Frequency Analysis of Snowmelt Flood Based on GAMLSS Model in Manas River Basin, China

Abstract

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

“…A Generalized Additive Model in Location, Scale, and Shape (GAMLSS) is a semi parametric regression model that analyzes the frequencies of stationary and non-station ary runoff and other features [17][18][19][20][21][22]29,30].…”

“…It supports a variety of random variable frequency distribution types and is extremely useful in constructing linear or nonlinear functional relationships between distribution function position parameters, scale parameters, shape parameters, and explanatory variables [18]. The GAMLSS framework has been widely applied in non-stationary frequency analysis, modeling, and forecasting in hydrology [19][20][21][22]. This GAMLSS feature also allows for cross-driving interactions between runoff and the driving elements, or between the driving elements themselves.…”

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

“…Among the several nonstationary flood frequency approaches, the time-varying moment method is widely utilized. Through incorporating factors into the covariates of model parameters, the time-varying moment method could quantify and even forecast the variation of extreme flood risks under changing environments [2][3][4][5][6]. For instance, Lopez and Frances (2013) applied the generalized additive models for location, scale and shape (GAMLSS) on flood risk impacted by multiple climate indices such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Mediterranean Oscillation and so on [7].…”