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Lake surface water temperature (LSWT) is a key parameter in lake energy budget and is highly vulnerable to climate change. However, the long‐term trends in LSWT across China and their driving factors remain uncertain. Here, we used a calibrated lake model to simulate LSWT over 1979–2018 for 91 large lakes (>100 km2) across China. Simulations reveal an overall LSWT warming trend (0.040°C yr−1, p < 0.05), but with large spatial variations. The majority of these lakes show significant warming trends (84%, 0.053°C yr−1), while a significant cooling trend is found in the seven lakes in the northwestern Tibetan Plateau (−0.064°C yr−1). LSWT of approximately 42% of the lakes increases more rapidly than the corresponding ambient air temperature. Regionally, the warming trend is highest for lakes in the Eastern Plain (0.049°C yr−1) and the lowest in the Yunnan‐Guizhou Plateau (0.016°C yr−1). The increases in simulated LSWT also vary across seasons, with a higher rate in winter and spring than in summer and autumn. Changes in air temperature, downward longwave radiation, and wind speed are the most important climatic drivers for LSWT changes. Lake surface warming could be more rapid under future global warming, necessitating greater attention to lake‐atmosphere interactions. Plain Language Summary: Lake surface water temperature (LSWT) is an essential factor in regulating heat and water exchange between lake surface and air. Previous studies have relied on satellite or in situ data to study changes in LSWT at various scales. However, the spatiotemporal variations in LSWT at a national scale and their driving factors are still poorly understood for large countries such as China. Here, we used a one‐dimensional lake model (FLake) to simulate the spatiotemporal variability of LSWT for 91 large lakes (>100 km2) across China from 1979 to 2018. Our simulations reveal a significant warming trend for most lakes, although a few lakes in the northwestern Tibetan Plateau show a cooling trend. Interestingly, 42% (38 of 91) of these lakes showed faster warming rates than nearby air temperatures. LSWT increased faster in spring and winter than in summer and autumn. Lake surface warming is mainly explained by locally rising air temperature, increasing downward longwave radiation, and weakened wind speed. Under the projected warmer climate in the future, lake surface warming is expected to continue, with important implications for lake management and climate change mitigation. Key Points: Lake model revealed a significant annual increase of 0.040°C (p < 0.05) in the surface water temperature of large lakes across China from 1979 to 2018The simulated surface water temperature warmed faster than the local surface air temperature in approximately 42% of the studied lakesChanges in air temperature, downward longwave radiation, and wind speed are the primary contributors to the trends in lake surface water temperature [ABSTRACT FROM AUTHOR]

Root-zone soil moisture (RZSM) is an important variable in land–atmosphere interactions, notably affecting the global climate system. Contrary to satellite-based acquisition of surface soil moisture, RZSM is generally obtained from model-based simulations. In this study, in situ observations from the Naqu and Pali networks that represent different climatic conditions over the Tibetan Plateau (TP) and a triple collocation (TC) method are used to evaluate model-based RZSM products, including Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.5a and 3.5b), Global Land Data Assimilation System (GLDAS) (versions 2.1 and 2.2), and the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5). The evaluation results based on in situ observations indicate that all products tend to overestimate but could generally capture the temporal variation, and ERA5 exhibits the best performance with the highest R (0.875) and the lowest unbiased RMSE (ubRMSE; 0.015 m3 m−3) against in situ observations in the Naqu network. In the TC analysis, similar results are obtained: ERA5 has the best performance with the highest TC-derived R (0.785) over the entire TP, followed by GLEAM v3.5a (0.746) and GLDAS-2.1 (0.682). Meanwhile, GLEAM v3.5a and GLDAS-2.1 outperform GLEAM v3.5b and GLDAS-2.2 over the entire TP, respectively. Besides, possible error causes in evaluating these RZSM products are summarized, and the effectiveness of TC method is also evaluated with two dense networks, finding that TC method is reliable since TC-derived R is close to ground-derived R, with only 6.85% mean relative differences. These results using both in situ observations and TC method may provide a new perspective for the soil moisture product developers to further enhance the accuracy of model-based RZSM over the TP. Significance Statement: The purpose of this study is to better understand the quality and applicability of GLEAM, GLDAS, and ERA5 RZSM products over the TP using both in situ observations and the triple collocation (TC) method, making it better applied to climate and hydrological research. This study provides four standard statistical metrics evaluation based on in situ observations, as well as the reliable metric, that is, correlation coefficient (R) derived from TC method, and highlights that TC-based evaluation could supplement the ground-based validation, especially over the data-scarce TP region. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth’s ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R 2 higher than 0.56, RMSE less than 0.1 cm 3 cm −3 , and Bias less than 0.07 cm 3 cm −3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP. [ABSTRACT FROM AUTHOR]