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This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope

Abstract Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

In this article, we present the first demonstration of the FengYun-3E (FY3E) Global Navigation Satellite System Occultation Sounder II-Reflectometry (GNOS-R) payload's capacity to detect near-surface soil freeze/thaw (F/T) states. This study offers an initial analysis of the F/T retrieval algorithm applied to data collected from the Arctic Circle, underscoring the GNOS-R's potential to deliver long-term near-surface soil F/T products. Data for the period extending from the launch day of GNOS-R (Day of Year (DOY) 179, 2021) to DOY 270 in 2022 were analyzed using the surface reflectivity (SR) ratio factor to discriminate F/T variations. Comparisons were made with soil moisture active passive (SMAP) F/T products, serving as an auxiliary analysis. We found a strong consistency between SR ratio factor and SMAP F/T values, with the accuracy of the F/T retrieval algorithm exceeding 60%. These findings corroborate the efficacy of the GNOS-R payload aboard FY3E in monitoring F/T patterns at higher latitudes, specifically, the Arctic Circle. The outcomes of this study will be beneficial for future F/T detection efforts using spaceborne Global Navigation Satellite System-Reflectometry payloads.

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

Numerous hybrid improper ferroelectrics have been discovered in bulk oxides with layered perovskite structures. In contrast, the competition between the interlayer rumpling and oxygen octahedral rotation suppresses the ferroelectricity in layered perovskite material with trivalent cation at the B-site. In the present work, single-phase dense La2Sr(Sc1−xFex)2O7 ceramics with double-layered Ruddlesden–Popper structures have been prepared, and room-temperature ferroelectricity is discovered in the ceramics with x ≤ 0.10. The ferroelectric polarization and coercive field decrease with increasing content of Fe3+ cations, consistent with the decline of oxygen octahedral rotation and tilting angles. Although the linear relationship between the Curie temperature and the tolerance factor for La2Sr(Sc1−xFex)2O7 ceramics is established, the line is far away from that for A2+3B4+2O7 ceramics due to the large interlayer rumpling in the present ceramics. Although no single-phase multiferroic has been discovered in this work, an effective way to introduce magnetism into hybrid improper ferroelectric is provided. [ABSTRACT FROM AUTHOR]

Synergistic reinforcement of dual-phase particles with different sizes was always considered superior to single-phase reinforcement in composites, but there were few studies to investigate the role of each part in enhancing properties. In this work, 40 vol% (TiB2p-TiCp)/Al–Cu–Mg composites with different proportions of TiB2p and TiCp (3:1, 2:1, 1:1, 1:2 and 1:3), were fabricated by the combustion synthesis to contrast the strengthening effect of dual-phase particles from the interface stability and the characteristics of dual-phase particles. Calculated by the first principle, TiB2 (3.12 J/m2) presented better interfacial stability with Al matrix than TiC (1.61 J/m2). Under the circumstance of the same volume fraction, the composites with a higher proportion of TiB2p usually show higher yield and maximum compressive strength and effectively prevent the crack growth of composites. However, excess submicron sized TiB2p tends to produce cracks at the hexagon edge when the loads were applied and formed crack loops delaminating materials. When the TiB2p/TiCp proportion was 1:1, the plasticity and strength were beneficially balanced at the elevated temperature. Otherwise, the composite with TiB2: TiC = 3:1 possessed the best wear resistance (coefficient of friction: 0.22) at a sliding velocity of 0.32 mm/s, 30 N load) among other composites (AR12, AR11, AR21, and AR31) at 473 K, which reduced 45%, 64%, 73% and 86%, respectively. The main wear mechanism was abrasive wear when the TiB2 proportion was dominant. Submicron sized TiB2p with better interface bonding can keep the rigidity of the composites to resist the plastic flow, while TiC nanoparticles are easier to be extracted during the wear process.

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV3h and PWV6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV3h product performed slightly better than PWV6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases.

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1.