# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 5 Conclusions  
This review paper is an attempt to understand the studies being conducted in the Indian Himalayan region, which contributes almost 15% of the global rainfall-induced landslides. The article focuses only on landslide activity which was triggered due to rainfall as it reflects a significant portion to landslides in the Indian Himalaya and it is the only typology that can be managed by early warnings and forecasting models. The review reveals that there are several avenues where significant progress is required, especially in the aspect of climate change, use of high spatial and temporal resolution data, and new methodologies such as physical methods and computational approaches. The study highlights key topics in which the research has focused: susceptibility mapping, identification, and slope stability analysis. The conclusions from each sub-section are as follows:  
1. Landslide assessment (including identification, threshold estimation, and monitoring of landslides). For landslide identification and mapping, focus needs to shift in three key directions: 1. Use of automated approaches involving the use of computational techniques. 2. Use of higher temporal resolution datasets and assessment of their reliability. 3. Application of the current techniques towards other significant landslide prone Himalayan regions. In terms of rainfall threshold studies, the number of articles were less than 10 with most of the work focused on the use of statistical models to define a single threshold, and majorly the Eastern Himalayas have been covered. The thresholds developed show large differences when calculated for regional and local scale, therefore it is suggested to develop thresholds at local scale to improve the understanding of the region and help in setting up an operational landslide early warning system. In general, the thresholds are very low if compared with other literature thresholds, thus confirming the basic assumption that the India Himalaya is very susceptible to landsl. Additional research needs to be conducted on the use of physical models, including campaigns aimed at gathering input data for more complex models; moreover, efforts need to be made on the combination of empirical and physical models for a better understanding of landslide initiation. 2. Landslide monitoring has been performed using both ground instrumentation and satellite data, however a general multi-scale approach that extensively covers the whole region is missing. 3. The focus needs to shift on include climatic factors for landslide assessment as climate change is unequivocal. The use of climate models needs to be conducted with caution, especially when downscale projections are considered. As the climatic conditions are quite varied from west to east, focus should also be on the use of appropriate down-scaling models. 4. The studies on landslide susceptibility was found to have a regional bias and more research needs to be conducted in the Jammu and Kashmir Himalayas and the northeastern belt. Emphasis has primarily been on specific states and regions such as Uttarakhand, Darjeeling, and some areas of Himachal Pradesh. In modeling aspects, the use of computational approaches needs to be emphasized as it has proved to be better than traditional methods. The analysis should start focusing on the use of hybrid models and big data analytics for regional to site specific analysis, thereby understanding the heterogeneity and uncertainty of the region. 5. There is a serious lack of ground-based rainfall data in large parts of the Himalayan region which has been highlighted by several works. A solution to this flaw is to start using remote sensing data (e.g., satellite radar rainfall estimates) to compare and find the best dataset to be used for individual sections in the Himalayan region. Conceptualization, A.D. and R.S. ; formal analysis, A.D. and S.S.; data curation, R.S. ; writing--original draft preparation, A.D. and S.S.; writing--review and editing, B.P. and A.M.A. ; and supervision, B.P. and R.S.