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

Evaluation of MODIS and VIIRS Cloud-Gap-Filled Snow-Cover Products for Production of an Earth Science Data Record

MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and 5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 sqkm, which is 0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s

The recent developments in cloud removal approaches of MODIS snow cover product

The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.</p

Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia

Abstract Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas

Satellite remote sensing observations of snow cover extent during the melt-out season in the Thompson-Okanagan Region, British Columbia from 2003-2019

Snow is a critical component of the earth’s overall energy budget and it contributes significantly to water resources especially in mountainous regions, coining the term the “water towers” for downstream communities (Viviroli et al., 2006). Studies have shown an increase in snow cover variability due in part by climate change. Most evident throughout the research is an earlier freshet period throughout the northern hemisphere, elevation-dependent warming in mountainous regions and regional climate models indicating transitions from snow to rain dominated basins (Pepin et al., 2015; Rangwala & Miller, 2012). Studies throughout British Columbia have shown evidence of earlier peak runoff from river gauges, a decrease in snow duration and increases in temperature by 1.4ᵒ (Shrestha et al., 2012; Kang et al., 2014; Islam et al., 2017). The Thompson Okanagan region is a semi-arid snow dominated region located in the southern portion of British Columbia (Kang et al., 2014). The spring freshet in Thompson Okanagan is affected by large atmospheric systems as well, including the Pacific North American Pattern (PNA), the Pacific Decadal Oscillation (PDO) and the Oceanic Nino Index (ONI). This research focuses on identifying variations in snow cover during the spring freshet (April 1st-June 30th) in Thompson Okanagan with remote sensing observations from 2003-2019. Snow cover mapping is achieved using visible-infrared observations of snow. High albedo is easily distinguishable in the visible spectrum; however, cloud contamination impedes analysis using visible infrared observations. Steps to mitigate the impact of cloud cover adopted a multi-step methodology. This improved the ability to characterize snow cover extent variability during the spring freshet. The methodology includes: i) a daily combination of Terra/Aqua (from 2003-2012) and VIIRS (from 2012-2019) observations; ii) an adjacent temporal deduction (ATD) technique which replaces cloud pixels with non-cloudy pixels from +/-2 adjacent days; iii) a spatial filter to interpolate snow in cloudy pixels; iv) and the identification of a regional snowline elevation above which cloud-labelled pixels are classified as snow, and cloud pixels below the elevation for no-snow are classified as no-snow. This methodology significantly reduced cloud cover from an average of 71.5% to 1.6% annually. Using stratified random sampling approach, reference points were gathered for a range of elevation bands for four watersheds within the region to test the snow mapping accuracy. The last day of snow (LDS) was extracted for each point from 2003-2019. Large scale atmospheric patterns (Pacific Decadal Oscillation (PDO), Pacific-North American (PNA) teleconnection pattern and Oceanic Nino Index (ONI)) were analyzed using simple and multiple linear regression to assess the variability within the LDS dataset that could be explained by these patterns. This analysis showed that the PNA did not significantly account the variability, but the PDO did with an R2 value reaching 64%, with a significance level of >95%. The simple linear regression models showed that the ONI explained 78% of the LDS variation during the March-April-May (MAM) months, with p>95%; this was more than any other 3-month interval studied. Also, the ONI R2 value decreased as elevation increased. Overall, El Nino years showed snow disappearance of ~23 days earlier than La Nina years at low elevation, ~18 days sooner at mid elevation and ~13 days sooner at high elevations. Earlier snow melt-out during El Nino phases have implications for water resources in the region, for residential and crop use as well as economic impacts for tourism (Westering, 2016; Winkler et al., 2017). This also contributes to area burned in forest fires and rapid melting snow can cause flooding in surrounding urban areas within Thompson Okanagan. Extending the study period into the future could allow further insights on potential effects of climate change within the region

Evolución espaciotemporal de la cobertura de nieve de las cuencas de alta montaña entre los 29°S - 37°S mediante el uso de imágenes satelitales y su influencia en el caudal

Tesis (Geóloga)La cobertura de nieve y hielo en zonas de cordillera representa una información importante en los procesos hidrológicos. Los datos obtenidos desde estaciones terrestres son escasos y pobremente distribuidos, con ello la estimación a partir de observaciones de sensores remotos se ha consolidado como una alternativa atractiva. Determinar la cobertura de nieve es particularmente importante durante primavera y verano en terreno montañoso, en el cual la nieve puede derretirse rápidamente provocando grandes variaciones espaciales en la cubierta de nieve. Este trabajo presenta el estudio y cálculo de la variabilidad espaciotemporal de la cubierta de nieve, en las cuencas de alta montaña entre los 29°S – 37° S durante los años 2003 al 2020 y su asociación con los valores de caudales obtenidos de las estaciones seleccionadas ubicada en el área de estudio. Para determinar la cobertura de nieve se emplearon imágenes satelitales del sensor Moderate Resolution Imaging Spectroradiometer (MODIS) a partir de productos diarios de cobertura de nieve (Snow Cover MOD10A1), todo el procesamiento se llevó a cabo a través de la plataforma online Google Earth Engine (GEE). Para la obtención de datos de caudal se utilizaron los datos de las estaciones fluviométricas de la DGA. Para determinar la relación entre estas dos variables, se utilizó la relación estadística coeficiente de determinación (R2), el cual inicialmente al evaluar estas variables día a día generó valores bajos de R2 (0,0 - 0,4) en la mayoría de las cuencas (desfase 0). Posteriormente, se evaluó la relación a partir de un desfase consecutivo de la variable caudal, este valor comenzó a mejorar y generó un patrón de variación temporal de la relación R2 (valores sobre 0,5). Finalmente, producto del patrón de variación del R2, las cuencas se agruparon en tres grupos, según el día de desfase en el cual presentaran su mayor R2, siendo las cuencas de los extremos su mayor R2 a los 60 días de desfase, las cuencas centrales a los 120 días y una única cuenca RV6 a los 160 días.Snow and ice cover in mountain range areas represents important information in hydrological processes. The data obtained from ground stations are scarce and poorly distributed, thus the estimation from remote sensing observations has established itself as an attractive alternative. Determining snow cover is particularly important during spring and summer in mountainous terrain, where snow can melt rapidly causing large spatial variations in snow cover. This work presents the study and calculation of the spatiotemporal variability of the snow cover, in the high mountain basins between 29 ° S - 37 ° S during the years 2003 to 2020 and its association with the flow values obtained from the stations located in the study area. To determine the snow cover, satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were used from daily snow cover products (Snow Cover MOD10A1), all the processing was carried out through the online platform Google Earth Engine. (CARAMBA). To obtain flow data, it will be used in the data from the DGA fluviometric stations. To determine the relationship between these two variables, the statistical relationship coefficient of determination (R2) was used, which first when evaluating these variables day by day generated low values of R2 (0.0 - 0.4) in most of the watersheds (lag 0). Subsequently, the relationship was evaluated from a consecutive lag of the flow variable, this value began to improve and generated a pattern of temporal variation of the R2 relationship (values above 0.5). Finally, as a result of the R2 variation pattern, the basins were grouped into three groups, according to the day of lag on which they presented their greatest R2, the extreme basins being their highest R2 on day 60 of lag, the central basins at 120 days and a single RV6 basin at 160 days

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Variability of the precipitation and moisture sources of the Tianshan Mountains, Central Asia

Das Tianshan-Gebirge, als „Wasserturm“ Zentralasiens, hat entscheidenden Einfluss auf die Wasserressourcen der Region. Untersuchungen von 1950 bis 2016 zeigen, dass der Jahresniederschlag in den meisten Teilen des Gebirges zunahm, außer im westlichen Tianshan, wo er abnahm. Es gibt hoch- und niedrigfrequente Schwankungen im Niederschlag mit 3-, 6-, 12- und 27-jährigen Quasiperioden. Auf Dekadenskala gab es zwei Trockenperioden (1950–1962, 1973–1984) und zwei Feuchtperioden (1962–1972, 1985–2016). Seit 2004 ist eine kontinuierliche Feuchtezunahme zu verzeichnen. Zusammenhänge wurden zwischen Zirkulationsmustern und dem Niederschlag identifiziert. Das East Atlantic-West Russia (EATL/WRUS)-Muster korreliert positiv mit dem Winter-Niederschlag. Das Scandinavia (SCAND)-Muster beeinflusst den Sommerniederschlag. Das Silk Road-Muster (SRP) war im Zeitraum 1964-1984 relevant. Die Feuchtigkeitsquellen für den Tianshan-Niederschlag stammen zu 93,2% von kontinentalen Quellen und nur begrenzt aus dem Ozean. Zentralasien ist die Hauptfeuchtequelle für das Gebirge. Im westlichen Tianshan kommt die Feuchtigkeit hauptsächlich von Zentralasien von April bis Oktober und von Westasien von November bis März. Im östlichen Tianshan tragen Ost- und Südasien sowie Sibirien konstant zur Feuchtigkeit im Sommer bei. Der Beitrag der Feuchtigkeit aus dem Nordatlantik zum Sommerniederschlag im nördlichen, zentralen und östlichen Tianshan zeigt einen abnehmenden Trend, obwohl dieser Beitrag ohnehin begrenzt ist. In Monaten mit extremem Winterniederschlag stammt die größte Zunahme der Feuchtigkeit im westlichen Tianshan aus Westasien, während Europa einen wichtigen Beitrag zu den extremen Winterniederschlägen im nördlichen Tianshan leistet. Im östlichen Tianshan ist die Feuchtigkeitszufuhr aus Ost- und Südasien sowie aus Sibirien während der extremen Niederschlagsmonate sowohl im Winter als auch im Sommer erhöht.The Tianshan Mountains, the "water tower" of Central Asia, are crucial water sources. Precipitation variability and water vapor transport impact water distribution. The study assessed 1950-2016 precipitation using Mann-Kendall tests and EEMD on GPCC data. Multi-timescale precipitation variations were analyzed with NCEP/NCAR reanalysis, and moisture sources during 1979–2017 with ERA–Interim data. Most of Tianshan had increasing annual precipitation, except Western Tianshan, which experienced a downtrend. Precipitation exhibited 3- and 6-year cycles and 12- and 27-year cycles. On the decadal scale, two dry and two wet periods occurred, with continuous humidification since 2004. A significant positive correlation was found between East Atlantic-West Russia EATL/WRUS circulation pattern and winter precipitation. SCAND influenced Tianshan's summer precipitation, with a wet period after 1988 due to enhanced water vapor flux. SCAND and EAP strengthened water vapor fluxes to Tianshan. SRP impacted Tianshan's summer precipitation during 1964–1984. About 93.2% of Tianshan's moisture comes from continental sources. Central Asia dominates moisture supply. Western Tianshan receives moisture mainly from Central Asia (April to October) and Western Asia (November to March). Almost 13.0% of Eastern Tianshan's summer moisture originates from East and South Asia and Siberia, with steady contributions. Moisture from the North Atlantic Ocean to summer precipitation in Northern, Central, and Eastern Tianshan shows a decreasing trend, but limited overall contribution. Extreme winter precipitation in Western Tianshan is linked to moisture from West Asia. Europe significantly contributes to extreme winter precipitation in Northern Tianshan. Eastern Tianshan sees enhanced moisture from East and South Asia and Siberia during extreme precipitation months in winter and summer