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Based on spatial variation and time, climate change has various levels of impacts on different communities and sometime with the state of development as well. The rural mountainous households that depend on natural resources for subsistence livelihoods and agriculture are particularly vulnerable with changing climate. Livelihood vulnerability assessment at local level is imperative to formulate appropriate adaptation policy and programs to address their livelihood challenges. This paper explored two vulnerability assessment indices, livelihood vulnerability index and IPCC vulnerability index by surveying 150 households from three village development committees (VDCs) in Lamjung district, Nepal. Data related to climate variables, natural disasters, water and food security, health, socio-demographics, livelihood strategies, and social network were collected and combined into indices. Both indices differed based on well-being status, gender of the household head and location across the households of three VDCs. The analysis was based on indices constructed from selected indicators measuring exposure, sensitivity, and adaptive capacity. Results indicated that very poor and poor households, and female-headed households were more vulnerable than medium, well-off and male-headed households. The availability of livelihood diversified strategies, education, establishment of early warning system to climate extreme will help to reduce vulnerability to climate change in the study areas. The findings help in designing priority areas of intervention for adaptation plan to reduce vulnerability and enhance the resilience of the mountainous households to climate change.

The COVID-19 pandemic has exceeded over sixty-five million cases globally. Different approaches are followed to mitigate its impact and reduce its spreading in different countries, but limiting mobility and exposure have been de-facto precautions to reduce transmission. However, a full lockdown cannot be sustained for a prolonged period. An evidence-based, multidisciplinary approach on risk zoning, personal and transmission risk assessment in near real-time, and risk communication would support the optimized decisions to minimize the impact of coronavirus on our lives. This paper presents a framework to assess the individual and regional risk of COVID-19 along with risk communication tools and mechanisms. Relative risk scores on a scale of 100 represent the integrated risk of influential factors. The personal risk model incorporates age, exposure history, symptoms, local risk and existing health condition, whereas regional risk is computed through the actual cases of COVID-19, public health risk factors, socioeconomic condition of the region, and immigration statistics. A web application tool ( http://www.covira.info ) has been developed, where anyone can assess their risk and find the guided information links primarily for Nepal. This study provides regional risk for Nepal, but the framework is scalable across the world. However, personal risk can be assessed immediately from anywhere.

As in many other parts of the world, the urban areas of the South Asian region are increasingly expanding. While cities today are the heart of commercial, technological and social development, they are also vulnerable to a variety of natural and anthropogenic threats. The complex urban infrastructure, and the ever-expanding population in cities, exacerbate the impacts of climate change and increase the risk of natural hazards. Throughout history, various hydrological disasters including floods, tidal surges, and droughts, and non-hydrological disasters such as earthquakes, landslides, and storms have led to catastrophic social, economic and environmental impacts in numerous South Asian cities. Disaster risk reduction is therefore central to ensure sustainability in urban areas. Although Nature-based Solutions (NbS) are identified as a promising strategy to reduce risk and increase resilience, there appears to be a lack of evidence-based approaches. NbS are measures that can be practiced to obtain benefits of nature for the environmental and community development through conserving, managing, and restoring ecosystems. Against this backdrop, the South Asian cities provide opportunities to evaluate capacities for achieving Nature-based Resilience (NbR) through NbS. This study documents insights from five cities of five different countries of the South Asian region which are subjected to a wide array of disasters: Barishal (Bangladesh), Phuentsholing (Bhutan), Gurugram (India), Kathmandu (Nepal), and Colombo (Sri Lanka). The primary objective of this study is to provide evidence on how NbS are being practiced. Thus, some success stories in cities under consideration are highlighted: restoration of natural canals through integrated development plans and community participation (Barishal), concepts of Gross National Happiness (GNH) and minimal nature interventions (Phuentsholing), “Greening cities’’ including eco-corridors, vegetation belts, biodiversity parks (Gurugram), proper land use planning aims at different disasters (Kathmandu), and wetland restoration and management with multiple benefits (Colombo). These cases could therefore, act as a “proxy” for learning from each other to prepare for and recover from future disasters while building NbR.

Lakes in monsoon-dominated regions are highly vulnerable to climate change and eutrophication. Chlorophyll-a, a measure of phytoplankton biomass, is a critical indicator for detecting changes in trophic state. Readily available satellite images combined with machine learning techniques can enable long-term monitoring of chlorophyll-a in lakes. We evaluated 24 combinations of models and satellite images for Phewa Lake, Nepal (eight algorithms across three satellite combinations). An ensemble learning model combining a Support Vector Regression (SVR) and Random Forest (RF) based on Sentinel-2 imagery achieved the best relative performance amongst the tested models, although overall predictive accuracy was moderate. Although microwave imagery from Sentinel-1 can penetrate clouds, and therefore provide continuous monitoring during periods of persistent cloud cover, Sentinel-2 achieved higher accuracy (MAE = 0.2 mg/m 3 ), due to the availability of high spectral resolution images and red-edge sensitivity. Analysis of Sentinel-2 images of Phewa Lake from 2018 to 2024 revealed relative seasonal patterns of chlorophyll-a consistent with limnological processes, with relatively higher concentrations during post-monsoon than other seasons. Model-generated maps showed relatively homogeneous spatial distributions of chlorophyll-a in post-monsoon, winter, and pre-monsoon, but highly heterogeneous and dynamic spatial patterns during monsoon, a season of high inflows and mixing. Remote sensing combined with machine learning offers a low-cost and scalable approach for freshwater monitoring that is particularly valuable in monsoonal and low-income countries. In Nepal, which has more than 5,000 lakes, such approaches have strong potential for national-scale monitoring and management. An effort to implement and validate machine learning models in other lakes can be beneficial for sustainable monitoring.

Various remote sensing products and observed data sets were used to determine spatial and temporal trends in climatic variables and their relationship with snow cover area in the higher Himalayas, Nepal. The remote sensing techniques can detect spatial as well as temporal patterns in temperature and snow cover across the inaccessible terrain. Non-parametric methods (i.e. the Mann–Kendall method and Sen's slope) were used to identify trends in climatic variables. Increasing trends in temperature, approximately by 0.03 to 0.08 °C year⁻¹ based on the station data in different season, and mixed trends in seasonal precipitation were found for the studied basin. The accuracy of MOD10A1 snow cover and fractional snow cover in the Kaligandaki Basin was assessed with respect to the Advanced Spaceborne Thermal Emission and Reflection Radiometer-based snow cover area. With increasing trends in winter and spring temperature and decreasing trends in precipitation, a significant negative trend in snow cover area during these seasons was also identified. Results indicate the possible impact of global warming on precipitation and snow cover area in the higher mountainous area. Similar investigations in other regions of Himalayas are warranted to further strengthen the understanding of impact of climate change on hydrology and water resources and extreme hydrologic events.

The diminishing spring discharge in the Middle Mountain Zone (MMZ) in Nepal is a matter of concern because it directly affects the livelihoods of low-income farmers in the region. Therefore, understanding the impacts of changes in climate and land-use patterns on water demand and availability is crucial. We investigated the impact of climate change on streamflow and environmental flow, and the demand for spring-fed river water for irrigation using the limited meteorological data available for the Babai River Basin, Nepal. SWAT and CROPWAT8.0 were used to respectively calculate present and future streamflow and irrigation water demand. Three general circulation models under two representative concentration pathways (RCPs 4.5 and 8.5) for the periods of 2020–2044, 2045–2069, and 2070–2099 were used to investigate the impact of climate change. Results indicate that the catchment is likely to experience an increase in rainfall and temperature in the future. The impact of the increment in rainfall and rise in temperature are replicated in the annual river flow that is anticipated to increase by 24–37%, to the historical data of 1991–2014. Despite this increase, projections show that the Babai River Basin will remain a water deficit basin from January to May in future decades.

South Asian countries face a disproportionate impact from disasters due to their unique topography, poverty, low literacy rates, and socio-economic status. Human activities, such as unplanned urbanization and poorly designed rural road networks, have further contributed to disasters in the region. The article explores the potential of nature-based solutions (NbS) as a means of addressing these challenges through the integration of green, blue, and grey infrastructure. The analysis evaluates the significance of NbS and examines policies and regional cooperation in Bangladesh, Bhutan, India, Nepal, and Sri Lanka, highlighting the importance of incorporating NbS into national policies and promoting collaboration among these countries. The study identifies the current low implementation of NbS in South Asia, with limited research in this area. While there are existing policy tools related to coastal zone management, water, forest, and urban development, policies related to NbS should be coherent, connected, and integrated with natural resources, climate change, disaster risk reduction, and socio-economic growth to achieve sustainable development in the region. Overall, the article emphasizes the need for effective policy implementation and research to enhance resilience to climate change and promote sustainable development in South Asia.

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

Background In the last two decades, Nepal has experienced an increase in both forest fire frequency and area, but very little is known about its spatiotemporal dimension. A limited number of studies have researched the extent, timing, causative parameters, and vulnerability factors regarding forest fire in Nepal. Our study analyzed forest fire trends and patterns in Nepal for the last two decades and analyzed forest fire-vulnerability risk based on historical incidents across the country. Results We analyzed the spatial and temporal patterns of forest fires and the extent of burned area using the Mann-Kendall trend test and two machine-learning approaches maximum entropy (MaxEnt), and deep neural network (DNN). More than 78% of the forest fire burned area was recorded between March and May. The total burned area has increased over the years since 2001 by 0.6% annually. The forest fire-vulnerability risk obtained from both approaches was categorized into four classes—very high, high, low, and very low. Conclusions Although burned area obtained from both models was comparable, the DNN slightly outperformed the MaxEnt model. DNN uses a complex structure of algorithms modeled on the human brain that enables the processing of the complex relationship between input and output dataset, making DNN-based models recommended over MaxEnt. These findings can be very useful for initiating and implementing the most suitable forest management intervention.

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.