scholarly journals Impacts of Some Climatic Variables on the Seasonal Productivity of Aman Rice at Dhaka Region, Central Part of Bangladesh

2016 ◽  
Vol 8 (2) ◽  
pp. 7-10
Author(s):  
SMSA Tuhin ◽  
MA Farukh ◽  
BS Nahar ◽  
MA Baten
Keyword(s):  
Crop Production ◽  
Climatic Variability ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Average Maximum ◽  
Average Maximum Temperature ◽  
Daily Sunshine

An agro-climatic study was conducted at Dhaka region of Bangladesh using 43 years (1970-2012) of climatic data (daily maximum temperature, seasonal total rainfall, daily average humidity, and daily sunshine hour) to observe the climatic variability and their impacts on the productivity of Aman rice. The average maximum temperature increased by 0.04°C in Aman season in Dhaka region. The average sunshine hours decreased by 0.05 in the season. The average humidity decreased by 0.14% in the season. The average seasonal rainfall increased slightly by 0.09 mm in the season. The Aman rice production increased by 0.03 t ha-1 in the region. The production year 2003 shows highest productivity due to less climatic devastation impact on the seasonal productivity of the rice. The climatic variables impact ( Savg > Havg > Tmax ) implies the seasonal productivity of Aman rice was mostly and inversely correlated with average sunshine (Savg) hour. However, most of the time the production showed increasing trend except some devastating natural calamities in the year of 1988 and 1998 which affected crop production seriously.J. Environ. Sci. & Natural Resources, 8(2): 7-10 2015

scholarly journals Walking is regulated by environmental temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuichi P. Obuchi ◽  
Hisashi Kawai ◽  
Juan C. Garbalosa ◽  
Kazumasa Nishida ◽  
Kenji Murakawa
Keyword(s):  
Environmental Temperature ◽  
Private Insurance ◽  
Movement Speed ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Curvilinear Relationship ◽  
Daily Maximum ◽  
Insurance Company ◽  
Average Maximum ◽  
Average Maximum Temperature

AbstractThe mechanisms that regulate human walking are not fully understood, although there has been substantial research. In our study, we hypothesized that, although walking can be volitionally modified, it is also involuntary and controlled by evolutionary factors, such as the relationship between temperature and movement speed in poikilotherms. This study aimed to determine the effects of environmental temperature on speed, step length, and cadence during unrestrained walking over long periods. Customers of a private insurance company were asked to use a background smartphone GPS application that measured walking parameters. Participants were 1065 app users (298 men and 767 women) aged 14–86 years. Observed walking speed and cadence were higher in winter (average maximum temperature: 10.2 °C) than in summer (average maximum temperature: 29.8 °C) (p < 0.001). The walking parameters were closely related to environmental temperature, with cadence most strongly correlated with daily maximum temperature (r = − 0.812, p < 0.001) and indicating a curvilinear relationship. A decrease in environmental temperature was found to increase cadence when the temperature was below 30 °C. The findings suggest that walking may be regulated by environmental temperature and potentially by the autonomic nervous system’s response to environmental temperature.

scholarly journals Spatial and temporal variation in weather events critical for boreal agriculture: I Elevated temperatures

2016 ◽  
Vol 25 (1) ◽  
Author(s):  
Pirjo Peltonen-Sainio ◽  
Pentti Pirinen ◽  
Hanna M Mäkelä ◽  
Otto Hyvärinen ◽  
Erja Huusela-Veistola ◽  
...  
Keyword(s):  
Production System ◽  
Crop Production ◽  
Elevated Temperatures ◽  
Climatic Variability ◽  
Temporal Variations ◽  
Maximum Temperature ◽  
Spatial And Temporal Variation ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Animal Farming

 Variation in temperature challenges crop production and animal farming. Elevated temperatures are often harmful, though may also open opportunities at high latitudes. Impacts depend on the vulnerability of the object, production system and their resilience to climatic variability. The station-wise temperature observations from the Finnish Meteorological Institute for a time period of 54 years (1961‒2014) were interpolated to a regular 10 km × 10 km grid covering the whole country. Several successive time slices were used to measure the likelihood for: 1) elevated temperatures of a) ≥1 °C above normal for three weeks, b) ≥2 °C above normal for two weeks and c) ≥3 °C above normal for one week, and 2) heatwaves with daily maximum temperature >25 °C for: a) 5 days (short) or b) 14 days (long episode). We also estimated the likelihood of warm winds in the early growing season which may enhance pest migration. We found large spatial and temporal variations in the likelihoods of elevated temperatures with many impacts on crop production, animal farming and welfare. In fact, only 1 °C temperature elevation may already be harmful, though in some cases also beneficial depending on region and vulnerability or adaptation of the object and production system. Though we show only some examples of the potential impacts of temperature variation on high latitude agro-ecosystems, these data are valuable as such for much wider applications in agriculture and beyond that.

scholarly journals Characteristics of thunderstorms and squalls over Indira Gandhi International (IGI) airport, New Delhi - Impact on environment especially on summer’s day temperatures and use in forecasting

MAUSAM ◽  
2021 ◽  
Vol 60 (4) ◽  
pp. 461-474
Author(s):  
R. K. JENAMANI ◽  
R. C. VASHISTH ◽  
S. C. BHAN
Keyword(s):  
Wind Speed ◽  
Environmental Changes ◽  
Maximum Wind Speed ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Peak Time ◽  
New Delhi ◽  
Maximum Wind ◽  
Average Maximum

In the present study, commencement timings and duration of thunderstorms (TS) and squalls at IGI airport, Palam, New Delhi have been analysed critically based on most recent eleven years data of 1995-2005 to find their favourable time of occurrences. Then utility of such data base in the aviation warning has been demonstrated. Environmental changes associated with these squalls have also been further analysed to understand their impact. Being recent May 2007 a very cool month over Delhi, the role of TS on controlling the day’s soaring temperature has also been studied from their data.  Results show TS are maximum in June followed by July whereas squalls are maximum in May followed by June. It shows more than 80% of TS in each season are of duration less than 3 hours with remaining are mostly 3 to 6 hours. The peak time period of commencement of both TS and squalls in the day differ with the progress of the months. For pre monsoon months, the most favourable timing of TS and squalls are 1200-1500 UTC while for monsoon, it starts earlier. Around 37% of the total TS during the period were associated with squalls. The average maximum wind speed in squall at IGI airport is about 68 kmph with highest maximum wind speed 139 kmph. On an average the environmental temperature falls by 5.6° C, humidity levels rises by 17.8% and mean sea level pressure rises by 1.6 hPa due to the occurrences of squalls. Study also shows daily maximum temperature rise is highly controlled by TS occurrences and May 2007, being a month of highest TS occurrences at the airport since 1995, became one of the coolest month in May over Delhi. The comparison of TS frequencies shows 12% increase in their annual activities since 1950-1980 with very high unusual increase of 51% in June and 26% in May. Since analysis of data from 1995 shows occurrences of TS are reversely but strongly correlated with summer temperatures and longer period temperature data since 1975 also confirms absence of significant trend in maximum temperature and higher temperature days in peak summer months of May and June till recent as expected due to high pollution, global warming and fast urbanization in the city, so it is the higher number of TS occurrences over the region from time to time which might have been main factor for controlling its significant rise.

scholarly journals Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data

2020 ◽  
Vol 18 (2) ◽  
pp. 207-223
Author(s):  
Salima Sultana Daisy ◽  
A. K. M. Saiful Islam ◽  
Ali Shafqat Akanda ◽  
Abu Syed Golam Faruque ◽  
Nuhu Amin ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Lead Time ◽  
Climatic Variables ◽  
Disease Spread ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Forecasting Model ◽  
Daily Maximum ◽  
Auto Regressive

Abstract Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000–2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = −0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p &lt; 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

Comparison of Crop Evapotranspiration by FAO, BREB and Pristley-Taylor methodsin mustard Crop in Central Bihar Region

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
JOYDEEP MUKHERJEE ◽  
SURAJIT MONDAL
Keyword(s):  
Energy Balance ◽  
Crop Production ◽  
Bowen Ratio ◽  
Moisture Stress ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Eastern Region ◽  
Crop Evapotranspiration ◽  
Ratio Energy

Untimely sowing, poor crop stands and absence of lack of high yielding varieties and lack of zone specific crop production technology, moisture stress and uncertain and extreme weather conditions are the major factor which govern the productivity of rapeseed-mustard in eastern India particularly in Bihar. This study was conducted in the experimental farm of ICAR Research Complex for Eastern Region, Patna, Bihar during 2011-12 and 2012-13. Bowen ratio energy balance (BREB) method is a micrometeorological method by combining Bowen ratio with energy balance equation of earth surface. The climate of the experimental site is semi-arid with dry hot summer and mild winters. Summers are long (early April–August) with monsoon setting in between July and September. May and June are the hottest months with mean daily maximum temperature ranging from 30 to 40°C. At 11.00 to 12.00 hours, the Rn reaches the maxima and its value reduces drastically after 15.00 hours. ET0 calculate by established empirical equation was compared with the pan evapotranspiration (ETpan) data and it was observed that PT method can be safely used to calculate ET0 in the study zone. The crop evapotranspiration (ETc) using Bowen Ratio Energy Balance method was also observed and compared with output from PT method. The ratio between LE/Rn attained the higher value at siliqua emergence (SE) and pod formation (PF) stages indicating higher water demand during the same crop growth period.

Probabilistic Forecasts from the National Digital Forecast Database

2008 ◽  
Vol 23 (2) ◽  
pp. 270-289 ◽  
Author(s):  
Roman Krzysztofowicz ◽  
W. Britt Evans
Keyword(s):  
Meteorological Data ◽  
Gaussian Model ◽  
Distribution Functions ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Point Estimate ◽  
Daily Maximum ◽  
Climatic Data ◽  
Estimation Of Parameters ◽  
Probabilistic Forecasts

Abstract The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, and a quantile function). The quantification of uncertainty is accomplished via Bayes theorem by extracting and fusing two kinds of information from two different sources: (i) a long sample of the predictand from the National Climatic Data Center, and (ii) a short sample of the official National Weather Service forecast from the National Digital Forecast Database. The official forecast is deterministic and hence deficient: it contains no information about uncertainty. The BPF remedies this deficiency by outputting the complete and well-calibrated characterization of uncertainty needed by decision makers and information providers. The BPF comes furnished with (i) the meta-Gaussian model, which fits meteorological data well as it allows all forms of marginal distribution functions, and nonlinear and heteroscedastic dependence structures, and (ii) the statistical procedures for estimation of parameters from asymmetric samples and for coping with nonstationarities in the predictand and the forecast due to the annual cycle and the lead time. A comprehensive illustration of the BPF is reported for forecasts of the daily maximum temperature issued with lead times of 1, 4, and 7 days for three stations in two seasons (cool and warm).

scholarly journals Modelling Trends of Climatic Variability and Malaria in Ghana Using Vector Autoregression

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Sylvia Ankamah ◽  
Kaku S. Nokoe ◽  
Wahab A. Iddrisu
Keyword(s):  
Relative Humidity ◽  
Vector Autoregression ◽  
Climatic Variability ◽  
Age Groups ◽  
Sub Saharan Africa ◽  
Climatic Variables ◽  
Health Concern ◽  
Maximum Temperature ◽  
Climatic Data ◽  
The Impact

Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.

scholarly journals Temporal Variability, Trends of Climatic Variables and Drought Analysis of Rajshahi and Sylhet District, Bangladesh

2021 ◽  
Vol 46 (2) ◽  
pp. 133-141
Author(s):  
Fatamatuj Sunny ◽  
Md Selim Miah ◽  
Md Younus Mia ◽  
Ruksana Haque Rimi
Keyword(s):  
Relative Humidity ◽  
Minimum Temperature ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Climate Index ◽  
Climatic Data ◽  
Climatic Indices ◽  
Sen’S Slope ◽  
Average Maximum ◽  
Average Rainfall

The study was conducted to quantify the change of selected climatic variables (rainfall, relative humidity, maximum and minimum temperature) over 50 years at Rajshahi and Sylhet districts in Bangladesh. Annual, seasonal, and monthly climatic data comparisons have been executed between 1968-1992 and 1993-2017 through trend analysis. The Mann-Kendall statistic and Sen's Slope model were used to reveal the trends and estimate the magnitude of change respectively. Prediction of the climatic variable of 10 years (2018-2027) was made based on the ARAR algorithm using MaxStat Pro software. Rainfall data were used to analyze drought by using climatic indices (De Mortone Aridity Index, IdM; Seleaninov Hydrothermic Index, IhS; Donciu Climate Index, IcD). Average rainfall was decreasing dramatically in monsoon season at Rajshahi and in both premonsoon and monsoon seasons at Sylhet. The negative change of average rainfall in the monsoon at Rajshahi from 1968-1992 to 1993-2017 was found 29.17 mm. The maximum temperature was increasing in all seasons in both Rajshahi and Sylhet. Annual Mannkendall trend test and Sen’s slope revealed that relative humidity was decreasing and maximum temperature was increasing significantly at Sylhet for the period 1993-2017. At Rajshahi, during 1968-1992, relative humidity was increasing by 0.247 % per year, and minimum temperature was decreasing 0.049℃ per year. Rainfall was decreasing insignificantly in both time scales. ARAR algorithm predicted that average maximum temperature might become comparatively higher than the previous 50 years. 1992 and 2010 were identified as drought years from all climatic indices, and 1969, 1981, and 1997 as excessive wet years at Rajshahi. No drought events were identified during 1968-2017 at Sylhet and the year 2017 to be an excessively wet year. IhS predicted 2020, 2025, and 2027 as drought years and 2024 as an excessive wet year at Sylhet. Asiat. Soc. Bangladesh, Sci. 46(2): 133-141, December 2020

scholarly journals Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily J. Wilkins ◽  
Peter D. Howe ◽  
Jordan W. Smith
Keyword(s):  
National Park ◽  
National Park Service ◽  
Spatial Behavior ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Weather ◽  
Visitor Behavior ◽  
Park Service ◽  
The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

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