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<p>Nepal, a mountainous country, is experiencing multiple disasters, majority of which are induced by Climate Change. Erratic rainfall, extremely high temperature during summer, cold waves are some of them. Nepal will experience the impacts of climate change through an increase in temperature, more frequent heat waves and shorter frost durations in the future (5AR IPCC). Nepal is witnessing the increased maximum temperature of 0.56<sup>o</sup>C per decade and the increment of the temperature is even higher in the mountain region (ICIMOD 2019). One of the major impacts of Climate Change among others, is glacier retreat and Glacial Lake Outburst Floods (GLOFS). Nepal has already experienced more than 26 GLOFS (UNDP and ICIMOD 2020), originated both from Nepal and China, Tibet.</p><p>The Imja Glacial Lake is located at 27° 53′ 55“ N latitude, 86° 55’ 20” E longitude and at an altitude of 5010 m in Everest Region of Nepal Himalayas.  Imja was identified during 1960s as a small supra lake, was later expanded to an area of <strong>1.28 Km<sup>2</sup></strong>, <strong>148.9 meter deep</strong>, holding <strong>75.2 million cubic meters </strong>of water in 2014.   Lake lowering by 3.4 metres and establishment of early warning system was done in 2016 by the Government of Nepal and UNDP with the support of Global Environment Facility.  Hydro-met stations & GLOF Sensors in the periphery and downstream  of Imja Lake and automated early warning sirens in six prime settlements in the  downstream of Imja  watershed  linking with  dynamic SMS Alert system along 50 km downstream of Imja Dudh Koshi River have been have been linked with community-based DRM institutions at local government level. This initiative is important for preparedness and response of GLOF Risk Reduction in the Imja Valley, benefitting 71,752 vulnerable people, both local and the tourists visiting the Everest Region of Nepal.</p><p>Early Warning System of Tsho Rolpa Glacial Lake, the biggest Glacial Lake of Nepal is another example in the such system. New inventory of Glacial Lakes has identified 47 critical lakes as priority lakes for GLOF Risk Reduction in Koshi, Gandaki and Karnali basins. In the new context of federal  governance system, the role of federal, province and local government and communities is crucial  for achieving the targets of  Sendai Framework for Disaster Risk Reduction , particularly target “g” and SDGs 11 and 13  through integrating  the targets in the regular planning and   its’ implementation for resilient and Sustainable Development of  Nepal.</p><p><strong>References:</strong></p><p>Glacial lakes and glacial lake outburst floods in Nepal. Kathmandu, ICIMOD 2011,  Nepal Disaster Report, Ministry of Home affairs (MoHA) , 2015, 2018 Annual Reports UNDP 2016, 2017 and 2018,  Imja Hydro-Meteorological and Early Warning System User Manual, Government of Nepal and UNDP, 2017 Project Completion Report: Community Based Flood and Glacial Lake Outburst Risk Reduction Project, Government of Nepal and UNDP, 2017,  Inventory of glacial lakes and identification of potentially dangerous glacial lakes in the Koshi, Gandaki, and Karnali River Basins of Nepal, the Tibet Autonomous Region of China, and India. Research Report, ICIMOD and UNDP, 2020</p><p> </p>

<p>Landslides are common in the mid-hill region of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal, the casual and triggering factors of the landslides are respectively the underlying geology, intense rainfall and unplanned construction of rural roads is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides at the watershed landscape is data driven, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available historical landslides date has been used to develop the rainfall threshold model for the two watersheds in central and western mid-hill regions respectively the Sindhukhola and Sotkhola in Bagmati and Karnali Provinces of Nepal. The watersheds are located in two distinct hydro-climatic regions in terms of rainfall amount and intensity. Historical daily (monsoonal) rainfall data of over four decades (1970-2016) were analyzed available from the Department of Hydrology and Meteorology (DHM)/Government of Nepal and five days’ antecedent rain was calculated. With the limitedly available temporal landslides data, correlation was examined among the 5-days antecedent rain (mm/5days) and daily rainfall (mm/day) portrayed the rainfall threshold (RT) model (Sindhukhola=180-1.07R<sub>T5adt</sub> and Sotkhola = 110-0.83*R<sub>T5adt</sub>). Utilizing the five days’ antecedent rain fitted into the model, results the threshold rainfall. Deducting the five days’ antecedent rains to the RT described the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in Y-axis and threshold exceedance R in X-axis) to visualize the changes in the threshold exceedance over time, whenever the threshold exceedance progressively and rapidly increased and crossed the threshold line and reached to the positive (> 0) zone, the plots allows for the landslides warning notice. In case of the threshold exceedance is further increased there is likely to have landslides in the watersheds. The model was validated with the 35 dated landslides recorded in monsoon 2015 in Sotkhola watershed. The result indicated that the model preserves 72% coefficient of determination (R<sup>2</sup>) where there were landslides in the watershed during 2015 monsoon. Due to the simplicity and at the data scarce situation, the model was found to be useful to forecast the landslides during the monsoon season in the region. The model; however, can be improved for better performance whenever the higher resolution time-series landslides data and automated weather stations are available in the watersheds. Linking this model to the proper landslide susceptibility map, and the real time rainfall data through mobile communication techniques, landslide early warning system can be established.</p><p><strong>KEYWORDS: </strong>landslide, rainfall threshold, data-scare, antecedent rainfall</p><p><strong>References:</strong></p><p>Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73(3-4), 247-265.</p><p>Jaiswal, P. and van Westen, C.J., 2009. Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2): 96-105.</p><p><strong>Acknowledgement:</strong> Comprehensive Disaster Risk Management Programme – UNDP in Nepal for the opportunity to conduct this research.</p>

<p><em>Keywords: Nature Based Solutions, Climate Change Adaptation, Disaster Risk Reduction, rural livelihood  , Nepal Himalayas</em></p><p><strong>Abstract:</strong></p><p>Climate has been changing and has considerable impacts on the livelihood of rural/urban communities all around the globe. The impacts however are more pronounced in the mountainous region such as in the Dudhkosi Basin (~4,063km<sup>2</sup>) in eastern Nepal. Studies suggested that the mountainous region of Nepal are climate-sensitive meaning that a small change in air temperature could bring significant impacts on the environmental degradation process and increase the frequency of climate-induced hazards and risk of disasters. In order to understand the dynamics of the changing climate on rural livelihood, this research attempted to model the environmental vulnerability of the basin considering the topographic and environmental attributes and assessed their contribution to the physical environmental degradation process. Multi-criteria based Decision Analysis (MCDA) approaches were implemented considering the seven primary topographic attributes such as slope, soil type, land use, NDVI, elevation, distance to drainage, and terrain wetness. The model was implemented for the three scenarios of rainfall such as historical and projected precipitation for RCP4.5 and RCP 8.5 as a climate change variable and distance to the rural roads as an anthropogenic factor to model the physical environmental degradation there by the vulnerability. The analysis depicted that the slope attributes has the highest contribution (20.21%) among all followed by precipitation, soil type, land use, distance to road, distance to drainage, terrain wetness and NDVI respectively 18.5%, 14.3%, 12.56%, 10.75%, 9.63%, 7.55% and 6.5%. The analysis clearly indicated that the slope is one of the most critical attributes where many settlements are located and have a significant contribution to the land-degradation process of the mountainous region. While the increasing trend of rainfall will cause more soil erosion and shallow landslides compounded due to the unplanned construction of rural roads thereby more area of the basin under the threat of degradation. The model also indicated that the increased amount of degraded land for the projected precipitation scenarios under RCP4.5 and RCP8.5 respectively increased from 7% to 15%. The increased amount of degradation will cause more communities living on the sloping terrain will turn to be vulnerable. The analysis demonstrated in this research suggested that the basin is at high threat of land degradation under the climate change scenarios for which an integrated basin management plan is to be developed. Integrated approach for Building Climate Resilience is the key in which the Nature-Based Solutions measures as climate change adaptation and disaster risk reduction. The measures also  improve the watershed conditions that subsequently enhance the quality of the lives of the people with increased economic opportunities and coping capacity for the climate change induced disasters and community resiliency.</p><p> </p>