Rainfall Trends, Rainfall Variability - Essay Example

ITECH 7407 Real Time Analytic Report Student name Admission number Supervisor Date of submission Executive Summary Rainfall variability and trends is widely known to be impacted by climate change in Ballarat, Australia and globally. Such changes will have dire consequences in various socio-economic sectors such as agriculture firms. The rationale behind this report is to design and deliver a real time analytic report to help the Precision Agriculture drive business and production performance in agricultural sector. This report offers elements of the business intelligence solution developed along with some recommendations based on the past records and future prediction of rainfall in Ballast. In order to determine future forecasts, past trends need to be analyzed and thought over so that future estimation can be made. The modeling mean monthly rainfall variability will also be vital in conveying agribusiness solutions so Precision Agriculture can gain an advantage in terms of analysis of future prospects of rain and hence make use of the facts obtained to generate better revenues and cut on losses. The impending effects of climate change on agribusiness are very much uncertain and hence affected the rainfall patterns which have fluctuated year after year. The variability since 2004 to 2016 were; 2004: 898.4, 2005: 761.4, 2006: 455, 2007: 788, 2008: 649.4, 2009: 688.6, 2010: 1107.8, 2011: 962.2, 2012: 802.4, 2013: 709.6, 2014: 640, 2015: 583.2 and 2016: 1014. The lowest amount of rainfall from 2000 to 2016 has been 455 mm annual in the year 2006, whereas the highest has been 1107.8 mm in the year 2010. The facts from studies that have been carried out in past few years from different places in Australia and globally reveal that climate change have caused rainfall variability. The report will give Precision Agriculture’s managers with a clear understanding of rainfall variability and way it has been impacted by climate change. The variability content is illustrated in terms of graphs to which enable possible users to digest information relevant to them. Last of all, a future forecast on possible trends has been made and recommendations availed on basis of calculations conducted. Precision Agriculture will use the data in advising their clients on best farming practices and best was of making profits while cutting on losses and risks. Table of Contents Introduction The reason behind this report is devising of a real time analytical solution that will help Precision Agriculture Firm in Ballarat with a business intelligence of boosting farming not only in Australia but also world at large. The report will offer a detailed resolutions and recommendations on rainfall variability and trends and way it has impacted on climate changes for Ballarat, other parts of Australia and globally. The analysis will be based on the past and present patterns of rainfall, and provide predictions of possible future rainfall patterns matched to the climate change (Timbal, 2009). In present days, climate change has taken a centre stage and attracted much concern in global arena. The concern has been on the unfavorable impact of climate change on way of life. Climate change is perceived as a main danger to mankind not only because it affect sustainable growth and development but also causes poverty. A number of development projects could remain as mirage if the harsh effects of climate change for example the way it impact on rainfall variability is not addressed. The information will be helpful in safeguarding crops from climate change, hence farmers being in position to get high and best quality production which could generate greater profits. Precision Agriculture is a provider of agribusiness intelligence solutions which endeavor to better their client’s productivity and profitability via more informed and researched decisions. The Precision’s focus is entirely coined behind data, insight and action. Whether the decision is on-farm or business, they collect, evaluate and interpret information to unearth agribusiness opportunities so as to offer savings and unlock farming potentials of its clients. Precision Agriculture offer practical farming support by submitting their findings to real life. Climate Change and Rainfall Trends During the last quarter of the 20th century, global food production was basically enough to sustain the growing population for many regions around the globe. However, in the last decade has been the reversal of the gains of food security because of increased demand for food, depleting of the natural resources and the deteriorating measures of conserving the environment. Climate change is reclined within unified trends and risks that are being faced in the agricultural sector (Hernández & Rodríguez, 2017). Other areas of concern that have driven the variability in rain levels comprises of hasty changes in biodiversity, depletion of land covers, oceanic acidification which is caused by disposal of harmful chemicals from industries. Climate change, and in particular increased rainfall variability is possibly the main challenge to food security since it’s essentially a climate-sensitive activity. As a result, the variation of rainfall amount across a region over time is key feature of the climate change of the region. Future trends of rainfall may be determined by analyzing the past and present trends. This is a step forward of delivering agribusiness intelligence solutions that Precision Agriculture may use the information of future prospects of rain to make informed decisions and advise its clients accordingly. The review of Ballarat Region shows that the area seems to exhibit seasonal similar rainfall levels, and the weather is 51°F with 87% humidity and wind N at 15 mph. The winter seasonal cycle is dominant and on a daily basis, the region is affected by rain-bearing fronts that move about with the mid-latitude westerly flow (Hernández & Rodríguez, 2017). Moreover, the similar weather conditions moving from the Western Region across to Southern Australia could affect rainfall variability of Ballarat. Past Analysis of Rainfall In this report, a detailed past trends of rainfall are presented from 2000-2016 as identified in the Australian region of Ballarat, where the data collected was analyzed and presented in a table form, representing a monthly data, that is from Jan. to Dec., over the 16 years period. Table 1.1. illustrates the data which comprises all mean rainfall from January to December for period 2000-2016. Table 1.1 Rainfall in Australian region Ballarat (2000-2016) Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Years Rainfall Mean rainfall (mm) 39.7 31.2 35.9 43.4 52.2 61.9 62.0 72.3 68.4 59.1 56.6 52.2 635.4 30 2000 2016 Highest rainfall (mm) 109.6 99.6 117.2 136.0 111.5 135.6 112.4 161.0 141.4 115.0 113.0 138.0 879.6 30 2000 2016 Lowest rainfall (mm) 0.8 0.0 4.0 8.2 11.0 12.8 19.8 13.0 26.2 5.0 18.5 4.0 301.8 30 2000 2016 Decile 1 rainfall (mm) 14.1 3.4 8.0 12.6 18.3 35.8 32.6 38.0 37.5 17.8 23.8 14.4 489.4 30 2000 2016 Decile 5 (median) rainfall (mm) 33.1 19.9 30.0 38.9 49.2 58.8 58.7 70.8 58.4 58.8 55.0 41.1 634.0 30 2000 2016 Decile 9 rainfall (mm) 74.6 79.5 74.8 70.4 90.8 86.7 96.4 105.3 110.0 95.7 85.7 107.6 775.4 30 2000 2016 Highest daily rainfall (mm) 55.9 66.6 36.4 88.8 42.6 28.0 46.4 49.6 46.0 64.0 45.8 60.6 88.8 30 2000 2016 Mean number of days of rain 7.5 5.9 8.9 11.4 14.4 17.7 19.8 18.7 16.5 14.7 11.7 10.8 158.0 30 2000 2016 Mean number of days of rain ≥ 1 mm 4.8 3.8 5.4 7.1 8.9 12.1 12.9 13.8 11.7 9.8 8.0 6.5 104.8 30 2000 2016 Mean number of days of rain ≥ 10 mm 1.1 0.9 1.0 1.2 1.3 1.7 1.4 1.4 1.7 1.7 1.7 1.5 16.6 30 2000 2016 Mean number of days of rain ≥ 25 mm 0.3 0.2 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.2 0.4 0.4 2.7 30 2000 2016 In the months of January, February and March, the mean rainfall was 39.7, 31.2 and 35.9 respectively, but increases from April to August when the region is experiencing heavy rains throughout the year, and come September it declines slowly to December. The same applied to mean number of days of rain, for instance on July being the highest followed by August. The month of February is the one that experiences the lowest rainfall in the year. Significance of the report to Precision Agriculture The variability of rainfall will assist the management at Precision Agriculture to see how potential concerns of climate change may trigger the possible trends of rainfall throughout the year. The trends will also be important in helping to foretell the agribusiness decisions like growth of crops which rely on rainfall levels. People may be advised to implement irrigation mitigation incase the levels of rain become minimal, however costs on irrigation may be saved in months that the region receives heavy rains. The preemptive measures will be useful in minimizing instances of damages that could be faced by farmers or other stakeholders in agribusiness. Figure 1.2 Monthly Mean Rainfall trends The y-axis which is vertical represents the monthly mean rainfall trend in mm, whereas the horizontal line also known as the x-axis represent the months, thus the graph shows the trend over the year span. It indicates the increasing and decreasing trends of mean rainfall monthly. Initially, at the start of the year, the amount of rainfall displayed is on decreasing trend, after which it start increasing from March to August and later decreases. Figure 1.3 Ballarat Region Rainfall Distributions The central region of Ballarat receives more rains compared to the northern parts. Moreover the rains are heavy in south-west parts as shown in figure 1.3. Figure 1.4 Monthly Rainfall Trends of Last 30 years compared to 2017 The trends of rainfall received this year in months of January to April was very high compared to the average mean of the last three decades. February recorded a mean of 44.2mm compared to 31.2 and April this year rained doubled compared to the last three decades, recording 101.0mm compared to 43.4. This could be attributed to some of global warming effects that are being experience around the globe, as the levels of rain fluctuates drastically. This is also illustrated in the figure 1.5 of the monthly rainfall bar chart. Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Years Mean rainfall (mm) for years 1987 to 2016 39.7 31.2 35.9 43.4 52.2 61.9 62.0 72.3 68.4 59.1 56.6 52.2 635.4 30 Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Years Rainfall (mm) for year 2017 38.6 44.2 44.4 101.0                   0 Figure 1.5 Monthly Rainfall Bar Chart Future Mean Rainfall Forecast After reviewing the mean rainfall trends for the last three decades, future forecasts has been modeled to have room for year 2017 so that it is possible to evaluate the forecast and prepare for likely unforeseen events. Figure 1.6 Forecasted Rainfalls Recommendations Precision Agriculture being a provider of agribusiness intelligence information to its clients, the trends and forecasts of mean rainfall while putting into consideration the climate change will be helpful in coming up with contingency plans for agricultural sectors leading to increased production (Gallant et al, 2013). Since the months of July – September receives a lot of rain; farmers can be advised to plan on how to grow crops that require more water. But in December and January, the levels of rainfall is minimal hence mitigation measures like irrigation can be put in place to support crops that needs a lot of water. Farmers may also be advised to adopt greenhouse farming which requires little water, and this could result into high production of horticultural produce like tomatoes and flowers. More studies should be embraced to continue analyzing the future trends of rainfall with changing climate. Additionally, more measures should be put in place to safeguard the environment and this will ensure conservation of our natural resources which is important to attraction of more rainfall. References Alston, J. M., & Anderson, K. P. (2016). Antipodean Agricultural and Resource Economics. Australian Journal of Agricultural and Resource Economics, 60(4), 493–710. Bill, M., Snow, B., Brian, L., & Peter, S. (2009). Agriculture in Australia: An Introduction. South Melbourne: Oxford University Press. Gallant, A. J., Reeder, M. J., Risbey, J. S., & Hennessy, K. J. (2013). The Characteristics of Seasonal-Scale Droughts in Australia,. International Journal of Climatology, 33(2), 1658-1672. Gartner, M. (2013). Business Intelligence. Retrieved from Gartner.com: http://www.gartner.com/it-glossary/business-intelligence-bi/ Hernández, J. C., & Rodríguez, G. M. (2017). Evolving Splines and Seasonal Unit Roots in Weekly Agricultural Prices. 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