scholarly journals Seasonal Evolution of Supraglacial Lakes on Baltoro Glacier From 2016 to 2020

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna Wendleder ◽  
Andreas Schmitt ◽  
Thilo Erbertseder ◽  
Pablo D’Angelo ◽  
Christoph Mayer ◽  
...  
Keyword(s):  
Time Series ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Lake Area ◽  
Reanalysis Dataset ◽  
Ice Dynamics ◽  
Seasonal Evolution ◽  
Supraglacial Lakes ◽  
Lake Evolution ◽  
Observation Period

The existence of supraglacial lakes influences debris-covered glaciers in two ways. The absorption of solar radiation in the water leads to a higher ice ablation, and water draining through the glacier to its bed leads to a higher velocity. Rising air temperatures and changes in precipitation patterns provoke an increase in the supraglacial lakes in number and total area. However, the seasonal evolution of supraglacial lakes and thus their potential for influencing mass balance and ice dynamics have not yet been sufficiently analyzed. We present a summertime series of supraglacial lake evolution on Baltoro Glacier in the Karakoram from 2016 to 2020. The dense time series is enabled by a multi-sensor and multi-temporal approach based on optical (Sentinel-2 and PlanetScope) and Synthetic Aperture Radar (SAR; Sentinel-1 and TerraSAR-X) remote sensing data. The mapping of the seasonal lake evolution uses a semi-automatic approach, which includes a random forest classifier applied separately to each sensor. A combination of linear regression and the Hausdorff distance is used to harmonize between SAR- and optical-derived lake areas, producing consistent and internally robust time series dynamics. Seasonal variations in the lake area are linked with the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) based on air temperature and precipitation data derived from the climate reanalysis dataset ERA5-Land. The largest aggregated lake area was found in 2018 with 5.783 km2, followed by 2019 with 4.703 km2, and 2020 with 4.606 km2. The years 2016 and 2017 showed the smallest areas with 3.606 and 3.653 km2, respectively. Our data suggest that warmer spring seasons (April–May) with higher precipitation rates lead to increased formation of supraglacial lakes. The time series decomposition shows a linear increase in the lake area of 11.12 ± 9.57% per year. Although the five-year observation period is too short to derive a significant trend, the tendency for a possible increase in the supraglacial lake area is in line with the pronounced positive anomalies of the SPI and STI during the observation period.

scholarly journals Modelling channelized surface drainage of supraglacial lakes

2015 ◽  
Vol 61 (225) ◽  
pp. 185-199 ◽  
Author(s):  
J. Kingslake ◽  
F. Ng ◽  
A. Sole
Keyword(s):  
Surface Water ◽  
Ice Sheets ◽  
Frictional Heat ◽  
Lake Area ◽  
Ice Dynamics ◽  
Surface Drainage ◽  
Supraglacial Lakes ◽  
Lake Model ◽  
Lake Drainage ◽  
Area Expansion

AbstractSupraglacial lakes can drain to the bed of ice sheets, affecting ice dynamics, or over their surface, relocating surface water. Focusing on surface drainage, we first discuss observations of lake drainage. In particular, for the first time, lakes are observed to drain >70 km across the Nivlisen ice shelf, East Antarctica. Inspired by these observations, we develop a model of lake drainage through a channel that incises into an ice-sheet surface by frictional heat dissipated in the flow. Modelled lake drainage can be stable or unstable. During stable drainage, the rate of lake-level drawdown exceeds the rate of channel incision, so discharge from the lake decreases with time; this can prevent the lake from emptying completely. During unstable drainage, discharge grows unstably with time and always empties the lake. Model lakes are more prone to drain unstably when the initial lake area, the lake input and the channel slope are larger. These parameters will vary during atmospheric-warming-induced ablation-area expansion, hence the mechanisms revealed by our analysis can influence the dynamic response of ice sheets to warming through their impact on surface-water routing and storage.

scholarly journals GLACIAL LAKE EVOLUTION BASED ON REMOTE SENSING TIME SERIES: A CASE STUDY OF TSHO ROLPA IN NEPAL

2020 ◽  
Vol V-3-2020 ◽  
pp. 633-639
Author(s):  
M. V. Peppa ◽  
S. B. Maharjan ◽  
S. P. Joshi ◽  
W. Xiao ◽  
J. P. Mills
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Glacial Lake ◽  
Lake Area ◽  
Glacial Lakes ◽  
Glacier Terminus ◽  
Lake Evolution ◽  
Frequency Monitoring ◽  
Downstream Communities ◽  
Sensing Time

Abstract. Himalayan glaciers have retreated rapidly in recent years. Resultant glacial lakes in the region pose potential catastrophic threats to downstream communities, especially under a changing climate. The potential for Glacial Lake Outburst Floods (GLOFs) has increased and studies have assessed the risks of those in Nepal and prioritised several glacial lakes for urgent and closer investigation. The risk posed by the Tsho Rolpa Glacial Lake is one of the most serious in Nepal. To investigate the feasibility of high-frequency monitoring of glacial lake evolution by remote sensing, this paper proposes a workflow for automated glacial lake boundary extraction and evolution using a time series of Sentinel optical imagery. The waterbody is segmented and vectorised using bimodal histograms from water indices. The vectorised lake boundary is validated against reference data extracted from rigorous contemporary unmanned aerial vehicle (UAV)-based photogrammetric survey. Lake boundaries were subsequently extracted at four different epochs to evaluate the evolution of the lake, especially at the glacier terminus. The final lake area was estimated at 1.61 km2, significantly larger than the areal extent last formally reported. A 0.99 m/day maximum, and a 0.45 m/day average, horizontal glacier retreat rates were estimated. The reported research has demonstrated the potential of remote sensing time series to monitor glacial lake evolution, which is particularly important for lakes in remote mountain regions that are otherwise difficult to access.

scholarly journals Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series

2021 ◽  
Vol 13 (2) ◽  
pp. 205
Author(s):  
Philipp Hochreuther ◽  
Niklas Neckel ◽  
Nathalie Reimann ◽  
Angelika Humbert ◽  
Matthias Braun
Keyword(s):  
Time Series ◽  
Observation Interval ◽  
Lake Area ◽  
Climate Data ◽  
Melt Pond ◽  
Automated Processing ◽  
Average Observation ◽  
Average Size ◽  
Lake Size ◽  
Sentinel 2

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.

scholarly journals Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7403
Author(s):  
Pavel P Fil ◽  
Alla Yu Yurova ◽  
Alexey Dobrokhotov ◽  
Daniil Kozlov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Shallow Groundwater ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Water Infiltration ◽  
Temporary Ponds ◽  
Small Water ◽  
Negative Exponential ◽  
Topographic Depressions

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

scholarly journals Monitoring Changes to Arctic Vegetation and Glaciers at Ny-Ålesund, Svalbard, Based on Time Series Remote Sensing

2021 ◽  
Vol 13 (19) ◽  
pp. 3845
Author(s):  
Guangbo Ren ◽  
Jianbu Wang ◽  
Yunfei Lu ◽  
Peiqiang Wu ◽  
Xiaoqing Lu ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
High Latitude ◽  
Global Climate ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Coastal Regions ◽  
Average Annual Temperature

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

scholarly journals PENGARUH UPAH, INVESTASI, DAN PERTUMBUHAN EKONOMI TERHADAP PERMINTAAN TENAGA KERJA DI KABUPATEN/KOTA SUMATERA BARAT

Jurnal Ecogen ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 553
Author(s):  
Putri Maya ◽  
Yulhendri Yulhendri
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Coefficient ◽  
Labor Demand ◽  
Secondary Data ◽  
Regression Method ◽  
Dominant Factor ◽  
Panel Regression ◽  
West Sumatra ◽  
Observation Period

Abstract:  One indicator used to measure economic development is employment. The huge population growth each year will certainly have an impact on increasing the number of the workforce and will certainly give meaning that the number of people looking for work will increase, along with that the workforce will also increase. This study aims to analyze the effect of wages, investment and economic growth on labor demand in West Sumatra. This study uses secondary data in the form of time series with an observation period of 2013-2017. Data obtained from the Central Statistics Agency (BPS), data analysis using the panel regression method with the program eviews. The results of this study include wages that negatively and significantly affect labor demand in West Sumatra. While investment and economic growth have a positive and significant effect on labor demand in West Sumatra. And the most dominant factor influencing labor demand in West Sumatra is the Economic Growth factor where the factor has the greatest regression coefficient among other factorsKeywords: labor, wages, investment, economic growth

scholarly journals Characterizing ecosystem-atmosphere interactions from short to interannual time scales

2007 ◽  
Vol 4 (3) ◽  
pp. 1405-1435
Author(s):  
M. D. Mahecha ◽  
M. Reichstein ◽  
H. Lange ◽  
N. Carvalhais ◽  
C. Bernhofer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
System Analysis ◽  
Singular System ◽  
Low Frequency ◽  
Data Adaptive ◽  
Effective Signal ◽  
Observation Space ◽  
Different Time Scales ◽  
Observation Period

Abstract. Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series are now partly comprising an observation period of one decade. In this study, we explored whether the observation period is already sufficient to detect cross relationships of the variables beyond the annual cycle as they are expected from comparable studies in climatology. We explored the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (NEE, GPP, Reco, VPD, LE, H, u, P) were influenced significantly by low frequency components (interannual variability). Furthermore we extracted a set of nonlinear relationships and found clear annual hysteresis effects except for the NEE-Rg relationship which turned out to be the sole linear relationship in the observation space. SSA provides a new tool to investigate these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing since it can be applied as novel gap filling approach relying on the temporal time series structure only.

scholarly journals Space-time variability of hydrological drought and wetness in Iran using NCEP/NCAR and GPCC datasets

2010 ◽  
Vol 14 (10) ◽  
pp. 1919-1930 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Reanalysis Data ◽  
Space Time ◽  
Hydrological Drought ◽  
Time Variability ◽  
Climate Conditions ◽  
Good Agreement

Abstract. Space-time variability of hydrological drought and wetness over Iran is investigated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the Global Precipitation Climatology Centre (GPCC) dataset for the common period 1948–2007. The aim is to complement previous studies on the detection of long-term trends in drought/wetness time series and on the applicability of reanalysis data for drought monitoring in Iran. Climate conditions of the area are assessed through the Standardized Precipitation Index (SPI) on 24-month time scale, while Principal Component Analysis (PCA) and Varimax rotation are used for investigating drought/wetness variability, and drought regionalization, respectively. Singular Spectrum Analysis (SSA) is applied to the time series of interest to extract the leading nonlinear components and compare them with linear fittings. Differences in drought and wetness area coverage resulting from the two datasets are discussed also in relation to the change occurred in recent years. NCEP/NCAR and GPCC are in good agreement in identifying four sub-regions as principal spatial modes of drought variability. However, the climate variability in each area is not univocally represented by the two datasets: a good agreement is found for south-eastern and north-western regions, while noticeable discrepancies occur for central and Caspian sea regions. A comparison with NCEP Reanalysis II for the period 1979–2007, seems to exclude that the discrepancies are merely due to the introduction of satellite data into the reanalysis assimilation scheme.

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