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Climate Change, Environment

Cloudburst in Arunachal; tourists stranded in flash flood, road closed

 

A calamitous cloudburst leading to massive rainfall and flash flood has made disaster in destrying many houses, bridges and roads in Tenga, Arunachal Pradesh.

Several hundred people were reported to be stranded while many others were missing in the flash flood which left a trail of devastation at Kaspi Nala near Nag-Mandir Tenga in West Kameng District of Arunachal Pradesh on Monday evening.

An RCC Bridge between Kaspi and Nagmandir has been washed away by floodwater.

The Army and paramilitary forces along with disaster management authorities have been deployed to rescue the victims.

Meanwhile, the West Kameng district administration has closed the Bhalukpong to Tawang  road.

The cloudburst triggered the flash flood on the evening of Monday, damaging over four houses, one boys’ hostel and one hilly restaurant along with several vehicles and motorcycles, according to tourists witnessed.

Earlier in the month of April, Bomdila, the headquarters of West Kameng district experienced cloudburst causing widespread damages to the places in proximity of the township.

The cloudburst was followed by torrential rain and hailstorm which created havoc in the township. According to Chandan Kumar Duarah, a science journalist says the cloudbust and flash flood attributed to massive deforestation, soil cutting in the region and climate change.

The rain lashed the district headquarters for over an hour resulting in chocking of drains and spread of debris all around.

At least 800 people were reported to be stranded while many others were missing in the flash flood which left a trail of devastation at Tenga in West Kameng District of Arunachal Pradesh on Monday evening.

The Army and paramilitary forces along with disaster management authorities have been deployed to rescue the victims.

The cloudburst was followed by torrential rain and hailstorm which created havoc in the township.

The rain lashed the district headquarters for over an hour resulting in chocking of drains and spread of debris all around.

Climate Change, International

European heat wave sees Germany put speed limits on autobahn

Large parts of western and central Europe sweated under blazing temperatures on June 26, with authorities in one German region imposing temporary speed limits on some stretches of the autobahn, the federal controlled-access higyway system designed for high-speed vehicular traffic, as a precaution against heat damage.

Authorities have warned that temperatures could top 40 degrees Celsius (104 Fahrenheit) in parts of the continent over the coming days as a plume of dry, hot air moves north from Africa.

The transport ministry in Germany’s eastern Saxony-Anhalt state said it has imposed speed limits of 100 kmph or 120 kmph on several short stretches of the highway until further notice. Those stretches usually have no speed limit.

On the evening of June 25, German railway operator Deutsche Bahn called rescue services to Duesseldorf Airport station as a precaution because two trains’ air conditioning systems weren’t working properly, but neither had to be evacuated.

In Paris, authorities banned older cars from the city for the day as a heat wave aggravates the city’s pollution.

Regional authorities estimate these measures affect nearly 60% of vehicles circulating in the Paris region, including many delivery trucks and older cars with higher emissions than newer models. Violators face fines.

Around France, some schools have been closed because of the high temperatures, which are expected to go up to 39 degrees Celsius (102 Fahrenheit) in the Paris area later this week and bake much of the country, from the Pyrenees in the southwest to the German border in the northeast.

Such temperatures are rare in France, where most homes and many buildings do not have air conditioning.

French charities and local officials are providing extra help for the elderly, the homeless and the sick this week, remembering that some 15,000 people, many of them elderly, died in France during a 2003 heat wave.

Prime Minister Edouard Philippe cited the heat wave as evidence of climate destabilization and vowed to step up the government’s fight against climate change.

About half of Spain’s provinces are on alert for high temperatures, which are expected to rise as the weekend approaches.

The northeastern city of Zaragoza was forecast to be the hottest on Wednesday at 39 degrees Celsius, building to 44 degrees Celsius on Saturday, according to the government weather agency AEMET.

In southwestern Europe, however, some people had other reasons to complain during their summer vacation- the Portuguese capital Lisbon, on Europe’s Atlantic coast, awoke cloudy and wet on Wednesday.                           AP

Climate Change

Acceleration of ice loss across the Himalayas over the past 40 years

Himalayan glaciers supply meltwater to densely populated catchments in South Asia, and regional observations of glacier change over multiple decades are needed to understand climate drivers and assess resulting impacts on glacier-fed rivers. Here, we quantify changes in ice thickness during the intervals 1975–2000 and 2000–2016 across the Himalayas, using a set of digital elevation models derived from cold war–era spy satellite film and modern stereo satellite imagery. We observe consistent ice loss along the entire 2000-km transect for both intervals and find a doubling of the average loss rate during 2000–2016 [−0.43 ± 0.14 m w.e. year−1 (meters of water equivalent per year)] compared to 1975–2000 (−0.22 ± 0.13 m w.e. year−1). The similar magnitude and acceleration of ice loss across the Himalayas suggests a regionally coherent climate forcing, consistent with atmospheric warming and associated energy fluxes as the dominant drivers of glacier change.

INTRODUCTION

The Intergovernmental Panel on Climate Change 5th Assessment Report estimates that mass loss from glaciers contributed more to sea-level rise than the ice sheets during 1993–2010 (0.86 mm year−1 versus 0.60 mm year−1, respectively), yet uncertainties for the glacier contribution are three times greater (1). Glaciers also contribute locally to water resources in many regions and serve as hydrological buffers vital for ecology, agriculture, and hydropower, particularly in High Mountain Asia (HMA), which includes all mountain ranges surrounding the Tibetan Plateau (23). Shrinking Himalayan glaciers pose challenges to societies and policy-makers regarding issues such as changing glacier melt contributions to seasonal runoff, especially in climatically drier western regions (3), and increasing risk of outburst floods due to expansion of unstable proglacial lakes (4). Yet, substantial gaps in knowledge persist regarding rates of ice loss, hydrological responses, and associated climate drivers in HMA (2).

Mountain glaciers are known to respond dynamically to a variety of drivers on different time scales, with faster response times than the large ice sheets (56). In the Himalayas, in situ studies document significant interannual variability of mass balances (79) and relatively slower melt rates on debris-covered glacier tongues over interannual time scales (1011). Yet, the overall effects of surface debris cover are uncertain, as many satellite observations suggest similar ice losses relative to clean-ice glaciers over similar or longer periods (1213). Because of the complex monsoon climate in the Himalayas, dominant causes of recent glacier changes remain controversial, although atmospheric warming, the albedo effect due to deposition of anthropogenic black carbon (BC) on snow and ice, and precipitation changes have been suggested as important drivers (1416).

Model projections of future Himalayan ice loss and resulting impacts require robust observations of glacier response to past and ongoing climate change. Recent satellite remote sensing studies have made substantial advances with improved spatial coverage and resolution to quantify ice mass changes during 2000–2016 (121718), and former records extending back to the 1970s have been presented for several areas using declassified spy satellite imagery (131922). These long-term records are especially critical for extracting robust mass balance signals from the noise of interannual variability (6). Many studies also report the highly heterogeneous behavior of glaciers in localized regions, with some glaciers exhibiting faster rates of ice loss during the 21st century (2022). Independent analyses document simultaneously increasing atmospheric temperatures at high-elevation stations in HMA (2326). To robustly quantify the regional sensitivity of these glaciers to climate change, a reliable Himalaya-wide record of ice loss extending back several decades is needed.

Here, we provide an internally consistent dataset of glacier mass change across the Himalayan range over approximately the past 40 years. We use recent advances in digital elevation model (DEM) extraction methods from declassified KH-9 Hexagon film (27) and ASTER stereo imagery to quantify ice loss trends for 650 of the largest glaciers during 1975–2000 and 2000–2016. All aspects of the analysis presented here only use glaciers with data available during both time intervals unless specified otherwise. We investigate glaciers along a 2000-km transect from Spiti Lahaul to Bhutan (75°E to 93°E), which includes glaciers that accumulate snow primarily during winter (western Himalayas) and during the summer monsoon (eastern Himalayas), but excludes complications of surging glaciers in the Karakoram and Kunlun regions where many glaciers appear to be anomalously stable or advancing (2). Our compilation includes glaciers comprising approximately 34% of the total glacierized area in the region, which represents roughly 55% of the total ice volume based on recent ice thickness estimates (1528). This diverse dataset adequately captures the statistical distribution of large (>3 km2) glaciers, thus providing the first spatially robust analysis of glacier change spanning four decades in the Himalayas. We extract DEMs from declassified KH-9 Hexagon images for the 650 glaciers, compile a corresponding set of modern ASTER DEMs, fit a robust linear regression through every 30-m pixel of the time series of elevations, sum the resulting elevation changes for each glacier, divide by the corresponding areas, and translate the volume changes to mass using a density conversion factor of 850 ± 60 kg m−3(see Materials and Methods).

RESULTS

Glacier mass changes

Our results indicate that glaciers across the Himalayas experienced significant ice loss over the past 40 years, with the average rate of ice loss twice as rapid in the 21st century compared to the end of the 20th century (Fig. 1). We calculate a regional average geodetic mass balance of −0.43 ± 0.14 m w.e. year−1 (meters of water equivalent per year) during 2000–2016, compared to −0.22 ± 0.13 m w.e. year−1 during 1975–2000 (−0.31 ± 0.13 m w.e. year−1 for the full 1975–2016 interval) (see Materials and Methods). A 30-glacier moving average shows a quasi-consistent trend across the 2000-km longitudinal transect during both time intervals (Fig. 1), and subregions have similar means and distributions of glacier mass balance. Some central catchments deviate somewhat from the Himalaya-wide mean during 2000–2016 (by approximately 0.1 to 0.2 m w.e. year−1) in the Uttarakhand (~79.0° to 80.0°E), the Gandaki catchment (~83.5° to 84.5°E), and the Karnali catchment (~81° to 83°E), which has fewer larger glaciers and relatively incomplete data coverage. Similar to previous in situ and satellite-based studies (1829), we observe considerable variation among individual glacier mass balances, with area-weighted SDs of 0.1 and 0.2 m w.e. year−1during each respective interval for the 650 glaciers. This variability most likely reflects different glacier characteristics such as sizes of accumulation zones relative to ablation zones, topographic shading, and amounts of debris cover. Yet, we find that, in our survey (using a rough average of 60 glaciers per 7000-km2 subregion), local variations tend to average out and mean values are similar across most catchments.

Fig. 1Map of glacier locations and geodetic mass balances for the 650 glaciers.Circle sizes are proportional to glacier areas, and colors delineate clean-ice, debris-covered, and lake-terminating categories. Insets indicate ice loss, quantified as geodetic mass balances (m w.e. year−1) plotted for individual glaciers along a longitudinal transect during 1975–2000 and 2000–2016. Both inset plots are horizontally aligned with the map view. Gray error bars are 1σ uncertainty, and the yellow trend is the (area-weighted) moving-window mean, using a window size of 30 glaciers.

Contrasting distributions of glacier mass balances are evident when comparing between time intervals. The 1975–2000 distribution has a negative tail extending to −0.6 m w.e. year−1, while the 2000–2016 distribution is more negative, extending to −1.1 m w.e. year−1 (Fig. 2A). During the more recent interval, glaciers are losing ice twice as fast on average (Fig. 2B), though this varies somewhat between subregions. For example, we find that the average rate of ice loss has increased by a factor of 3 in the Spiti Lahaul region, and by a factor of 1.4 in West Nepal. We also compile altitudinal distributions of ice thickness change for the glaciers and create a Himalaya-wide average thickness change profile versus elevation (Fig. 2, C and D). These distributed thinning profiles are a function of both thinning by mass loss and of dynamic thinning due to ice flow. We find that the 2000–2016 thinning rate (m year−1) profile is considerably steeper, which is likely caused by a combination of faster mass loss and widespread slowing of ice velocities during the 21st century (230).

Fig. 2Comparison of ice losses between 1975–2000 and 2000–2016 for the 650 glaciers.(A) Histograms of individual glacier geodetic mass balances (m w.e. year−1) during 1975–2000 (mean = −0.21, SD = 0.15) and 2000–2016 (mean = −0.41, SD = 0.24). Shaded regions behind the histograms are fitted normal distributions. (B) Result of dividing the modern (2000–2016) mass balances by the historical (1975–2000) mass balances for each glacier, showing the resulting distribution of the mass balance change (ratio) between the two intervals (mean = 2.01, SD = 1.36). In this case, the shaded region is a fitted kernel distribution. (C) Altitudinal distributions of ice thickness change (m year−1) separated into 50-m elevation bins during the two intervals. (D) Normalized altitudinal distributions of ice thickness change. Normalized elevations are defined as (z − z2.5)/(z97.5 − z2.5), where z is elevation and subscripts indicate elevation percentiles. This scales all glaciers by their elevation range (i.e., after scaling, glacier termini = 0 and headwalls = 1), allowing for more consistent comparison of ice thickness changes across glaciers with different elevation ranges. Note the abrupt inflection point in the 2000–2016 profile at ~0.1; this is likely due to retreating glacier termini. Shaded regions in the altitudinal distributions indicate the SEM estimated as σz/nz−−√, where σz is the SD of the thinning rate for each 50-m elevation bin and nz is the number of independent measurements when accounting for spatial autocorrelation (see Materials and Methods).

We multiply geodetic mass balances by the full glacierized area in the Himalayas between 75° and 93° longitude to estimate region-wide ice mass changes of −7.5 ± 2.3 Gt year−1 during 2000–2016, compared to −3.9 ± 2.2 Gt year−1 during 1975–2000 (−5.2 ± 2.2 Gt year−1 during the full 1975–2016 interval). Recent models using Shuttle Radar Topography Mission (SRTM) elevation data for ice thickness inversion estimate the total glacial ice mass in our region of study to be approximately 700 Gt in the year 2000 (see Materials and Methods) (1528). If this estimate is accurate, our observed annual mass losses suggest that of the total ice mass present in 1975, about 87% remained in 2000 and 72% remained in 2016.

Comparison of clean-ice, debris-covered, and lake-terminating glaciers

We study mass changes for different glacier types by separating glaciers into clean-ice (<33% area covered by debris), debris-covered (≥33% area covered by debris), and lake-terminating categories based on a Landsat band ratio threshold and manual delineation of proglacial lakes (see Materials and Methods). All three categories have undergone a similar acceleration of ice loss (Table 1), and debris-covered glaciers exhibit similar and often more negative geodetic mass balances compared to clean-ice glaciers over the past 40 years (Fig. 3). Altitudinal distributions indicate slower thinning for lower-elevation regions of debris-covered glaciers (glacier tongues where debris is most concentrated) relative to clean-ice glaciers, but comparatively faster thinning in mid- to upper elevations (Fig. 4). Lake-terminating glaciers concentrated in the eastern Himalayas exhibit the most negative mass balances due to thermal undercutting and calving (31), though they only comprise around 5 to 6% of the estimated total Himalaya-wide mass loss during both intervals.Table 1Himalaya-wide geodetic mass balances (m w.e. year−1).View this table:

Fig. 3Comparison between clean-ice (<33% debris-covered area) and debris-covered (≥33% debris-covered area) glaciers for seven subregions.Circle sizes are proportional to glacier areas, colors delineate clean-ice versus debris-covered categories, and boxplots indicate geodetic mass balance (m w.e. year−1). Box center marks (red lines) are medians; box bottom and top edges indicate the 25th and 75th percentiles, respectively; whiskers indicate q75 + 1.5 ⋅ (q75 − q25) and q25 − 1.5 ⋅ (q75 − q25), where subscripts indicate percentiles and “+” symbols are outliers.
Fig. 4Altitudinal distributions of ice thickness change (m year−1) for the 650 glaciers.Glaciers are separated by time interval (top) and category (<33% versus ≥33% debris-covered area) (bottom). (A) Altitudinal distributions of ice thickness change for clean-ice glaciers during 1975–2000 and 2000–2016. The y axes are normalized elevation as in Fig. 2. (B) Same as (A), but for debris-covered glaciers. (C) Altitudinal distributions of ice thickness change during 1975–2000 for clean-ice and debris-covered glaciers. (D) Same as (C), but for 2000–2016. (E) Altitudinal distributions of glacierized area for both glacier categories. Elevational extent of debris cover varies widely between individual glaciers, but is mostly concentrated in lower ablation zones. The clean-ice category includes 478 glaciers and the debris-covered category includes 124 glaciers.

Approximation of required temperature change

As a first approximation of the consistency between observed glacier mass balances and available temperature records, we estimate the energy required to melt the observed ice losses and conservatively estimate the atmospheric temperature change that would supply this energy via longwave radiation to the glaciers, using a simple energy balance approach (Materials and Methods). We propagate significant uncertainties associated with input from global climate reanalysis data, scaling of temperatures from coarse reanalysis grids to specific glacier elevations, and averaging of climate data over the glacierized region. Results suggest that the observed acceleration of ice loss can be explained by an average temperature ranging from 0.4° to 1.4°C warmer during 2000–2016, relative to the 1975–2000 average. This approximately agrees with the magnitude of warming observed by meteorological stations located throughout HMA, which have recorded air temperatures around 1°C warmer on average during 2000–2016, relative to 1975–2000 (Fig. 5). More comprehensive climate observations and models will be essential for further investigation, but these simple energy constraints suggest that the acceleration of mass loss in the Himalayas is consistent with warming temperatures recorded by meteorological stations in the region.

Fig. 5Compilation of previously published instrumental temperature records in HMA.(A) Regional temperature anomalies, relative to the 1980–2009 mean temperatures for each record. The yellow trend (23) from the quality-controlled and homogenized climate datasets LSAT-V1.1 and CGP1.0 recently developed by the China Meteorological Administration (CMA), using approximately 94 meteorological stations located throughout the Hindu Kush Himalayan region. The orange trend (44) is from a similar CMA dataset derived from 81 stations more concentrated on the eastern Tibetan Plateau. The blue trend (24) is from three decades of temperature data from 13 mountain stations located on the southern slopes of the central Himalayas. The black trend is the 5-year moving mean. (B) Temperature anomalies from high-elevation stations at the Chhota Shigri glacier terminus (25); Dingri station in the Everest region (26); average from the Kanzalwan, Drass, and Patseo stations (45); and average of 16 stations above 4000 m elevation on the Tibetan Plateau and eastern Himalayas (46). Here, temperature anomalies are relative to the mean of each record. The gray trend line is the 5-year moving mean. (C) Difference in mean temperature (°C) between the two intervals, i.e., the mean of the 2000–2016 interval relative to the mean of the 1975–2000 interval.

DISCUSSION

Implications for dominant drivers of glacier change in the Himalayas

The parsing of Himalayan glacier energy budgets is not a straightforward task owing to the scarcity of meteorological data, in combination with the complex climate and topography of the region (2). Furthermore, the Himalayas border hot spots of high anthropogenic BC emissions, which may affect glaciers by direct heating of the atmosphere and decreasing albedo of ice and snow after deposition (14). While improved analyses combining observations and high-resolution atmospheric and glacier energy balance models will be required to quantify these effects, the pattern of ice loss we observe has important implications regarding dominant climate influences on Himalayan glacier mass balances. Our results suggest that any drivers of glacier change must explain the region-wide consistency, the doubling of the average rate of ice loss in the 21st century compared to 1975–2000, and the observation that clean-ice, debris-covered, and lake-terminating glaciers have all experienced a similar acceleration of mass loss.

Some studies have suggested a weakening of the summer monsoon and reduced precipitation as primary reasons for negative glacier mass balances, particularly in the Everest region (16). While decreasing accumulation rates may account for a significant portion of the mass balance signal for some glaciers, an extreme Himalaya-wide decrease in precipitation would be required to explain the extensive ice losses we observe, especially given that monsoon-dominated glaciers with high accumulation rates are known to be much more sensitive to temperature than accumulation changes (532). Regional studies of precipitation trends in the Himalayas do not suggest a widespread decrease in precipitation over the past four decades (Supplementary Materials). A uniform BC albedo forcing across the Himalayas is another possible explanation, although BC concentrations measured in snow and ice in the Himalayas have been found to be spatially heterogeneous (1433), and high-resolution atmospheric models also show large spatial variability of deposited BC originating from localized emissions in regions of complex terrain (1434). Future analyses focused on quantifying the spatial patterns of BC deposition will reveal further insights, yet given the rather homogeneous pattern of mass loss we observe across the 2000-km Himalayan transect, a strong, spatially heterogeneous mechanism seems improbable as a dominant driver of glacier ice loss in the region.

Debris-covered glaciers

Similar thinning rates of debris-covered (thermally insulated) glaciers relative to clean-ice glaciers have been observed by previous studies and have been not only ascribed to surface melt ponds and associated ice cliffs acting as localized hot spots to concentrate melting but also attributed to declining ice flux causing dynamic thinning and stagnation of debris-covered glacier tongues (2). While we cannot yet directly deconvolve relative contributions from these processes, we find that average thinning rates for debris-covered glaciers are slower than clean-ice glaciers at low elevations (glacier tongues where debris is most concentrated), which agrees with reduced melt rates from field studies. In turn, debris-covered glaciers tend to have comparatively faster thinning at mid-range elevations, where debris cover is sparser and also where the majority of total glacierized area resides (Fig. 4). Models of debris-covered glacier processes suggest that this pattern of thinning may cause a reduction in down-glacier surface gradient, which, in turn, reduces driving stress and ice flux and explains why debris-covered ablation zones become stagnant (35). We also find that clean-ice glaciers exhibit a much more pronounced steepening of the thinning profile over time, compared to debris-covered glaciers. It may be that both glacier types experience a uniform thinning phase followed by a changing terminus flux and retreat phase, but the clean-ice glaciers are in a later phase of response to recent climate change (36).

Comparison with previous studies in the Himalayas

To compare our results with previous remote sensing studies that derive mass changes from various sensors (including Hexagon, SRTM, SPOT5, ICESat, and ASTER), we restrict our results to the approximate geographical regions covered by each corresponding study (12131722) and then compute area-weighted average geodetic mass balances. In addition, we compare individual glacier mass balances for the Everest and Langtang Himal regions, where mass changes were previously estimated using declassified Corona and Hexagon imagery (131920). Despite factors such as variable spatial resolutions, distinct void-filling methods, heterogeneous spatial and temporal coverages, and different definitions of glacier boundaries, we find that our average mass balances generally agree with previous analyses and overlap within uncertainties (table S1). However, because of the significant variability of individual glacier mass changes within subregions, our results also highlight the importance of sampling a large number of glaciers to obtain a robust average trend for any given area.

Comparison with benchmark mid-latitude glaciers and global average

To gain perspective on mass losses from these low-latitude glaciers in the monsoonal Himalayas, we compare our results with benchmark mid-latitude glaciers in the European Alps, as well as with a global average mass balance trend (fig. S1) (37). The Alps contain the most detailed long-term glaciological and high-elevation meteorological records on Earth, and the climatic sensitivity and behavior of these European glaciers are well understood compared to glaciers in HMA. Air temperatures in the Alps show an abrupt warming trend beginning in the mid-1980s, and Alpine mass balance records display a concurrent acceleration of ice loss, with a continual strongly negative mass balance after that time. Himalayan weather station data indicate a more gradual warming trend, with the strongest warming beginning in the mid-1990s (fig. S1, A and B). We find that mass balance in the Himalayas is less negative compared to the Alps and the global average, despite close proximity to a known hot spot of increasing BC emissions with rapid growth and accompanying combustion of fossil fuels and biomass in South Asia (38). The concurrent acceleration of ice loss observed in both the Himalayas and Europe over the past 40 years coincides with a distinct warming trend beginning in the latter part of the 20th century, followed by the consistently warmest temperatures through the 21st century in both regions.

Conclusion

Our analysis robustly quantifies four decades of ice loss for 650 of the largest glaciers across a 2000-km transect in the Himalayas. We find similar mass loss rates across subregions and a doubling of the average rate of loss during 2000–2016 relative to the 1975–2000 interval. This is consistent with the available multidecade weather station records scattered throughout HMA, which indicate quasi-steady mean annual air temperatures through the 1960s to the 1980s with a prominent warming trend beginning in the mid-1990s and continuing into the 21st century (2326). We suggest that degree-day and energy balance models focused on accurately quantifying glacier responses to air temperature changes (including energy fluxes and associated feedbacks) will provide the most robust estimates of glacier response to future climate scenarios in the Himalayas.

MATERIALS AND METHODS

Hexagon

U.S. intelligence agencies used KH-9 Hexagon military satellites for reconnaissance from 1973 to 1980. A telescopic camera system acquired thousands of photographs worldwide, after which film recovery capsules were ejected from the satellites and parachuted back to Earth over the Pacific Ocean. With a ground resolution ranging from 6 to 9 m, single scenes from the mapping camera cover an area of approximately 30,000 km2 with overlap of 55 to 70%, allowing for stereo photogrammetric processing of large regions. Images were scanned by the U.S. Geological Survey (USGS) at a resolution of 7 μm and downloaded via the Earth Explorer user interface (earthexplorer.usgs.gov). Digital elevation models were extracted using the Hexagon Imagery Automated Pipeline methodology, which is coded in MATLAB and uses the OpenCV library for Oriented FAST and Rotated BRIEF (ORB) feature matching, uncalibrated stereo rectification, and semiglobal block matching algorithms (27). The majority of the KH-9 images here were acquired within a 3-year interval (1973–1976), and we processed a total of 42 images to provide sufficient spatial coverage (fig. S2).

ASTER

The ASTER instrument was launched as part of a cooperative effort between NASA and Japan’s Ministry of Economy, Trade and Industry in 1999. Its nadir and backward-viewing telescopes provide stereoscopic capability at 15-m ground resolution, and a single DEM covers approximately 3600 km2. Approximately 26,000 ASTER DEMs were downloaded via the METI AIST Data Archive System (MADAS) satellite data retrieval system (gbank.gsj.jp/madas), a portal maintained by the Japanese National Institute of Advanced Industrial Science and Technology and the Geological Survey of Japan. To use all cloud-free pixels (including images with a high percentage of cloud cover), no cloud selection criteria were applied when downloading the images. We used the Data1.l3a.demzs geotiff product, which has a spatial resolution of 30 m. After downloading, the DEMs were subjected to a cleanup process: For a given scene, any saturated pixels (i.e., equal to 0 or 255) in the nadir band 3 (0.76 to 0.86 μm) image were masked in the DEM. Next, the SRTM dataset was used to remove any DEM values with an absolute elevation difference larger than 150 m (relative to SRTM), which effectively eliminated the majority of errors caused by clouds. While more sophisticated cloud masking procedures are possible, the ASTER shortwave infrared detectors failed in April 2008, making cloud detection after this time impossible using standard methods. We examined existing cloud masks derived using Moderate Resolution Imaging Spectroradiometer images as another option (tonolab.cis.ibaraki.ac.jp/ASTER/cloud/). However, these are not optimized for snow-covered regions and often misclassify glacier pixels as clouds. Instead, our large collection of multitemporal ASTER scenes, the SRTM difference threshold, and our robust linear trend fitting algorithm [see description of Random Sample Consensus (RANSAC) in the “Trend fitting of multitemporal DEM stacks” section] effectively excluded any remaining erroneous cloud elevations after the initial threshold. As a final step, all ASTER DEMs were coregistered to the SRTM using a standard elevation–aspect optimization procedure (39). We did not apply fifth-order polynomial correction procedures to the ASTER DEMs for satellite “jitter” effects and curvature bias as done in some previous studies (18). We found that while these types of corrections may reduce the overall average elevation error in a scene, the polynomial fitting can be unwieldy and may introduce unwanted localized biases. By stacking many ASTER DEMs (with 20.5 being the average number of observations per pixel stack during the ASTER trend fitting, see fig. S3E) and using a robust fitting procedure, we found that biases do not correlate across overlapping scenes, and thus tend to cancel out one another. Furthermore, the elevation change results from this portion of our study overlap within uncertainties with Brun et al. (18) (Supplementary Materials) who did perform polynomial corrections. This suggests that for a large-scale regional study using a high number of overlapping ASTER scenes, the satellite jitter and curvature bias corrections have a relatively minimal impact on the final results.

Glacier polygons

To delineate glaciers during all portions of the analysis, we used manually refined versions of the Randolph Glacier Inventory (RGI 5.0) (40). Starting with the original RGI dataset, we edited the glacier polygons to reflect glacier areas during 1975, 2000, and 2016. For the 1975 edit, we used a combination of Hexagon imagery, the Global Land Survey (GLS) Landsat Multispectral Scanner mosaic (GLS1975), and glacier thickness change maps derived from differencing the Hexagon and modern ASTER DEMs, which are particularly useful for debris-covered glacier termini that often have spectral characteristics indistinguishable from surrounding terrain. Debris-covered areas for each glacier were delineated using a Landsat DN TM4/TM5 band ratio with a threshold of 2.0, and glaciers with ≥33% debris cover were assigned to the debris-covered category. For the 2000 edit, we used the GLS2000 Landsat Enhanced Thematic Mapper Plus mosaic, along with glacier thickness change maps derived from differencing ASTER DEMs. For the 2016 edit, we used a custom mosaic of Landsat 8 imagery with acquisition dates spanning 2014–2016. The individually edited glacier polygons were used for all ice volume change and geodetic mass balance computations. The polygons were also used during alignment of the DEMs, where the shapefiles were converted to raster masks with a dilation (morphological operation) of 250 m on the binary rasters. This effectively excluded unstable terrain surrounding the glaciers during the DEM alignment process, such as steep avalanching slopes and unstable moraines.

Trend fitting of multitemporal DEM stacks

Glacier polygons were processed individually—all DEMs from a given time interval (1975–2000 or 2000–2016) that overlap a polygon were selected and resampled to the same 30-m resolution using linear interpolation. The overlapping DEMs were sampled with a 25% extension around each glacier to include nearby stable terrain for alignment and uncertainty analysis (fig. S4). After ensuring that there is no vertical bias, the aligned DEMs were sorted in temporal order as a three-dimensional matrix, and linear trends were fit to every pixel “stack” (i.e., along the third dimension of the matrix) using the RANSAC method. During each RANSAC iteration, a random set of two elevation pixels per stack were selected. A linear trend was fit to these two values, and then all remaining elevation pixels were compared to the trend. Any elevation pixels within 15 m of the trend line were marked as inliers. This process was repeated for 100 iterations, and the iteration with the greatest number of inliers was selected. A final linear fit was performed using all inliers from the best iteration, and this trend was used for each pixel stack’s thickness change estimate. The thickness change maps were subjected to outlier removal using thresholds for maximum slope, maximum thickness change, minimum count per pixel stack, minimum timespan per pixel stack, maximum SD of inlier elevations per pixel stack, and maximum gradient of the thickness change map (fig. S3). In addition, the thickness change pixels were separated into 50-m elevation bins, and pixels falling outside the 2 to 98% quantile range were excluded. Any bins with less than 100 pixels were removed and then interpolated using the two adjacent bins. Before computing ice volume change for the glaciers, the final thickness change maps were visually inspected, any remaining erroneous pixels (which occurred almost exclusively in low-contrast, snow-covered accumulation zones) were manually masked, and a 5 × 5 pixel median filter was applied. We did not attempt to perform seasonality corrections, as no seasonal snowfall records are available and because nearly all the Hexagon DEMs were acquired during winter, thus minimizing any seasonality offsets between regions. For the 1975–2000 interval, we used the Hexagon DEMs and sampled ASTER thickness change trends at the start of the year 2000. For regions with multiple overlapping Hexagon DEMs, we used the same RANSAC method. During the 1975–2000 interval, only two DEMs were available for most glaciers. In these cases, the RANSAC iterations were unnecessary, and we simply differenced the two available DEMs. We did not use SRTM for any thickness change estimates; thus, no correction for radar penetration was necessary.

Mass changes

To compute (mean annual) ice volume changes for individual glaciers, all thickness change pixels falling within a glacier polygon were transformed to an appropriate projected WGS84 UTM coordinate system (zones 43 to 46, depending on longitude of the glacier). Pixel values (m year−1) were then multiplied by their corresponding areas (pixel width × pixel height) and summed together. The resulting ice volume change was then divided by the average glacier area to obtain a glacier thickness change. We used the average of the initial and final glacier areas for a given time interval and excluded slopes greater than 45° to remove any cliffs and nunataks. Last, the glacier thickness change was multiplied by an average ice-firn density (41) of 850 kg m−3 and then divided by the density of water (1000 kg m−3) to compute glacier geodetic mass balance in m w.e. year−1. Because of cloud cover, shadows, and low radiometric contrast, some glacier accumulation zones had gaps in the DEMs and resulting thickness change maps. This is particularly evident in the Hexagon DEMs for the Spiti Lahaul region owing to extensive cloud cover. To fill these gaps, we tested two different void-filling methods for comparison. In the first method, we defined a circular search area with a radius of 50 km around the center of a given glacier. All thickness change pixels from glaciers in this surrounding area were binned (into 50-m elevation bins, and following the same outlier-removal procedure given in the preceding section), and any missing data in the glacier were set to this “regional bin” mean value at the corresponding elevation. In the second method, we filled data gaps using an interpolation procedure, where voids in an individual glacier were linearly interpolated using bin values at upper and lower elevations relative to the missing data (those belonging to the same glacier), and assumed zero change at the highest elevation bin (headwall). Both methods yielded similar results (table S1). In addition, no obvious trends were apparent when geodetic mass balance was plotted versus percent data coverage or glacier size (fig. S5). While smaller glaciers exhibited more scatter, the average mass balance was similar for all glacier sizes. These observations indicate that our representative sample of glaciers, while biased toward larger glaciers, adequately captures the statistical distribution of glacier mass balances in the Himalayas.

To calculate regional geodetic mass balances, we separated glaciers into four subregions (Spiti Lahaul, West Nepal, East Nepal, and Bhutan) as defined by Brun et al. (18). We then calculated the average mass balance for each of these four subregions, weighted by individual glacier areas. Last, we calculated a final average mass balance for the Himalayas, weighted by the total glacierized area (from the RGI 5.0 database) in each of the four subregions, between 75° to 93° longitude. Because of the relatively homogeneous mass balance distribution, we found that this approach resulted in similar values (well within the uncertainties) compared to simply calculating the area-weighted average mass balance of the 650 measured glaciers. To obtain the total mass changes in Gt year−1, we multiplied each subregion mass balance by its total glacierized area and then summed the results from all subregions to get Himalaya-wide totals of −3.9 Gt year−1 for 1975–2000 and −7.5 Gt year−1 for 2000–2016. To calculate contributions to sea-level rise, we used a global ocean surface area of 361.9 × 106 km2 (fig. S4G).

To estimate the total ice mass present in our region of study, we used ice thickness estimates from Kraaijenbrink et al. (15), who used the Glacier bed Topography version 2 model to invert for ice thickness (28) with input from the SRTM DEM (acquired in February of 2000). The ice thickness estimates from (15) did not include glaciers smaller than 0.4 km2, and to estimate the additional mass contribution from these smallest glaciers (along with any other glaciers that are missing thickness estimates), we fit a second-order polynomial to the logarithm of glacier volumes versus the logarithm of glacier areas and evaluated this fit equation for any glaciers without volume data (fig. S6). We then converted glacier volume to mass using a density value of 850 kg m−3. Over our region of study, the ice volumes from the thickness data amounted to 649 Gt, with an additional contribution of 35 Gt from the fitting procedure, for a total of 684 Gt.

Uncertainty assessment

We quantified statistical uncertainty for individual glaciers using an iterative random sampling approach. For a given glacier, the SD of elevation changes from the surrounding stable terrain (σz) was first calculated. For any missing thickness change pixels within the glacier polygon, we also included an extrapolation uncertainty σe. This accounts for additional error in regions with incomplete data, i.e., those glacier regions filled by extrapolating thickness changes from surrounding glaciers, or linear interpolation assuming zero change at the headwall, as described in the previous section. We found that in the Himalaya-wide altitudinal distributions, the maximum SD of thickness change in any 50-m elevation bin above 5000 m is 0.56 m year−1. Nearly all regions with incomplete data coverage are above this elevation, resulting from poor radiometric contrast for snow-covered glacier accumulation zones. We thus conservatively set σe equal to 0.6 m year−1. We then combined both sources of error to get σp for every individual thickness change pixelσp=σ2z+σ2e−−−−−−√(1)

To account for spatial autocorrelation, we started with a normally distributed random error field (with a mean of 0 and an SD of 1) the same size as the thickness change map and then filtered it using an n-by-n moving window average to add spatial correlation, where n is defined as the spatial correlation range divided by the spatial resolution of the thickness change map. We used 500 m for the spatial correlation range, which is a conservative value based on semivariogram analysis in mountainous regions from previous studies (182142). The resulting artificial error field En (now with spatial correlation) is scaled by the σp values and added to the thickness change map ΔH as follows, where (xy) are pixel coordinatesΔHE(x,y)=ΔH(x,y)+En(x,y)⋅σp(x,y)σn(2)

If thickness change data exist at a given pixel location (xy) on the glacier, σn is the SD of the set of all En values where data exist (i.e., where σe is equal to zero). Conversely, if thickness change data do not exist at a given pixel location (xy) on the glacier, σn is the SD of the set of all En values where data do not exist (i.e., where σe is equal to 0.6 m year−1). In this way, the second term of Eq. 2 assigns larger uncertainties to regions with incomplete data. Last, all glacier thickness change pixels in ΔHE were summed together to compute a volume change with the introduced error. This procedure was repeated for 100 iterations, and the volume change uncertainty σΔV was computed as the SD of the resulting distribution (fig. S4). For region-wide volume change estimates, we conservatively assumed total correlation between glaciers and computed region-wide uncertainty as follows, where g is the total number of glaciers (~17,400)σΔV region=∑1gσΔV(3)

For glaciers where thickness change data are not available, a measure of uncertainty is still required to factor into the final regional uncertainty estimate. For these glaciers, we estimated σΔVas (42)σΔV=σ2z region⋅Acor5⋅A−−−−−−−−−−−√(4)Acor=π⋅L2(5)

In this case, σz region is the region-wide SD of elevation change over stable terrain (0.42 m year−1) (fig. S7), Acor is the correlation area, L is the correlation range (500 m), and A is the glacier area. Last, all σΔV and σΔV regjon estimates were combined with an area uncertainty (43) of 10% and a density uncertainty (41) of 60 kg m−3 using standard uncorrelated error propagation.

Sensitivity of region-wide glacier mass change estimates

We further tested the sensitivity of our region-wide estimates to potential biases, including (i) the exclusion of small glaciers, (ii) incomplete data coverage for many glacier accumulation zones during 1975–2000, and (iii) void-filling technique. First, we note that our geodetic mass balance analysis only includes glaciers larger than 3 km2. This is because mass balance uncertainties increase with decreasing glacier size, and we find that uncertainties often exceed the magnitude of mass changes for glaciers smaller than ~3 km2. To test whether the neglected small glaciers appreciably affect the result, we also computed mass balances using all available glaciers (i.e., all glaciers with ≥33% data coverage, including those smaller than 3 km2). We find that including the full set of smaller glaciers changes the region-wide geodetic mass balance estimates by a maximum of 0.04 m w.e. year−1 (fig. S4G). Next, we note that the Hexagon DEMs in particular have poor data coverage over glacier accumulation zones (figs. S8 and S9). However, the vast majority of thinning occurs in glacier ablation zones, and the amount of thinning decreases with elevation in a quasi-linear fashion, especially in mid- to upper regions of the glaciers where data gaps are most common. Thus, we hypothesize that we can extrapolate and interpolate with reasonable confidence over accumulation areas. To test the robustness of this assumption, we used the 2000–2016 glacier change data. The ASTER data over this interval have superior radiometric contrast and adequately capture elevation change trends for most accumulation zones. We first set all 2000–2016 thickness change pixels to be empty where the 1975–2000 data are missing to simulate the same data gaps over accumulation zones as in the 1975–2000 data. We then performed the same geodetic mass balance calculations and found that the region-wide geodetic mass balance only changes by 0.01 m w.e. year−1 (fig. S4G, comparing test 3 to test 1). Last, we performed two separate void-filling methods for all tests (see the “Mass changes” section for descriptions of void-filling methods) and observed a maximum change in geodetic mass balance of 0.04 m w.e. year−1. Overall, the relatively small impact of each test suggests that our results are robust to the exclusion of small glaciers, incomplete data coverage over glacier accumulation zones, and void-filling technique.

SUPPLEMENTARY MATERIALS

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/6/eaav7266/DC1

Fig. S1. Comparison of Himalayan temperature trends and regional mass balance with benchmark mid-latitude glaciers and a global average trend.

Fig. S2. Coverage of glacierized area in the Himalayas.

Fig. S3. Trend fit examples for two large glaciers using ASTER DEMs during 2000–2016, histograms of ASTER pixel counts and timespans per stack (glacier averages), and outlier thresholds.

Fig. S4. Illustration of uncertainty estimation procedure for a single iteration/glacier and Himalaya-wide sensitivity tests.

Fig. S5. Geodetic mass balances during 1975–2000 and 2000–2016 plotted against various parameters.

Fig. S6. Log-log plot of glacier volumes versus areas used to estimate the total ice mass present in our region of study.

Fig. S7. Analysis of elevation change for nonglacier pixels (stable terrain) during both intervals.

Fig. S8. Thickness change maps used in the analysis.

Fig. S9. Thickness change maps for the three remaining Himalayan regions.

Table S1. Geodetic mass balance comparisons with prior studies.

References (4770)

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

  1. . M. Maurer1,2,*
  2. J. M. Schaefer1,2
  3. S. Rupper3 and 
  4. A. Corley1
Climate Change, Sc. & Environment

Declassified Spy Photos Reveal The True Scale of Melting of The Himalayan Glaciers

Mike  Mcrae 

MIKE MCRAE
Just under half a century ago a system of satellites codenamed Hexagon was circling the globe and snapping high-resolution shots of the changing landscape… not to mention a Russian airfield or two.

With the Cold War long melted, those images were declassified back in 2002, providing rich pickings for all kinds of research. Now scientists have used these pictures to present a startling new perspective on the Himalaya’s vanishing glaciers.

A team of US researchers from Columbia University and the University of Utah have made detailed measurements on changes to the thickness of ice in the Himalayas between two time periods; from 1975 to 2000, and then 2000 to 2016.

In some ways, what they found might not come as a great shock, if you’ve been paying attention to the climate crisis.

“It looks just like what we would expect if warming were the dominant driver of ice loss,” says the study’s lead author Joshua Maurer from Columbia University’s Lamont-Doherty Earth Observatory.

The team stitched together galleries of images of the Himalayas taken by the Keyhole-9 ‘Hexagon’ photographic reconnaissance satellites, ending up with an overview of some 650 glaciers spanning the famous mountain range.

They then developed a process to turn the 3D map into a form that provided information on elevations.

By comparing the results with modern stereo satellite imagery from NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) program, Maurer and his team could calculate annual changes to ice coverage.

Since the turn of the millennium, glaciers have thinned by an average of just under half a metre (roughly 1.5 feet) per year. Over the preceding decades, that loss was half; closer to 22 centimetres, or just under 10 inches.

That’s averaged out as well. While some glaciers at higher elevations are holding steady, there are rivers of ice closer to sea level that are losing on average 5 metres (16 feet) a year.

Of course, glaciers can thin out over time for a number of reasons. Lower precipitation, for example, or fine particulates from pollution increasing localised warming by darkening the ice and absorbing sunlight.

These factors can almost certainly contribute to the melting of large patches here and there, but the sheer scale of the change implies a more global effect.

To test their suspicions, the team also compiled data on temperatures taken by ground stations and compared these with rates of melting across the map.

Sure enough, both sets of figures lined up neatly enough to reveal that our warming planet can certainly account for the ice loss.

“This is the clearest picture yet of how fast Himalayan glaciers are melting over this time interval, and why,” says Maurer.

Further west, mountain ranges such as the Alps have attracted attention for accelerated melting of their icy peaks in the 1980s.

While it took a little longer to come up to speed, it’s now clear the Himalayas are rocketing ahead. Given the area they cover and their position, we can expect the melting of their glaciers to be a catastrophe of immense proportions.

Seasonal snowmelts contribute significant quantities of water to major river systems such as the Indus, where hundreds of millions rely on its flow and volume for drinking water, farming, and hydroelectricity.

Increased melting might temporarily be a boon, but in the long term, millions of people will face an increasing risk of water crisis.

Tragically, pooling meltwater is putting communities at greater risk of cataclysmic flooding as elevated lakes burst at the seams, sending walls of water crashing downhill.

In the 1970s, US authorities launched the Hexagon system of spy satellites partially in hope of having advanced warning of a building global threat.

Thankfully, that particular type of threat never eventuated. But now, nearly 50 years later, the same library of pictures has given us strong evidence of a much more serious threat. This time, it’s real.

This research was published in Science Advances.

Climate Change, Environment

Odisha to plant palms to arrest lightning bolts

Satyasundar Barik

The Odisha government has decided to revive the traditional practice of planting palm trees to deal with the issue of deaths caused by lightning every year. Approximately 500 lives are lost annually due to lightning in the State. Palm trees, being the tallest ones, act as a good conductor when lightning strikes.

Palm tree plantations will come up along the forest boundaries on National and State Highways and in common land in coastal villages. The State Forest and Environment Department has issued instructions to all regional conservators of forests and divisional forest officers in this regard.

Traditional practice

“Earlier, planting palm trees was a traditional practice in villages, but this has now been discontinued due to urbanisation and development. The tree has a wide range of uses — its fruits are eaten, the stem is valuable as wood, and baskets and mats are woven with the leaves. It is also learnt to be helpful as a bulwark against lightning casualties,” said D. Swain, principal chief conservator of forests.

“Lightning usually hits the tallest object first. The palm tree being the tallest among other trees in its surroundings works as a lightning conductor, decreasing deaths by lightning,” said Mr. Swain. Palm trees also protect coastal areas from storms and cyclones, while its roots protect embankments from soil erosion.

According to Bishnupada Sethi, managing director, Odisha State Disaster Management Authority (OSDMA), as many as 1,256 lightning deaths took place in the State in the last three years, most of them (about 85%) in the May-September period. Lightning deaths account for about 27% of the total number of ‘disaster deaths’.

The OSDMA has taken up a massive awareness drive, educating people on how to react during a thunderstorm.

The neighbouring Bangladesh, which also sees many deaths every year due to lightning strikes, has announced a similar programme to plant one million palm trees.

Climate Change

Increasing Lightning Death needs a new policy in Bangladesh and Northeast India

 The maximum lightning incidents are attributable to climate change in the entire Indian subcontinent, central Bangladesh and Northeast India in the Brahmaputra Basin. The Indian Meteorological Department (IMD) has recently issued a weather forecast for Assam and Meghalaya that, thunderstorm accompanied by lightning mostly occurred on March 5 and 6, 2019. The Weather Channels also predicted rain or snow accumulation in the east and Northeast India till Thursday evening. The fury of nature has been left many parts of Northeastern region of India in tatters. Incessant rainfall in most areas of Garo Hills in Meghalaya has left trails of destruction with houses, schools, and trees strewn in the aftermath of this horrific weather. While some districts of Assam and West Meghalaya have been partially affected, the districts of North and East Garo Hills in the state of Meghalaya were worst hit. Most lightning deaths and injuries occur when people are caught outdoors in the summer months during the afternoon and evening. Deaths from lightning strikes is now one of the most discussed subjects in the country. Most of the victims are the lone breadwinners in their families. The maximum lightning incidents in the entire Indian subcontinent occur in central Bangladesh and the states of Meghalaya, West Bengal, and Assam before the monsoon season (March-May) with 40 lightning strikes per square kilometer. The data of the National Crime Records Bureau (NCRB) says lightning kills more people in India than any other natural calamity. According to a 2014 NCRB report, out of 20,201 accidental deaths attributable to natural causes, 12.8 percent were due to a lightning strike. The 2014 report published by Indian Meteorological Department (IMD) reported the period between March 15 to June 15, 2014, Assam experienced the highest number of thunderstorms followed by Arunachal Pradesh in March, Meghalaya in April and Tripura in May and June. During the entire period, the frequency was the highest during the night (30 percent) followed by evening (21 percent). In Bangladesh the lighting strike death toll is unbelievable. On last May 2018, 29 people died from lightning in 12 districts in 24 hours, and almost all of them are farm workers. Earlier, at least 12 people died in March, and 58 people died in April 2018 in parts of Bangladesh, according to government data. In the last two days of April last year, as many as 33 people were killed as storms swept across the country, said Disaster Management Minister Mofazzal Hossain Chowdhury Maya. The number of deaths was 160 in 2015, 170 in 2014, 185 in 2013, 201 in 2012 and 179 in 2011. Lightning poses a significant threat as an increasing number of people are losing life due to the natural disaster every year, experts say. Scores of people die every year after being struck by lightning during the rainy season in Bangladesh, which runs from April to October. The officials say the numbers are exceptionally high this year. Every day 10 to 12 people are dying from a lightning strike. Authorities declared lightning strike to be a natural disaster after 82 people were killed in a single day in May 2016. Independent monitors estimated that some 349 Bangladeshis died from lightning that year. In Bangladesh, the thunderstorm usually occurs from March to May, but sometimes it takes place until October or November. Owing to a sudden change in weather, heavy rain and strong gales that originate in the Bay of Bengal, end up causing lightning strikes and loss of lives in the Bangladesh and Indian Northeast. According to a new study, the above numbers can dramatically increase if the current rate of global warming continues. As reported in the journal of Science, it is expected to see a 12% increase in lightning activity for every 1 degree centigrade (1.8 degrees F) of warming, meaning the U.S. could experience a 50% increase in strikes by the turn of the century. In affected regions, people suffer “light dumb” disorder and significantly suffer a moderate headache. Many people succumb to severe heart failures. In Bangladesh, there are records of people suffering light heart failure and neural damage. Moreover, some suffered from moderate skin irritation and headache and some with severe heart failure and neural damage disease. Is climate change responsible? Lightning emerged as a new natural disaster in the Northeast Indian states and the Bay of Bengal area. The Brahmaputra flows through the region and ends at the confluence of the Bay of Bengal. This entire region is prone to lightning because of its complex topography, killing many people every year. Studies have shown thunderstorms are very frequent during the pre-monsoon season over northeastern India and Bangladesh. They are especially distinctive by their nature and severity compared to other storms, which occur over some other regions or during some different seasons. Lightning, as well as thunder and storms, are hazardous. Mostly they appear together. Anyone can strike and kill people, and also trigger potentially devastating wildfires. Studies exploring how lightning could change with rising temperatures are few and far between, and those that have been conducted have produced wildly different results. For the current study, scientists from the University of California, Berkeley, started by examining the relationship between atmospheric variables and lightning rates. They hypothesized that two factors– precipitation (the amount of water that hits the ground) and the amount of energy available to make atmospheric air rise– could predict lightning flash rate. These variables can both be used as measures of storm convection (the vertical movement of air), a process that is known to generate lightning which requires two key ingredients: water in all three states (liquid, solid and gas) and quickly rising clouds to keep the ice suspended. Next, they applied these variables to 11 different climate models, all of which assume that there will be no significant drops in greenhouse gas emissions, and found that lightning would likely increase by around 12% per 1 degree Centigrade. Since it is predicted that temperatures will be around 4°C higher at the end of the century, this means there could be a 50% increase in strikes in the US by 2100. This could potentially mean more human injuries and more wildfires since around half of all fires are started by lightning. The entire Bay of Bengal, a part of Assam, Meghalaya and West Bengal are prone to lightning because of the complex topography. Studies have shown thunderstorms are very frequent during the pre-monsoon season in northeastern India and Bangladesh. They are especially distinctive by their nature and severity compared to other storms, which occur over some other regions or during some different seasons. Presently most scientists believe, with the increase in global temperature, the intensity of thunderstorms and lightning will magnify in intensity. The thundercloud formation because of excess heat over Bangladesh is resulting in thunderbolts and lightning, particularly in the regions where water bodies are high, such as Haor areas. The wind convergence occurs in active convection which is the upward movement of warm and moist air. The subsequent instability results in widespread precipitation with chances of thunderstorms. According to Prof Rashid, the temperature rose in April in Bangladesh, which has caused water to vaporize and leads to rain, clouds, and lightning. Bangladesh is witnessing increasing numbers of casualties from lightning, a natural disaster, for the last few days, mainly because of the rise in temperature that is leading to the formation of upper air circulation in the geographical region, experts say. The geographical location of Bangladesh with the Himalayas in the north, the Bay of Bengal in the south, as well as the Indian Ocean and Arabian sea in the proximity, it is adding to the creation of thunderstorms in the region. It is to be noted that Northeast India, together with Bangladesh, is one of the most thunderstorm-prone regions in the world, substantiated by Tetsuya Fujita of the University of Chicago in 1973. Fujita along with Allen Pearson had developed the Fujita Pearson Scale for measuring the damage caused by tornadoes. Of all the severe thunderstorm events in the Northeast region during the 55 years of the study period, about 30 percent of the incidents resulted from storms (nor’easters), with hail and lightning accounting for 18 percent and 10 percent of all recorded events. While severe thunderstorms can develop at any time of the year, over half of the severe thunderstorm events occurred in the region during March, April, and May, peaking during the latter months. A secondary peak in severe thunderstorm events occurs in September and is likely due to the impact of tropical cyclones or their remnants flowing from the warm waters of the Bay of Bengal. The data compiled by the ICRC on the occurrence of severe thunderstorm incidents show that they are first seen on an isolated day in February under the influence of a western disturbance, and it becomes a familiar feature during the hot afternoons of April to May to early morning hours of the next days. Summer monsoon season with 60 percent incidents is the most favored time of the year for the occurrence of lightning strikes in Assam, followed by pre-monsoon season with 32 percent of the incidents. During the 55-year study period, it was reported that 22 people died on an average per year from severe thunderstorm hazards in Northeast India. More than 60 percent of these death cases were due to lightning. In general, severe thunderstorm impacts like loss of life and injury, loss of livelihood and damage to infrastructure are significantly more on impoverished and vulnerable rural population in the western part of Assam. The total climatology of lightning activity showed that the region of the west of Assam experiences higher lightning activity. Another study published in the International Journal of Climatology in September 2015, which was carried out by Hupesh Choudhury, Partha Roy, Sarbeswar Kalita and Sanjay Sharma states that during the pre-monsoon season, the frequency of lightning is quite significant in the Northeast due to the interaction of moisture-laden wind with the complex topography of the region. The Meghalaya plateau and foothills of Patkai hill range, in particular, experience severe lightning. Iqbal R Tinmaker and Kaushar Ali of the Indian Institute of Tropical Meteorology finds almost the same result attributed to space-time variation of lightning activity over Northeast India. They revealed lightning flash rate density is the maximum over the west of northeast India. The study, published in Meteorologische Zeitschrift in April 2012, said this high flash rate density is attributable to the topography and the geography of the region, along with the moisture availability. The 2014 report published by Indian Meteorological Department (IMD) said the highest number of thunderstorms in each month of the storm period (March 15 to June 15, 2014) was recorded in Assam, followed by Arunachal Pradesh in March, Meghalaya in April and Tripura in May and June. During the entire period, the frequency was highest during the night (30 percent) followed by evening (21 percent). Apart from agriculture fishing is at risk condition at the time of thunderstorm and lightning and fishes were at very risk condition during TS ( thunder and storm) and lightning. Moreover, these (TS) affected agricultural production very much. For TS and lightning, agrarian land was unsuitable for agricultural production. Trees and crops were uprooted, damaged and fired. So, people lose their property and fail with their regular lifestyle. A thunderbolt struck farmers while they were working at paddy field and harvesting paddy field. Lack of Awareness It is observed, casualties are increasing because of a lack of awareness among people. We find that most illiterate and lack of knowledge about lighting as well as thunderstorm and they assume it as a supernatural phenomenon or God’s fury. Awareness is crucial to reduce the toll and its harmful impact. Routine research works involving government and NGO and government regulation are needed to mitigate the menace. Mohan Kumar Das, the senior research fellow of the Institute of Water and Flood Management (IWFM) at Bangladesh University of Engineering and Technology (Buet), said deaths from lighting could also be avoided if people take some cautious steps according to BMD. The Bangladesh government is deeply concerned about the peril of such incidents, but measures are not adequate. Moreover, Meteorologists from the developing world say lightning incidents and their impacts remain under-reported as they are sporadic, making them difficult to record. It is observed, the shortage of adequate tall trees in rural areas could be a reason for the rise in the number of deaths from lightning. So people should be aware of lightning protect forest and danger of standing under a lone high tree during bad weather. Bangladesh Government authority has recorded almost all records of lightning death, but governments in India have not done it. Despite being the most lightning-prone zone in the Northeast, Assam and Meghalaya governments do not have any separate programme to create awareness among the people about lightning and TS. The state revenue and disaster management authority do not have any independent campaign for lightning. Awareness is critical to reducing the toll and harmful impact. Routine research work with broad public awareness, government, and NGO participation, and government regulations are necessary for a safe and sound environment. The Bangladesh government is more concerned about the tragic incidents. But state governments of Assam and Meghalaya as well as Central Government in India are not profoundly involved yet. It needs an urgent policy, program, and execution at the grass-root level to address the problem. The data compiled by the ICRC on the occurrence of severe thunderstorm incidents show that they are first seen on an isolated day in February under the influence of a western disturbance, and it becomes a familiar feature during the hot afternoons of April to May to early morning hours of the next days. Summer monsoon season with 60 percent incidents is the most favored time of the year for the occurrence of lightning strikes in Assam, followed by pre-monsoon season with 32 percent of the incidents. During the 55-year study period, it was reported that 22 people died on an average per year from severe thunderstorm hazards in Northeast India. More than 60 percent of these death cases were due to lightning. In general, severe thunderstorm impacts like loss of life and injury, loss of livelihood and damage to infrastructure are significantly more on impoverished and vulnerable rural population in the western part of Assam. The total climatology of lightning activity showed that the region of the west of Assam experiences higher lightning activity. Another study published in the International Journal of Climatology in September 2015, which was carried out by Hupesh Choudhury, Partha Roy, Sarbeswar Kalita and Sanjay Sharma states that during the pre-monsoon season, the frequency of lightning is quite significant in the Northeast due to the interaction of moisture-laden wind with the complex topography of the region. The Meghalaya plateau and foothills of Patkai hill range, in particular, experience severe lightning. Iqbal R Tinmaker and Kaushar Ali of the Indian Institute of Tropical Meteorology finds almost the same result attributed to space-time variation of lightning activity over Northeast India. They revealed lightning flash rate density is the maximum over the west of northeast India. The study, published in Meteorologische Zeitschrift in April 2012, said this high flash rate density is attributable to the topography and the geography of the region, along with the moisture availability. The 2014 report published by Indian Meteorological Department (IMD) said the highest number of thunderstorms in each month of the storm period (March 15 to June 15, 2014) was recorded in Assam, followed by Arunachal Pradesh in March, Meghalaya in April and Tripura in May and June. During the entire period, the frequency was highest during the night (30 percent) followed by evening (21 percent). Apart from agriculture fishing is at risk condition at the time of thunderstorm and lightning and fishes were at very risk condition during TS ( thunder and storm) and lightning. Moreover, these (TS) affected agricultural production very much. For TS and lightning, agrarian land was unsuitable for agricultural production. Trees and crops were uprooted, damaged and fired. So, people lose their property and fail with their regular lifestyle. A thunderbolt struck farmers while they were working at paddy field and harvesting paddy field. Lack of Awareness It is observed, casualties are increasing because of a lack of awareness among people. We find that most illiterate and lack of knowledge about lighting as well as thunderstorm and they assume it as a supernatural phenomenon or God’s fury. Awareness is crucial to reduce the toll and its harmful impact. Routine research works involving government and NGO and government regulation are needed to mitigate the menace. Mohan Kumar Das, the senior research fellow of the Institute of Water and Flood Management (IWFM) at Bangladesh University of Engineering and Technology (Buet), said deaths from lighting could also be avoided if people take some cautious steps according to BMD. Despite being the most lightning-prone zone in the Northeast, Assam and Meghalaya governments do not have any separate program to create awareness among the people about lightning and TS. The state revenue and disaster management authority do not have any independent campaign for lightning.

by Chandan kumar Duarah

– See more at: http://southasiajournal.net/increasing-lightning-death-needs-a-new-policy-in-bangladesh-and-northeast-india/

Climate Change

‘Bangladesh’s geography will naturally counter sea level rise until it becomes too rapid due to climate change’

Saqib Sarke

Sea level rise is a concern in all coastal regions in the world. Bangladesh happens to be among the most vulnerable areas. But for all the talk about how vulnerable the country is, the river laden land has evolved to create mechanisms that naturally counter sea level rise. Environmental scientist Jonathan Gilligan says this aspect is often not understood. 

This is not to say there is no risk from human induced sea level rise and all the commotion about part of the country submerging underwater deserves less attention. But the geographical area known as Bangladesh has for centuries acted as the big basin that paves the way for the mighty Brahmaputra and Ganges to unite with the sea at the Bay of Bengal. The land has learned how to survive rise of water. 

It does so through the fascinating natural mechanism of depositing sediment. That is how it has managed to stay above water for thousands and thousands of years, said Prof Gilligan. But this might change quite soon.

An associate professor at Vanderbilt University in the United States, Gilligan studies the interactions between human society and the environment. 

His work requires an interdisciplinary approach, and as a result he often works with teams that have social scientists, natural scientists and engineers. The interdisciplinary approach is necessary to understand how the actions of communities affect environment around them and how the changing environment – that is changed both by the actions of the community and also actions from the outside like climate change then affect the livelihood of the community. 

“And by trying to understand the two together, we can then understand better how the environmental change will affect the community and how the community might adapt,” he said. 

Gilligan, who worked in Sri Lanka on how paddy farmers are affected by water scarcity, has carried out a lot of research work in the southwest coastal areas of Bangladesh.

“Climate change has a lot of effects on the ground. My focus primarily is on how the coastal communities are affected by increased flood hazards due to subsidence of the land combined with sea level rise and also how increasing salinity is affecting community,” he said. 

To understand the effects of sea level rise Gilligan looks at how the communities are using their land, how the polders are affecting the changing rivers and how they are affecting the land inside the polder.

“My group is currently studying how the erosion of the river banks and the formation of ‘ghashland’ is going to affect communities. And how climate change interacts with all the activities along the river; and the combination of the land use and the communities with the sea level rise. You need to understand the combination to understand the effect on the communities.”

Gilligan says the impacts of climate change and sea level rise is going to be very visible in the next 20 to 30 years and they will become increasingly severe through the rest of this century, because the sea level rise is speeding up. 

“But what often doesn’t get understood is there’s also a tremendous natural resource that Bangladesh has and this is the sediment that is carried in the rivers. Over the last several thousand years there has been natural sea level rise, which is not as severe as the human sea level rise.”

“But with the natural sea level rise the land has risen at the same rate as the sea level rise, because there is a natural balance between the sea level rise and the sediment that comes  with the rivers and deposit on the land,” he said. 

This will allow Bangladesh to survive natural sea level rise as it has for millenia.  And if the sea level rise does not exceed a certain level the land will simply rise at the same rate as the sea. 

“But if the sea level rise become too rapid or something stops the sediments coming in the river, either of those would mean sea level rise would become much more severe,” said Gilligan.

With the Polar ice melting at unprecedented rates, the worse case scenario is not out of the picture. In fact, as scientists estimate, it is imminent. 

“Some lands can be rescued from the sea level rise, because there’s enough sediment to build up the land by several milimetres  per year. The question is how the rivers and the land and the polders are managed to try to get the maximum benefit from that sediment, to raise the land up as much as possible, so that the sea level rise does not drown too much.”

Gilligan is currently studying how the changes from human contribution are affecting migration. 

“There will be migration from places where the rise is too rapid. But migration does not happen just because of environmental stresses. It happens for economic reasons. What becomes very important to understand is that migration can be a valuable response to climate change. But it can also bring economic opportunities to pull people out of poverty,” he said. 

Bangladeshi people have traditionally employed many mechanisms to cope with the natural floods that occur regularly in the delta. Embankments are one of the methods that have been used for many years. A previous study by Gilligan found that embankments across the coastal plain of Bangladesh for preventing tidal inundation of the landscape also caused disruption in sediment flow. 

While these embankments were necessary for rice cultivation and to prevent food shortages in Bangladesh, these also cut off Bangladeshi lands from their riverine sediment supply. Consequently, most lands today have subsided far below mean high water levels, making them increasingly susceptible to severe flooding from waterlogging and the impacts of storm surges.

The study, where Gilligan was one of the researching scientists, found that embankments constructed since the 1960s are the main reason for lower land elevations along coastal areas in Bangladesh. Within this area, some are experiencing more than twice the rate of the most worrisome sea-level rise projections from the United Nations’ Intergovernmental Panel on Climate Change.

Gobeshona Conference_2019_7

Disruption to sediment transfer is also likely to happen from river diversion projects by India. The Farakka Barrage has caused significant political tension since the 90s between Bangladesh and its big neighbour. But Farakka actually does not stop sediment flow to any significant level, Gilligan said. 

“The Farakka Barrage has been very controversial. The research by some of my colleagues who I work closely with, they are geologists – that research is suggesting that Farakka Barrage has not been significantly affecting the sediment transport,” said Gilligan. 

However, this will not be the case if India implements its other river diversion projects.

“If India implements the big river diversion project that is being considered, that could have very serious impact. That could divert a lot of sediment out of the river. An it could cause harm to Bangladesh,” he said. 

Jonathan Gilligan is currently developing models to study how the diversion of sediment could affect the adaptive capacity of sea level rise within Bangladesh.

Climate Change

With rising sea levels, Bangkok struggles to stay afloat

BANGKOK: As Bangkok prepares to host climate-change talks, the sprawling city of more than 10 million is itself under siege from the environment, with dire forecasts warning it could be partially submerged in just over a decade.

A preparatory meeting begins Tuesday in Thailand’s capital for the next UN climate conference, a crunch summit in Poland at the end of 2018 to set rules on reducing greenhouse emissions and providing aid to vulnerable countries.

As temperatures rise, abnormal weather patterns — like more powerful cyclones, erratic rainfall, and intense droughts and floods — are predicted to worsen over time, adding pressure on governments tasked with bringing the 2015 Paris climate treaty to life.

Bangkok, built on once-marshy land about 1.5 metres (five feet) above sea level, is projected to be one of the world’s hardest hit urban areas, alongside fellow Southeast Asian behemoths Jakarta and Manila.

“Nearly 40 percent” of Bangkok will be inundated by as early as 2030 due to extreme rainfall and changes in weather patterns, according to a World Bank report.

Currently, the capital “is sinking one to two centimetres a year and there is a risk of massive flooding in the near future,” said Tara Buakamsri of Greenpeace.

Seas in the nearby Gulf of Thailand are rising by four millimetres a year, above the global average.

The city “is already largely under sea level”, said Buakamsri.

In 2011, when the monsoon season brought the worst floods in decades, a fifth of the city was under water. The business district was spared thanks to hastily constructed dikes.

But the rest of Thailand was not so fortunate and the death toll passed 500 by the end of the season.

Experts say unchecked urbanisation and eroding shorelines will leave Bangkok and its residents in a critical situation.

With the weight of skyscrapers contributing to the city’s gradual descent into water, Bangkok has become a victim of its own frenetic development.

Making things worse, the canals which used to traverse the city have now been replaced by intricate road networks, said Suppakorn Chinvanno, a climate expert at Chulalongkorn University in Bangkok.

“They had contributed to a natural drainage system,” he said, adding that the water pathways earned the city the nickname ‘Venice of the East’.

Shrimp farms and other aquacultural development — sometimes replacing mangrove forests that protected against storm surges — have also caused significant erosion to the coastline nearest the capital.

This means that Bangkok could be penned in by flooding from the sea in the south and monsoon floods from the north, said Chinvanno.

“Specialists anticipate more intense storms in this region in the years to come.”

Narong Raungsri, director of Bangkok’s Department of Drainage and Sewage, admitted that the city’s “weaknesses” stem from its small tunnels and the hyper-development of neighbourhoods.

“What used to act as water basins are now no more,” Raungsri said.

“Our system can only handle so much — we need to enlarge it.”

Today, the government is scrambling to mitigate the effects of climate change, constructing a municipal canal network of up to 2,600 kilometres with pumping stations and eight underground tunnels to evacuate water if disaster strikes.

Chulalongkorn University in 2017 also built in central Bangkok an 11-acre park specially designed to drain several million litres of rain and redirect it so surrounding neighbourhoods are not flooded.

But these ad-hoc fixes may not be enough.

“We need a clear policy of land management,” said Greenpeace’s Buakamsri, adding that the need for increased green spaces is outweighed by developers’ interests.

“The high price of land in Bangkok makes economic interests a priority.”

Climate Change

Snowfall On Tyrol’s Mountain Drops

by LISA FISCHER

The roller coaster of temperatures: From more than 30 degrees, the values ​​tumbled in recent days by more than 15 degrees down. It rained in the Tyrolean valleys – and on the mountains fell the first snowflakes. During the night on Sunday, the snowfall fell to around 1,500 meters.

Measuring stations at the Sonnblick and at the Rudolfshütte in the Hohe Tauern, there were about 40 centimeters of fresh snow, reported the Central Institute for Meteorology and Geo-dynamics. At the Stubai Glacier 20 centimeters of fresh snow lay in the morning, at the Hintertuxer Glacier it was 30 centimeters.

Below the valleys there was a hint of autumn: dark clouds and early temperatures of less than 10 degrees caused the Tyrolians to shiver. Low temperatures of 1.4 degrees were measured in Sillian, 2.7 degrees in Virgen and 3.2 degrees in Hochfilzen. This makes the Tyrolean resorts one of the coldest in Austria.

tyrols-mountain

The snow on Austria’s mountains led to traffic obstructions in alpine locations on Sunday morning. Some higher mountain roads from Tyrol to Styria were not passable after snowfall – at least temporarily. The Hahntennjoch was open again in the morning. The snow on the Turracher Straße (B95) in the Turracher Höhe area were cleared relatively quickly. The Sölkpass road (L704) had to be closed between Stein an der Enns (Liezen district) and Baierdorf in the Murau district. The same was true for the Kärntner Nockalm road.

But do not worry, the summer comes back in the new week with all your strength. While in the lowlands partly strong showers descend, from the Oberland the clouds break up more and more in the course of the Sunday. The daily maximums are between 11 and 19 degrees.

On Monday, the sun is shining again from the sky, after a cool autumn start the temperatures rise rapidly to 19 to 24 degrees. It will be a few degrees warmer on Tuesday. According to weather forecasts, up to 28 degrees in the Tyrol are possible again. Only now and then do a few harmless clouds move across the sky.

On Wednesday it will be summerly hot from Vorarlberg to Salzburg with peaks of around 31 degrees. From Thursday, the shower and thunderstorm to increase again, the temperatures to remain same on a late summer level.

Sources: tt news

Ahrntal, the pearl of Tyrol in the extreme north of Italy

Across the Ahrntal, from Campo Tures to the border with Austria, in one of the most pristine