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1.
MethodsX ; 10: 102062, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36845367

RESUMO

Hydrological modelling is a precondition for many scientific researches such as species distribution models, ecological models, agricultural suitability models, climatological models, hydrological models, flood and flash flood models, landslide models etc. Even the topographic control over many hydrological factors has also been studied. Over time different hydrological models have been developed and extensively used. Recently, these models have been used to prepare different types of conditional factors that are widely used in hazard modelling such as floods, flash floods, landslides etc. Quantitative analysis of the Digital Elevation Model (DEM) according to different models by engaging Geographic Information Systems (GIS) supports users to extract various types of information about landscapes where hydrological and topographic information are most important. Methods to prepare hydrological factors namely TWI, TRI, SPI, STI, TPI, stream density and distance to stream by processing DEM in GIS are discussed in this paper. These common hydrological factors are extensively used in many scientific research papers either for modelling or to measure their relationship with other environmental factors.•Hydrological factors have great importance in understanding the landscape and are widely used in scientific research, especially geo-environmental hazard mapping.•Physically based hydrological methods are engaged in ArcMap 10.5 software.•Commonly used hydrological factors are processed using freely available DEM and ArcMap 10.5 software.

2.
Data Brief ; 44: 108484, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35966949

RESUMO

This article reports on the dataset gathered following the census of 83 present-day Infralittoral Prograding Wedges (IPWs), surveyed on the inner continental shelf of the Central-Eastern Tyrrhenian Sea. The purpose of the census was to explore their bathymetric range and assess the observational laws governing this variability. The ensued dataset (Campania Region IPW Dataset, CRID) includes geographic, topographic and morpho-bathymetric indices, descriptive of each IPW and more, the exposure of each IPW to wave forcing (Geographical fetch, Effective fetch and extreme significant wave height, H S ). In this work, histograms contribute to describe all the variables and highlight the dominant features of each IPW. Location maps univocally links the geographic position of each IPW to the appropriate attribute record in the dataset. Further, thematic maps illustrate eight wave fields obtained by offshore-to-nearshore transformation by as many sea states scenarios with 200-year return period. Such wave fields are used as sources for significant wave height representing wave conditions over each IPW. This dataset could be implemented with new measures at a broader scale, by following analogue procedures for measurements, to enlarge the observational scale on IPWs and improve the numerical models which might eventually derive by the analysis of this dataset.

3.
Data Brief ; 20: 1676-1682, 2018 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-30263919

RESUMO

The data deals with the functions that automatically extracted lineaments from the Cartosat, ASTER and SRTM of Digital Elevation Model (DEM) of different spatial resolutions, in the software ArcGIS 10.4. The extracted lineaments result shows the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM gives the lowest number of lineaments reflects Cartosat and SRTM (Shuttle Radar Topography Mission) DEM shows a medium number of lineaments. Cartosat DEM is most appropriate for extraction of contours precisely rather than ASTER and SRTM. This study reveals the Cartosat DEM data is best to use extraction of lineaments in the Indian provinces, offers at most comprehensive geological structural info amongst all the data sets. The extracted lineaments lengths and densities are determined by the statistical method. Based on the data generated lineament density and rose diagram. Cartosat DEM data are the best suited for studying very small areas as through geological and structural information can be mined by using this data.

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