Accurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.

This study aims to analyze the atmospheric water vapor budget changes in the Yellow-Huai-Hai River basin and explore the possible relationship between water vapor budget and precipitation. The Fifth Phase of the Coupled Model Intercomparison Project is used to assess the future change of water vapor in the region. Corrected general circulation model outputs are evaluated, and multimodel ensemble is used to project the future atmospheric water vapor changes. Results show the following: (1) Water vapor in wet summer, which is transported from Bengal Bay and west Pacific Ocean and accounts for the largest part of annual transport, has similar distribution with precipitation in the study area; (2) Strong evidences indicate significant relationships between the precipitation and water vapor in humid area and semihumid area, but poor relationship in semiarid area; (3) The future climate of the region is projected to be wetter but has a "dry belt" located in the Hai River basin, the north of the Huai River basin and the southeast of the Yellow River basin during 2020s, which will reduce rapidly afterward; (4) Summer water vapor changes depend mainly on the meridional transport. The changes under RCP4.5 scenario are smaller than that under RCP8.5 and the increases are more significantly in 2080s under both scenarios. The water vapor has a larger increase in the Huai River basin than that in the other two basins. Future water vapor changes will likely lead to exacerbated problems caused by the uneven distribution of precipitation and produce serious challenges to water resource management in agriculture, industry and the environment.

As one of the three major black soil regions in the world, northeastern China has an important strategic position there. Since the 20th century, the local environment has undergone great changes under the influence of the natural economy, and it is particularly important to quantitatively assess the degree of change. However, there have been few long-term quantitative studies of environmental spatial-temporal variances in the three northeastern provinces. Therefore, in this study, four typical remote sensing indices of the normalized difference vegetation index (NDVI), land surface temperature (LST), normalized differential building–soil index (NDBSI) and wetness (WET) were employed to construct the remote sensing ecological index (RSEI) using a principal component analysis (PCA) method based on the Google Earth Engine (GEE) platform in northeastern China. The spatiotemporal variations in the eco-environmental quality were detected using linear slope and M–K test, and the direct and interactive effects of different influencing factors on the RSEI changes during 2000–2020 were explored based on geographic detection. The results show that the interannual variations in the RSEI show a fluctuating upward trend, with an increase percentage of 12.45% in the last two decades, indicating that the ecological quality of northeast China has gradually improved. Furthermore, that the western and eastern Heilongjiang provinces and western Jilin provinces contributed substantially to the improvement of environmental quality, while the environmental quality of Jilin provinces and central Liaoning provinces decreased to varying degrees. Compared with 2000, the area with a fair environmental quality grade had the greatest change, and had decreased by 60.69%. This was followed by the area with an excellent quality grade, which increased by 117%. Land-use type had the greatest impact on environmental changes in northeastern China, but the impact degree gradually decreased, while the impact of socioeconomic factors such as the gross production of agriculture, forestry, animal husbandry and fishery and population density on environmental quality gradually increased. The major reason for the decline of environmental quality in central Jilin and central Liaoning is that urbanization development had occupied a large amount of cropland. This shows that taking into account the virtuous cycle of an ecological environment while promoting urban and rural development may be an important task for northeastern China in the future.

Irrigation is a significant human activity that affects surface water fluxes in the Tarim River Basin. To quantitatively assess the irrigation impact of this activity on surface water fluxes in the Tarim River, a land surface hydrologic model was coupled with a modified irrigation scheme and a reservoir module and applied to simulate these fluxes. Modeling results indicate that the combined effect of the irrigation process and reservoir operation is prominent in the study area, from which 70–75% of the surface water is extracted and used for irrigation. This scenario can primarily be attributed to the significant amount of water losses as a result of evaporation and the seepage of canals and aqueducts. The effective utilization coefficient of the extracted surface water is only approximately 0.40. The irrigation water withdrawals increased with the recent rapid expansion of cultivated land. Therefore, the water flowing into the main stem of the Tarim River still shows a downward trend, despite the significant increase in the total discharge of headwater basins since the 1960s.