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 Evaluation of satellite-based land surface temperature differences for global flood detection
Tác giả hoặc Nhóm tác giả: H. T. Pham, L. Marshall, F. Johnson
Nơi đăng: The 23rd International Congress on Modelling and Simulation (MODSIM2019); Số: 2019;Từ->đến trang: 882;Năm: 2019
Lĩnh vực: Khoa học công nghệ; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
Floods result in loss of life, significant damages to environment and socio-economy. Early warning information therefore is necessary for helping global human relief organizations and national water resource services to response effectively to floods. However, conventional flood warning systems have been limited to use for emergency response because of sparse hydrological measuring, delays in accessing data, and inaccessibility in transboundary rivers. Satellite data provide global coverage and near-real time accessibility, and are often freely available. This suggests that it is possible to use space-based data for detecting or mapping floods in large-scale areas. Tremendous efforts have been devoted to identify potential flooding using remote sensing data but fast and universally robust approach to detect floods has not yet been developed for use in ungauged regions. Previous studies have suggested that the difference in land surface temperature between daytime and nighttime (DLST) could be a good indicator for flood monitoring. In dry seasons with low flows, land heats and cools quickly, thus DLST is high while during flood events, flood flow increases associated with changes in land conditions and DLST decreases. Although this relationship suggests that satellite-based DLST can be used to detect floods, the changes in LST and river discharge depend on a variety of local climate conditions, land cover, catchment properties and hydrologic characteristics. Here we evaluate the potential of DLST for global flood detection. We investigate the relationship between DLST and flood flows across contrasting conditions at global scale. The DLST values were derived from 0.05° daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products. Flood flows were extracted from daily Global Runoff Database Center (GRDC) data at more than 3000 stations. Regression trees governed by the different conditions were developed to examine the relationship between DLST and flood flows. The results in Australia show that strong negative correlations occurred in the hot and steppe regions. The tropical, monsoon, hot and high dense of vegetation coverage had moderate negative correlations while weak negative correlations were found in cool temperate regions with small catchment areas (Figure 1). The results suggest that the best condition under which DLST can be used to detect flood flows are high temperature and sparse vegetation coverage. The findings provide understanding of the conditions under which DLST may be useful for detecting floods or developing flood warning systems in large-scale spatial and sparsely gauged areas
ABSTRACT
Floods result in loss of life, significant damages to environment and socio-economy. Early warning information therefore is necessary for helping global human relief organizations and national water resource services to response effectively to floods. However, conventional flood warning systems have been limited to use for emergency response because of sparse hydrological measuring, delays in accessing data, and inaccessibility in transboundary rivers. Satellite data provide global coverage and near-real time accessibility, and are often freely available. This suggests that it is possible to use space-based data for detecting or mapping floods in large-scale areas. Tremendous efforts have been devoted to identify potential flooding using remote sensing data but fast and universally robust approach to detect floods has not yet been developed for use in ungauged regions. Previous studies have suggested that the difference in land surface temperature between daytime and nighttime (DLST) could be a good indicator for flood monitoring. In dry seasons with low flows, land heats and cools quickly, thus DLST is high while during flood events, flood flow increases associated with changes in land conditions and DLST decreases. Although this relationship suggests that satellite-based DLST can be used to detect floods, the changes in LST and river discharge depend on a variety of local climate conditions, land cover, catchment properties and hydrologic characteristics. Here we evaluate the potential of DLST for global flood detection. We investigate the relationship between DLST and flood flows across contrasting conditions at global scale. The DLST values were derived from 0.05° daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products. Flood flows were extracted from daily Global Runoff Database Center (GRDC) data at more than 3000 stations. Regression trees governed by the different conditions were developed to examine the relationship between DLST and flood flows. The results in Australia show that strong negative correlations occurred in the hot and steppe regions. The tropical, monsoon, hot and high dense of vegetation coverage had moderate negative correlations while weak negative correlations were found in cool temperate regions with small catchment areas (Figure 1). The results suggest that the best condition under which DLST can be used to detect flood flows are high temperature and sparse vegetation coverage. The findings provide understanding of the conditions under which DLST may be useful for detecting floods or developing flood warning systems in large-scale spatial and sparsely gauged areas
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