Various hydrological models have been designed to simulate moisture transformation in the water-cycle system between atmospheric water, surface water, soil water and groundwater. But few have been designed specially for oases in arid desert areas where the ecology and the environment are vulnerable because of unwise water-land resources utilization. In order to analyze the moisture transformation in the Hotan Oasis in the Taklimakan Desert in China, and hence to provide scientific references for the rational exploitation and allocation of the limited water-land resources, for the purpose of ensuring that the vulnerable ecology and environment there can be gradually improved and the social economy there can develop sustainably, a dissipative hydrological model for the Hotan Oasis (DHMHO) was developed. It was an outcome of years of systematic study on the moisture transformation in arid areas and on the water-land conditions in the Hotan Oasis. Based on statistics, DHMHO introduces two empirical equations whereby we dynamically calibrated model parameters with monthly data from year 1971 to 1995. Then the calibrated parameters were used to model the moisture movement from 1996 to 2003 and thereafter rationality check and error analysis were conducted. The error analysis results show that the absolute relative errors between simulated and observed groundwater depth values are almost (11 of 12 points) within 20%, and C. Zhao et al. those in annual watershed outflow simulation are mostly (six of eight points) within 20% with an average annual Nash-Sutcliffe Efficiency Coefficient (NSEC) of 0.80. With DHMHO and IPCC assessment, we also simulated the moisture transformation and dissipation in the Hotan Oasis from the year 2011 to 2020. Results show details of the water resources in the Hotan Oasis in the next decade and hence are expected to provide scientific references for establishing rational exploitation and allocation policies on the local water-land resources in the future.

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed that (1) the SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

Understanding the spatial-temporal dynamics of evapotranspiration in relation to climate change and human activities is crucial for the sustainability of water resources and ecosystem security, especially in regions strongly influenced by human impact. In this study, a process-based evapotranspiration (ET) model in conjunction with the Global Land Surface Satellite (GLASS) LAI dataset was used to characterize the spatial-temporal pattern of evapotranspiration from 1982 to 2016 over the Gan River basin (GRB), the largest sub-basin of the Poyang Lake catchment, China. The results showed that the actual annual ET (ETa) weakly increased with an annual trend of 0.88 mm year−2 from 1982 to 2016 over the GRB, along with a slight decline in annual potential ET (ETp). On an ecosystem scale; however, only the evergreen broadleaved forest and cropland presented a positive ETa trend, while the rest of the ecosystems demonstrated negative trends of ETa. Both correlation analysis and sensitivity analysis revealed a close relationship between ETa inter-annual variability and energy availability. Attribution analysis illustrated that contributions of climate change and vegetation greening on the ETa trend were −0.48 mm year−2 and 1.36 mm year−2, respectively. Climate change had a negative impact on the ETa trend over the GRB. However, the negative effects have been offset by the positive effects of vegetation greening, which mainly resulted from the large-scale revegetation in forestland and agricultural practices in cropland. It is concluded that large-scale afforestation and agricultural management were the main drivers of the long-term evolution of water consumption over the GRB. This study can improve our understanding of the interactive effects of climate change and human activities on the long-term evolution of water cycles.

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The influences of natural factors on groundwater depth in Hotan Oasis were analyzed by grey relational analysis and multiple linear regression models set up to test the precision of the analysis. The results show that temperature and evaporation are the most important influencing factors of groundwater depth. Groundwater depth sinks deeper with increasing temperature and evaporation. Runoff recharges groundwater and is the second important factor. Groundwater depth gets shallower with increasing runoff and groundwater response lags by one to two months. The influence of wind speed, water temperature or humidity on groundwater depth is relatively low. There is no significant correlation between precipitation and groundwater depth. The groundwater depth regression models have high precision with confidence levels above α = 0.01.