Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, na & iuml;ve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed.

Study region: The Sanjiangyuan, located on the Tibetan Plateau, is the headwater of the three large Asia Rivers- the Yangtze, Yellow and Lancang (upper Mekong) Rivers.Study focus: Mountain glacier melt runoff, an important buffer against drought, is enhancing with climate warming. Projection of glacier (especially small glaciers) runoff change is imperative for adapting to climate change and mitigating relevant risks. We aim to provide an up-to-date knowledge of the glacier area and runoff change for 2016-2099 in the Sanjiangyuan.New hydrological insights for the region: Projections based on CMIP6 archive show that 1) glacier area in the Sanjiangyuan for the four SSPs will shrink by 36 +/- 12 % (SSP1-2.6), 42 +/- 20 % (SSP2-4.5), 49 +/- 19 % (SSP3-7.0) and 61 +/- 15 % (SSP5-8.5) by the end of the 21st century. Small glacier dominated Lancang River basin is more sensitive to climate change than large glacier abundant Yangtze River basin and Yellow River basin. The Lancang River basin is pro-jected to experience the greatest relative glacier area shrinkage, 10 % of glacier area and 55 % of glacier number will disappear for SSP5-8.5; 2) annual glacier runoff in the Yangtze River and Yellow River will reach peak water around 2080 under SSP3-7.0, while the Lancang River is already in or near peak water timing for all SSPs. Higher emission scenario tends to yield later peak water timing due to the changes in snow melt.