Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August, 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses

Current approaches to modelling natural flood management sites

This record includes an extended abstract and MP4 presentation. Presented at the 42nd WEDC International Conference

Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites

Nature-based solutions have gained popularity as an approach to tackling hydro-meteorological hazards (HMHs) in both urban and rural settings. Despite this popularity, challenges persist regarding the evidence base for their effectiveness and data scarcity at the feature or site scale. Flood modelling is a common approach to quantifying the effectiveness of NbS; however, the accuracy of these models heavily depends on the accuracy of the DEM, land cover, and hydraulic/hydrological data utilised. Remote and rural settings often face data scarcity due to the challenging nature of data collection, and insufficient funding for monitoring. Additionally, NbS features vary in size and scale, with many being small (<1 m in width), posing challenges for accurate representation in national LiDAR datasets. Technological advancements in remote sensing technologies, such as unmanned aerial vehicles, handheld LiDAR, and GPS-GNSS, offer opportunities to gather high-resolution, high-accuracy data in these challenging locations. This article proposes a methodological framework for collecting elevation data at remote NbS sites that can tackle areas affected by both sparse and dense vegetation cover. This approach proves valuable in both pre-NbS implementation, through facilitating NbS opportunity and environmental risk identification, and post-NbS implementation, through aiding in geo-spatial feature location, improving existing DEM data for flood modelling, and monitoring temporal changes.</p

Remote-sensing disturbance detection index to identify spatio-temporal varying flood impact on crop production

© 2019 Elsevier B.V. Flooding is the most common type of natural hazards that can interrupt crop growth and reduce production. Current understanding of flood impact on crops is largely obtained from broad-scale studies without considering the influence of localized variations. Due to the highly localized features of flooding, it is essential to develop an effective and systematic approach to investigate and better understand the spatio-temporal varying flood disturbances at fine spatial scales. Based on the pixel-based time series of Enhanced Vegetation Index (EVI) data, two satellite-based flood disturbance detection indices (DIs), i.e. EVI and peak EVI, are developed to recognize the difference between the signals induced by natural variations and instantaneous/non-instantaneous flood impact in crop growth processes. To define flood impact, the actual and predicted normal values of temporal trajectories of EVI and peak EVI during the crop growing seasons are compared to detect and remove the interference from the crop's intra-annual natural variations. A range of natural variations are considered to discern the signal induced by the crop's inter-annual natural variations. Furthermore, recovery of crops from flooding is also considered by comparing the peak EVI during crop growing seasons to detect the final flood impact. Using the Northeast China as a case study area, we successfully demonstrate the capacity of these two DIs to identify spatio-temporal varying flood impact on crop production. The DIs also reveal positive response of crops to extreme precipitation under certain conditions. Further analysis demonstrates the non-linear relationships between flood disturbances and terrain slope, distance from rivers, and flow accumulation area, which enable the development of empirical regression models to sufficiently capture the variation of flood damage extent. The research findings confirm that the two DIs proposed in this work are useful in detecting flood disturbances to crops and facilitating informed decision-making in agricultural flood management

Discrimination of Cys from Hcy by an Iridium(III) Complex Based on Time-Dependent Luminescence

A phosphorescent probe, Ir­(ppy)₂Cl­(DMF) (<b>Ir2</b>), for cysteine (Cys) is reported. The recognition is based on the time-dependent emission of <b>Ir2</b>-Cys. Importantly, this probe can discriminate Cys rapidly from homocysteine (Hcy). Probe <b>Ir2</b> displays a highly selective 72 nm of luminescent red-shift toward Cys within 30 min with an obvious emissive color change from green to orange. After 30 min, the emission band blue shifts gradually with an emissive change from orange to green. TDDFT combined with CV studies indicates that the different emission profile is ascribed to the different binding modes of <b>Ir2</b> to Cys with time. The probe can be used to determine Cys in semiaqueous solution

Climatology and convective mode of severe hail in the United Kingdom

Severe or large hail, with diameter ≥20 mm, is a hazard associated with severe convective storms that can cause significant damage. In the UK, the rarity and small footprint of severe hail events makes obtaining well-documented hail reports difficult, and the reports are spread across multiple databases. In this study, three databases of UK severe hail reports are merged for the first time. The combined event set (1979–2022), comprising >800 reports, is used to investigate interannual variability and the seasonal, spatial and size distributions of severe hail. The seasonal cycle peaks in early–mid summer, and the peak month has shifted from June to July since around 2005. The distribution of reported hail size is exponential, with a slower decay (larger hail) during summer. The time of day, basic convective mode (isolated, clustered or linear), and presence or absence of supercellular characteristics are assessed for 274 of the reports since 2006, using composite radar rainrate data. The diurnal cycle is strong year-round, peaking during the late afternoon (1500–1800 UTC). 53% of severe hail events are associated with isolated cells, 33% with clusters, and 14% with linear storms. Around 35% of severe hail-producing storms are probable supercells, increasing to 70% for storms producing ≥40 mm hail. This demonstrates that the prevalence of supercells producing very large hail extends to temperate maritime climates. These results may be of relevance in other regions with a relatively low incidence of severe hail in the present climate. This comprehensive analysis of severe and potentially impactful hail in the UK provides novel insight into its characteristics, enabling improved assessment of climate risk from this hazard. </p

Additional file 1: Figure S1. of TNF-α promotes extracellular vesicle release in mouse astrocytes through glutaminase

Dynamic light scattering measurements demonstrate that treatment of mouse astrocytes with TNF-α does not affect the distribution of EVs. a Immunofluorescent staining for GFAP (red) in primary mouse astrocytes. Scale bars all indicated 50 μm. b A viability assay for astrocytes treated with TNF-α after 24 h. c Western Blot for calreticulin in astrocytes and EVs, calreticulin was a marker of endoplasmic reticulum, actin was a loading control. d EVs were isolated from serum-free culture of control and TNF-α-treated group after 24 h, and the size of EVs were determined by dynamic light scattering (DLS). The results were shown by intensity percent. (PDF 1048 kb

Additional file 2: Figure S2. of TNF-α promotes extracellular vesicle release in mouse astrocytes through glutaminase

TNF-α induces GLS release from the mitochondria in mouse astrocytes. a After 72 h stimulation, mitochondria from mouse astrocytes were isolated, and then subjected to Western blotting analysis using anti-glutaminase and anti-ATP5A antibodies. ATP5A was a mitochondria marker and actin was a loading control. Gls−/− mouse astrocytes were treated with TNF-α for 48 h and then stained with DCFH-DA. Cells were washed with serum-free culture medium, and then subjected to fluorescence microscope (b) and fluorescent microplate reader (c). (PDF 769 kb

Quantitative Metabolomic Profiling of Plasma, Urine, and Liver Extracts by ¹H NMR Spectroscopy Characterizes Different Stages of Atherosclerosis in Hamsters

Atherosclerosis (AS) is a progressive disease that contributes to cardiovascular disease and shows a complex etiology, including genetic and environmental factors. To understand systemic metabolic changes and to identify potential biomarkers correlated with the occurrence and perpetuation of diet-induced AS, we applied ¹H NMR-based metabolomics to detect the time-related metabolic profiles of plasma, urine, and liver extracts from male hamsters fed a high fat and high cholesterol (HFHC) diet. Conventional biochemical assays and histopathological examinations as well as protein expression analyses were performed to provide complementary information. We found that diet treatment caused obvious aortic lesions, lipid accumulation, and inflammatory infiltration in hamsters. Downregulation of proteins related to cholesterol metabolism, including hepatic SREBP2, LDL-R, CYP7A1, SR-BI, HMGCR, LCAT, and SOAT1 was detected, which elucidated the perturbation of cholesterol homeostasis during the HFHC diet challenge. Using “targeted analysis”, we quantified 40 plasma, 80 urine, and 60 liver hydrophilic extract metabolites. Multivariate analyses of the identified metabolites elucidated sophisticated metabolic disturbances in multiple matrices, including energy homeostasis, intestinal microbiota functions, inflammation, and oxidative stress coupled with the metabolisms of cholesterol, fatty acids, saccharides, choline, amino acids, and nucleotides. For the first time, our results demonstrate a time-dependent metabolic progression of multiple biological matrices in hamsters from physiological status to early AS and further to late-stage AS, demonstrating that ¹H NMR-based metabolomics is a reliable tool for early diagnosis and monitoring of the process of AS