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Liulin springs discharge is simulated using grey system and ARIMA model, respectively. According to the hydrological characteristics, the Liulin springs discharge series can be divided into two periods: 1957 to 1973, the spring discharge was in natural state; from 1974 to 2009, the spring discharge was impacted by both climate change and human activities. The data of the first period is used to calculate the spring discharge in natural state and the model is extrapolated, which can obtain the second period's spring discharge in natural state. The contribution of human activities in depletion of Liulin Springs can be acquired by subtracting the observed discharge from simulated spring discharge in the second period. Thus, the effects of human activities from climate change is differentiated. According to the results, both GM(1,1) decomposition model and ARIMA model were suitable for spring discharge simulation. The empirical studies shows that the grey system GM(1,1) model has a high precision for the index series simulation. But for the spring discharge with large periodic fluctuation, it can only achieve accuracy through periodic amendment. ARIMA model can reflect time-lag effect of precipitation on spring discharge very well, which can accurately simulate the quantitative relationship between spring discharge and precipitation.