Carbonaceous FexP Synthesized via Carbothermic Reduction of Dephosphorization Slag as Hydrogen Evolution Catalyst for Water Splitting

Developing the high-efficiency and cheap non-noble catalysts towards hydrogen evolution reaction (HER) is of significance for water splitting. Herein, for the first time, we report a simple method of acid leaching combined with carbothermic reduction with dephosphorization slag to construct a carbonaceous FexP/C catalyst. In alkaline medium, the corresponding overpotential when the output current density was 10 mA cm−2 (η10) was only 145 mV. Additionally, there was no obvious attenuation after 3000 cycles, which showed significantly better activity and stability than that of non-carbonaceous FexP catalysts prepared by gas–solid phosphating. The structure and composition of FexP/C were characterized by X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy, and inductively coupled plasma atomic emission spectrometer. The electrochemical properties of the electrode were evaluated by cyclic voltammetry, linear scanning voltammetry, electrochemical impedance spectroscopy, and cyclic stability. The results showed that the prepared FexP/C was composed of FeP-Fe2P mixed nanocrystals supported on amorphous carbon. Compared with FexP, the synergistic catalysis of the FeP and Fe2P phases as well as the interactive support effect between the FeP-Fe2P mixed nanocrystals and the amorphous carbon support will attribute the rich active sites for electrocatalytic reaction and reduce the charge transfer resistance. Thus, FexP/C has good hydrogen evolution activity and stability. Overall, the preparation of catalysts with high additional value based on dephosphorization slag was preliminarily explored

Drought Propagation Patterns under Naturalized Condition Using Daily Hydrometeorological Data

Drought propagation pattern forms a basis for establishing drought monitoring and early warning. Due to its regional disparity, it is necessary and significant to investigate the pattern of drought propagation in a specific region. With the objective of improving understanding of drought propagation pattern in the Luanhe River basin, we first simulated soil moisture and streamflow in naturalized situation on daily time scale by using the Soil and Water Assessment Tool (SWAT) model. The threshold level method was utilized in identifying drought events and drought characteristics. Compared with meteorological drought, the number of drought events was less and duration was longer for agricultural and hydrological droughts. The results showed that there were 3 types of drought propagation pattern: from meteorological drought to agricultural/hydrological drought (M-A/H), agricultural/hydrological drought without meteorological drought (NM-A/H), and meteorological drought only (M). To explain the drought propagation pattern, possible driven factors were determined, and the relations between agricultural/hydrological drought and the driven factors were built using multiple regression models with the coefficients of determination of 0.4 and 0.656, respectively. These results could provide valuable information for drought early warning and forecast

A Deep Learning Method Based on Two-Stage CNN Framework for Recognition of Chinese Reservoirs with Sentinel-2 Images

The development of effective and comprehensive methods for mapping and monitoring reservoirs is essential for the utilization of water resources and flood control. Remote sensing has the great advantages of broad spatial coverage and regular revisit to meet the demand of large-scale and long-term tasks of earth observation. Although there already exist some methods for coarse-grained identification of reservoirs at region-level in remote sensing images, it remains a challenge to recognize and localize reservoirs accurately with insufficiency of object details and samples annotated. This study focuses on the fine-grained identification and location of reservoirs with a two-stage CNN framework method, which is comprised of a coarse classification between aquatic and land areas of image patches and a fine detection of reservoirs in aquatic patches with precise geographical coordinates. Moreover, a NIR RCNN detection network is proposed to make use of the multi-spectral characteristics of Sentinel-2 images. To verify the effectiveness of our proposed method, we construct a reservoir and dam dataset of 36 Sentinel-2 images which are sampled in various provinces across China and annotated at the instance level by manual work. The experimental results in the test set show that the two-stage CNN method achieves an average recall of 80.83% nationwide, and the comparison between reservoirs recognized by the proposed model and those provided by the China Institute of Water Resources and Hydropower Research verifies that the model reaches a recall of about 90%. Both the indicator evaluation and visualization of identification results have shown the applicability of the proposed method to reservoir recognition in remote sensing images. Being the first attempt to make a fine-grained identification of reservoirs at the instance level, the two-stage CNN framework, which can automatically identify and localize reservoirs in remote sensing images precisely, shows the prospect to be a useful tool for large-scale and long-term reservoir monitoring

Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images

Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution

Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images

Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution

Artificial and Natural Water Bodies Change in China, 2000–2020

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China’s surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration

Artificial and Natural Water Bodies Change in China, 2000–2020

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China’s surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration

Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product

The Tibetan Plateau (TP) has the largest number of inland lakes with the highest elevation on the planet. Mapping the distribution of lake water in space and time is crucial for scientific research of interactions among the regional cryosphere, hydrosphere, and atmosphere. In this study, a lake water surface mapping algorithm is developed for Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 surface reflectance images, which is used to produce the 8-day lake water surface data set (lake water surface area larger than 1 km2) of theTP (Qinghai–Tibet Plateau) for the period of 2000–2012. The accuracy analysis indicate that compared with water surface data of the 134 sample lakes extracted from the 30 m Landsat Thematic Mapper (TM) images, the average overall accuracy of the results is 91.81% with average commission and omission error of 3.26% and 5.38%; the results also show strong linear (the coefficient of determination R2 is 0.9991) correlation with the global MODIS water mask data set with overall accuracy of 86.30%; and the lake area difference between the Second National Lake Survey and this study is only 4.74%, respectively. This study provides reliable data set for the lake change research of theTP in the recent decade

Time-series surface water reconstruction method (TSWR) based on spatial distance relationship of multi-stage water boundaries

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments, are faced with drawbacks like cloud influence, which hinders the direct extraction of data from time-series remote sensing images. This study proposes a Time-series Surface Water Reconstruction method (TSWR). The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine (GEE). Then, we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later periods. When applied to 10 large rivers around the world, this method yielded an overall accuracy of 98% for water extraction, an RMSE of 0.41 km2. Furthermore, time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image availability. These findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images, but also to depict a more pronounced time variation characteristic. The successful application of this method on GEE demonstrates its importance for use on large scales or in global studies