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您的检索式：作者名="SHRESTHA Krishna Kumar"
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| 1 | Predicting the impact of climate change on the distribution of two threatened Himalayan medicinal plants of Liliaceae in Nepal显示文摘Predicting the potential distribution of medicinal plants in response to climate change is essential for their conservation and management. Contributing to the management program, this study aimed to predict the distribution of two threatened medicinal plants, Fritillaria cirrhosa and Lilium nepalense. The location of focal species gathered from herbarium specimen housed in different herbaria and online databases were geo-referenced and checked for spatial autocorrelation. The predictive environmental variables were selected, and MaxE nt software was used to model the current and future distributions of focal species. Four Representative Concentration Pathway(RCP) trajectories of the BCC-CSM1.1 model were used as the future(2050) projection layer. The MaxEnt modelling delineated the potential distribution of F. cirrhosa and L. nepalense. The current suitability is projected towards Central and Eastern Hilly/Mountainous regions. Both species gainmaximum suitability in RCP 4.5 which decline towards other trajectories for L. nepalense. Overall, both the focal species shift towards the north-west, losing their potential habitat in hilly and lower mountainous regions by 2050 across all trajectories. Our results highlight the impact of future climate change on two threatened and valuable species. The results can be further useful to initiate farming of these medicinally and economically important species based on climatically suitable zone and for designing a germplasm conservation strategy. | RANA Santosh Kumar RANA Hum Kala GHIMIRE Suresh Kulllar SHRESTHA Krishna Kumar RANJITKAR Sailesh | 2017 | Journal of Mountain Science2017，14，3： | 5 |
| 2 | Determining bioclimatic space of Himalayan alder for agroforestry systems in Nepal显示文摘Himalayan alder species are proven to be very useful in traditional as well as contemporary agroforestry practice. These nitrogen-fixing trees are also useful in the land restoration. Therefore, understanding the distribution of Himalayan alder and the potential zone for plantation is meaningful in the agroforestry sector. Suitable climatic zones of Alnus spp. were modelled in Max Ent software using a subset of least correlated bioclimatic variables for current conditions(1950 -2000), topographic variables(DEM derived) and Landuse Landcover(LULC) data. We generated several models and selected the best model against random models using ANOVA and t-test. The environmental variables that best explained the current distribution of the species were identified and used to project into the future. For future projections, ensemble scenarios of climate change projection derived from the results of 19 Earth System Models(ESM) were used. Our model revealed that the most favorable conditions for Alnus nepalensis are in central Nepal in the moist north-west facing slope, whereas for Alnus nitida they are in western Nepal.The major climatic factor that contributes to Alnus species distribution in Nepal appears to be precipitation during the warmest quarter for A. nepalensis and precipitation during the driest quarter for A. nitida. Future projections revealed changes in the probability distribution of these species, as well as where they need conservation and where they can be planted. Also, our model predicts that the distribution of Alnus spp. in hilly regions will remain unchanged, and therefore may represent sites that can be used to revitalize traditional agroforestry systems and extract source material for land restoration. | Santosh Kumar Rana Hum Kala Rana Krishna Kumar Shrestha Suresh Sujakhu Sailesh Ranjitkar | 2018 | Plant Diversity2018，40，1： | 1 |
| 3 | Pavement performance model for road maintenance and repair planning: a review of predictive techniques显示文摘This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users. | Krishna Singh Basnet Jagat Kumar Shrestha Rabindra Nath Shrestha | 2023 | Digital Transportation and Safety2023，2，4： | 0 |
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