Glacial lake outburst floods(GLOFs)are among the most severe cryospheric hazards in the Himalaya.While previous studies have primarily focused on the characteristics and causes of GLOFs,strategies for mitigating their...Glacial lake outburst floods(GLOFs)are among the most severe cryospheric hazards in the Himalaya.While previous studies have primarily focused on the characteristics and causes of GLOFs,strategies for mitigating their disaster impacts remain underexplored.This study introduces China’s Glacial Lake Management System(GLMS)and evaluates its potential for regional replication in reducing damage caused by GLOFs.We find that while GLOF frequency shows a statistically insignificant decrease from 1990 to 2023,downstream damage has intensified,yet appears relatively mitigated within China across the Himalaya following the implementation of the GLMS.Further hydrodynamic modelling suggests that glacial lakes will continue to expand in the future,with total growth expected to triple relative to the 2000-2020 period.These expansions could increase GLOF exposure by over 27%for high-risk lakes and by more than 40%in regions outside China without targeted interventions.However,implementing GLMS engineering measures could reduce the intensity of future floods by 24%,with even greater reductions outside China—29%compared to 21%within China.Building on China’s lake management experience and recognizing the transboundary nature of GLOFs,the comprehensive framework we propose for region-wide glacial lake risk reduction across the Himalaya integrates engineering measures,early warning systems,and community responses.This framework addresses the urgent need for proactive and coordinated mitigation strategies in densely populated high-mountain regions.展开更多
In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for u...In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for univariate generalized linear model E(y|X) = μ(X′β). Given uncorrelated residuals {e i = Y i ? μ(X i ′ β0), 1 ? i ? n} and other conditions, we prove that $$ \hat \beta _n - \beta _0 = O_p (\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n^{ - 1/2} ) $$ holds, where $ \hat \beta _n $ is a root of the above equation, β 0 is the true value of parameter β and $$ \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n $$ denotes the smallest eigenvalue of the matrix S n = ∑ i=1 n X i X i ′ . We also show that the convergence rate above is sharp, provided independent non-asymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas (1976) for classical linear regression models, we point out that the necessary condition guaranteeing the weak consistency of QMLE is S n ?1 → 0, as the sample size n → ∞.展开更多
We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mix...We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mixing) responses under mild conditions. The LIL is useful to derive the asymptotic bounds for the discrepancy between the empirical process of the log-likelihood function and the true log-likelihood. The strong consistency of some penalized likelihood-based model selection criteria can be shown as an application of the LIL. Under some regularity conditions, the model selection criterion will be helpful to select the simplest correct model almost surely when the penalty term increases with the model dimension, and the penalty term has an order higher than O(log log n) but lower than O(n). Simulation studies are implemented to verify the selection consistency of Bayesian information criterion.展开更多
基金supported by the National Natural Science Foundation of China(42571153,42301150,42301140,and ERGTPES project 42588201)the China Post-Doctoral Program for Innovative Talents(BX20230387)+5 种基金the Basic Excellent Research Group for Tibetan Plateau Earth System,the Department of Science and Technology of Xizang(XZ202403ZY0028)the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0201)support from the HINTERLANDS project(High mountains in the Anthropocene:from landscape dynamics to hazards and risksPRIMUS/25/SCI/005)realized at the Charles University,Faculty of Scienceand Johannes Amos Comenius Programme(P JAC),project No.CZ.02.01.01/00/22_008/0004605Natural and anthropogenic georisks.
文摘Glacial lake outburst floods(GLOFs)are among the most severe cryospheric hazards in the Himalaya.While previous studies have primarily focused on the characteristics and causes of GLOFs,strategies for mitigating their disaster impacts remain underexplored.This study introduces China’s Glacial Lake Management System(GLMS)and evaluates its potential for regional replication in reducing damage caused by GLOFs.We find that while GLOF frequency shows a statistically insignificant decrease from 1990 to 2023,downstream damage has intensified,yet appears relatively mitigated within China across the Himalaya following the implementation of the GLMS.Further hydrodynamic modelling suggests that glacial lakes will continue to expand in the future,with total growth expected to triple relative to the 2000-2020 period.These expansions could increase GLOF exposure by over 27%for high-risk lakes and by more than 40%in regions outside China without targeted interventions.However,implementing GLMS engineering measures could reduce the intensity of future floods by 24%,with even greater reductions outside China—29%compared to 21%within China.Building on China’s lake management experience and recognizing the transboundary nature of GLOFs,the comprehensive framework we propose for region-wide glacial lake risk reduction across the Himalaya integrates engineering measures,early warning systems,and community responses.This framework addresses the urgent need for proactive and coordinated mitigation strategies in densely populated high-mountain regions.
基金supported by the President Foundation (Grant No. Y1050)the Scientific Research Foundation(Grant No. KYQD200502) of GUCAS
文摘In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for univariate generalized linear model E(y|X) = μ(X′β). Given uncorrelated residuals {e i = Y i ? μ(X i ′ β0), 1 ? i ? n} and other conditions, we prove that $$ \hat \beta _n - \beta _0 = O_p (\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n^{ - 1/2} ) $$ holds, where $ \hat \beta _n $ is a root of the above equation, β 0 is the true value of parameter β and $$ \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n $$ denotes the smallest eigenvalue of the matrix S n = ∑ i=1 n X i X i ′ . We also show that the convergence rate above is sharp, provided independent non-asymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas (1976) for classical linear regression models, we point out that the necessary condition guaranteeing the weak consistency of QMLE is S n ?1 → 0, as the sample size n → ∞.
文摘We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mixing) responses under mild conditions. The LIL is useful to derive the asymptotic bounds for the discrepancy between the empirical process of the log-likelihood function and the true log-likelihood. The strong consistency of some penalized likelihood-based model selection criteria can be shown as an application of the LIL. Under some regularity conditions, the model selection criterion will be helpful to select the simplest correct model almost surely when the penalty term increases with the model dimension, and the penalty term has an order higher than O(log log n) but lower than O(n). Simulation studies are implemented to verify the selection consistency of Bayesian information criterion.
