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

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

Additional file 1: Figure S1. of Integrated biogeography of planktonic and sedimentary bacterial communities in the Yangtze River

Map of the Yangtze River basin showing all the sampling sites in this study. Lines indicate the mainstream river and its tributaries, the former having a continuum of 4300 km (i.e., the actual sinuous channel length, equivalent to 2.05 times the straight line distance of 2102 km from start to the end sampling sites). Black dots indicate sampling points in the midstream; red dots represent sampling points in tributaries. (TIFF 5094 kb

Additional file 4: Figure S3. of Integrated biogeography of planktonic and sedimentary bacterial communities in the Yangtze River

Correlation distribution in the most populated phyla (coded with different colors) among both persistent and transient bacterial OTUs (> 0.1% relative abundance) with significant associations (P < 0.05) in water samples (a) and sediment samples (b). The Spearman value (> 0.5 or < − 0.5) was plotted to represent the degree of (positive or negative) correlation with higher absolute value as robust correlation. (TIFF 6045 kb

Additional file 10: Figure S8. of Integrated biogeography of planktonic and sedimentary bacterial communities in the Yangtze River

The ranks of the dissimilarities within and between groups for planktonic (a) and sedimentary (b) bacterial communities estimated by ANOSIM (analysis of similarity statistics). The samples are grouped by seasons and landform types. (TIFF 6556 kb

Ordonnance du roy, portant deffense aux brigadiers & dragons des six compagnies particulieres que Sa Majesté à fait mettre sur pied, & distribuer par brigades dans ses provinces de Soissonnais, Picardie & Champagne pour empescher le fauxsaunage, la vente du tabac en fraude & marchandises de contrebande, d'en favoriser le commerce ni de prendre de l'argent de ceux qui le feront, pour ensuite les laisser aller, sur peine de mort ; comme aussi de rançonner des habitans ou particuliers, en exigeant d'eux de l'argent ou autre chose sous pretexte de fauxsaunage, vente de tabac en fraude ou marchandises de contrebande, sur peine des galeres perpetuelles . Du 5. janvier 1718

[Acte royal. 1718-01-05. Paris]Avec mode text