中文摘要：

本文运用GM（1，1）分解模型和ARIMA模型分别模拟柳林泉流量。根据影响特征将泉水流量变化分为两个时段研究：1957—1973年泉水流量处于自然状态；1974--2009年泉水流量受气候变化和人类活动双重影响。运用第1时段的数据建模获得自然状态下泉水流量的模型，将模型外推，获得第2阶段自然状态下泉水流量，然后根据水量平衡原理，减去同期实测流量，获得人类活动对泉水流量衰减的贡献。GM（1，1）分解模型的结果显示，从20世纪70年代到21世纪初柳林泉衰减量为2．26m3／s；ARIMA模型的结果为2．32m3／s；与同期实际衰减量2．27m3／s比，相对误差分别为0．44％和2．20％，表明两种模型都适用于泉水流量的模拟。对比人类活动和气候变化对柳林泉流量衰减的贡献，两个模型得到同样结果，即人类活动的贡献是气候变化的8～9倍。实证研究显示，GM（1，1）模型适用于指数序列的模拟，对具有周期波动的泉水流量，可通过周期修正提高精度；而ARIMA模型能够较好地反映泉水流量相对于降水量的时间滞后效应，能比较准确地模拟泉水流量与降水量的量化关系。

英文摘要：

The discharge of the Liulin spring is simulated with GM（1,1） decomposition model and ARIMA model respectively. According to the hydrological characteristics, the Liulin spring flow series could be di- vided into two periods. First, from 1957 to 1973 the spring flow was under natural state; second, from 1974 to 2009 the spring flow was impacted by both climate change and human activities. Using the data of first pe- riod, the spring flow under the natural state is fitted with GM（1,1） decomposition model and ARIMA mod- el, and then the models are extrapolated to obtain the second periods＇ spring flow under the natural state. According the water balance principle, the spring flow decrement contributed by human activities is acquired by subtracting the observed discharge from simulated spring flow of the second period under the natural state. Thus, it is differentiated the effects of human activities from climate change. The simulated Liulin Springs＇ attenuation from 1970s to early 21st century is 2.26 m3/s by GM （1,1） decomposition model and 2.36 m3/s by ARIMA model with the relative error being 0.44% and 2.20% respectively, showing both GM （1,1） decomposition model and ARIMA model are suitable for spring flow simulation. Comparing the effects of human activities and climate change to the depletion of the Liulin Spring＇s discharge, the authors find that the contribution of human activities is 8 to 9 times higher than that of the climate change. The empirical studies have shown that the GM （1,1） model is of high precision in simulating the exponential series. It can also improve accuracy by periodic amendment, when simulate the spring flow with periodic fluctuations. ARIMA model could reflect time-lag between precipitation and spring discharge and accurately simulate their quantitative relation.