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Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW. [ABSTRACT FROM AUTHOR]

In China, flash floods are one of the main natural disasters causing loss of life and damage to infrastructure. The threat of flash floods is exacerbated with climate change and increased human activities, such that the number of disasters has shown a clear upward trend in recent years. However, due to the scarcity of instrumental data or overly short timeseries, we are still lacking critical data to understand spatio-temporal patterns and driving factors of extreme flash floods. This missing knowledge is however crucial for a proper management of these hazards, especially in remote mountain environments. In forested catchments, dendrogeomorphology allows the reconstruction of past process activity based on growth disturbances (GDs) in trees that have been affected by past flash floods. Therefore, in our study, for the first time, we reconstruct past flash floods in the Qilian Mountains, northeast Tibetan Plateau, over past centuries. To this end, we sampled 99 Qinghai spruce (Picea crassifolia) trees affected by flash floods, with a total of 194 increment cores, and identified 302 GDs induced by past flash floods. These GDs have been caused by at least 21 flash floods that we are able to reconstruct over the last 170 years. The position of GDs within tree rings and the intra-seasonal dating of past events also allowed discussion of the likely synoptic situations that may have led to the triggering of flash floods in the past. Logistic regression analysis confirms that significant correlation exists between cumulative maximum 5-day August–September precipitation and reconstructed flash floods, which is corresponding to the majority of scars and related tangential rows of traumatic resin ducts (TRDs) found in the latewood portion of growth rings. These results support the idea that abundant precipitation occurring at the end of the summer season and early fall is the key factor driving flash floods in our study area. Our research not only fills the gaps regarding historical flash flood histories in the Qilian Mountains, but also provides a scientific basis for the region's response to climate change and flood prevention and reduction. [ABSTRACT FROM AUTHOR]

In recent decades, the continuously changed glaciers in the Himalayas not only affected process of atmospheric flow and water cycle in the plateau but also increased the frequency of secondary disasters, such as ice collapse and outburst floods. Therefore, the monitoring of Himalayas glacial change is of great significance in the aspects of climate change and disaster prevention and reduction. The Himalayas glacier outlines in China were extracted by ratio threshold and visual interpretation based on the Landsat TM/ETM+/OLI data and glacier catalogue data. Based on the ASTER GDEM data, the distribution and change characteristics at different altitudes were studied, and the surface moraine was identified to study the influence of glacial area variations. In addition, Glaciers were divided into marine and continental glaciers in this study, and the distribution and changes of the two types of glaciers were analyzed. Also, a comprehensive analysis of a long time series was performed. The results showed that: (1) From 1990 to 2015, the glaciers in the study area showed an overall trend of melting; the annual melting speed gradually accelerated from 0.48%/a to 0.75%/a. The total melting glacial area was 828.16 km2, and the melting rate of the glaciers in the western section was the highest, at 0.63%. (2) The total area of continental glaciers was larger than that of marine glaciers, and its reduction was relatively larger, too. (3) The average size of debris-covered glaciers was 10 times that of debris-free glaciers, but their rate of change (8.1%) was 1/2 of that of debris-free glaciers (17.8%). (4) The number of glaciers gradually decreased as the size of glaciers increased. The glaciers with grades of >50 km2, 0.5∼1 km2, and 1∼2 km2 had large change rates, which were 20.1%, 19.1%, and 18.5%, respectively. In summary, the Himalayas glaciers in China were melting at an accelerated rate and their numbers were gradually decreasing from 1990 to 2015. The location, type, elevation, size and debris cover of a glacier are all important factors influencing glacier change. It has been found that the lower the elevation or the smaller the size of the glacier, the greater the likelihood of glacier ablation, and the debris can inhibit the melting of glaciers to a certain extent. [ABSTRACT FROM AUTHOR]