state-of-the-art field-based and the remote sensing-based methods for monitoring debris-covered glacier characteristics and lakes and their evolution under future climate change. We advocate a holistic process-based framework for assessing hazards associated with moraine-dammed glacio-terminal lakes that are a projected end-member state for many debris-covered glaciers under a warming climat