Heihe River Basin is the second largest inland river basin in China, where water supply service in the upper reach has greater influence on the sustainable development of middle and lower reaches. This study analyzed the influence of land use/land cover change (LUCC) on the water supply service in the upper reach by carrying out scenario simulation. Firstly, we analyzed the LUCC and climate change in the upper reach during 1990–2005; then the water supply service, which was represented by the annual water yield, was estimated with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Thereafter three scenarios (precipitation change and LUCC change combined, LUCC change only, and precipitation change only) were established to analyze the impacts of LUCC and precipitation change on the water yield. The results show that the LUCC exerted great influence on water yield, while the impact of precipitation change is even more significant than that of LUCC. Although there are still some uncertainties, the results of this study can still provide valuable reference information for ecological conservation and water resource management in the upper reach of the Heihe River Basin.

Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km² and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants.

Water resources play a vital role in human life and agriculture irrigation, especially for agriculture-dominant developing countries and regions. Improving agricultural water use efficiency has consequently become a key strategic choice. This study, based on Zhangye City’s economic characteristics and data, applies a static Computable General Equilibrium (CGE) model with a constant elasticity of substitution (CES) composited water−land resources account to assess the impact of improving agricultural water use efficiency on economy, water conservation and land reallocation. Results reveal that: Zhangye City’s GDP increases by 0.10% owing to an increasing average technical level by improving agricultural water use efficiency; total water consumption decreases by 122 million m³, 69% of which comes from a reduction of surface water use; and land demand increases by 257.43 hectares mainly due to agricultural land demands. With respect to the sectors’ output, export-oriented sectors with higher water intensities in the agricultural sectors benefit most. In contrast, land-intensive sectors contract the most, as the rental price of land rises. Therefore, agricultural water conservation technology should be introduced considering more in surface water. Furthermore, higher demand for agricultural land would reduce land availability for other sectors, thus inhibiting urbanization pace on a small scale.