Oasis landscape change and its pattern dynamics are considered one of the vital research areas on global land use and landscape change in arid regions. An agricultural oasis is the main site of food security and ecosystem services in arid areas. Recently, the dramatic exploitation of agricultural oases has affected oasis stability, inducing some ecological and environmental issues such as water shortage and land degradation. In this study, the Qira oasis on the southern margin of Tarim Basin, Northwest China, was selected as a study area to examine the spatiotemporal changes in an agricultural oasis and the influence on oasis landscape pattern. Based on the integration of Thematic Mapper, Enhanced Thematic Mapper Plus, and GF-1 images, the agricultural Qira oasis has rapidly increased, with annual change rates of −0.3%, 1.6%, 3.7%, and 1.5% during 1970–1990, 1990–2000, 2000–2013, and 2013–2016, respectively. With the agricultural oasis expansion, the agricultural land has increased from 91.10 km² in 1970 to 105.04 km² in 2016. The percentage of farmland area has increased by 15.3% in 2016 compared with that in 1970. The natural vegetation is decreasing owing to the reclamation of desert–oasis ecotone. The oasis landscape change and pattern are mainly affected by agricultural expansion under water-saving technological utilization, land use policy, and regional economic development demand. The expansion of agricultural oasis is alarming due to human overexploitation. Thus, the government should adjust the layout of agricultural development and pay considerable attention to the oasis environment sustainability. This study can provide a valuable reference on the impact of climate change and human activities on a landscape.

Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R²), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R² of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R² of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.

Recently, a wide range of quantitative research on the identification of environmental flow requirements (EFRs) has been conducted. However, little focus is given to EFRs to maintain multiple ecosystem services in oasis areas. The present study quantifies the EFRs in oasis areas of Tarim Basin, Xinjiang, Northwest China on the basis of three ecosystem services: (1) maintenance of riverine ecosystem health, (2) assurance of the stability of oasis–desert ecotone and riparian (Tugai) forests, and (3) restoration of oasis–desert ecotone groundwater. The identified consumptive and non-consumptive water requirements are used to quantify and determine the EFRs in Qira oasis by employing the summation and compatibility rules (maximum principle). Results indicate that the annual maximum, medium, and minimum EFRs are 0.752 × 10⁸, 0.619 × 10⁸, and 0.516 × 10⁸ m³, respectively, which account for 58.75%, 48.36%, and 40.29% of the natural river runoff. The months between April and October are identified as the most important periods to maintain the EFRs. Moreover, the water requirement for groundwater restoration of the oasis–desert ecotone accounts for a large proportion, representing 48.27%, 42.32%, and 37.03% of the total EFRs at maximum, medium, and minimum levels, respectively. Therefore, to allocate the integrated EFRs, focus should be placed on the water demand of the desert vegetation’s groundwater restoration, which is crucial for maintaining desert vegetation to prevent sandstorms and soil erosion. This work provides a reference to quantify the EFRs of oasis areas in arid regions.

The dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.