Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

As a component of arid ecosystems, groundwater plays an important role in plant growth; therefore, it is essential to use deterministic models to reconstruct the process of groundwater level change. Typically, the linearized solution of the one‐dimensional (1‐D) Boussinesq equation yields acceptable performance in simulating transient conditions over short recharge periods in ephemeral stream systems, but the ability of this solution to simulate multiyear changes in groundwater levels is limited. In this study, an improved groundwater hydraulics (GH‐D2) model is built based on the groundwater hydraulics (GH) solution of the 1‐D Boussinesq equation to simulate multiyear changes in the groundwater level in ephemeral stream systems. The model is validated in the lower reaches of the Tarim River to simulate groundwater level fluctuations within the scope of influence of the river (300, 500, 750, 1050 m) over a 16‐year period (2000 to 2015). To evaluate the performance of the models, the bias, mean absolute error, root mean squared error, Nash‐Sutcliffe efficiency (NSE), and coefficient of determination (R2) are calculated. The results show that the improved GH‐D2 model, which considers ephemeral streamflow, unsteady flow theory and the delayed response effect of groundwater level changes, performs well in simulating multiyear changes in the groundwater level in the ephemeral stream system. The observed and simulated values of the groundwater level at different river distances are consistent, and the model provides a new basis for multiyear simulations of groundwater level fluctuations in ephemeral stream systems.

Soil salinity is an active and complex part of soil property in arid and semiarid irrigation areas that restricts the sustainability of agriculture production. Knowledge of seasonal distributions and migration of soil salinity is important for the management of agriculture. In this study, three-dimensional (3-D) geostatistical methods were used to construct seasonal 3-D spatial distribution maps of soil salinity, and then the quantitative analysis methods were used to study the seasonal accumulation patterns of soil salinity for the 0–150 cm soil depth in cold and semiarid irrigated rice fields. The results revealed that there were different spatial distribution and migration patterns of soil salinity in autumn 2015, spring 2016, autumn 2016, and spring 2017. The migration of soil salinity had a dispersion trend from autumn to spring, and the area of non-saline soil increased. Whereas there was an accumulation trend from spring to autumn, and the area of non-saline soil decreased. There were about 10–20% of the study area had experienced transitional changes of different soil salinity levels in different seasons. The correlation coefficient showed that there were significant positive correlations among the five depth increments (30 cm) in different seasons, and the correlations of soil salinity were higher in adjacent layers than in nonadjacent layers. The ECe values were higher in the topsoil (0–30 cm) and deeper subsoil (120–150 cm), indicating that soil soluble salts accumulated in the soil surface due to evaporation and accumulated in the bottom due to leaching and drainage. Microtopography was the major factor influencing spatial distribution of soil salinity in different seasons. The ECe values were generally higher in the swales or in areas with rather poor drainage, whereas the values were lower in relatively higher-lying slopes or that were well-drained. The results provide theoretical basis and reference for studying the variation of seasonal soil salinity in irrigated fields.

Estimating the snow cover change in alpine mountainous areas (in which meteorological stations are typically lacking) is crucial for managing local water resources and constitutes the first step in evaluating the contribution of snowmelt to runoff and the water cycle. In this paper, taking the Jingou River Basin on the northern slope of the Tianshan Mountains, China as an example, we combined a new moderate-resolution imaging spectroradiometer (MODIS) snow cover extent product over China spanning from 2000 to 2020 with digital elevation model (DEM) data to study the change in snow cover and the hydrological response of runoff to snow cover change in the Jingou River Basin under the background of climate change through trend analysis, sensitivity analysis and other methods. The results indicate that from 2000 to 2020, the annual average temperature and annual precipitation in the study area increased and snow cover fraction (SCF) showed obvious signs of periodicity. Furthermore, there were significant regional differences in the spatial distribution of snow cover days (SCDs), which were numerous in the south of the basin and sparse in the central of the basin. Factors affecting the change in snow cover mainly included temperature, precipitation, elevation, slope and aspect. Compared to precipitation, temperature had a greater impact on SCF. The annual variation in SCF was limited above the elevation of 4200 m, but it fluctuated greatly below the elevation of 4200 m. These results can be used to establish prediction models of snowmelt and runoff for alpine mountainous areas with limited hydrological data, which can provide a scientific basis for the management and protection of water resources in alpine mountainous areas.

No abstract