Youcun Liu

A Bayesian analysis of nonstationary generalized extreme value distribution of annual spring discharge minima

In many areas throughout the world, extensive groundwater pumping has facilitated significant social development and economic growth, but has typically resulted in a decrease in groundwater level and a decline and change in spring discharge. The declining trend and changing seasonality of spring discharge lead to nonstationarity in hydrological processes. When we apply the generalized extreme value distribution to karst spring discharge, several assumptions including independence, identical distribution, and stationarity must be met. To investigate the response of spring discharge to extensive groundwater development and extreme climate change, a nonstationary generalized extreme value (NSGEV) model is proposed by assuming the location parameter to be the sum of a linear and a periodic temporal function to describe the declining trend and seasonality of spring discharge. Bayes’ theorem treats parameters as random variables and provides ways to convert the prior distribution of parameters into a posterior distribution. Statistical inferences based on posterior distribution can provide a more comprehensive representation of the parameters. In this paper we use Markov Chain Monte Carlo method, which can solve high-dimensional integral computation in the Bayes equation, to estimate the parameters of NSGEV model. Then the NSGEV model was used to calculate the distribution of minimum discharge values of Niangziguan Springs in North China. The results show that NSGEV model is able to represent the distribution of minimum values and to predict the cessation time of Niangziguan Springs discharge with two controllable variables: time and return period. With a 100-year return level, flow cessation of Niangziguan Springs would occur in April 2022. Moreover, the probability of Niangziguan Springs discharge cessation is 1/27 in 2025, and 1/19 in 2030. This implies that the probability of Niangziguan Springs cessation will increase dramatically with time.

A statistical model for karst spring discharge estimation under extensive groundwater development and extreme climate change

ABSTRACT To acquire better understanding of spring discharge under extreme climate change and extensive groundwater pumping, this study proposed an extreme value statistical decomposition model, in which the spring discharge was decomposed into three items: a long-term trend; periodic variation; and random fluctuation. The long-term trend was fitted by an exponential function, and the periodic variation was fitted by an exponential function whose index was the sum of two sine functions. A general extreme value (GEV) model was used to obtain the return level of extreme random fluctuation. Parameters of the non-linear long-term trend and periodic variation were estimated by the Levenberg-Marquardt algorithm, and the GEV model was estimated by the maximum likelihood method. The extreme value statistical decomposition model was applied to Niangziguan Springs, China to forecast spring discharge. We showed that the modelled spring discharge fitted the observed data very well. Niangziguan Springs discharge is likely to continue declining with fluctuation, and the risk of cessation by August 2046 is 1%. The extreme value decomposition model is a robust method for analysing the nonstationary karst spring discharge under conditions of extensive groundwater development/pumping, and extreme climate changes. Editor D. Koutsoyiannis; Associate editor J. Ward

Preliminary study on reaction of mountain runoff to climate change in Urumqi River, China

Characteristics and trends of precipitation, temperature and runoff variation in the mountain watershed of Urumqi River basin in the past over 40 years were analyzed on basis of the measured data at the relational hydrological and weather stations in the area. The results show that the mountain temperature has significantly increased during the past 48 years; the increase of temperature in autumn was higher than other seasons. The annual precipitation varied with a slightly upward trend in the same period and the increase mainly appeared in summer and winter. The runoff showed an obvious increase trend; it is very sensitive to mountain precipitation and temperature. On the basis of the above study, we found that the increase trend in runoff was more significant than that in precipitation in the study area. That was the synactic results of variation of groundwater, ice-snow meltwater and precipitation caused by global climate change.