为分析西北地区地下水干旱时空演变趋势及对气象干旱的动态响应,利用GRACE和GLDAS数据定量评估地下水储量变化,构建地下水干旱指数GRACE-GDI分析地下水干旱的时空演变特征,并利用Pearson相关系数分析了地下水干旱对气象干旱的动态响应关系.结果表明:西北地区地下水储量总体上以0.25 cm/a的速率枯竭;河西走廊、六盘山区、青海南部地下水干旱发生频率较高,陕南地区、柴达木盆地地下水干旱发生频率较低,西北地区多年平均地下水干旱面积比例为29.0％;地下水干旱与气象干旱的响应关系存在明显的空间异质性,其中呈显著正相关关系的区域占59.3％,且由于气候变暖和植被改善,在准噶尔盆地、吐鲁番盆地、青海湖流域、阿尔泰山等地区响应程度增加;干旱响应时间主要为1～6月和19～24月.

Understanding the relationship among different types of drought is crucial for drought mitigation and early warnings. Much attention has been recently focused on the propagation from meteorological drought (MD) to hydrological drought (HD); however, the influences of human activities on drought propagation have rarely been explored. The novelty of the study was to propose an effective framework to quantify the impacts of human activities on MD-HD propagation. We adopted the framework to comprehensively evaluate the anthropic impacts on hydrological drought variations and time, thresholds, and probabilities of MD-HD propagation in the Weihe River Basin (WRB) during different periods. The results showed that human activities did significantly disturb HD variations and MD-HD propagation characteristics. Specifically, human activities increased the frequency and extremes of HD and weakened its correlation with MD. The MD-HD propagation characteristics showed spatiotemporal differences across three subbasins because of the different levels of human activities. The thresholds of MD triggering different levels of HD generally became larger with change rates from 1% to 143% and 3% to more than 189% during two periods, respectively. Meanwhile, we also found that the thresholds became distinctly smaller, which could only be observed in spring and winter. Moreover, the relationship between natural and human-induced probabilities of HD occurrence showed three patterns with the increase of MD severity. The quantitative results of this study can provide guide information on adaptation strategies to promote drought preparedness in the WRB. The proposed framework can be also applied in other regions to improve the understanding of hydrological drought mechanisms.

【Objective】 The increased demand for water due to economic development coupled with dwindling water supply is the double whammy facing most provinces in north China. Given agriculture is the biggest water use sector, understanding the change in agricultural water demand is critical to improving water resources management. The purpose of this paper is to present a new method to estimate agricultural water use changes at provincial scale in the north of China. 【Method】 The proposed method was based on discrete wavelet transform (DWT), fractional-order grey model (FGM(1,1)), weighted Markov Chain (WMC), and autoregressive moving average (ARMA) model. The time series of agricultural water demand was firstly decomposed into approximate series and detailed series, respectively, using DWT. FGM(1,1) was then used to describe the approximate series, with the errors corrected by WMC. The Fisher optimal segmentation method was used to divide the state intervals of the predicted errors, and the state intervals were predicted using a probability transfer matrix. The predicted intervals and values of the approximate series were obtained from the boundary values and median of the predicted error state intervals. In comparison, the detailed series were predicted using the ARMA model based on the Akaike Information Criteria. These were used to predict the agricultural water demand and its intervals. We applied the models to agricultural water demand in Shaanxi and Inner Mongolia provinces, with data measured from 2002 to 2015 used to train the model and those measured from 2016 to 2019 to validate the model. We compared the results calculated from the proposed model with those estimated from the traditional GM (1,1), DWT- GM(1,1)-ARMA, and DWT-FGM(1,1)-ARMA models. 【Result】 The average absolute error of the proposed model for the two provinces was 1.25% and 1.01%, respectively, much less than those given rise to by other models. The predicted agricultural water demand intervals showed that after correction by WMC, the model provided reliable short-term fluctuation intervals in agricultural water demand in both provinces. 【Conclusion】 The proposed model for predicting agricultural water demand at provincial scale was accurate and robust. It can also predict the intervals which describe the short-term fluctuation in agricultural water demand. The model has an implication in helping improve water management and developing sustainable agriculture.