Monitoring variations in terrestrial water storage (TWS) is of great significance for the management of water resources. However, it remains a challenge to continuously monitor TWS variations using in situ observations and hydrological models because of a limited number of gauge stations and the complicated spatial distribution characteristics of TWS. In contrast, the Gravity Recovery and Climate Experiment (GRACE) could overcome the aforementioned restrictions, providing a new reliable means of observing TWS variation. Thus, GRACE was employed to investigate TWS variations in Northwest China (NWC) between April 2002 and March 2016. Unlike previous studies, we focused on the interactions of multiple climatic and vegetational factors, and their combined effects on TWS variation. In addition, we also analyzed the relationship between TWS variations and socioeconomic water consumption. The results indicated that (i) TWS had obvious seasonal variations in NWC, and showed significant decreasing trends in most parts of NWC at the 95% confidence level; (ii) decreasing sunshine duration and wind speed resulted in an increase in TWS in Qinghai province, whereas the increasing air temperature, ameliorative vegetational coverage, and excessive groundwater withdrawal jointly led to a decrease in TWS in the other provinces of NWC; (iii) TWS variations in NWC had a good correlation with water storage variations in cascade reservoirs of the upper Yellow River; and (iv) the overall interactions between multiple climatic and vegetational factors were obvious, and the strong effects of some climatic and vegetational factors could mask the weak influences of other factors in TWS variations in NWC. Hence, it is necessary to focus on the interactions of multiple factors and their combined effects on TWS variations when exploring the causes of TWS variations.

The Huai River Basin (HRB) is an important grain and industrial production area in eastern China with frequent droughts. Under the background of current climate change, the hydrological and meteorological characteristics of the basin may be changed, which may lead to the changes of regional drought characteristics. It should be paid more attention on the drought research under climate change and the difference between different drought indices. Coupled Model Intercomparison Project phase 5 (CMIP5) as an important tool for climate change research has been used in the study and the study chosen three global circulation models (GCMs)—such as CNRM-CM5 (CNR), HadGEM2-ES (Had), and MIROC5 (MIR)—to gather an ensemble model (EnM) for providing the future climate information. The Standardized Precipitation Index (SPI) and Humidity Index (HI) were used to evaluate and compare the drought situations in the past and the future periods with two representative concentration path scenarios (RCP4.5 & RCP8.5). Some sequence statistics methods, such as Mann–Kendall test and run theory, were carried out to analyze the trend and the changes of extreme drought frequency and characteristics values. The research showed that the simulation accuracy of the EnM would better. SPI and HI take different factors into count and thus lead to differences in describing drought trend, extreme drought frequency, and characteristic values, such as drought severity, drought duration, mean drought severity, and max drought intensity. The research showed that both SPI and HI showed the same wetting or drying trend in the same timescales (except winter) as in the historical data. However, the future annual and seasonal drought trend reflected by SPI shows a wetting trend while HI shows a drying trend. Both in the past and the future, extreme drought frequency and characteristic values reflected by HI are higher than SPI. The drought trend is greater, and the extreme drought frequency and characteristics tend to be strengthened under RCP8.5. Low precipitation and high potential evapotranspiration (PET), especially the PET caused by temperature rise, are the main influencing factors of drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis and we should strengthen the comparative study of different drought indices in future drought analysis under climate change.

Huai River Basin (HRB) is an important food and industrial production area and a frequently drought-affected basin in eastern China. It is necessary to consider the future drought development for reducing the impact of drought disasters. Three global circulation models (GCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5), such as CNRM-CM5 (CNR), HadGEM2-ES (Had) and MIROC5 (MIR), were used to assessment the future drought conditions under two Representative Concentration Pathways (RCPs) scenarios, namely, RCP4.5 and RCP8.5. The standardized precipitation evapotranspiration index (SPEI), statistical method, Mann-Kendall test, and run theory were carried out to study the variations of drought tendency, frequency, and characteristics and their responses to climate change. The research showed that the three CMIP5 models differ in describing the future seasonal and annual variations of precipitation and temperature in the basin and thus lead to the differences in describing drought trends, frequency, and drought characteristics, such as drought severity, drought duration, and drought intensity. However, the drought trend, frequency, and characteristics in the future are more serious than the history. The drought frequency and characteristics tend to be strengthened under the scenario of high concentration of RCP8.5, and the drought trend is larger than that of low concentration of RCP4.5. The lower precipitation and the higher temperature are the main factors affecting the occurrence of drought. All three CMIP5 models show that precipitation would increase in the future, but it could not offset the evapotranspiration loss caused by significant temperature rise. The serious risk of drought in the future is still higher. Considering the uncertainty of climate models for simulation and prediction, attention should be paid to distinguish the effects of different models in the future drought assessment.

The authors wish to make a correction to their paper