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Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct "single-peak" pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. [ABSTRACT FROM AUTHOR]

Model simulation plays a significant role in the water resources cycle, and the simulation accuracy of models is the key to predicting regional water resources. In this research, the Qianhe tributary at the Weihe River basin in Western China was selected as the study area. The tributary was divided into 29 sub-basins and 308 hydrological response units according to the spatial raster data and attribute data of the hydrology, meteorology, topography, land use, and soil types. On this basis, a soil and water assessment tool (SWAT) model for runoff simulation and evaluation of this region was established. A sensitivity test and parameter calibration were then executed on 15 parameters involved with surface runoff, soil flow, and shallow underground runoff. The simulation results demonstrate a calibration and verification error of 3.06–10.08%, with very small uncertainties throughout the simulation, whereas they exhibit relatively large errors in the simulation of the dry period (winter) but, in contrast, quite small errors in the rainy period (summer). In addition, the simulated runoff with a low value is overestimated. When the annual, monthly, and daily runoff are 4–13.5 m3/s, 4–69.8 m3/s, and 40–189.3 m3/s, respectively, the relative error is smaller, and the simulation results are more accurate. The sensitive parameters predominantly affecting the runoff simulation of the basin include soil evaporation compensation, runoff curve coefficient, vegetation transpiration compensation, and saturated hydraulic conductivity in this region. In the case of hypothetical land use change scenarios, we observe a great reduction in simulated runoff in arable land, woodland, and grassland, while we observe an increment in construction and residential land and wasteland. The annual and monthly runoff are increased by above 54.5%. With the increase in cultivated land and forestland, the annual and monthly runoff decrease by 24.6% and 6.8%, respectively. In the case of hypothetical scenarios under 24 climate combinations, if the precipitation remains unchanged, the increase and decrease in temperature by 1 °C leads to a decline and increment of runoff by −0.72% and 5.91%, respectively. With regard to the simulation for the future under the RCP2.6 and RCP8.5 climate scenarios, downscaling was employed to predict the runoff trend of the future. In short, this study provides a method for runoff inversion and water resources prediction in small mountainous watersheds lacking hydrological and meteorological observation stations. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of "more in the east and less in the west" was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin. [ABSTRACT FROM AUTHOR]