The Influence of River Morphology on the Remote Sensing Based Discharge Estimation: Implications for Satellite Virtual Gauge Establishment

Monitoring of river discharge is a key process for water resources management, soil and water conservation, climate change, water cycling, flood or drought warning, agriculture and transportation, especially for the sustainable development of rivers and their surrounding ecological environment. Continuous and comprehensive discharge monitoring was usually impossible before, due to sparse gauges and gauge deactivation. Satellite remote sensing provides an advanced approach for estimating and monitoring river discharge at regional or even global scales. River morphology is generally considered to be a direct factor that affects the accuracy of remote sensing estimation, but the specific indicators and the extent to which it affects the estimation accuracy have not yet been explored, especially for medium to small rivers (width < 100 m). In this paper, six sites with hydrological gauges in the upper Heihe River Basin (HRB) of northwestern China and the Murray Darling Basin (MDB) of southeastern Australia were selected as the study cases. River discharge was estimated from Landsat imagery using the C/M method accordingly. River gradient, sinuosity, and width were obtained from Digital Elevation Model data for each site. Global Surface Water Dataset (GSWD) was also employed for indicating the dynamic status of river morphology. A series of methods were applied to analyze the influence of river morphology on estimation accuracy qualitatively and quantitatively, based on which we established inference about the theory of selecting satellite virtual gauges (SVGs). The results confirm the feasibility of the C/M method for discharge estimation, with the accuracy affected by multiple river morphological indicators. Among them, river width was found to be the most significant one. Moreover, water occurrence and water extent extracted from GSWD also have impact on the discharge estimation accuracy. Another independent river section in MDB was set as an example to demonstrate the reasonability of the established theory. It is anticipated that this study would promote the application of remote sensing for discharge estimation by providing practical guidance for establishing appropriate SVGs

The Influence of River Morphology on the Remote Sensing Based Discharge Estimation: Implications for Satellite Virtual Gauge Establishment

Monitoring of river discharge is a key process for water resources management, soil and water conservation, climate change, water cycling, flood or drought warning, agriculture and transportation, especially for the sustainable development of rivers and their surrounding ecological environment. Continuous and comprehensive discharge monitoring was usually impossible before, due to sparse gauges and gauge deactivation. Satellite remote sensing provides an advanced approach for estimating and monitoring river discharge at regional or even global scales. River morphology is generally considered to be a direct factor that affects the accuracy of remote sensing estimation, but the specific indicators and the extent to which it affects the estimation accuracy have not yet been explored, especially for medium to small rivers (width < 100 m). In this paper, six sites with hydrological gauges in the upper Heihe River Basin (HRB) of northwestern China and the Murray Darling Basin (MDB) of southeastern Australia were selected as the study cases. River discharge was estimated from Landsat imagery using the C/M method accordingly. River gradient, sinuosity, and width were obtained from Digital Elevation Model data for each site. Global Surface Water Dataset (GSWD) was also employed for indicating the dynamic status of river morphology. A series of methods were applied to analyze the influence of river morphology on estimation accuracy qualitatively and quantitatively, based on which we established inference about the theory of selecting satellite virtual gauges (SVGs). The results confirm the feasibility of the C/M method for discharge estimation, with the accuracy affected by multiple river morphological indicators. Among them, river width was found to be the most significant one. Moreover, water occurrence and water extent extracted from GSWD also have impact on the discharge estimation accuracy. Another independent river section in MDB was set as an example to demonstrate the reasonability of the established theory. It is anticipated that this study would promote the application of remote sensing for discharge estimation by providing practical guidance for establishing appropriate SVGs

Discharge Estimation Using Harmonized Landsat and Sentinel-2 Product: Case Studies in the Murray Darling Basin

Quantifying river discharge is a critical component for hydrological studies, floodplain ecological conservation research, and water resources management. In recent years, a series of remote sensing-based discharge estimation methods have been developed. An example is the use of the near infrared (NIR) band of optical satellite images, with the principle of calculating the ratio between a stable land pixel for calibration (C) and a pixel within the river for measurement (M), applying a linear regression between C/M series and observed discharge series. This study trialed the C/M method, utilizing the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance product on relatively small rivers with 30~100 m widths. Two study sites with different river characteristics and geographic settings in the Murray-Darling Basin (MDB) of Australia were selected as case studies. Two independent sets of HLS data and gauged discharge data for the 2017 and 2018 water years were acquired for modeling and validation, respectively. Results reveal high consistency between the HLS-derived discharge and gauged discharge at both sites. The Relative Root Mean Square Errors are 53% and 19%, and the Nash-Sutcliffe Efficiency coefficients are 0.24 and 0.69 for the two sites. This study supports the effectiveness of applying the fine-resolution HLS for modeling discharge on small rivers based on the C/M methodology, which also provides evidence of using multisource synthesized datasets as the input for discharge estimation

Serum Saturated Fatty Acids including Very Long-Chain Saturated Fatty Acids and Colorectal Cancer Risk among Chinese Population

The association between circulating saturated fatty acids (SFAs) including very long-chain SFAs (VLCSFAs) and colorectal cancer (CRC) risk has not been clearly established. To investigate the association between serum SFAs and CRC risk in Chinese population, 680 CRC cases and 680 sex and age-matched (5-year interval) controls were recruited in our study. Serum levels of SFAs were detected by gas chromatography. Unconditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between serum SFAs and CRC risk. Results showed that total SFAs were positively associated with the risk of CRC (adjusted OR quartile 4 vs. 1 = 2.64, 95%CI: 1.47–4.74). However, VLCSFAs were inversely associated with CRC risk (adjusted OR quartile 4 vs. 1 = 0.51, 95%CI: 0.36–0.72). Specifically, lauric acid, myristic acid, palmitic acid, heptadecanoic acid, and arachidic acid were positively associated with CRC risk, while behenic acid and lignoceric acid were inversely associated with CRC risk. This study indicates that higher levels of total serum SFAs and lower levels of serum VLCSFAs were associated with an increased risk of CRC in Chinese population. To reduce the risk of CRC, we recommend reducing the intake of foods containing palmitic acid and heptadecanoic acid such as animal products and dairy products, and moderately increasing the intake of foods containing VLCSFAs such as peanuts and canola oil

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

The optimization of the membrane electrode assembly (MEA) is crucial for enhancing the performance of proton exchange membrane water electrolysis. Nevertheless, achieving global optimization of all manufacturing parameters of the MEA poses challenges due to their high-dimensional complexity and limited experimental data. In this study, machine learning (ML) techniques were introduced to tackle this intricate engineering challenge. 58 MEAs were fabricated and tested to construct a comprehensive database enriched with features and ample data. This was achieved through a data expansion method that involves altering the operating temperature of the electrolyzer. The XGBoost was employed to perform regression predictions on high-dimensional variables, achieving a remarkable coefficient of determination (R2) value of 0.99926. The SHAP (SHapley Additive exPlanations) method and the genetic algorithm were applied for model interpretation and global optimization, respectively. By utilizing the insights provided by the SHAP method, we could narrow the decision variable dimensionality down to 5 key variables, achieving results that are comparable to full-variable optimization while notably reducing time costs by 67.9%. Guided by ML, the MEA with globally optimized variables achieved a voltage of only 1.828 V at 3 A cm–2. The study presents an approach that integrates intelligent optimization techniques with data-driven methods for high-dimensional variable optimization. This contribution provides valuable insights into energy conversion and storage technologies in the chemical industry

Additional file 1: Table S1. of A practical prognostic lncRNA signature for lung squamous cell carcinoma

Patient IDs in the training and testing sets. (XLSX 13 kb

Topotactic fabrication of transition metal dichalcogenide superconducting nanocircuits

Abstract Superconducting nanocircuits, which are usually fabricated from superconductor films, are the core of superconducting electronic devices. While emerging transition-metal dichalcogenide superconductors (TMDSCs) with exotic properties show promise for exploiting new superconducting mechanisms and applications, their environmental instability leads to a substantial challenge for the nondestructive preparation of TMDSC nanocircuits. Here, we report a universal strategy to fabricate TMDSC nanopatterns via a topotactic conversion method using prepatterned metals as precursors. Typically, robust NbSe2 meandering nanowires can be controllably manufactured on a wafer scale, by which a superconducting nanowire circuit is principally demonstrated toward potential single photon detection. Moreover, versatile superconducting nanocircuits, e.g., periodical circle/triangle hole arrays and spiral nanowires, can be prepared with selected TMD materials (NbS2, TiSe2, or MoTe2). This work provides a generic approach for fabricating nondestructive TMDSC nanocircuits with precise control, which paves the way for the application of TMDSCs in future electronics

Additional file 3: Table S3. of A practical prognostic lncRNA signature for lung squamous cell carcinoma

Univariate Cox regression result. (XLSX 10 kb