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.

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 EC_e 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 EC_e 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.

Under climate change, river ecological base flow becomes particularly critical for the health and stability of inland river ecosystems in arid regions. Taking three typical seasonal inland rivers, including the Kriya River, the Niya River, and the Qarqan River, all on the northern slope of the Kunlun Mountains, as research objects, meteorological data from 1958 to 2019 and hydrological data from 1978 to 2014 were selected to analyze the characteristics of ecological base flow under climate change. The results show that in the past 60 years, the temperature in the Kriya River, Niya River, and Qarqan River basins has warmed at rates of 0.235 °C•(10a)⁻¹, 0.223 °C•(10a)⁻¹, and 0.177 °C•(10a)⁻¹, respectively, while the precipitation has increased at the rates of 0.88 mm•(10a)⁻¹, 3.90 mm•(10a)⁻¹, and 7.92 mm•(10a)⁻¹. The Tennant method was determined as the best algorithm for ecological base flow of inland rivers in arid regions. The interannual maximum values of ecological base flow all occurred in 2010, with three rivers increasing by 5.098 m³·s⁻¹, 2.416 m³·s⁻¹, and 11.343 m³·s⁻¹ respectively, and the regional average increasing by 6.286 m³·s⁻¹. The maximum ecological base flow within the year is in July, and the minimum value is in January, with a high proportion of the flood season. The monthly ecological base flow of rivers has a strong correlation with the monthly temperature and precipitation, with the strongest correlation in the Qarqan River basin and the correlation coefficients of 0.876 and 0.917. We propose ecological base flow guarantee objectives for the three rivers during the dry season, and clarify that the flood season is the key point of ecological base flow guarantee. The guarantee objectives are 0.974 m³·s⁻¹, 0.154 m³·s⁻¹, and 2.167 m³·s⁻¹, for the three rivers, respectively. Our research results can provide scientific reference for the management and regulation of water resources and ecological protection of the northern slope of the Kunlun Mountains.

The Tarim River is located in the arid areas of northwestern China, where groundwater (GW) and surface water (SW) in different landscape units have undergone regular and duplicate transformation processes, greatly improving the utilization of water resources. Investigation of the interaction between groundwater and surface water is critical to determine proper water resources planning and management in the Tarim River region. A new approach of coupling the soil-vegetation-atmosphere transport model (SVAT) with the groundwater-surface water interaction model (GSI) is presented in this paper. Usually, the surface water recharges to groundwater and groundwater-soil water exchange are not considered in the SVAT model. However, in reality, the soil water content profiles and soil heat profiles are intensely affected by shallow groundwater table, especially in arid riparian zones where groundwater levels fluctuate substantially. A new method linking the SVAT model with the GSI model is proposed in this ...

River ecological baseflow is key to river ecosystem health and stability and has become particularly important with global climate change aggravation. By considering the Niya River Basin in Xinjiang, based on meteorological data from 1958 to 2021 and hydrological data from 1978 to 2018, the Tennant method was determined to be the best basin ecological baseflow calculation method, the M–K test was used to analyze the abrupt ecological baseflow and climate change characteristics, and the ecological baseflow regression response and sensitivity coefficient models concerning climate change were established. The results showed that 75% of the ecological baseflow in the Niya River Basin ranged from 15 to 31 m³•s⁻¹ in 1978–2018, the average annual temperature increased by 1.6°C at a 0.22°C•(10a)⁻¹ rate, and the annual precipitation increased by 6.3 mm at a 0.98 mm•(10a)⁻¹ rate. The prediction accuracy of the regression model was good, R² exceeded 0.7, the ecological baseflow response to climate change lagged, and precipitation greatly impacted ecological baseflow. The basin sensitivity coefficient showed a decreasing trend from upstream to downstream, with the annual maximum value in 2010, the minimum value in 1984, the monthly maximum value in April and the monthly minimum value in November. Based on the climate change trend and the social water use of the basin, the ecological baseflow protection targets and measures were proposed according to the season and the hydrological period for actual water resource management and scheduling of the river in this and similar regions.