# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 1 Introduction  
Glacial lakes are lakes of glacial origin and they commonly occur in high mountain areas. Under the action of multiple factors, glacial lakes can suddenly release large amounts of water, which can have devastating effects on lives and livelihoods. Outbursts from glacial lakes have repeatedly caused the loss of human lives as well as severe damage to local infrastructure. However, the hazard potential of glacial lakes remains uncertain in several high mountain ranges around the world, especially in the context of the accelerating rates of glacier retreat as a result of atmospheric warming [1; 2]. As a consequence of global warming, the rates of glacier retreat and melting have increased, further leading to the increase and expansion of glacial lakes [3; 4; 5]. As glaciers recede in response to climatic warming, the number and volume of potentially hazardous moraine-dammed lakes in high mountain areas are increasing [6]. These lakes develop behind unstable ice-cored morines and have the potential to burst catastrophically, producing devastating glacial lake outburst floods (GLOFs). In the past 50 years, there have been at least 20 major outbursts in the Himalayas, most of which are concentrated in Tibet [7]. Therefore, it is of great practical significance for the social and economic development of Tibet to carry out the disaster assessment of glacial lake outbursts. GLOFs are the result of the comprehensive effects of the climatic background and geological conditions [8]. Chinese and international scholars have conducted extensive research on GLOFs. Researchers usually divide relevant evaluation indexes into qualitative, quantitative, and semi-quantitative according to the characteristics of the glacial lake concerned [9; 10]. [PERSON] et al. [11] evaluated GLOFs according to the heat index and glaciallake outburst risk index. [PERSON] et al. [12] used the fuzzy evaluation method to select eight indicators for evaluating the risk of GLOF in Luozha County. [PERSON] et al. [13] used Landsat TM satellite remote sensing data to evaluate the risk of glacial lakes in the southern section of the Sichuan-Tibet highway by means of onsite investigation, interviewing, and surveying. [PERSON] [14] selected 188 glacial lakes in Columbia as samples and established a probability equation of glacial lake outburst risk using logistic regression and four indexes. [PERSON] et al. [15] used the fuzzy matter-element extension method to comprehensively evaluate the risk of GLOFs in the Parlung Zangbo River basin. In the risk assessment and modeling of GLOFs, it is of great significance to select an appropriate model corresponding to the influence factors of glacial lakes, which would ensure the accurate and reliable estimation of the hazard level index [16; 17; 18]. The logistic regression model is a regression model based on the maximum likelihood estimation method. It is an excellent and widely used model for evaluating landslide susceptibility. It can be used to effectively determine the correspondence between landslides and non-landslides (1-0) and nonlinear correlations between landslides and impact factors. This model has simple calculation steps and offers high adaptability to evaluation factors required by independent variables. Evaluation factors can be either discretely distributed or continuously distributed [19]. Scholars in China and abroad have also made useful attempts and explorations using logistic regression models to evaluate the susceptibility of moraine lakes to outbursts [20; 21; 22]. [PERSON] et al. [23] described the probability of glacial lake formation on a pixel basis for Himalayan glaciers using logistic regression. [PERSON] et al. [24] developed a logistic regression predictive model for estimating the hazard probability of the Grasshopper Glacier Lake. [PERSON] et al. [25] also used logistic regression to classify the drained/undrained lakes in the southern Coast Mountains of British Columbia, Canada.