Unobservable Components Modelling of Monthly Average Maximum and Minimum Temperature Patterns in India 1981–2015

2018 ◽  
Vol 176 (1) ◽  
pp. 463-482 ◽  
Author(s):  
Narasimha Murthy Kaipa Viswanath ◽  
Saravana Ramachandran
Keyword(s):  
Minimum Temperature ◽  
Monthly Average ◽  
Average Maximum

scholarly journals Evaluation of Climate Change on Streamflow, Sediment, and Nutrient Load at Watershed Scale

Climate ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 165
Author(s):  
Prem B. Parajuli ◽  
Avay Risal
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Minimum Temperature ◽  
Future Climate ◽  
Maximum Temperature ◽  
Nutrient Load ◽  
Climate Scenarios ◽  
Monthly Average ◽  
Average Maximum ◽  
Monthly Streamflow

This study evaluated changes in climatic variable impacts on hydrology and water quality in Big Sunflower River Watershed (BSRW), Mississippi. Site-specific future time-series precipitation, temperature, and solar radiation data were generated using a stochastic weather generator LARS-WG model. For the generation of climate scenarios, Representative Concentration Pathways (RCPs), 4.5 and 8.5 of Global Circulation Models (GCMs): Hadley Center Global Environmental Model (HadGEM) and EC-EARTH, for three (2021–2040, 2041–2060 and 2061–2080) future climate periods. Analysis of future climate data based on six ground weather stations located within BSRW showed that the minimum temperature ranged from 11.9 °C to 15.9 °C and the maximum temperature ranged from 23.2 °C to 28.3 °C. Similarly, the average daily rainfall ranged from 3.6 mm to 4.3 mm. Analysis of changes in monthly average maximum/minimum temperature showed that January had the maximum increment and July/August had a minimum increment in monthly average temperature. Similarly, maximum increase in monthly average rainfall was observed during May and maximum decrease was observed during September. The average monthly streamflow, sediment, TN, and TP loads under different climate scenarios varied significantly. The change in average TN and TP loads due to climate change were observed to be very high compared to the change in streamflow and sediment load. The monthly average nutrient load under two different RCP scenarios varied greatly from as low as 63% to as high as 184%, compared to the current monthly nutrient load. The change in hydrology and water quality was mainly attributed to changes in surface temperature, precipitation, and stream flow. This study can be useful in the development and implementation of climate change smart management of agricultural watersheds.

scholarly journals SARIMA modeling of the monthly average maximum and minimum temperatures in the eastern plateau region of India

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 841-848
Author(s):  
ENAKSHI SAHA ◽  
ARNAB HAZRA ◽  
PABITRA BANIK
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Minimum Temperature ◽  
Information Criteria ◽  
Temperature Series ◽  
Plateau Region ◽  
Akaike Information Criteria ◽  
Monthly Average ◽  
Standardized Residuals ◽  
Average Maximum

The SARIMA time series model is fitted to the monthly average maximum and minimum temperature data sets collected at Giridih, India for the years 1990-2011. From the time-series  plots, we observe that the patterns of both the series are quite different; maximum temperature series contain sharp peaks in almost all the years while it is not true for the minimum temperature series and hence both the series are modeled separately (also for the sake of simplicity). SARIMA models are selected based on observing autocorrelation function (ACF) and partial autocorrelation function (PACF) of the monthly temperature series. The model parameters are obtained by using maximum likelihood method with the help of three tests [i.e., standard error, ACF and PACF of residuals and Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and corrected Akaike Information Criteria (AICc)]. Adequacy of the selected models is determined using diagnostic checking with the standardized residuals, ACF of residuals, normal Q-Q plot of the standardized residuals and p-values of the Ljung-Box statistic. The models ARIMA (1; 0; 2) × (0; 1; 1)12  and ARIMA (0; 1; 1) × (1; 1; 1)12  are finally selected for forecasting of monthly average maximum and minimum temperature values respectively for the eastern plateau region of India.  

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981–2015

2018 ◽  
Vol 131 (4) ◽  
pp. 775-787 ◽  
Author(s):  
K. V. Narasimha Murthy ◽  
R. Saravana ◽  
K. Vijaya Kumar
Keyword(s):  
Minimum Temperature ◽  
Stochastic Modelling ◽  
Monthly Average ◽  
Average Maximum

scholarly journals A positive association between cryptosporidiosis notifications and ambient temperature, Victoria, Australia, 2001–2009

2015 ◽  
Vol 13 (4) ◽  
pp. 1039-1047 ◽  
Author(s):  
Lillian Kent ◽  
Michelle McPherson ◽  
Nasra Higgins
Keyword(s):  
Climate Change ◽  
Ambient Temperature ◽  
Rural Areas ◽  
Minimum Temperature ◽  
Negative Binomial ◽  
Negative Binomial Regression Model ◽  
Public Health Response ◽  
Monthly Average ◽  
Average Maximum ◽  
Average Minimum Temperature

Increased temperatures provide optimal conditions for pathogen survival, virulence and replication as well as increased opportunities for human–pathogen interaction. This paper examined the relationship between notifications of cryptosporidiosis and temperature in metropolitan and rural areas of Victoria, Australia between 2001 and 2009. A negative binomial regression model was used to analyse monthly average maximum and minimum temperatures, rainfall and the monthly count of cryptosporidiosis notifications. In the metropolitan area, a 1 °C increase in monthly average minimum temperature of the current month was associated with a 22% increase in cryptosporidiosis notifications (incident rate ratio (IRR) 1.22; 95% confidence interval (CI) 1.13–1.31). In the rural area, a 1 °C increase in monthly average minimum temperature, lagged by 3 months, was associated with a 9% decrease in cryptosporidiosis notifications (IRR 0.91; 95% CI 0.86–0.97). Rainfall was not associated with notifications in either area. These relationships should be considered when planning public health response to ecological risks as well as when developing policies involving climate change. Rising ambient temperature may be an early warning signal for intensifying prevention efforts, including appropriate education for pool users about cryptosporidiosis infection and management, which might become more important as temperatures are projected to increase as a result of climate change.

scholarly journals The effect of average temperature on suicide rates in five urban California counties, 1999–⁠2019: an ecological time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie M. Nanos ◽  
Mavra Qamar ◽  
David N. Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Suicide Rate ◽  
Minimum Temperature ◽  
The United States ◽  
Additive Models ◽  
Maximum Temperature ◽  
Mortality Data ◽  
Average Temperature ◽  
Suicide Rates ◽  
Average Maximum

Abstract Background Suicide is among the top 10 leading causes of premature morality in the United States and its rates continue to increase. Thus, its prevention has become a salient public health responsibility. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. The purpose of the present study is to evaluate the association between average temperature and suicide rates in the five most populous counties in California using mortality data from 1999 to 2019. Methods Monthly counts of death by suicide for the five counties of interest were obtained from CDC WONDER. Monthly average, maximum, and minimum temperature were obtained from nCLIMDIV for the same time period. We modelled the association of each temperature variable with suicide rate using negative binomial generalized additive models accounting for the county-specific annual trend and monthly seasonality. Results There were over 38,000 deaths by suicide in California’s five most populous counties between 1999 and 2019. An increase in average temperature of 1 °C corresponded to a 0.82% increase in suicide rate (IRR = 1.0082 per °C; 95% CI = 1.0025–1.0140). Estimated coefficients for maximum temperature (IRR = 1.0069 per °C; 95% CI = 1.0021–1.0117) and minimum temperature (IRR = 1.0088 per °C; 95% CI = 1.0023–1.0153) were similar. Conclusion This study adds to a growing body of evidence supporting a causal effect of elevated temperature on suicide. Further investigation into environmental causes of suicide, as well as the biological and societal contexts mediating these relationships, is critical for the development and implementation of new public health interventions to reduce the incidence of suicide, particularly in the face increasing temperatures due to climate change.

scholarly journals Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimation

2020 ◽  
Author(s):  
Mohammad Taghi Sattari ◽  
Halit Apaydin ◽  
Shahab Shamshirband ◽  
Amir Mosavi
Keyword(s):  
Deep Learning ◽  
Minimum Temperature ◽  
Meteorological Parameters ◽  
Short Term Memory ◽  
Sunshine Duration ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Ann Model ◽  
Learning Methods ◽  
Average Maximum

Abstract. Proper estimation of the reference evapotranspiration (ET0) amount is an indispensable matter for agricultural water management in the efficient use of water. The aim of study is to estimate the amount of ET0 with a different machine and deep learning methods by using minimum meteorological parameters in the Corum region which is an arid and semi-arid climate with an important agricultural center of Turkey. In this context, meteorological variables of average, maximum and minimum temperature, sunshine duration, wind speed, average, maximum, and minimum relative humidity are used as input data monthly. Two different kernel-based (Gaussian Process Regression (GPR) and Support Vector Regression (SVR)) methods, BFGS-ANN and Long short-term memory models were used to estimate ET0 amounts in 10 different combinations. According to the results obtained, all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others. In kernel-based GPR and SVR methods, Pearson VII function-based universal kernel was the most successful kernel function. Besides, the scenario that is related to temperature in all scenarios used, including average temperature, maximum and minimum temperature, and sunshine duration gave the best results. The second-best scenario was the one that covers only the sunshine duration. In this case, the ANN (BFGS-ANN) model, which is optimized with the BFGS method that uses only the sunshine duration, can be estimated with the 0.971 correlation coefficient of ET0 without the need for other meteorological parameters.

scholarly journals Exploring the Sensitivity of Subtropical Stand Aboveground Productivity to Local and Regional Climate Signals in South China

Forests ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 71 ◽  
Author(s):  
Hua Zhou ◽  
Yang Luo ◽  
Guang Zhou ◽  
Jian Yu ◽  
Sher Shah ◽  
...  
Keyword(s):  
Regional Climate ◽  
Mixed Forest ◽  
Single Species ◽  
Forest Productivity ◽  
Maximum Temperature ◽  
Forest Types ◽  
Monthly Average ◽  
Aboveground Productivity ◽  
Average Maximum ◽  
Average Maximum Temperature

Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Niño-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 °C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Niño periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Niña periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.

scholarly journals Evaluation of SRE Scenarios for Penang, Selangor and Johor in Peninsular Malaysia using PRECIS Regional Climate Model (RCM)

2018 ◽  
Vol 65 ◽  
pp. 05020
Author(s):  
Kah Seng Chin ◽  
Kok Weng Tan
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Peninsular Malaysia ◽  
Observation Data ◽  
Future Projections ◽  
Monthly Average ◽  
Average Maximum ◽  
Maximum Temperatures ◽  
Historical Observation

Climate change is unambiguous as there is much evidence from around the world showing that changes have already occurred. This phenomenon is in response to an array of human activities, notably the release of greenhouse gases; an understanding of the rate, mode and scale of this change is now of literally vital importance to society. Researchers utilize climate models to study the dynamics of our changing climate and also to make future projections. Climate models are basic representation of many interactions within the Earth’s climate which includes the atmosphere, land surface, oceans and ice. These models are typically quantitative in nature and range from simple depictions of the climate to very complex ones. In this present study, downscaled PRECIS regional climate models (RCMs) were used to project the average minimum and average maximum temperatures and average precipitation for Penang, Selangor and Johor in Peninsular Malaysia. The RCM projections for these three states were developed based on ECHAM4 A2 and ECHAM5 A1B scenarios for the years 1980 to 2069 and ECHAM4 B2 scenario for the years 2010 to 2069. Bias correction will be applied to the simulated historical data to remove common systematic model errors. Historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall from the Malaysian Meteorological Department (MMD) will be used in the bias correction. Finally, a RCM scenario which matches with the historical observation data of the three states for future projections will be recommended.

scholarly journals Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

2021 ◽  
Vol 25 (2) ◽  
pp. 603-618
Author(s):  
Mohammad Taghi Sattari ◽  
Halit Apaydin ◽  
Shahab S. Band ◽  
Amir Mosavi ◽  
Ramendra Prasad
Keyword(s):  
Minimum Temperature ◽  
Short Term Memory ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Accurate Estimation ◽  
Support Vector ◽  
Learning Approaches ◽  
Ann Model ◽  
Average Maximum ◽  
Semi Arid

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.

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