scholarly journals Climatic records and linkage along an altitudinal gradient in the southern slope of Nepal Himalaya

2017 ◽  
Vol 53 ◽  
pp. 47-56 ◽  
Author(s):  
Binod Dawadi
Keyword(s):  
Regression Models ◽  
Similar Rate ◽  
High Mountain ◽  
Climate Data ◽  
Southern Slope ◽  
Nepal Himalaya ◽  
Lower Elevation ◽  
Similar Range ◽  
Weather Stations ◽  
Highly Correlated

To validate the climatic linkages under different topographic conditions, observational climate data at four automated weather stations (AWS) in different elevations, ranging from 130 m asl. to 5050 m asl., on the southern slope of the Nepal Himalayas was examined. the variation of means and distribution of daily, 5-days, 10-days, and monthly average/sum of temperature/ precipitation between the stations in the different elevation was observed. Despite these differences, the temperatures records are consistent in different altitudes, and highly correlated to each other while the precipitation data shows comparatively weaker correlation. The slopes (0.79-1.18) with (R2 >0.64) in the regression models for high Mountain to high Himalaya except in November and 0.56-1.14 (R2 >0.50) for mid-hill and high Mountain except January, December, June indicate the similar rate of fluctuation of temperature between the stations in the respective region. These strong linkages and the similar range of fluctuation of temperature in the different elevation indicate the possibilities of their use of lower elevation temperature data to represent the higher elevation sites for paleoclimatic calibration. However, the associations of precipitation between the stations at the different elevation are not as strong as the temperature due to heterogeneous topographical features and steep altitudinal contrast.

A Short note on Linkage of Climatic Records between Terai and Mid mountain of Central Nepal

2020 ◽  
Vol 3 (4) ◽  
Author(s):  
Binod Dawadi ◽  
Ram Hari Acharya ◽  
Dipendra Lamichhane ◽  
Saroj Pudasainee ◽  
Ishwar Kumar Shrestha
Keyword(s):  
Monsoon Season ◽  
Short Note ◽  
Similar Rate ◽  
Climatic Data ◽  
Southern Slope ◽  
Spatial And Temporal Patterns ◽  
Plain Area ◽  
Lower Elevation ◽  
Similar Range ◽  
Highly Correlated

The steep South to North (S-N) gradient and complex topography results in higher variations in the spatial and temporal patterns of climate within a short distance in the southern slope of the Nepal Himalayas. Therefore, to validate the climatic linkages of climatic between the stations under two distinct topographic conditions, we examine observational climatic data from 106 m a.s.l. and 1801 m a.s.l., as a representative stations in a  plain area and hilly area of on the southern slope of the of Nepal. The analysis of 13129 daily average temperature and 13147 daily total precipitation showed that the variation in their means and distribution of daily, 5-day, 10-day, and monthly average/sum of temperature/precipitation between the stations in the different elevation. Despite of these the differences, the temperatures records are consistent in different altitudes, and highly correlated to each other while the precipitation data shows a comparatively weaker correlation. The slopes (0.85-1.6) with R2 >0.50 in the regression models for the lower elevation and higher station in Mid mountain region except monsoon season indicate the similar rate of fluctuation of temperature between the stations in the respective region. Precipitation also shows the similar trend with higher variation between the stations in the different topographic setting. These strong linkages and a similar range of fluctuation of climatic parameters in the different elevation indicate the possibilities of their use of lower elevation climatic data to represent climate in the higher elevation sites

scholarly journals Does the High Elevation Climate along Mt. Everest can be Represented by Lower Elevation Stations?

2021 ◽  
Vol 26 (2) ◽  
pp. 99-109
Author(s):  
Binod Dawadi ◽  
Shankar Sharma ◽  
Kalpana Hamal ◽  
Nitesh Khadka ◽  
Yam Prasad Dhital ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Factors ◽  
Monsoon Season ◽  
High Elevation ◽  
High Mountain ◽  
Climate Data ◽  
Southern Slope ◽  
Mountain Areas ◽  
Temperature And Precipitation ◽  
Lower Elevation

Climate change studies of the high mountain areas of the central Himalayan region are mostly represented by the meteorological stations of the lower elevation. Therefore, to validate the climatic linkages, daily observational climate data from five automated weather stations (AWS) at elevations ranging from 2660 m to 5600 m on the southern slope of Mt. Everest were examined. Despite variations in the means and distribution of daily, 5-day, 10-day, and monthly temperature and precipitation between stations located at a higher elevation and their corresponding lower elevation, temperature records in the different elevations are highly correlated. In contrast, the precipitation data shows a comparatively weaker correlation. The slopes of the regression model (0.82–1.13) with (R2>0.74) for higher altitude (5050 m and 5600 m) throughout the year, 0.83–1.12 (R2>0.68) except late monsoon season for the station at 4260 m and 5050 m asl indicated the similar variability of the temperature between those stations. Similarly, Namche (3570 m) temperature changes by 0.81–1.32°C per degree change in corresponding lower elevation Lukla station (2660 m), except for monsoon season. However, inconsistent variation was observed between the station with a large altitudinal difference (2940 m) at Lukla and Kala Patthar (5600 m). In general, climate records from corresponding lower elevation can be used to quantitatively assess climatic information of the high elevation areas on the southern slope of Mt. Everest. However, corrections are necessary when absolute values of climatic factors are considered, especially in snow cover and snow-free areas. This study will be beneficial for understanding the high-altitude climate change and impact studies.

scholarly journals Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (13) ◽  
pp. 2548
Author(s):  
Luthfan Nur Habibi ◽  
Tomoya Watanabe ◽  
Tsutomu Matsui ◽  
Takashi S. T. Tanaka
Keyword(s):  
Spatial Variability ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Plant Density ◽  
Density Measurement ◽  
Critical Factor ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Climate Data

The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m−2. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m−2. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m−2. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.

scholarly journals Variabilidade Espacial da Erosividade das Chuvas para o Estado da Paraíba (Variability of Spatial Erosivity of Rainfall in the Paraíba State)

2014 ◽  
Vol 7 (4) ◽  
pp. 691
Author(s):  
Bernardo Starling Dorta do Amaral ◽  
João Filadelfo de Carvalho Neto ◽  
Richarde Marques da Silva ◽  
José Carlos Dantas
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Rainfall Characteristics ◽  
R Factor ◽  
Erosivity Index ◽  
The Mean ◽  
Weather Stations ◽  
Soil Loss Equation

As características específicas das chuvas variam entre regiões, e o conhecimento da sua potencialidade erosiva é necessário para o planejamento dos recursos hídricos. Este estudo determinou a erosividade, analisou a variabilidade espacial da precipitação e o coeficiente de chuva para o Estado da Paraíba mediante técnicas de Sistemas de Informação Geográfica. Para a realização deste estudo foram utilizados dados climatológicos de 98 estações climatológicas da Embrapa, com séries de 1911 a 1990. Em seguida as informações sobre a erosividade foram processadas cartograficamente. O valor médio anual da erosividade das chuvas com base no índice EI30 para o Estado da Paraíba foi de 5.032,03 MJ.mm/ha/h, valor que representa o Fator “R” da Equação Universal de Perdas de Solo (USLE). As equações de regressão entre erosividade e precipitação e coeficiente de chuva não foram significativas. As principais conclusões são que: (a) os índices de erosividade encontrados são maiores na zona litorânea do que nas demais porções do Estado, e (b) as erosividades encontradas variaram de acordo com os valores da precipitação.   A B S T R A C T Specific rainfall characteristics vary among regions and their erosion potential must be known for the planning of water resources. This study analyzed the erosivity and rainfall variability and precipitation coefficient for Paraíba State based on Geographic Information Systems techniques. In order In this paper 98 climatological stations of Embrapa were used, with rainfall data of 1911 to 1990. For this study we use d climate data from 98 weather stations of Embrapa, with series from 1911 to 1990. Additionally we processed the information of the erosivity index cartographically by year and microregions. The mean annual value of erosivity was 5,032.03 MJ.mm/ha/h, which is to be used as “R” Factor in the Universal Soil Loss Equation (USLE) for Paraíba State and surrounding regions with similar climatic conditions. The main conclusions are that: (a) erosivity indexes are higher in coastal areas than in inland areas, and (b) the erosivity range according to the precipitation.   Keywords: erosivity, rainfall, water resources   

scholarly journals Climatic water indices for viticulture

2018 ◽  
Vol 53 (6) ◽  
pp. 765-768
Author(s):  
Marco Antônio Fonseca Conceição ◽  
Jorge Tonietto ◽  
Reginaldo Teodoro de Souza
Keyword(s):  
Climate Data ◽  
Water Index ◽  
Historical Series ◽  
Weather Stations ◽  
Water Indices

Abstract: The objective of this work was to evaluate the performance of vineyard water indices in different grape-growing regions. The climate data used come from the historical series of weather stations located in 18 countries. The evaluated indices were the following: dryness, Zuluaga, humidity, aridity, moisture, and the grapevine water index. The grapevine water index and the indices of drought, moisture, and aridity exhibit similar performances, which makes them suitable to be used equivalently in climatological studies of grapevine regions.

scholarly journals Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia

2020 ◽  
Vol 66 (256) ◽  
pp. 175-187 ◽  
Author(s):  
David R. Rounce ◽  
Tushar Khurana ◽  
Margaret B. Short ◽  
Regine Hock ◽  
David E. Shean ◽  
...  
Keyword(s):  
Mass Balance ◽  
Parameter Uncertainty ◽  
Large Scale ◽  
Evolution Model ◽  
Model Parameters ◽  
High Mountain ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Model Parameter Uncertainty ◽  
Geodetic Mass Balance

AbstractThe response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

scholarly journals Local knowledge based perceptions on climate change and its impacts in the Rakaposhi valley of Gilgit-Baltistan, Pakistan

2019 ◽  
Vol 12 (2) ◽  
pp. 222-237 ◽  
Author(s):  
Laxmi Dutt Bhatta ◽  
Erica Udas ◽  
Babar Khan ◽  
Anila Ajmal ◽  
Roheela Amir ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Local Knowledge ◽  
Plant Distribution ◽  
Precipitation Pattern ◽  
Climate Data ◽  
Local Perceptions ◽  
Content Type ◽  
Knowledge Based ◽  
Weather Stations

Purpose The purpose of this paper is to understand local perceptions on climate change and its impacts on biodiversity, rangeland, agriculture and human health. Design/methodology/approach A household survey with 300 interviewees and focus group discussions with key stakeholders were conducted and validated at two steps, using the climate data from the nearest weather stations and reviewing literatures, to correlate the local perceptions on climate change and its impacts. Findings Majority of the respondents reported an increase in temperature and change in the precipitation pattern with increased hazardous incidences such as floods, avalanches and landslides. Climate change directly impacted plant distribution, species composition, disease and pest infestation, forage availability, agricultural productivity and human health risks related to infectious vector-borne diseases. Research limitations/implications Because of the remoteness and difficult terrain, there are insufficient local weather stations in the mountains providing inadequate scientific data, thus requiring extrapolation from nearest stations for long-term climate data monitoring. Practical implications The research findings recommend taking immediate actions to develop local climate change adaptation strategies through a participatory approach that would enable local communities to strengthen their adaptive capacity and resilience. Social implications Local knowledge-based perceptions on climate change and its impacts on social, ecological and economic sectors could help scientists, practitioners and policymakers to understand the ground reality and respond accordingly through effective planning and implementing adaptive measures including policy formulation. Originality/value This research focuses on combining local knowledge-based perceptions and climate science to elaborate the impacts of climate change in a localised context in Rakaposhi Valley in Karakoram Mountains of Pakistan.

An alternative interpretation of climate data: Intelligence

2017 ◽  
Vol 40 ◽  
Author(s):  
Robert J. Sternberg
Keyword(s):  
Life History ◽  
Statistical Analysis ◽  
Life History Strategy ◽  
Alternative Interpretation ◽  
Self Control ◽  
Climate Data ◽  
State Variable ◽  
Target Article ◽  
Highly Correlated ◽  
Underlying Variable

AbstractThe CLASH model proposed in the target article is plausible but less than parsimonious. I suggest that statistical analysis probably would find slower life history strategy, greater focus on the future, and greater self-control to be highly correlated and perhaps unifactorial, because they are all manifestations of a single underlying variable, namely, intelligence. I suggest how intelligence as a state variable plausibly could explain the differences observed by the authors.

Emergency department and ‘Google flu trends’ data as syndromic surveillance indicators for seasonal influenza

2014 ◽  
Vol 142 (11) ◽  
pp. 2397-2405 ◽  
Author(s):  
L. H. THOMPSON ◽  
M. T. MALIK ◽  
A. GUMEL ◽  
T. STROME ◽  
S. M. MAHMUD
Keyword(s):  
Emergency Department ◽  
Linear Regression ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Linear Regression Models ◽  
Physician Visits ◽  
Influenza Activity ◽  
Ed Visits ◽  
Highly Correlated

SUMMARYWe evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data – total ED visits attributed to influenza-like illness (ILI) (‘ED ILI volume’) and percentage of visits attributed to ILI (‘ED ILI percent’) – and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

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