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Lake water is a crucial resource in the global hydrological cycle, providing substantial freshwater resources and regulating regional climates. High-resolution remote sensing satellites, such as Landsat, provide unprecedented opportunities for the continuous monitoring of lake area changes. However, limitations imposed by revisit cycles and cloud cover often result in only a few usable images being taken per month for a single lake, restricting our understanding of daily-scale lake dynamics. Leveraging recent advancements in AI-driven remote sensing technologies, we developed an innovative deep learning algorithm, MosaicFormer, a Transformer-based model designed for spatiotemporal fusion across diverse remote sensing applications. We used it to integrate observations from MODIS and Landsat, producing seamless daily Landsat-scale images. To demonstrate its effectiveness, we applied the model to lake monitoring, showcasing its ability to reconstruct high-resolution water body dynamics with limited Landsat data. This approach combines Masked Autoencoders (MAEs) with the Swin Transformer architecture, effectively capturing latent relationships between images. Testing on public benchmarks demonstrated that our method outperforms all traditional approaches, achieving robust data fusion with an overall R2 of 0.77. A case study on lake water monitoring reveals that our method captures daily variations in the surface area of Hala Lake, providing accurate and robust results. The results indicate that our method demonstrates significant advantages and holds substantial potential for large-scale remote sensing-based environmental monitoring.

In the Qaidam Basin, meteorological stations are sparsely distributed, and the observational precipitation data are therefore not comprehensive, which significantly hampers the accurate assessment and optimal allocation of regional water resources. This study aims to evaluate the accuracy of three precipitation products (MSWEP V2, GPM IMERG V6, and TRMM 3B43) against gauged-based precipitation data from nine meteorological stations and 12 hydrologic stations in the Qaidam Basin from 2001 to 2016. The results reveal the following: (1) At the annual and monthly scales, the MSWEP product has the highest accuracy, followed by the GPM product. However, the TRMM product only reveals a correlation at the monthly scale; (2) the MSWEP product performs better in the wet season than the dry season, and the TRMM product has an abnormally high value of precipitation in the wet season. Moreover, it was shown that the accuracy of the GPM product is superior to that of the TRMM product; however, it has a low detection capability in some mountainous areas; and (3) the average error of each precipitation product at the meteorological stations is smaller than that at the hydrological stations.

The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction.

To address the global warming issue, China is prioritizing the development of clean energy sources such as wind and solar power under its “dual carbon target”. However, the expansion of these resources is constrained by their intermittency and the spatial and temporal distribution of wind and solar energy. This paper systematically reviews the evolution of wind and solar energy reserves, their development potential, and their current status in China from a geographical perspective. In conjunction with existing research, this paper anticipates future exploration in the realm of wind–solar complementary development or multi-energy complementary development, viewed through the lens of resource quantity. The anticipated findings are intended to furnish a theoretical foundation for further studies on the development and utilization of wind and solar energy resources within China.

Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain.

Lakes are sensitive indicators of climatic change and are important resources for regional economic development. In recent years, there have been many studies related to the changes in lake area and lake volume. However, further studies are still required to understand the responses of lakes to climatic change and human activities. This paper utilizes lakes in the Qaidam Basin, an inland arid region of China, as the object of study and investigates the characteristics of variability in lake changes and its driving forces by combining multi-source remote sensing, model simulations and historical data. We first analyzed the spatiotemporal pattern of climatic change in the basin under the background of global warming. The response of lake water volume to climatic change and human activities is then discussed. Finally, the main factors that affect the change in lake water volume in different regions of the basin are delineated. The water volume of lakes in the Qaidam Basin increased by 3.81 km3 from 1990 to 2020. Particularly since the 21st century, the water volume of lakes has increased rapidly, and an increasingly abrupt change appeared around 2015. The increases in precipitation and vegetation area are the main and secondary factors that led to the increase in total lake water volume in the basin, respectively. However, the main influencing factors still vary in different regions. The impact of air temperature, evaporation, and changes in the cropland area on the change in lake water volume is generally not obvious. Human activities, such as the development of salt lakes and damming, have led to substantial changes in the spatial pattern of lakes in the middle of the basin and are associated with the replacement, genesis, and disappearance of Yiliping Lake, Yahu Lake and West Taijinar Lake, respectively. This study reveals the changing characteristics of climate and lake water volume in inland arid basins in China, which are highly important to understand the responses of lakes to climatic change and human activities, and provides a scientific basis for the rational development and utilization of lake resources in arid basins.

Drought is one of the most severe natural disasters globally, with its frequency and intensity escalating due to climate change, posing significant threats to agricultural production. This is particularly critical in major food-producing regions, where drought profoundly impacts crop yields. Such impacts can trigger food crises in affected regions and disrupt global food trade patterns, thereby posing substantial risks to global food security. Based on historical data, this study examines the yield response characteristics of key crops—maize, rice, soybean, spring wheat, and winter wheat—under drought conditions during their growth cycles, highlighting variations in drought sensitivity among major food-producing countries. The findings reveal that maize and soybean yield in China, the United States, and Brazil are among the most sensitive and severely affected by drought. Furthermore, using precipitation simulation data from CMIP6 climate models, the study evaluates drought trends and associated crop yield risks under different future emission scenarios. Results indicate that under high-emission scenarios, crops face heightened drought risks during their growth cycles, with China and the United States particularly vulnerable to yield reductions. Additionally, employing copula functions, the study analyzes the probability of simultaneous drought occurrences across multiple countries, shedding light on the evolving trends of multicountry drought events in major food-producing regions. These findings provide a scientific basis for assessing global food security risks and offer policy recommendations to address uncertainties in food supply under climate change.

China is rich in wind- and solar-energy resources. In recent years, under the auspices of the “double carbon target,” the government has significantly increased funding for the development of wind and solar resources. However, because wind and solar energy are intermittent and their spatial distribution is uneven, the profits obtained by the developers of wind- and solar-energy resources are unstable and relatively low. For this reason, we analyze in this article the spatiotemporal variations in wind and solar energy resources in China and the temporal complementarity of wind and solar energy by applying a Spearman correlation coefficient based on the Daily Value Dataset of China Surface Climate Data V3.0. Finally, we also strive to harmonize regions where wind and solar resources are less complementary by introducing hydro-energy resources. The results reveal that wind energy and solar energy resources in China undergo large interannual fluctuations and show significant spatial heterogeneity. At the same time, according to the complementarity of wind and solar resources, over half of China’s regions are suitable for the complementary development of resources. Further research shows that the introduction of hydro-energy resources makes it feasible to coordinate and complement the development of wind- and solar-energy resources in areas where the complementarity advantage is not significant. This has a significant effect on increasing the profit generated by the complementary development of two or more renewable resources.

Accurate rainfall estimations based on ground-based rainfall observations and satellite-based rainfall measurements are essential for hydrological and environmental modeling in the Qaidam Basin of China. We evaluated the accuracy of daily and monthly scale Tropical Rainfall Measuring Mission (TRMM) rainfall products in the Qaidam Basin. A Geographically Weighted Regression (GWR) was used to estimate the spatial distribution of the TRMM product error using altitude and geographical latitude and longitude as independent variables. Finally, a rainfall model was developed by combining ground-based and satellite-based rainfall measurements, and the model precision was validated with a cross-validation method based on rainfall gauge measurements. The TRMM precipitation observations may contain errors compared with the ground-measured precipitation, and the error for daily data was higher than that for monthly data. A time series of TRMM rainfall measurements at the same location showed errors at certain time intervals. The ground-based and satellite-based rainfall GWR model improved the error in the TRMM rainfall products. This rainfall estimation model with a 1-km spatial resolution is applicable in the Qaidam Basin in which there is a sparse network of rainfall gauges, and is significant for spatial investigations of hydrology and climate change.