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Hazard from Himalayan Glacier Lake Outburst Floods

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Hazard from Himalayan Glacier Lake Outburst Floods Hazard from Himalayan glacier lake outburst floods Georg Veha,1, Oliver Korupa,b, and Ariane Walza aInstitute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany; and bInstitute of Geosciences, University of Potsdam, 14476 Potsdam-Golm, Germany Edited by Andrea Rinaldo, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, and approved November 27, 2019 (received for review August 27, 2019) Sustained glacier melt in the Himalayas has gradually spawned (17). While the distribution and dynamics of moraine-dammed more than 5,000 glacier lakes that are dammed by potentially lakes have been mapped extensively in recent years (12, 18–21), unstable moraines. When such dams break, glacier lake outburst objectively appraising the current Himalayan GLOF hazard has floods (GLOFs) can cause catastrophic societal and geomorphic remained challenging. The high-alpine conditions limit detailed impacts. We present a robust probabilistic estimate of average fieldwork, leading researchers to extract proxies of hazard from GLOFs return periods in the Himalayan region, drawing on 5.4 increasingly detailed digital topographic data and satellite imagery. billion simulations. We find that the 100-y outburst flood has These data allow for readily measuring or estimating the geometry of +3.7 6 3 an average volume of 33.5 /−3.7 × 10 m (posterior mean and ice and moraine dams, the possibility of avalanches or landslides 95% highest density interval [HDI]) with a peak discharge of entering a lake, or the water volumes released by outbursts (9, 18, 20). +2,000 3 −1 15,600 /−1,800 m ·s . Our estimated GLOF hazard is tied to Ranking these diagnostics in GLOF hazard appraisals has mostly the rate of historic lake outbursts and the number of present lakes, relied on expert judgment (18, 22), because triggers and condi- which both are highest in the Eastern Himalayas. There, the esti- tioning factors are largely unknown for most historic GLOFs (23). − mated 100-y GLOF discharge (∼14,500 m3·s 1) is more than 3 times Guided by studies on earthquakes, landslides, wildfires, and that of the adjacent Nyainqentanglha Mountains, and at least an floods, we use the magnitude and frequency of GLOFs in a given order of magnitude higher than in the Hindu Kush, Karakoram, area and period as an objective metric of hazard. GLOFs have − and Western Himalayas. The GLOF hazard may increase in these occurred at an average rate of ∼1.3 y 1 in the Himalayas in the regions that currently have large glaciers, but few lakes, if future past 3 decades (23), although of differing size, which is conven- projected ice loss generates more unstable moraine-dammed lakes tionally described by the released flood volume V0 or the as- Q than we recognize today. Flood peaks from GLOFs mostly attenu- sociated peak discharge p. These parameters are difficult to ENVIRONMENTAL SCIENCES ate within Himalayan headwaters, but can rival monsoon-fed dis- measure during dam failure but can be estimated eventually from charges in major rivers hundreds to thousands of kilometers the surface areas of glacier lakes. We thus scaled the manually downstream. Projections of future hazard from meteorological mapped areas of all 5,184 Himalayan moraine-dammed lakes floods need to account for the extreme runoffs during lake out- (>0.01 km2) to volumes using a Bayesian robust regression of bursts, given the increasing trends in population, infrastructure, maximum lake depth versus area from 24 bathymetrically surveyed and hydropower projects in Himalayan headwaters. lakes (Materials and Methods and SI Appendix,Fig.S2andTable S1). Our approach assumes that all lakes are equally susceptible to atmospheric warming | meltwater lakes | GLOF | extreme-value statistics | outburst and that any incision depth is possible, which is consistent Bayesian modeling with data from Himalayan dam breaks (SI Appendix,Fig.S3). We used a Bayesian variant of a physical dam-break model (24, 25) to Q V onsoonal floods are among the most destructive natural predict peak discharge p from the product of 0 and the breach k Materials and Methods SI Appendix Mhazards in the greater Himalayan region and the adjacent erosion rate ( and ,Figs.S4and mountain ranges of the Hindu Kush, Karakoram, Nyainqentanglha, and Hengduan Shan (Fig. 1). Regional projections for the Significance lower Indus, Ganges, and Brahmaputra rivers hold that flood frequencies will rise noticeably in the 21st century (1, 2), putting Glacier lake outburst floods (GLOFs) have become emblematic the livelihoods of 220 million people at risk (3). In Himalayan of a changing mountain cryosphere. The Himalayas suffered headwaters, such prognoses have disregarded episodic, but po- the highest losses from these sudden pulses of meltwater but tentially destructive, floods from the sudden emptying of moraine- lack a quantitative appraisal of GLOF hazard. We express the dammed lakes. Such glacier lake outburst floods (GLOFs) occur hazard from Himalayan glacier lakes by the peak discharge for largely independently of hydrometeorological floods, but can a given return period. The 100-y GLOF has a mean discharge of − surpass their peak discharges by orders of magnitude in the upper ∼15,600 m3·s 1, comparable to monsoonal river discharges river reaches (4–6). Glacier lakes dammed by abandoned mo- hundreds of kilometers downstream. The Eastern Himalayas raines are susceptible to outburst, triggered by ice or debris falls, are a hotspot of GLOF hazard that is 3 times higher than in any strong earthquake shaking, internal piping, or overtopping waves other Himalayan region. The size of growing glacier lakes and that exceed the shear resistance of the dam (7–9). These triggers the frequency of lake outbursts determine GLOF hazard, which mostly happen unrecorded in remote terrain, eroding the needs to be acknowledged better in flood hazard studies. impounding barriers within minutes to hours, and releasing sediment-laden floods that may travel >100 km downstream (7). Author contributions: G.V. and O.K. designed research; G.V. and O.K. performed research; With little to no warning, communities and infrastructure down- G.V. and O.K. analyzed data; and G.V., O.K., and A.W. wrote the paper. stream have often been caught unprepared, suffering loss of human The authors declare no competing interest. lives and livestock, and damage to roads, buildings, and hydro- This article is a PNAS Direct Submission. power facilities (10–12). An objective and reproducible hazard Published under the PNAS license. assessment of such dam-break floods is key to human safety and Data deposition: All input data are available at Zenodo (http://doi.org/10.5281/zenodo. sustainable development, and is repeatedly emphasized in re- 3523213), and model codes are available at GitHub (https://github.com/geveh/GLOFhazard). search and media coverage of atmospheric warming, dwindling 1To whom correspondence may be addressed. Email: [email protected]. glaciers, and growing meltwater lakes (3, 13, 14). This article contains supporting information online at https://www.pnas.org/lookup/suppl/ GLOFs have gained growing attention in the Himalayas (15, 16), doi:10.1073/pnas.1914898117/-/DCSupplemental. where these disasters have had the highest death toll worldwide www.pnas.org/cgi/doi/10.1073/pnas.1914898117 PNAS Latest Articles | 1of6 Downloaded by guest on September 23, 2021 A BC Fig. 1. Moraine-dammed glacier lakes in the Himalayas. (A) Distribution of moraine-dammed lakes in our study region in 1° × 1° bins. Bubbles are scaled to the total lake area, and color-coded to abundance. Reported GLOFs (yellow triangles) have occurred most frequently in the past 8 decades in regions where glacier lakes are largest (23). (B) Location of the Himalayas between the Indian subcontinent and the Tibetan Plateau. (C) Histogram of glacier lake areas. S5). In summary, we obtained 4.6 × 108 and 4.9 × 109 scenarios of Discussion V Q 0 and p, respectively, for all moraine-dammed lakes in the We offer a consistent and reproducible estimate of present Himalayas. These numbers ensure that we have sufficiently ex- GLOF hazard in the Himalayas. Our Bayesian estimates explore plored the physically plausible space for fitting an extreme-value the parameter space of plausible flood volumes and associated distribution to these key flood diagnostics. We further stacked our peak discharges with roughly a million outburst scenarios for any simulations, accounting for contemporary mean annual GLOF −1 given lake. Our approach expands previous hazard appraisals by rates between 0.03 and 0.71 y in 7 subregions of the Himalayas, explicitly accounting for regionally varying GLOF rates. The and estimated GLOF return periods in terms of the 100-y flood V Q Materials and Methods estimated GLOF return periods are consistent with the fre- volume 100 and discharge p100 ( ) (26, 27). quency of reported flood volumes and discharges since 1935 SI Appendix Results (Fig. 3 and , Fig. S6 and Table S2). The 3 largest reported flood volumes of 71.6, 19.5, and 17.2 × 106 m3 from the The predicted flood volumes and peak discharges span more than 7 lakes Sangwang Tsho (1954), Sabai Tsho (1998), and Lugge Tsho orders of magnitude (Fig. 2). Based on a mean posterior rate of 1.26 − GLOFs y 1 over the past 3 decades (23), we estimate a contemporary (1994), respectively, had return periods of 237, 56, and 49 y, +3.7 6 3 −1 +2,000 3 −1 respectively, according to our predictions. The highest reported V of 33.5 /− × 10 m ·s and Q of 15,600 /− m ·s in − 100 3.7 p100 1,800 peak discharge (15,920 m3·s 1) drained from Lake Zhangzangbo the entire study area (Fig. 3). Regionally differing GLOF rates in 1981; we estimate this as a 100-y event in the greater Himalayan cause variation in V100 and Qp100.
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