Comparison of Spatial Interpolation Methods for Mapping Daily Air Temperature 

2021 ◽  
Author(s):  
Lasyamayee L Sahoo ◽  
Subashisa Dutta
Keyword(s):  
Minimum Temperature ◽  
Evaluation Method ◽  
Heat Waves ◽  
Interpolation Method ◽  
Climatic Variability ◽  
Labour Productivity ◽  
The State ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Interpolation Methods

<p>The sparsely distributed meteorological centers fails to provide enough information regarding spatial patterns. Even at places where dense meteorological stations are available, it is difficult to develop realistic gridded data due to the complex topography and climatic variability. Some of the climate as well as hydrological model require spatially continuous datasets as inputs. It is possible to obtain a continuous surface of raster datasets with the help of interpolation methods where each value is assigned based on surrounding values using specific mathematical formulas. For present study, various interpolation methods, like Inverse distance weighted, ordinary krigging, thin plate smoothing spline; has been compared for maximum and minimum temperature. Error in the interpolated data was analyzed by independent cross validation method, in which measurements like root mean square error (RMSE), mean squared relative error (MSRE), coefficient of determination (r<sup>2</sup>) and coefficient of efficiency (CE) were adopted for performance evaluation. Method with minimum error was chosen for developing the final map. It provides an effective way for mapping the meteorological variables in a topographically diverse region. In this case, an Indian state Odisha is chosen as study area. The state consists of 10 different agro-climatic zones and sees several weather systems across the year. The area suffers with floods, drought, heat waves and costal erosion almost every year with variable intensity. Strong heat waves in summer affect the human health, agriculture, construction efficiency and labour productivity. As three-fourth of the state is filled with mountains and high lands, monitoring network is sparsely distributed. Despite small latitudinal difference, temperature changes considerably with respect to both space and time. Here interpolation method plays a vital role to avoid uncertainty in modelling. Based on the generated maps, vulnerable areas on the basis of maximum temperature in summer and minimum temperature in winter is identified. Several indicators and vulnerability indices has been used.</p>

scholarly journals CLIMATIC VARIABILITY AND LIVELIHOOD VULNERABILITY IN KADUNA STATE, NIGERIA

2017 ◽  
Vol 1 (1) ◽  
pp. 36
Author(s):  
Muhammad, N. ◽  
Manu, H.I. ◽  
Maina- Bukar, Y. ◽  
Abdullahi, Y.R.
Keyword(s):  
Local Government ◽  
International Development ◽  
Minimum Temperature ◽  
Climatic Variability ◽  
Vulnerability Index ◽  
The State ◽  
Maximum Temperature ◽  
Sample Population ◽  
Climatic Zones ◽  
Farming Households

Purpose: This paper focused on livelihood vulnerability induced by climatic variability amongst farming households in Kaduna state, Nigeria. Methodology: The research used a sample population of 400 using Taro Yamane formula which represents about 0.05% of the population of the three selected local government areas and it purposively targeted farming households heads (FHHH) in one of each of the three eco-climatic zones in the state. Kagarko, BirninGwari and Makarfi local government areas were based on their eco-climatic location and rurality to represent humid, sub-humid and dry sub humid zones of the state respectively. A multi stage sampling technique was further adopted in which farming districts and villages were selected for the administration of 400 structured questionnaires proportionately distributed proportionately to the three local government areas. The Department for International Development (DFID) sustainable livelihoods framework was adopted in the design of the structured questionnaires. Coefficient of Variation (CV %) was deployed to determine the variability of rainfall and temperature of the three eco-climatic zones of the past thirty six years (1981-2016) which was employed into the Micah Hahn’s Livelihood Vulnerability Index model.The results show that Kagarko (humid) had a CV% of 105.43 of rainfall, 9.06 CV% of maximum temperature and CV% of 17.63 in minimum temperature. BirninGwari (sub-humid) had a CV% of 119.64 in rainfall, CV% of 14.17 in maximum temperature and CV% of 15.92 in minimum temperature while Makarfi (dry sub-humid) had a CV% of 124.71 in rainfall, CV%  of 9.72 in maximum temperature and 16.29 CV% in minimum temperature. The livelihood vulnerability index (LVI) of Kagarko was calculated to be 0.35, Makarfi and BirninGwari were calculated to be 0.36 respectively and vulnerability spider diagrams were used to capture and compare results. On a vulnerability scale of 0-1, the three eco-climatic zones were found to be very vulnerable to climatic variability. The paper has proved the applicability of Co-efficient of Variation (CV %) into the LVI model which is a departure from previous users who have consistently deployed Mean Standard Deviation into the model. Results: This study will serve as a spring board to meet the Sustainable Development Goals (SDGs) targets on vulnerable communities in Kaduna state. It is discovered that farmers in Makarfi and BirninGwari, even though in different eco-climatic zones of sub-humid and dry sub humid zones respectively, share equal level of livelihood vulnerability index of 0.36 while Kagarko area which is in humid zone, is having 0.35. These indicated that all the areas are within the very vulnerable values on a vulnerability scale of 0-1. The vulnerability levels of the study area can be attributed to weak Natural, Financial and Physical capitals. Recommendations: The paper recommended Integrated Farmers’ Livelihoods Support Strategy (IFLISS) so as to build the resilience of farming households’ livelihood capitals and reduce vulnerability levels.

Effect of environment factors on milk production and lactation length under different seasons in crossbred cattle

2015 ◽  
Author(s):  
S.S. Mote ◽  
D.S. Chauhan* and Nilotpal Ghosh1
Keyword(s):  
Milk Yield ◽  
Minimum Temperature ◽  
Summer Season ◽  
Climatic Variables ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Temperature Minimum ◽  
Crossbred Cattle ◽  
Lactation Length ◽  
Significant Regression

The study was undertaken to evaluate the effect of different macro climatic variables on lactation milk yield and lactation length of Holdeo (Holstein Friesian x Deoni) crossbred cattle. Milk data of 145 Holdeo crossbred cows with 619 lactation records and the meteorological data over a period of 15 years (1995-2009) were obtained from Cattle Cross Breeding Project, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani and University Meteorological Observatory, respectively. It was observed that maximum temperature has significant correlation with lactation milk yield; whereas maximum temperature, minimum temperature, sunshine hours and wind speed have significant correlation with lactation length. Regression analysis indicated that all the climatic variables except minimum temperature exhibited significant regression results with lactation milk yield, and maximum temperature, minimum temperature and maximum humidity have significant regression results with lactation length. All the climatic variables considered in the study accounted for 75 % and 65 % direct variation on lactation milk yield and lactation length, respectively, as verified by the value of coefficient of determination (R2). It was observed that lactation milk yield (1136.56 + 21.04 kg.) and lactation length (295.29 + 5.51 days) were highest among the cows calved during winter season as compared to rainy and summer season. All the climatic variables considered in the study accounted for 57% , 56 % and 48 % direct variation on milk yield and 68% , 53 % and 46 % direct variation on lactation length in rainy, winter and summer season, respectively, as verified by the value of coefficient of determination (R2). This research indicated that crossbred cows were sensitive to seasonal changes on their lactation performance. The optimum ranges of temperature; humidity and THI for better performance of crossbred in subtropical region of India were found to be 19-26 oC, 52-66 % and 65-68 %, respectively.

Effect of environment factors on milk production and lactation length under different seasons in crossbred cattle

2016 ◽  
Author(s):  
S. S. Mote ◽  
D. S. Chauhan ◽  
Nilotpal Ghosh
Keyword(s):  
Milk Yield ◽  
Minimum Temperature ◽  
Summer Season ◽  
Climatic Variables ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Temperature Minimum ◽  
Crossbred Cattle ◽  
Lactation Length ◽  
Significant Regression

The study was undertaken to evaluate the effect of different macro climatic variables on lactation milk yield and lactation length of Holdeo (Holstein Friesian x Deoni) crossbred cattle. Milk data of 145 Holdeo crossbred cows with 619 lactation records and the meteorological data over a period of 15 years (1995-2009) were obtained from Cattle Cross Breeding Project, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani and University Meteorological Observatory, respectively. It was observed that maximum temperature has significant correlation with lactation milk yield; whereas maximum temperature, minimum temperature, sunshine hours and wind speed have significant correlation with lactation length. Regression analysis indicated that all the climatic variables except minimum temperature exhibited significant regression results with lactation milk yield, and maximum temperature, minimum temperature and maximum humidity have significant regression results with lactation length. All the climatic variables considered in the study accounted for 75 % and 65 % direct variation on lactation milk yield and lactation length, respectively, as verified by the value of coefficient of determination (R2). It was observed that lactation milk yield (1136.56 + 21.04 kg.) and lactation length (295.29 + 5.51 days) were highest among the cows calved during winter season as compared to rainy and summer season. All the climatic variables considered in the study accounted for 57% , 56 % and 48 % direct variation on milk yield and 68% , 53 % and 46 % direct variation on lactation length in rainy, winter and summer season, respectively, as verified by the value of coefficient of determination (R2). This research indicated that crossbred cows were sensitive to seasonal changes on their lactation performance. The optimum ranges of temperature; humidity and THI for better performance of crossbred in subtropical region of India were found to be 19-26 oC, 52-66 % and 65-68 %, respectively.

scholarly journals Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria

2021 ◽  
Author(s):  
Mohamed Sanusi Shiru ◽  
Eun-Sung Chung
Keyword(s):  
Minimum Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Projection ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Climatic Research Unit ◽  
Spatial Assessment ◽  
Taylor Diagram

Abstract This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

scholarly journals Reweigh Temperature as Estimator for Evaluation and Prediction

2020 ◽  
pp. 66-71
Author(s):  
M. K. Awasthi ◽  
Deepak Patle
Keyword(s):  
Minimum Temperature ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Subtropical Climate ◽  
Data Set ◽  
Hourly Data ◽  
Weather Underground ◽  
Region Temperature ◽  
Research Outcome

This study aimed to develop estimator for evaluation of reweigh temperature for prediction research extent. Research conducted in Jabalpur district of Madhya Pradesh, India, which comes under the humid subtropical climate region. Temperature recorded at one hour, two hour or three hour either side of maximum temperature may be averaged to get a plateaued value for that much time period. Hourly data on temperature recorded at Weather Underground site are regrouped into different temperature forms namely average of maximum and minimum temperature (Tav), weighted temperature (Twt), maximum temperature (Tmax), Temperature plateaued one hour, two hour and three hour either side of maximum temperature (Tp2, Tp4 and Tp6 respectively). These temperature forms are plotted for all twelve months. Integration of Tav and Tmax was done for estimation of weighted temperature. Values of coefficient of determination raised from fitting of linear regression between each of temperature form; Tmax, Tav, Twt, Tp2 Tp4 and Tp6 with actual pan evaporation. Data set comprises of daily records separately for all twelve months. Daily records are also regrouped into four more categories i.e. for whole year (365 days), hot months (April-May), cold months (December- January) and wet months (July-August). Though the r-squared values are found very low and explains that temperature alone cannot be taken as predictor of evaporation, which is a well comparative fact, but the purpose of presenting these values here to show the comparative effect of different temperature forms on evaporation. In hot months, the Twt with r-squared values of 0.49 seems to be more correlated than other temperature forms. But, in cold months Tmax, Tp2, Tp4 and Tp6 have more influence on evaporation than the Tav or Twt. The research outcome of the present study will be helpful to estimation of reweigh temperature rather average of maximum and minimum temperature for use in prediction research work.

scholarly journals Spatio-temporal modelling of the duration of the cotton cycle in the State Of Goiás, Brazil

2011 ◽  
Vol 31 (4) ◽  
pp. 652-662
Author(s):  
Jorge C. dos A. Antonini ◽  
Euzebio M. da Silva ◽  
Nori P. Griebeler ◽  
Edson E. Sano
Keyword(s):  
Pearson Correlation ◽  
The State ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Model Parameters ◽  
Crop Fields ◽  
Time Space ◽  
Spatio Temporal ◽  
Temperature Records ◽  
Comparative Results

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.

Extreme temperatures analysis: A study for Campinas, Brazil

2021 ◽  
Author(s):  
Guilherme Correia ◽  
Ana Maria Ávila
Keyword(s):  
Minimum Temperature ◽  
Temperature Increase ◽  
Heat Waves ◽  
Vulnerable Groups ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
P Value ◽  
Significance Level ◽  
Historical Series ◽  
Maximum Temperatures

<p>Extreme events such as heat waves have adverse effects on human health, especially on vulnerable groups, which can lead to deaths, thus they must be faced as a huge threat. Many studies show general mean temperature increase, notably, minimum temperatures. The scope of this work was to assess daily data of a historical series (1890-2018) available on the Instituto Agronômico de Campinas (IAC), in Campinas, using a suite of indices derived from daily temperature and formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI) and evaluate trends. To compute the extreme indices RClimDex 1.1 was used. The significance test is based on a t  test, with a significance level of 95% (p-value<0,05). Temperature increase is undoubtedly through many indices, especially from 1980, as there is a continuous rise of the temperature. Annual mean maximum temperature rose from 26°C to 29°C, whereas many years consistently have more than 50 days with maximum temperatures as high as 31°C and more than 20% of the days within a year are beyond the 90th percentile of the daily maximum temperatures. Annual mean minimum temperature rose from 14°C to 18°C, whereas many years consistently have more than 150 days with minimum temperatures as high as 18°C and more than 30% of the days within a year are beyond the 90th percentile of the daily minimum temperatures. Therefore, results indicate the increase of minimum temperature is greater than the increase of maximum temperatures.</p>

scholarly journals ASSESSMENT OF LONG TERM CLIMATIC VARIABILITY OF UTTARAKHAND

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Sujeet Kumar ◽  
Shakti Suryavanshi
Keyword(s):  
Minimum Temperature ◽  
Monsoon Season ◽  
Climatic Variability ◽  
Kendall Test ◽  
Summer Season ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Mountain Areas ◽  
Mk Test ◽  
Increasing Trend

A trend analysis was performed for historic (1901-2002) climatic variables (Rainfall, Maximum Temperature and Minimum Temperature) of Uttarakhand State located in Northern India. In the serially independent climatic variables, Mann-Kendall test (MK test) was applied to the original sample data. However, in the serially correlated series, prewhitening is utilized before employing the MK test. The results of this study indicated a declining trend of rainfall in monsoon season for seven out of thirteen districts of Uttarakhand state. However, an increasing trend was observed in Haridwar and Udhamsingh Nagar districts for summer season rainfall. For maximum and minimum temperature, a few districts exhibited a declining trend in monsoon season whereas many districts exhibited an increasing trend in winter and summer season. Mountain dominated areas (as Uttarakhand state) are specific ecosystems, distinguished by their diversity, sensitivity and intricacy. Thus the variability of rainfall and temperature has a severe and rapid impact on mountainous ecosystems. Nevertheless, mountains have significant impacts on hydrology, which may further threaten populations living in the mountain areas as well as in adjacent, lowland regions.

scholarly journals The Association between the Mapping Distribution of Melioidosis Incidences and Meteorological Factors in an Endemic Area: Ubon Ratchathani, Thailand (2009–2018)

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Jaruwan Wongbutdee ◽  
◽  
Wacharapong Saengnill ◽  
Jutharat Jittimanee ◽  
Pawana Panomket ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Meteorological Factors ◽  
Interpolation Method ◽  
Public Health Problem ◽  
Kriging Interpolation ◽  
Maximum Temperature ◽  
Tropical Regions ◽  
Kriging Interpolation Method ◽  
Average Rainfall

Abstract Melioidosis is a public health problem in the tropical regions, occurring to meteorological variability. For 10 years of melioidosis outbreaks, we create probability maps of melioidosis distribution during 2009–2018 and determine the association with meteorological factors. The monthly average rainfall and incidence of melioidosis were high from July to September but they not significantly associated (P = 0.576). However, the monthly maximum and minimum temperature were significantly associated with melioidosis incidence (P = 0.002 and P = 0.029, respectively). We estimated the spatial distribution of rainfall and maximum and minimum temperature using the Co-Kriging interpolation method which found that the spatial distribution of the melioidosis incidence was significantly associated with rainfall in 2009, 2010, and 2015; with the maximum temperature in 2009, 2010, 2011, 2013, and 2015; and with the minimum temperature in 2010, 2011, and 2015. Our finding approach may support information and classify a pattern for melioidosis distribution. Keywords: Incidence, Melioidosis, Meteorological factors

scholarly journals Analysis of Anomalies and Trends of Climate Change Indices in the State of Zacatecas, Mexico

2020 ◽  
Author(s):  
Oscar Pita-Díaz ◽  
David Ortega-Gaucin
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Agricultural Sector ◽  
Negative Impact ◽  
Southern Oscillation ◽  
Climate Scenario ◽  
The State ◽  
Data Availability ◽  
Sufficient Evidence ◽  
Maximum Temperature

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

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