• Energy Research
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• Chinese Academy of Sciences close

The Nectarian-aged Crisium basin exhibits an extremely thin crust and complicated lunar geological history. This large multi-ring impact basin is characterized by prolonged lunar volcanism ranging from the Imbrian age to the Eratosthenian period, forming the high-Ti mare unit, low-Ti mare basalts, and very low-Ti mare unit. We produced an updated geological map of the Crisium basin and defined four mare units (Im1: 3.74 Ga; Im2: 3.49 Ga; Im3: 3.56 Ga; EIm: 2.49 Ga) in terms of distinct composition and mineralogy. Olivine was widely determined in the Ti-rich Im1, implying the hybridization source in the lunar mantle with the occurrence of small-scale convective overturn. The major phase of low-Ti basaltic volcanism occurred c.a. 3.5 Ga, forming Im2 and Im3 in the western area. The youngest mare unit (EIm) has slight variations of pyroxene compositions, implying a decrease of calcic content of basaltic volcanisms with time. Later, distal material transports from large impact events in highlands could complicate the mixing of local mare basalts in the Copernicus age, especially the Im3 unit. The identified olivine-bearing outcrops and widely Mg-rich materials (Mg# > 70, where Mg# = molar 100 × Mg/(Mg + Fe)) in the western highlands, assumed to be the occurrence of the Mg-suite candidates, require future lunar exploration missions to validate.

Lakes on the Tibetan Plateau (TP) are an indicator of global climate change. The study on the factors driving lake change on the TP can help us understand its response to climate change. In this study, Landsat and ICESat data were used to obtain the variations of area, water level, and storage of Hala Lake and the area of glaciers in the Hala Lake Basin during 1987–2018. Combined with meteorological data, climate change trends and the factors driving Hala Lake change in the last 30 years were analyzed. The contribution of glacier mass loss to lake recharge was estimated by the water balance of Hala Lake. The results showed that Hala Lake has experienced three stages: slight expansion (1987–1994), shrinkage (1995–2001) and rapid expansion (2002–2018) during the study period. The rate of glacial melting continued to decline during the study period. Precipitation was the main factor that drove the hydrological characteristic changes in Hala Lake. The step change points of annual precipitation and temperature occurred in 2001, almost the same time that Hala Lake began expanding rapidly.

Climate change has significantly influenced water resource patterns in arid regions. Applying effective water-saving measures to improve irrigation efficiency and evaluate their future water-saving capabilities is crucial for ensuring the sustainable development of irrigation agriculture. Based on the daily meteorological data from 15 global climate models (GCMs) in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this study used the AquaCrop model to perform high-resolution (0.1° × 0.1°) grid simulations of cotton yields and irrigation requirements. The study also investigated the ability of film-mulched drip irrigation (FMDI) to improve future irrigation efficiency under two shared socio-economic pathways (SSP245 and SSP585) in the Tarim River Basin (TRB), Central Asia, from 2025 to 2100. The results showed that the cotton yield and irrigation water productivity (WPI) in the TRB exhibited an upward trend of 13.82 kg/ha/decade (80.68 kg/ha/decade) and 0.015 kg/m3/decade (0.068 kg/m3/decade), respectively, during the study period. The cotton yield and WPI were higher in the northern, northwestern plains, and northeastern intermountain basin areas, where they reach over 4000 kg/ha and 0.8 kg/m3/decade. However, the cotton yield and WPI were lower in the southwestern part of the study area. Therefore, large-scale cotton production was not recommended there. Furthermore, compared to flood irrigation, the use of FMDI can, on average, improve the WPI by approx. 25% and reduce irrigation water requirements by more than 550 m3/ha. Therefore, using FMDI can save a substantial amount of irrigation water in cotton production, which is beneficial for improving irrigation efficiency and ensuring the future stable production of cotton in the TRB. The research results provide a scientific reference for the efficient utilization and management of water resources for cotton production in the TRB and in similar arid regions elsewhere in the world.

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume change of Lake Qinghai, analyzed the climate and land use changes in Lake Qinghai catchment, and distinguished the contributions of climate change and local human activities to water volume change. The results showed that lake water volume decreased by 9.48 km3 from 1975 to 2004 and increased by 15.18 km3 from 2005 to 2020. The climate in Lake Qinghai catchment is becoming warmer and more pluvial, and the changes in land use have been minimal. Based on the Soil and Water Assessment Tool (SWAT), land use change, climate change and interaction effect of them contributed to 7.46%, 93.13% and −0.59%, respectively, on the variation in surface runoff into the lake. From the perspective of the water balance, we calculated the proportion of each component flowing into and out of the lake and found that the contribution of climate change to lake water volume change was 97.55%, while the local human activities contribution was only 2.45%. Thus, climate change had the dominant impact on water volume change in Lake Qinghai.

Wind erosion is one of the most widespread and severe natural hazards in arid, semiarid, and semihumid regions worldwide. The Three-North region (TNR) (Northeast China, North China, and Northwest China) of China includes 90% of the wind erosion area in China. In response to the harsh environmental conditions in the TNR, China initiated a series of ecological programs, including the Three-North Afforestation Program and Grain for Green. However, little is known about the effect of these ecological programs on wind erosion. Therefore, within our study, we estimated the spatiotemporal variations in wind erosion in the TNR between 1981–2020 with a revised wind erosion model and analyzed its driving mechanism. Then, the ecological programs’ effects on wind erosion changes was identified. The results showed the following. (1) From 1981 to 2020, wind erosion showed a clear downward trend of 99.02 t km−2 a−1, with a slope. On average, the areas of mild, moderate, severe, more severe, and very severe wind erosion accounted for 28.76%, 7.17%, 3.92%, 3.72%, and 13.29% of the total in the TNR, respectively. (2) Wind erosion variation was inconsistent in different parts of the TNR. The wind erosion expressed a long-term decreasing trend in Northeast China and the Loess Plateau, a nonsignificant change in North Central China, and an increasing trend in Northwest China. (3) On average, ecological programs were very limited in reducing erosion at the regional scale, with a contribution of approximately 5.93% in the TNR because of the relatively small scope of ecological programs’ implementation. Climate change played a key role in adjusting wind erosion; wind speed, temperature, and precipitation affected 57.58% of the TNR. Human interference (proportion of cropland and grassland areas in a 1 km ×1 km grid) affected 8.78% of the TNR. Thus, the persistent complement of ecological programs, reasonable human activities, and timely observation is a method to alleviate wind erosion in the TNR.

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations.

The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.