This project report summarizes a study on using meteorological data to predict rainfall in Rwanda, with a case study of Rwampara swamp. The report was submitted by two students in partial fulfillment of their bachelor's degree in water and environmental engineering. It describes the background of the study, objectives, methodology, results and conclusions regarding rainfall prediction and its implications for agriculture in Rwampara swamp. Meteorological data from Kigali airport weather station spanning 42 years was analyzed using SPSS software to develop a regression model for rainfall prediction.
This is the Final Project Report.
"USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA "
Prepared by NDACYAYISENGA Telesphore
and
BYUKUSENGE VILANY
Unde Guidance of Supervisor
Eng .MAJORO Felicien
Academic year 2013-2014.
This the power point of Final Project.
"USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA "
Prepared by NDACYAYISENGA Telesphore
and
BYUKUSENGE VILANY
Unde Guidance of Supervisor
Eng .MAJORO Felicien
Academic year 2013-2014.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
This is the Final Project Report.
"USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA "
Prepared by NDACYAYISENGA Telesphore
and
BYUKUSENGE VILANY
Unde Guidance of Supervisor
Eng .MAJORO Felicien
Academic year 2013-2014.
This the power point of Final Project.
"USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA "
Prepared by NDACYAYISENGA Telesphore
and
BYUKUSENGE VILANY
Unde Guidance of Supervisor
Eng .MAJORO Felicien
Academic year 2013-2014.
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
SyQwest Bathy-2010 Sub Bottom Profiler used in Tarbela Reservoir StudySyQwest Inc.
Hydrographic echo sounders are used to measure the depth to the seafloor by using the properties of acoustic waves. The principle of echo-sounders is basic - by measuring the two-way travel time between the acoustic waves transmitted on sea surface and those reflected at seafloor.
In this study, an integrated approach for hydrographic surveying is introduced and evaluated in terms of its efficiency in comparison with the traditional methods of hydrographic surveying. The approach develops an integrated environment of hydrographic surveying comprising human, hardware and software. The process of surveying starts from in-house planning using specialized geo-spatial softwares. Then, on site a combination of computer hardware, echosounder, differential global positioning system (DGPS), survey vessel and survey crew is made. Post-processing is performed after conducting a survey in order to improve quality of data by filtering errors and producing the end product like reservoir underwater terrain, development of reservoir stage-area and stage-storage relationships, etc. The study was applied to Tarbela Reservoir, Pakistan.
DSD-SEA 2018 Software Application in Integrated Water Resources Management in...Deltares
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Certification: Thakur Polytechnic
More Details on https://www.kaushikgupta.in
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3D Model Animations of Artificial Recharge Wells in Resolving Fresh Water Qua...AM Publications
Artificial recharge wells was the technology to solve fresh water avaibility problems especially in small or very small islands. In small or very small islands the fresh water became scarce because of the size and structure of the islands, which caused sea water intrussion to ground water, was easy to be occured. The artificial recharge wells was intalled, developed, and used in Pari island to tackle that problems. The measurements and analysis showed that the ground water in the island was tend to brackish, with the hope by this artificial recharge wells the people in that island will no longer difficult in finding source of fresh water. Because the importance of this research was significance in trying to solve one of the problems found in Small Island in indonesia. so, it was needed to be modelled in 3D animations in order for researchers can study the work mechanism of the wells to improve and apply it for others region, and people will know about the technology itself. The making of 3D model animations was through series of stages and process, they were pre-production, production, and post-production. The making of this 3D model animations with the support of softwares, they were 3Ds Max, Adobe after Effect, and Adobe Premier
Intensity–duration–frequency (IDF) curves are among the most demandable information in meteorology, hydrology and engineering water resources design, planning, operation, and management works. The IDF Curves accessible are for the most part done by fitting arrangement of yearly greatest precipitation force to parametric dispersions. Intensity-durationfrequency (IDF) curves represent the relationship between storm intensity, storm duration and return period. Environmental change is relied upon to intensify the boundaries in the atmosphere factors. Being prone to harsh climate impacts, it is very crucial to study extreme rainfall-induced flooding for short durations over regions that are rapidly growing. One way to approach the extremes is by the application of the Intensity-Duration-Frequency (IDF) curves. The annual maximum rainfall intensity (AMRI) characteristics are often used to construct these IDF curves that are being used in several infrastructure designs for urban areas. Thus, there is a necessity to obtain high temporal and spatial resolution rainfall information. Many urban areas of developing countries lack long records of short-duration rainfall. The shortest duration obtained is normally at a daily scale/24 h. This paper suggests their generation based on annual daily maximum rainfall (ADMR) records. Rainfall data of 23 (Twenty three) hydrological years of all stations were used. Maximum rainfall frequency analysis was made by LogNormal Distribution method.
Indonesian Disaster Data and Information in 2016 showed that flood has reached a soaring 32.2% overall. In one of the common flood region (2016), Tangerang, the flood had impacted 30,949, and destroys more than 400 residentials. In spite of this dreadful fact, Tangerang has no systematically ways of detecting the flood patterns. Therefore, there is urgency for a system that is able to detect potential flood risks in Tangerang. This study explores a mean to systematically find flood patterns in Tangerang and attempt to visualize the risks based on 11 years of data on four major river stations within Tangerang vicinity. All the data obtained from Ciliwung Cisadane River Basin Center (BBWS) between 2009 until 2017 with total data of 368,184 rows. This study proposes an interactive dashboard based on the water level data covering rivers of Angke, Pesanggrahan, and Cisadane. Three clustering methods are implemented, the K-Medoids, DBScan, and x-means, to segregate the water level data, taken from four stations obtained from Ciliwung Cisadane River Basin Center (BBWS), into meaningfull periodic flood patterns. The output of this research is an interactive dashboard created based on the newly found patterns. The dashboard is designed to be simple and easy to use for non-technical persons. We believe that the output of this research could be implemented into the decision-making process taken by the Ciliwung Cisadane River Basin Center (BBWS) in order to improve countermeasure attempts on the potentially flooded areas.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
SyQwest Bathy-2010 Sub Bottom Profiler used in Tarbela Reservoir StudySyQwest Inc.
Hydrographic echo sounders are used to measure the depth to the seafloor by using the properties of acoustic waves. The principle of echo-sounders is basic - by measuring the two-way travel time between the acoustic waves transmitted on sea surface and those reflected at seafloor.
In this study, an integrated approach for hydrographic surveying is introduced and evaluated in terms of its efficiency in comparison with the traditional methods of hydrographic surveying. The approach develops an integrated environment of hydrographic surveying comprising human, hardware and software. The process of surveying starts from in-house planning using specialized geo-spatial softwares. Then, on site a combination of computer hardware, echosounder, differential global positioning system (DGPS), survey vessel and survey crew is made. Post-processing is performed after conducting a survey in order to improve quality of data by filtering errors and producing the end product like reservoir underwater terrain, development of reservoir stage-area and stage-storage relationships, etc. The study was applied to Tarbela Reservoir, Pakistan.
DSD-SEA 2018 Software Application in Integrated Water Resources Management in...Deltares
Presentation by Mr. Irfan Sudono (Ministry of Public Works and Housing, Indonesia) at the Seminar Cutting Edge Hydro Software for South-East Asia, during the Deltares Software Days South-East Asia 2018. Thursday, 6 September 2018, Yogyakarta.
IoT Based Water Management and Supervision SystemKaushik Gupta
Final Year Project
IoT Based Water Management and Supervision System -2019
Certification: Thakur Polytechnic
More Details on https://www.kaushikgupta.in
Presentation by Vladimir Smakhtin & Giriraj Amarnath at the Advisory Committee meeting of the Integrated Drought Management Program held in Geneva, 9 September, 2014.
3D Model Animations of Artificial Recharge Wells in Resolving Fresh Water Qua...AM Publications
Artificial recharge wells was the technology to solve fresh water avaibility problems especially in small or very small islands. In small or very small islands the fresh water became scarce because of the size and structure of the islands, which caused sea water intrussion to ground water, was easy to be occured. The artificial recharge wells was intalled, developed, and used in Pari island to tackle that problems. The measurements and analysis showed that the ground water in the island was tend to brackish, with the hope by this artificial recharge wells the people in that island will no longer difficult in finding source of fresh water. Because the importance of this research was significance in trying to solve one of the problems found in Small Island in indonesia. so, it was needed to be modelled in 3D animations in order for researchers can study the work mechanism of the wells to improve and apply it for others region, and people will know about the technology itself. The making of 3D model animations was through series of stages and process, they were pre-production, production, and post-production. The making of this 3D model animations with the support of softwares, they were 3Ds Max, Adobe after Effect, and Adobe Premier
Intensity–duration–frequency (IDF) curves are among the most demandable information in meteorology, hydrology and engineering water resources design, planning, operation, and management works. The IDF Curves accessible are for the most part done by fitting arrangement of yearly greatest precipitation force to parametric dispersions. Intensity-durationfrequency (IDF) curves represent the relationship between storm intensity, storm duration and return period. Environmental change is relied upon to intensify the boundaries in the atmosphere factors. Being prone to harsh climate impacts, it is very crucial to study extreme rainfall-induced flooding for short durations over regions that are rapidly growing. One way to approach the extremes is by the application of the Intensity-Duration-Frequency (IDF) curves. The annual maximum rainfall intensity (AMRI) characteristics are often used to construct these IDF curves that are being used in several infrastructure designs for urban areas. Thus, there is a necessity to obtain high temporal and spatial resolution rainfall information. Many urban areas of developing countries lack long records of short-duration rainfall. The shortest duration obtained is normally at a daily scale/24 h. This paper suggests their generation based on annual daily maximum rainfall (ADMR) records. Rainfall data of 23 (Twenty three) hydrological years of all stations were used. Maximum rainfall frequency analysis was made by LogNormal Distribution method.
Indonesian Disaster Data and Information in 2016 showed that flood has reached a soaring 32.2% overall. In one of the common flood region (2016), Tangerang, the flood had impacted 30,949, and destroys more than 400 residentials. In spite of this dreadful fact, Tangerang has no systematically ways of detecting the flood patterns. Therefore, there is urgency for a system that is able to detect potential flood risks in Tangerang. This study explores a mean to systematically find flood patterns in Tangerang and attempt to visualize the risks based on 11 years of data on four major river stations within Tangerang vicinity. All the data obtained from Ciliwung Cisadane River Basin Center (BBWS) between 2009 until 2017 with total data of 368,184 rows. This study proposes an interactive dashboard based on the water level data covering rivers of Angke, Pesanggrahan, and Cisadane. Three clustering methods are implemented, the K-Medoids, DBScan, and x-means, to segregate the water level data, taken from four stations obtained from Ciliwung Cisadane River Basin Center (BBWS), into meaningfull periodic flood patterns. The output of this research is an interactive dashboard created based on the newly found patterns. The dashboard is designed to be simple and easy to use for non-technical persons. We believe that the output of this research could be implemented into the decision-making process taken by the Ciliwung Cisadane River Basin Center (BBWS) in order to improve countermeasure attempts on the potentially flooded areas.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Micro RNA genes and their likely influence in rice (Oryza sativa L.) dynamic ...Open Access Research Paper
Micro RNAs (miRNAs) are small non-coding RNAs molecules having approximately 18-25 nucleotides, they are present in both plants and animals genomes. MiRNAs have diverse spatial expression patterns and regulate various developmental metabolisms, stress responses and other physiological processes. The dynamic gene expression playing major roles in phenotypic differences in organisms are believed to be controlled by miRNAs. Mutations in regions of regulatory factors, such as miRNA genes or transcription factors (TF) necessitated by dynamic environmental factors or pathogen infections, have tremendous effects on structure and expression of genes. The resultant novel gene products presents potential explanations for constant evolving desirable traits that have long been bred using conventional means, biotechnology or genetic engineering. Rice grain quality, yield, disease tolerance, climate-resilience and palatability properties are not exceptional to miRN Asmutations effects. There are new insights courtesy of high-throughput sequencing and improved proteomic techniques that organisms’ complexity and adaptations are highly contributed by miRNAs containing regulatory networks. This article aims to expound on how rice miRNAs could be driving evolution of traits and highlight the latest miRNA research progress. Moreover, the review accentuates miRNAs grey areas to be addressed and gives recommendations for further studies.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
Diabetes is a rapidly and serious health problem in Pakistan. This chronic condition is associated with serious long-term complications, including higher risk of heart disease and stroke. Aggressive treatment of hypertension and hyperlipideamia can result in a substantial reduction in cardiovascular events in patients with diabetes 1. Consequently pharmacist-led diabetes cardiovascular risk (DCVR) clinics have been established in both primary and secondary care sites in NHS Lothian during the past five years. An audit of the pharmaceutical care delivery at the clinics was conducted in order to evaluate practice and to standardize the pharmacists’ documentation of outcomes. Pharmaceutical care issues (PCI) and patient details were collected both prospectively and retrospectively from three DCVR clinics. The PCI`s were categorized according to a triangularised system consisting of multiple categories. These were ‘checks’, ‘changes’ (‘change in drug therapy process’ and ‘change in drug therapy’), ‘drug therapy problems’ and ‘quality assurance descriptors’ (‘timer perspective’ and ‘degree of change’). A verified medication assessment tool (MAT) for patients with chronic cardiovascular disease was applied to the patients from one of the clinics. The tool was used to quantify PCI`s and pharmacist actions that were centered on implementing or enforcing clinical guideline standards. A database was developed to be used as an assessment tool and to standardize the documentation of achievement of outcomes. Feedback on the audit of the pharmaceutical care delivery and the database was received from the DCVR clinic pharmacist at a focus group meeting.
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
Summary of the Climate and Energy Policy of Australia
Final project report of telesphore and vilany
1. A PROJECT REPORT Submitted by NDACYAYISENGA Télesphore (REG.NO: GS 20111583) AND BYUKUSENGE Vilany (REG.NO: GS 20111369) Under the Guidance of Mr. MAJORO Félicien Submitted in partial fulfilment of the requirements for the award of BACHELOR OF SCIENCE DEGREE IN WATER AND ENVIRONMENTAL ENGINEERING
DEPARTMENT OF CIVIL ENGINEERING AND ENVIRONMENTAL TECHNOLOGY SCHOOL OF ENGINEERING (Nyarugenge Campus) COLLEGE OF SCIENCE AND TECHNOLOGY P.O. Box: 3900 Kigali, Rwanda. MAY 2014 “USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA, CASE STUDY: RWAMPARA SWAMP”
PROJECT ID: CEET/WEE/2013-14/18
2. ii
COLLEGE OF SCIENCE AND TECHNOLOGY SCHOOL OF ENGINEERING (Nyarugenge Campus) P.O. Box: 3900 Kigali, Rwanda. DEPARTMENT OF CIVIL ENGINEERING AND ENVIRONMENTAL TECHNOLOGY C E R T I F I C A T E This is to certify that the Project Work entitled “using meteo data for rainfall prediction in RWANDA, case study: RWAMPARA swamp” is a record of the original bonafide work done by NDACYAYISENGA Telesphore (REG. No: GS20111583 ) and BYUKUSENGE Vilany (REG.No:GS20111369) in partial fulfilment of the requirement for the award of Bachelor of Science Degree in Water and Environmental Engineering of College of Science and Technology under the University of Rwanda during the Academic Year 2013-2014. …………………………… …………………………… SUPERVISOR HEAD OF DEPARTMENT Mr. MAJORO Félicien Dr. G. S. KUMARAN Submitted for the final Project Defense Examination held at School of Engineering (Nyarugenge Campus), College of Science and Technology, on ………………………………..........................
3. iii
DECLARATION
We, NDACYAYISENGA Telesphore (Reg. No: GS 20111583) and BYUKUSENGE Vilany (Reg No: GS 20111369) declare that this project entitled” USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA, CASE STUDY: RWAMPARA SWAMP “is based on an original work conducted by ourselves for the award of bachelor Science degree in WATER AND ENVIRONMENTAL ENGINEERING at College of Science and Technology. It has never been submitted in any other higher learning institution, at our best knowledge, for the same academic purposes. SIGNATURE................... SIGNATURE........................ Date: / /2014 Date: / /2014 NDACYAYISENGA Telesphore BYUKUSENGE Vilany REG. No: GS 20111583 REG. No: GS 20111369
4. iv
DEDICATION
This project is dedicated to:
Our parents;
Families;
Our brothers;
Our sisters;
Friends; and
Our classmates;
5. v
ACKNOWLEDGEMENT
It is with profound joy and great happiness that we are deeply thankful to the almighty God who guided and protected us through all this time. We equally thank our research project supervisor Eng. Félicien MAJORO who consistently and coherently worked with us in order to help us achieve our goals and GASANA Emelyne helped us to use SPSS. We are pleased to thank our families and all family members for their support and advice. Our special thanks are addressed to the government of Rwanda for its appreciable policy of promoting education at all levels. Finally our sincere acknowledgements go to the entire administration of UR-CST and the whole academic staff for providing to us quality academic services throughout these four years.
6. vi
ABSTRACT The field study was carried out at RWAMPARA swamp, located especially between NYARUGENGE and KICUKIRO Districts, the agriculture is very important and play great role in the community where has both insufficient and abundance water or rainfall affect crops production such as beets, onions, carrots, small vegetations, maize, etc. In this study, we use many theories of rainfall prediction and the factors affecting rainfall to precipices on earth surface and their losses. There are many software and models used in rainfall prediction such as SPSS, ACCESS, ANFIS, NWP, Neural Networks and Matrix Decomposition Method used in different countries. The use of SPSS software in prediction of rainfall was selected because it is the one of software which is generate the simulation of model and analysis of output data or forecasts data in rainfall prediction at Rwampara swamp using data from meteo-Rwanda Kigali AERO station of 42 years from 1972 to 2013. Also we used CROPWAT and CLIMWAT to analyze crop water requirement and irrigation needed in RWAMPARA. The processing historical rainfall data in SPSS software are showing predicted rainfall for next two years where Rainfall (1168.0mm for 2014 and 1194.7mm for 2015) = -121.021+3.669 Humidity+4.434 Temperature to facilitate the agricultural activities in study area. In this report, there is crop patterned related to rainfall predicted and irrigation water requirement of 160.8mm/decade, effective rain of 200.2mm/decade, Crop Evapotranspiration of 328.6mm/decade needed for some crops such as small vegetations from April to July 2014 and type of crops according to rainfall predicted and creation of agriculture patterns.
7. vii
TABLE OF CONTENTS
DECLARATION ................................................................................................................................... iii
DEDICATION ........................................................................................................................................iv
ACKNOWLEDGEMENT ....................................................................................................................... v
ABSTRACT ...........................................................................................................................................vi
TABLE OF CONTENTS ....................................................................................................................... vii
LIST OF TABLES .................................................................................................................................. xi
LIST OF FIGURES................................................................................................................................ xii
LIST OF APPENDICES ....................................................................................................................... xiii
LIST OF ABREVIATION .................................................................................................................... xiv
CHAPTER I: INTRODUCTION ............................................................................................................. 1
1.1 BACKGROUND OF THE STUDY ......................................................................................... 1
1.2 PROBLEM STATEMENT ...................................................................................................... 2 1.3 OBJECTIVES OF THE PROJECT .......................................................................................... 2 1.3.1 General objective ............................................................................................................. 2
1.3.2 Specific objectives ........................................................................................................... 2
1.4 SCOPE OF THE PROJECT ..................................................................................................... 3
1.5 JUSTIFICATION OF THE PROJECT ..................................................................................... 3
1.5.1 Research significance ....................................................................................................... 3
1.5.2 Public and administrative significance .............................................................................. 3
1.5.3 Academic significance ..................................................................................................... 3
CHAPTER II: LITERATURE REVIEW ................................................................................................. 4
2.1 GENERALITIES ON HYDROLOGY ..................................................................................... 4
2.1.1 Water resources of Rwanda .............................................................................................. 4
2.1.2 Hydrology and hydrologic cycle ....................................................................................... 4
2.1.3 Scope of hydrology .......................................................................................................... 6
8. viii
2.2 PRECIPITATION .................................................................................................................... 6 2.2.1 Types of precipitation ....................................................................................................... 7
2.2.2 Measurement of precipitation ........................................................................................... 7
2.2.3 Analysis of rainfall data.................................................................................................... 8
2.3 WATER LOSSES .................................................................................................................. 10
2.3.1 Definition of water losses ............................................................................................... 10
2.3.2 Evaporation and evapotranspiration ................................................................................ 10
2.3.3 Hydrometeorology ......................................................................................................... 11
2.3.4 Infiltration ...................................................................................................................... 11
2.4 SOIL-WATER-IRRIGATION RELATIONSHIP ................................................................... 12
2.4.1 Definitions ..................................................................................................................... 12
2.4.2 Crop water requirement .................................................................................................. 12
2.4.3 Effect of rainfall ............................................................................................................. 13
2.4.4 Net irrigation requirement (NIR) .................................................................................... 13 2.5 FACTORS AFFECTING RAINFALL ................................................................................... 14 2.5.1 Weather and Meteorology .............................................................................................. 14 2.5.2 Evaporation and Evapotranspiration ............................................................................... 14
2.6 USING SPSS SOFTWARE IN RAINFALL PREDICTION ................................................... 16
2.6.1 Definition ....................................................................................................................... 16
2.6.2 Types of software used in rainfall prediction................................................................... 16
2.6.3 Types of time series data ................................................................................................ 16
2.6.4 Process used in SPSS software by box-Jenkins modeling ................................................ 17
2.6.5 Autocorrelation .............................................................................................................. 19
2.6.6 Stationary time series ..................................................................................................... 20
2.6.7 Data that is non stationary in the mean............................................................................ 20
2.6.8 Identifying potential model ............................................................................................. 21
2.6.9 Estimating the component of a time series using SPSS ................................................... 21
9. ix
2.6.10 Basic concepts in analysis of time series data.................................................................. 22
2.6.11 Autoregressive (AR) model ............................................................................................ 24
2.6.12 Prediction interval .......................................................................................................... 26
2.6.13 Forecasting..................................................................................................................... 26
CHAPIII: MATERIALS AND METHODOLOGY................................................................................ 27
3.1 SITE DESCRIPTION ............................................................................................................ 27
3.1.1 Site localization .............................................................................................................. 28
3.1.2 Soil type ......................................................................................................................... 28
3.1.3 Rainfall pattern............................................................................................................... 28
3.1.4 Meteo factors of study area ............................................................................................. 28
3.2 RESEARCH TOOLS ............................................................................................................. 29
3.2.1 Digital camera ................................................................................................................ 29
3.2.2 Global Positioning System (GPS) ................................................................................... 30
3.3 RESEARCH METHODOLOGY ........................................................................................... 31
3.3.1 Contour map of the study area ........................................................................................ 31
3.3.2 Questionnaire and interview ........................................................................................... 31
3.3.3 Meteo data collection ..................................................................................................... 32
3.3.4 Use of Cropwat window 8.0 ........................................................................................... 32
3.3.5 Use of SPSS window 11.0 .............................................................................................. 32
3.3.6 Books and e-book ........................................................................................................... 34
CHAPITER IV: RESULTS AND DISCUSSIONS ................................................................................ 35
4.1 SURVEY MAP AND MAIN FEATURES OF SITE .............................................................. 35
4.2 INTERVIEW RESULTS ....................................................................................................... 36
4.2.1 Rwampara site................................................................................................................ 36
4.2.2 RWANDA meteorology agency ..................................................................................... 36
4.3 METEO DATA INFLUENCING RAINFALL PATTERNS AT RWAMPARA ..................... 36
4.4 EVALUATION OF RAINFALL MODEL ............................................................................. 37
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4.4.1 Modeling procedures ...................................................................................................... 37
4.4.2 Modeling and simulation ................................................................................................ 37
4.4.3 Level of acceptance of the model .................................................................................... 40
4.4.4 Importance of the model ................................................................................................. 41
4.5 CROP WATER REQUIREMENT FOR DIFFERENT CROPS .............................................. 41
4.6 RAINFAL PREDICTION ...................................................................................................... 42
4.6.1 Measurement of the accuracy ......................................................................................... 42
4.6.2 Rainfall pattern for agriculture of Rwampara swamp ...................................................... 45
4.7 PLANTING CROPS AND SOWING DATE ......................................................................... 45
4.7.1 Planting crops ................................................................................................................ 45
4.7.2 Sowing date ................................................................................................................... 46
CHAPTER V: CONCLUSION AND RECOMMENDATION ............................................................... 48
5.1 CONCLUSION ..................................................................................................................... 48
5.2 RECOMMENDATIONS ....................................................................................................... 49
REFERENCES ..................................................................................................................................... 50
APPENDICES ...................................................................................................................................... 52
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LIST OF TABLES
TABLE 3.1: AVERAGE METEO DATA COLLECTION .......................................................................... 29
TABLE 4. 1: REGRESSION COEFFICIENTS ..................................................................................... 38
TABLE 4. 2: IRRIGATION WATER REQUIREMENT ............................................................................. 41
TABLE 4. 3: ERROR MEASUREMENT ............................................................................................... 43
TABLE 4. 4: RAINFALL FORECASTING RESULT FOR TWO YEARS....................................................... 44
TABLE 4. 5: SOWING DATE PROGRAM AND TYPES OF CROPS ........................................................... 47
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LIST OF FIGURES
FIGURE 2. 1: HYDROLOGICAL CYCLE ............................................................................................... 5
FIGURE 2. 3: SPSS MODELING PROCESS ......................................................................................... 18
FIGURE 2. 4: MODELING IDENTIFICATION PROCESS ........................................................................ 21
FIGURE 3. 1: CULTURE OF RWAMPARA SWAMP .............................................................................. 27
FIGURE 3. 2: DIGITAL CAMERA ..................................................................................................... 30
FIGURE 3. 3: GPS ......................................................................................................................... 30
FIGURE 3. 4: CONTOUR MAP OF RWAMPARA .................................................................................. 31
FIGURE 4. 1: SURVEY MAP OF RWAMPARA .................................................................................... 35
FIGURE 4. 2: RAINFALL TIME PLOT MODEL ..................................................................................... 39
FIGURE 4. 3: FORECASTING MODEL ............................................................................................... 40
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LIST OF APPENDICES
APPENDIX 1: Meteo data APPENDIX 2: Questionnaires APPENDIX 3: Model output APPENDIX 4: GPS Coordination
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LIST OF ABREVIATION UR: University of Rwanda CST: College of Science and Technology CEET: Civil Engineering and Environmental Technology WEE: Water and Environmental Engineering SPSS: Statistical Packages of Social Sciences UHF: Ultra High Frequency UCL: upper confidence limit LCL: Lower confidence limit NIR: Net Irrigation Requirement SARIMA: seasonal autoregressive integrated moving average ARIMA: autoregressive integrated moving average SMA: seasonal moving average MA: moving average AR: autoregressive ARMA: autoregressive moving average
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SWC: Soil Water Content SAWC: Soil Available Water Capacity SAS: Seasonal Adjusted series SAF: Seasonal Adjusted Factor STC: Seasonal Trends cycle D: transformation Difference Q: number of moving average values P: number of autoregressive values SIMSEM: Simulated structural equation Modeling MINITERE: Ministry of foreign affairs MINAGRI: Ministry of Agricultural WMO: World Meteorology Organization FAO: Food and Agriculture Organization
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CHAPTER I: INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Rwanda, officially the Republic of Rwanda, is a sovereign state in state in central and East Africa of capital of Kigali. Located a few degrees south of the Equator for coordinate‟s latitude: 1º04‟and 51‟ south and 28º45‟ and 31º15‟ East. Rwanda is bordered by Uganda, Tanzania, Burundi, and the Democratic Republic of the Congo. Rwanda has area of 26,338 kilometer square (km2) and 5.3% of water. Water generates in Rwanda is coming from the precipitation related cycle which is use in agricultural activities. (Safaris, 2013) The broad aim of this study was to develop objectives means of assessing the performance of Meteo-RWANDA rainfall prediction used to support the agriculture cost due to unprepared irrigation. Within this broad remit a more specific aim was to establish performance criteria to be applied to the seasonal rainfall prediction, to the annually updates and announcing the sowing date for cultivators. A prediction or forecast is a statement about the way things will happen in the future, often but not always based on experience or knowledge. While there is much overlap between prediction and forecast, a prediction may be a statement that some outcome is expected, while a forecast is more specific, and may cover a range of possible outcomes. (wiki, http://en.wikipedia.org/wiki/Prediction, 2013) In our project, we have predicted rainfall patterns for announcing sowing dates to save irrigation expenses. The rainfall patterns are characterized by four seasons, a short rainy season from September to November and a longer rainy season between March and May. Between these seasons are two dry periods, a short one between December and February and a long one from June to August. Rainfall ranges from about 900mm to 1500mm in the RWANDA areas. Agriculture is a vital sector for the sustained growth of developing countries, especially agriculture based in RWANDA. A significant portion of the Rwandan‟s population 80 percent of rural inhabitants still depends on agriculture for employment and sustenance. (EDPRS2, 09 April 2013)
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1.2 PROBLEM STATEMENT
The Rwanda Meteorological service does not have enough capacity to predict proper rainfall because of insufficient materials or irresponsible laborers. Whether Rwanda is in a drought or much less rains than expected, both scenarios will have a serious impact on the agricultural sector with reduced harvest and potentially even a food shortage. Analysis of rainfall trends show that rainy seasons are tending to become shorter with higher intensity. This tendency has led to decreases in agricultural production and events such as droughts in dry areas (BUGESERA) such cause the cost of irrigation to increase; and floods or landslides in areas experiencing heavy rains. Heavy rains have been being observed especially in North and Western province. These heavy rains coupled with a loss of ecosystems services resulting from deforestation and poor agricultural practices have resulted in soil erosion ,rock falls, landslides and floods which destroy crops, houses and other infrastructure (roads, bridges, hospitals and schools ) as well as loss of human and animal life . 1.3 OBJECTIVES OF THE PROJECT 1.3.1 General objective
The general objectives of this research is to produce a feasibility study of rainfall prediction project to encourage the Rwampara swamp„s farmers to use rainfall predicted for the future season.
1.3.2 Specific objectives
To identify various factors affecting rainfall,
To analyze the effect of rainfall on agriculture,
Collection of the rainfall data from Meteo-Rwanda Kanombe airport station,
To use SPSS software to simulate rainfall prediction,
Prediction of seasonal rainfall patterns and advising cultivators on sowing dates to save irrigation expenses.
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1.4 SCOPE OF THE PROJECT
The scope of this study is about rainfall prediction and analyzing for agriculture activities in RWAMPARA. In fact, this analysis will conduct to the prediction of seasonal rainfall patterns and advising cultivators on sowing dates. The detailed of soil analysis of the area will not be performed such as seepage and agronomic of soil and exact sowing date of each crop because of loss of materials.
1.5 JUSTIFICATION OF THE PROJECT
1.5.1 Research significance
For final year students , it is very important to put the class theories into practice .This project is also in line with requirements for them to get a bachelor‟s degree will help us to get bachelor degree.
1.5.2 Public and administrative significance
This project will improve the agriculture production, environmental sustainable and personal activities such as irrigation during dry period and rainy period.
1.5.3 Academic significance
This study may be served as the reference by students interested in rainfall for agriculture seasons prediction and hydrological information of Rwanda.
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CHAPTER II: LITERATURE REVIEW
2.1 GENERALITIES ON HYDROLOGY
Hydrology is a branch of Earth science. The importance of hydrology in the assessment, development, utilization, and management of the water resources, of any region is being increasingly realized at all levels. It was in view of this that the United Nations proclaimed the period of 1965-1974 as the International Hydrological decade during which ,intensive efforts in hydrologic education research ,development of analytical techniques and collection of hydrological on a global basis ,were promoted in Universities ,Research Institutions , Government Organizations. (Roghunath, 2007)
2.1.1 Water resources of Rwanda
Rwanda is a country located in great Lakes Region of Africa .Its topography gradually rises from the East at an average altitude of 1,250m to the North and West where it culminates in a mountain range called “Congo-Nile Ridge ” varying from 2,200m to 3,000m and a volcano formation, the highest volcano being 4,507m high. The country is divided by a water divide line called “Congo-Nile Ridge”. To the west of this line lies the Congo River basin which covers 33% of the national territory, which receives 10% of the total national waters. To the east lies the Nile River basin, whose area covering 67% of the Rwandan territory and delivers 90% of the national waters {Ministry of Lands, Environment, Forests, Water and Mines (MINITERE, 2004)}.
2.1.2 Hydrology and hydrologic cycle
Hydrology is the science, which deals with the occurrence, distribution and disposal of water on the planet earth; it is the science which deals with the various phases of the hydrologic cycle. Hydrologic cycle is the water transfer cycle, which occurs continuously in nature; the three important phases of the hydrologic cycle are: Evaporation and Evapotranspiration, Precipitation and Runoff. Evaporation from the surfaces ponds, lakes, reservoirs, dams, seas, oceans, and soon; and transpiration from surface vegetation (plant leaves of cropped land and forests, and soon) take place. These vapors rise to the sky and are condensed at higher altitudes by condensation nuclei and form clouds, resulting in droplet growth.
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The clouds melt and sometimes burst resulting in precipitation of different forms like rain, sleet, snow, hail, mist, dew and front. A part of this precipitation flows over the land called “runoff” after infiltrate into the soil which builds up the groundwater table. The surface runoff joins the streams, rivers and other water is stored in reservoirs or dams. A portion of surface runoff and groundwater flows back to oceans, lake, wells, and soon; again evaporation restarts from the water surfaces and the cycle repeats.
Hydrologic engineering differs from hydrology primarily in that an engineering application is implied. Thus engineering considerations deal mostly with estimating, predicting or forecasting precipitation or streamflow. Of these three phases of hydrologic cycle, namely, evaporation, precipitation and runoff, it is the “rainfall and runoff phase”, which is important to a water and environmental engineer since he is concerned with the storage of surface runoff and quantity of rainfall in the catchment area or watershed for crop water requirement and design of storages capacity for irrigation, municipal water supply, hydropower, and soon. (Roghunath, 2007)
(Geofreekz, 2010)
Figure 2. 1: hydrological cycle
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2.1.3 Scope of hydrology
The study of hydrology helps us to know:
a) The maximum probable rainfall that may occur at a given site and its frequency; this is required for the crop water needed, irrigation requirement, safe design of drains and culverts, dams and reservoirs, channels and other water regulation control structures.
b) The water yield from a basin or region, its occurrence, quantity and frequency, and soon; this is necessary for the planning of irrigation program, crop needed, design of dams, municipal water supply, water power, river navigation, and soon.
c) The groundwater development for which a knowledge of the hydrology of the area, means that formation of soil, recharge facilities like streams and reservoirs, rainfall pattern, climate, cropping pattern, and soon are required.
d) The maximum intensity of storm and its frequency for the design of drainage project in the area. (Roghunath, 2007)
2.2 PRECIPITATION Precipitation is the primary mechanism for transporting water from the atmosphere to the surface of the earth. The main forms of precipitation include drizzle, rain, snow, graupel and hail. In meteorology, precipitation (also known as one of the classes of hydrometeors, which are atmospheric water phenomena) is any product of the condensation of atmospheric water vapor that falls under gravity (wiki, 2013). Precipitation occurs when a local portion of the atmosphere becomes saturated with water vapor, so that the water condenses and precipitates. Thus, fog and mist are not precipitation but suspensions, because the water vapor does not condense sufficiently to precipitate. Two processes, possibly acting together, can lead to air becoming saturated: cooling the air or adding water vapor to the air. Generally, precipitation should fall to the surface; an exception is virga which evaporates before reaching the surface. The precipitation occurs when a local portion of the atmosphere becomes saturated with water vapor, so that the water condenses and “precipitates” Thus, fog and mist are not precipitation but suspensions, because the water vapor does not sufficiently to precipitate. (Roghunath, 2007)
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2.2.1 Types of precipitation
The precipitation may be due to:
Thermal convection (convectional precipitation), this type of precipitation is in the form of local whirling thunder storms and is typical of the tropics. The air close to the warm earth gets heated and rises due to its low density, cools adiabatically to form a cauliflower shaped cloud, which finally bursts into a thunder storm. When accompanied by destructive winds, they are called “tornados”.
Conflict between two air masses (frontal precipitation), when two air masses due to contrasting temperatures and densities clash with each other, condensation and precipitation occur at the surface of contact; this surface of contact is called a “front or front surface”. If a cold air mass drives out a warm air mass, it is called a “warm front”.
Orographic lifting (orographic precipitation), the mechanical lifting of moist air over mountain barriers, causes heavy precipitation on the windward side.
Cyclonic (cyclonic precipitation), this type of precipitation is due to lifting of moist air converging into a low pressure belt, i.e. due to pressure differences created by the unequal heating of the earth‟s surface. (Roghunath, 2007)
2.2.2 Measurement of precipitation
Rainfall may be measured by a network of rain gauges which may either be of non-recording or recording type. The non-recording rain gauge used in India is the Symon‟s rain gauge. It consists of a funnel with a circular rim of 12.7cm diameter and a glass bottle as a receiver. The cylindrical metal casing is fixed vertically to the masonry foundation with the level rim 30.5cm above the ground surface. The rain falling into the funnel is collected in the receiver and is measured in a special measuring glass graduated in mm of rainfall; when full it can measure 1.25cm of rain.
Recording rain gauge: this is also called “self-recording, automatic or integrating rain gauge”. This type of rain gauge has an automatic mechanical arrangement consisting of
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clockwork, a drum with a graph paper fixed around it and a pencil point, which draws the mass curve of rainfall. From this mass curve, the depth of rainfall, in a given time, the rate or intensity of rainfall at any instant during a storm, time of onset and cessation of rainfall, can be determined. The gauge is installed on a concrete or masonry platform 45cm2 in the observatory enclosure by the side of the ordinary rain gauge at a distance of 2-3m from it. The gauge is so installed that the rim of the funnel is horizontal and at a height of exactly 75cm above ground surface. The self-recording rain gauge is generally used in conjunction with an ordinary rain gauge exposed close by, for use as standard, by means of which the readings of the recording rain gauge can checked and if necessary adjusted. There are three types of recording rain gauges like tipping bucket gauge, weighing gauge and float gauge.
Automatic-radio-reporting rain gauge: this type of rain gauge is used in mountainous areas, which are not easily accessible to collect the rainfall data manually. As in the tipping bucket gauge, when the buckets fill and tip, they give electric pulses equal in number to the mm of rainfall collected which are coded into messages and impressed on a transmitter during broadcast. At the receiving station, these coded signals are picked up by UHF receiver. (Roghunath, 2007)
2.2.3 Analysis of rainfall data
Rainfall during a year, season or monthly (or a number of years) consists of several storms .The characteristics of a rainstorm are:
i. Intensity(cm/hr)
ii. Duration (min , hr ,or days)
iii. Frequency(once in 5 years or once in 10, 20, 40, 60, or 100)
iv. Areal extent (i.e. area over which it is distributed).
Correlation of rainfall records: Suppose a number of years of rainfall records observed on recording and non recording rain-gauges for a river basin are available; then it is possible to correlate
The intensity and duration of storms
The intensity, duration and frequency of storms
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If there are storms of different intensity and various durations, then a relation may be obtained by plotting the intensities (i, or cm/h) against durations (t, min, or hr) of the respective storms either on the natural graph paper ,or a double log(log-log) paper, and relations of the form given below may be obtained :
i. 푖=푎 푡+푏 .N. Talbot‟s formula (for t=5-120min)……… (2.1)
ii. 푖=푘 푡푛 ………. (2.2)
iii. 푖=푘푡푥 ………. (2.3)
Where t= duration of rainfall or its part a, b, k, n and x are constants for a given region. Since x is usually negative equations (2.2) and (2.3) are same and are applicable for duration t>2hrs. On the other hand ,if there are rainfall records for 30 to 40 years ,the various storms during the period of record may arranged in the descending order of their magnitude(of maximum depth). When arranged like this in the descending order, if there are a total number of n items and the order number or rank of any particular storm(maximum depth or intensity) is m, then the recurrence interval T (also known as return period ) of the storm magnitude is given by one of the following equations:
1. California method (1923),T= 푛 푚 ………………………(2.4)
2. Hazen‟s method (1930), 푇=푛 푚− 12 ..……………………(2.5) 3. Kimball‟s method, (Weibull, 1939) 푇=푛+1 푚 …………………… (2.6) And the frequency F (expressed as per cent of time) of that storm magnitude (having recurrence interval T) is given by 퐹=1 푇 푋 100% …………………… (2.7) (Roghunath, 2007)
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2.3 WATER LOSSES
2.3.1 Definition of water losses The hydrologic equation states that: rainfall – losses =runoff ………. (2.8) In the previous we discussed precipitation and its measurement. The various water losses that occur in nature are enumerated below. If these losses are deducted from the rainfall, the surface runoff can be obtained. Interception loss due to surface vegetation, i.e. held by plant leaves. Interception loss: the precipitation intercepted by foliage (plant leaves, forests) and buildings and returned to atmosphere (by evaporation from plant leaves) without reaching the ground surface is called interception loss. (Roghunath, 2007) Effective rain = Rainfall – Interception loss …………………… (2.9)
2.3.2 Evaporation and evapotranspiration Evaporation from water and soil surface and transpiration through plants can account for significant volumes of water. Evaporation is the process during which a liquid changes into a gas. The process of evaporation of water in nature is one of the fundamental components of the hydrological cycle by which are one of the vapors through absorption of heat energy. This is the only form of moisture transfer from land and oceans into the atmosphere. Considerable quantity of water is lost by evaporation from the soil surface. Sunlight, temperature, wind velocity and humidity are the main climate factors influencing the rate and extent of evaporation. More the fine aggregates of black soil, more the heat absorbed resulting in more loss of water. The basic principle is to cover them with vegetation, mulching, keeping soil surface loose by tillage operation, use of wind brake etc. That can help to reduce evaporation losses.
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Evaporation may also directly affect soil moisture conditions. If there is too much moisture in the soil, the farm machinery can get bogged down because it has to work too hard. If the soil is too dry, however, the plants may be easily stressed due to the lack of available water and crust may sometimes form on top of the soil. This crust may be so impermeable that when it rains on top of the crusty soil, the rain runs right off rather than soaking in .Each plant type has its own unique evapotranspiration rate. The combination of two separated processes whereby water are lost on the one hand from the soil surface by evaporation and on the other hand from the crop by transpiration is referred to as evapotranspiration (ET). (John A. Roberson, 1997)
2.3.3 Hydrometeorology Hydrometeorology is branch of meteorology that deals with problems involving the hydrologic cycle, the water budget and the rainfall statics of storms. The boundaries of hydrometeorology are not clear cut, and the problems of the hydrometeorologists overlap with those of the climatologists, the hydrologist, the cloud physicist, and weather forecaster. Considerable emphasis is placed on determining, theoretically or empirically, the relationships between meteorological variables and the maximum precipitation reaching the ground. These analyses often serve as the bases for the design of flood-control and water usage structures, primarily dams and reservoirs. Other concerns of hydrometeorologists include the determination of rainfall probabilities, the space and time distribution of rainfall and evaporation, the recurrence interval of major storms, snow melt and runoff, and probable wind tides and waves in reservoirs. The whole field of water quality and supply is of growing importance in hydrometeorology.
2.3.4 Infiltration Infiltration is the process by which water on the ground surface enters the soil. Infiltration is governed by two forces which are gravity and capillary action. While smaller pores offer greater resistance to gravity, very small pores pull water through capillary action in addition to and even against the force of gravity.
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Infiltration rate in soil science is a measure of the rate at which a particular soil is able to absorb rainfall or irrigation. It is measured in inches per hour (inch/hr) or millimeters per hour (mm/hr). The rate decreases as the soil becomes saturated. If the precipitation rate exceeds the infiltration rate, runoff will usually occur unless there is some physical barrier. (Roghunath, 2007)
2.4 SOIL-WATER-IRRIGATION RELATIONSHIP
2.4.1 Definitions
Soil-plant-water relationships describes those properties of soils and plants that affect the movement, retention, and use of water essential to plant growth. It can be divided and treated as: soil-plant relation, soil-water relation and plant-water relations.
2.4.2 Crop water requirement
It is defined as “the depth of water needed to meet the water loss through evapotranspiration (ETcrop) of a disease free crop growing in large fields under non-restricting soil conditions including soil water and fertility and achieving full production potential under the given growing environment”. That is, it is the quantity of water required by the crop in a given period to meet its normal growth under a given set of environmental and field conditions. The determination of water requirements is the main part of the design and planning of an irrigation system. The water requirement is the water required to meet the water losses through:
Evapotranspiration (ET);
Unavoidable application losses; and
Other needs such as leaching and land preparation.
The water requirement of crops may be contributed from different sources such as irrigation, effective rainfall, and soil moisture storage and groundwater contributions. (Charlotte, 2013) Hence, WR = IR + ER + S + GW ………………………… (2.11)
Where, IR = Irrigation requirement, ER = Effective rainfall, S = carry over soil moisture in the crop root zone, GW = groundwater contribution.
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2.4.3 Effect of rainfall
The primary source of water for agricultural production, for large parts of the world and Rwanda, is rainfall. Rainfall is characterized by its amount, intensity and distribution in time. All crops need water to grow and to produce yields. The most important source of water for crop growth is rainfall.
When rainfall is insufficient, irrigation water may be supplied to guarantee a good harvest. One of the main problems of the irrigator is to know the prediction of rainfall and the amount of water that has to be applied to the field to meet the water needs of crops; in other words the irrigation requirement needs to be determined. Too little water during the growing season causes the plants to wilt. Long periods during which the water supply is insufficient, result in loss of yield. In addition, the irrigation requirement needs to be determined for proper design of the irrigation system and for establishment of the irrigation schedules. (docrep, Httt://www.fao.org/docrep/r4082e/4082e03.htm)
2.4.4 Net irrigation requirement (NIR) Net irrigation water requirement (NIWR) is the quantity of water necessary for crop growth. It is expressed in millimeters per year (mm/yr) or in cubic meters per hectare per year (m3/ha/yr) {1mm= 10m3/ha}. It depends on the cropping pattern and the climate. Information on irrigation efficiency is necessary to be able to transform NIWR into gross irrigation water requirement (GIWR), which is the quantity of water to be applied in reality, taking into account water losses. Multiplying GIWR by the area that is suitable for irrigation gives the total water requirement for that area. In our study water requirements are expressed in m3/month. In order to be able to do this at the scale of Area, assumptions have to be made on the definition of areas to be considered homogeneous in terms of rainfall, potential evapotranspiration, cropping pattern, cropping intensity and irrigation efficiency (docrep, 2014). Net irrigation requirement depend on: Depth of water, exclusive of effective precipitation, or groundwater, that is required for meeting crop evapotranspiration for production and other related uses. Such uses may include water required for leaching, frost protection, cooling and chemigation.
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2.5 FACTORS AFFECTING RAINFALL Rain is liquid water in the form of droplets that have condensed from atmospheric water vapor and precipitated that is, become heavy enough to fall under gravity. Rain is a major component of the water cycle and is responsible for depositing most of the fresh water on the earth. It provides suitable conditions for many types of ecosystem, as well as water for hydroelectric power plants and crop irrigation. Changes in rainfall and other forms of precipitation will be one of the most critical factors determining the overall impact of climate change. Rainfall is much more difficult to predict than temperature but there are some statements that scientists can make with confidence about the future. (John A. Roberson, 1997) 2.5.1 Weather and Meteorology Temperature and precipitation are two characteristics of weather most familiar to all of us. Quantitatively, each is governed by energy given off by the sun and distribution and absorption of that energy on the earth. All weather, and hence all precipitation, is governed by movement of the air mass surrounding the earth. Motion of that air mass is unsteady and turbulent. 2.5.2 Evaporation and Evapotranspiration Evaporation from water and soil surfaces and transpiration through plants, can account for significant volumes of water. The process of evaporation and evapotranspiration occurs at the water surface and vegetations where molecules of water develop sufficient energy to escape bonds with the water and become vapor molecules in the air. Evaporation from a water body is a function of air and water temperatures, the moisture gradient at the water surface, and wind. Wind moves the moisture away from the lake‟s surface and, thus, increases the moisture gradient, increasing the rate of evaporation.
a) Temperature Higher temperatures affect the conditions for cloud formation and rainfall. Heavy rain showers, such as summer thunderstorms, are influenced more by temperature than rain from
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larger widespread rain systems. Heavy rain has far-reaching consequences for society, and these could worsen at higher temperatures.
b) Wind
Wind is the movement of air caused by the uneven heating of the earth by the sun. It does not have much substance you cannot see it or hold it but you can feel its force. It can dry our cloves in summer, blow clouds and condense it and chill us to the bone in winter. It is strong enough to carry sailing ships across the ocean and rip huge trees from the ground. It is the great equalizer of the atmosphere, transporting heat, moisture, pollutants, and dust great distances around the globe. Landforms, processes, and impacts of wind are called Aeolian landforms, processes, and impacts.
c) Humidity
Humidity is the amount of water vapor in the air. Water vapor is the gaseous state of water and is invisible. Humidity indicates the likelihood of precipitation, dew, or fog. Higher humidity reduces the effectiveness of sweating in cooling the body reducing the rate of evaporation of moisture from the skin and the leaves of crops. There are three main measurement s of humidity: absolute, relative and specific.
Absolute humidity is the water content of air;
Relative humidity, expressed as a percent, measures the current absolute humidity relative to the maximum for that temperature;
Specific humidity is a ratio of the vapor content of the mixture to the total air content on a mass basis.
There are other factors affecting rainfall which are climate, sunshine, topography, human activities and vegetation cover.
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2.6 USING SPSS SOFTWARE IN RAINFALL PREDICTION
2.6.1 Definition
SPSS is a statistical package used for conducting statistical analyses ,manipulating and presenting data
Acronym statistical packages for the social science but now it is known as predictive analysis software
Its statistical capabilities range from simple percentages to complex analyses including multiple regressions and general linear models.
2.6.2 Types of software used in rainfall prediction
There is main software used in rainfall prediction:
SPSS (Statistical Package for Social Sciences) software (PAKISTAN, Ethiopia, India)
ANFIS (Adaptive Neuro-Fuzzy Inference System) ,THAILAND
Satellite Rainfall Estimates (Remote Sensing and GIS )
ACCESS (Australian Community climate and Earth-System Simulator), AUSTRALIA
NWP (Numerical Weather Prediction), USA
Matrix Decomposition method (UK)
STATA(UK)
Neural Networks (USA)
2.6.3 Types of time series data
Time series data can have two main forms i.e. continuous and discrete. A continuous time series is one in which the variable being examined is defined continuously in time. Means defined at each point in time. Examples: mean temperature at specific site, amount of rainfall at specific site, the wind speed at specific site, air humidity, and weather condition. Many time series are not defined at each point in time, but only at specific time (discrete time series). Examples: seasonal production for crops, monthly rainfall, monthly mean temperature, monthly air humidity, and maximum o r minimum daily temperature.
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In most case data are not measured continuously, but measured at specific points in time (such as hourly or daily). Sometimes, they are measured more frequently, and then applied average to give say, average hourly wind speed or mean temperature or relative humidity or rainfall. Forecasting in the time series means that we extend the historical data into the future where the measurements are not available yet. If a time series can be predicted exactly, it is said to be “deterministic”. However, most time series are stochastic (random) in that the future is only partly determined by past data, so that exact predictions are impossible and must be replaced by the ideal that future data have a probability distribution which are conditioned by a knowledge of past data. Therefore, the subject matter of time series and forecasting main objective is focused on “understanding the past and forecasting the future”.
2.6.4 Process used in SPSS software by box-Jenkins modeling Box-Jenkins Modeling is made using time series analysis by several methods, one which is the Autoregressive Integrated Moving Average (ARIMA) or Box-Jenkins method, being called the (p, d, q) model, too (Box and Jenkins, 1976). In the (p, d, q) model, p denotes the number of autoregressive values, d is the order of differencing, representing the number of times required to bring the series to a kind of statistical station or equilibrium and q denotes the number of moving average values. In ARIMA model, (p, d, q) is called non-seasonal part of the model, p denotes the order of connection of time series with its past and q denotes the connection of the series with factors effective in its construction. At the first stage, the primary values of p, d and q are determined using the autocorrelation function (ACF) and partial autocorrelation function (PACF). A careful study of the autocorrelation and partial autocorrelation diagrams and their elements, will provide a general view on the existence of the time series, its trend and characteristics. This general view is usually a basis for selection of the suitable model. Also, the diagrams are used to confirm the degree of fitness and accuracy of selection of the model. At the second stage, it is examined whether p and q (representing the autoregressive and moving average values, respectively) could remain in the model or must exit it. At the third stage, it is evaluated whether the residue values are stochastic with normal distribution or not.
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It is then that one can say the model has good fitness and is appropriate. If the time series is of seasonal type, then the modeling has two dimensional states, and in principle, a part of the time series variations belongs to variations in any season and another part of it belongs to variations between different seasons. A special type of seasonal models that shows deniable results in practice and coin sides with the general structure of ARIMA models is devised by Box and Jenkins (1976), which is called multiplicative seasonal model. It is in the form of ARIMA (p, d, q) (P, D, Q) then, for the model being ideal, the schemes must be used to test the model and for the comparison purpose, so as the best model is chosen for forecasting: 푿풕=푿풕−ퟏ±푿풕−ퟐ±푿풕−ퟑ±푿풕−풏±풁풕 ………… (2.12) (Arash Asadi, 2013)
Chart shows description of SPSS process
Figure 2. 2: SPSS modeling process
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Time sequence plot: It is similar to X-Y graphs, and is used to display time versus value data pairs. A time Plot data item consists of two data values which are the time and the value. Which translate into the x and y- coordinates, respectively. Each data item is displayed as a symbol, but you can add a line.
2.6.5 Autocorrelation Correlation (often measured as a correlation coefficient) indicates the strength and direction of linear relationship between two random variables. Pearson correlation coefficient is given by equations: 풓 풙풚= 푺풙풚 푺풙푺풚 …………… (2.13) Where Sxy is the covariance between x and y, Sx and Sy are standard deviation for x and y variables respectively.
푺풙풚= (풙풏풊 =ퟏi-풙 ) (yi -풚 ) / (n-1) ......................... (2.14) Therefore rxy can be given as 풙풊−풙 풚풊−풚 풙풊−풙 ퟐ 풏풊 =ퟏ 풏풊 =ퟏ/ 풚풊−풚 ퟐ ………….. (2.15) It lies in the range [-1, 1] and measures the strength of the linear association between the two variables. A value of +1 indicates that the variables move together perfectly; a value of -1 indicates that they move in opposite directions. The primary difference between time series models and other types of models is that lag values of the target variable are used a predictor variables, whereas other models use other variables as predictors. There, in time series, an autocorrelation is the correlation between the target variable and lag values for the same variable. Autocorrelation measure the correlation if any, between observations at different apart and provide useful descriptive information. It is also an important tool in model building and often provides variable clues to a suitable probability model for a given set of data. For time series data yt the autocorrelation coefficient at lag k is given by: 풓풌= (풚풕−풚 풕)(풚풕+풌−풚 풕)/ (풚풕−풚 풕)ퟐ푵풊 =ퟏ 푵−풌 풕=ퟏ ……………. (2.16)
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2.6.6 Stationary time series A time series is said to be stationary if there is no systematic change in mean (no trend) and if there is no systematic change in variance in which if strictly periodic variations have been removed. Therefore, a time series yt; t= 1, 2, is called to be stationary if its statistical properties do not depend on time t. A time series may be stationary in respect to one characteristic such the mean, but not stationary in respect to other characteristics such as the variance. Stationary in variance can sometimes be produced by taking logarithmic transformation.
2.6.7 Data that is non stationary in the mean If the data are not stationary in the mean, then the data show some sort of “trend “or “cyclical” fluctuation. Thus, allowing either a straight forward increase or decrease, or a cyclical up and down movement. The presence of such non stationary is indicated firstly by a trend in the plot of the data; secondly, it is indicated on the ACF by the autocorrelation “dying away” very slowly. The PACF will in this case show a partial auto correlation at lag 1 of nearly unity. A method of dealing with such data is to take differences of the data. If this is the correct of choice of degree of differencing, then one will be able to identify a model based on the ACF and PACF. In some cases, it is necessary to difference the data twice, in which case the ACF and PACF of the first differences will still show trend. Previous ARMA models can be extended in the same way to data is non stationary, And such models are called auto regressive integrated moving models ARIMA (p; d; q) models. The p and q are as in the ARMA models, while the d indicates the degree of the differencing used (d=1 for first difference, d=2 for second differences) In general, it is seldom necessary to go above second differences.
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2.6.8 Identifying potential model The identification of potential models is based on patterns of the autocorrelation (ACF) and partial auto correlation (PACF) functions. These are plots of the autocorrelations and partial autocorrelations at various lags, against the size of lag. Thus in the autocorrelation plot, the size of the autocorrelation is more or less equal to the size of the data minus 2. In model fitting the principle of parsimony is in general a rule to seek simplest models as much as possible. For example in time series, if neither AR (p) nor MA (q) models are plausible, it is natural to try ARMA (p, q). And in accordance with the principle of parsimony, to use as small as p and q as possible, starting therefore with p=q=1
Figure 2. 3: Modeling identification process
2.6.9 Estimating the component of a time series using SPSS Using SPSS we can estimate the components of seasonal time series .This is called seasonal decomposition in SPSS , and is done using Seasonal Decomposition from the time series submenu of analyze.
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To use this decomposition, the following conditions should be satisfied The time series has annual seasonality The time series (or transformation of it) may be described adequately by an additive model. The time variable sand periodicity has been defined in SPSS using defines dates. Then SPSS give us the estimated factors. Here period is the period of the cycle which 12 months. Period to 12 are the months from January to December. The estimated seasonal factors give us largest and lowest number which indicates the seasonal peak and through, respectively. Note that the estimated seasonal factors sum to zero. After this seasonal decomposition analysis, in the data view panel of the SPSS Data Editor, the following four new variables will obtained: ERR_1, SAS_1, SAF_1, and STC_1. 1) SAS_1 (Seasonal Adjusted Series) contains seasonally adjusted series, which is obtained by subtracting the estimated seasonal component (SAF_1) (Seasonal adjusted Factor) from the time series. In seasonally adjusted time series (SAS_1), the seasonality has been removed from the original time series, leaving the trend component and irregular component. 2) STC_1 (Seasonal Trend Cycle) is a smoothed version of SAS_1; it is called the trend- cycle component in SPSS. This name indicates that annual seasonality has been removed, and that the trend and any cycles of period greater than one year remain. 3) ERR_1 (Error) is an estimate of the irregular component; it is equal to the seasonally adjusted series minus the trend cycle component.
2.6.10 Basic concepts in analysis of time series data The special feature analysis is the fact that successive observations are dependent and that the analysis must take into account the time order of observations. When successive observations are dependent, future values may be predicted from past observations. A time series is said to be stationary if there is no systematic change in mean (no trend), if there is no systematic
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change in variance and if there is no systematic change in variance and if strictly periodic variations have been removed. Much of the probability theory of time series is concerned with stationary time series, and for this reason time series analysis often requires one to transform a non-stationary series into a stationary one so as to use this theory. Trend can defined as “a long term change in the mean level”. The simplest type of trend is familiar “linear trend + Error” for which the observation at time t is a random variable Xt, given by Xt = α+βt+Єt where α and β are constants and Єt denotes a random error term with zero mean. As we know special type of filtering, which is particularly useful for removing a trend is simply to differentiate a given time series until it becomes stationary. This method is an integral part of the so called “Box-Jenkins procedure”. For non-seasonal data, first order differencing is usually sufficient to attain apparent stationary. But occasionally, second order differencing may be required. The analysis of time series which exhibit seasonal variation depends on whether one wants to: Measure the seasonal effect and/or Eliminate seasonality For series showing little trend, it is usually adequate to estimate the seasonal effect for a particular period (e.g.: April) by finding the average of each April observation divided minus the corresponding yearly average in the additive case, or the April observation divided by the yearly average in the multiplicative case. Generally, a time series analysis consists of two steps: 1. Building a model that represents a time series; and 2. Using the model to predict future data or values. If a time series has a regular pattern, then value of the series should be a function of previous values. If Y is the target (rainfall) value that we are trying to model and predict, and Yt the value of Y at time t, then the goal is to create a model of the form: 풀풕=풇 풀풕−ퟏ,풀풕−ퟐ,풀풕−ퟑ,…,풀풕−풏 +풆풕 ………………… (2.17)
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Where Yt-1 is the value of Y for the previous observation, Yt-2 is the value two observations ago, etc, and et represents error that does not follow a predictable pattern (this is called a random shock). Values of variables occurring prior to the current observation are called lag values. The goal of building a time series model is the same as the goal for other types of predictive models which is to create a model such that the error between the predicted value of the target variable and the actual value is as small as possible. The main objective in investigating time series is forecasting future values of the observed series. This can be done through the model which adequately describes the behavior of the observed variable and the required forecast. Time series data corresponds to the sequence of values for a single variable in ordinary data analysis. Each case (row) in the data represents an observation at a different time the observations must be taken at equally spaced time interval.
2.6.11 Autoregressive (AR) model AR model is a common approach for modeling univariate time series. Therefore, with a stationary series in place, a process yt is said to be an autoregressive process of order p abbreviated as AR (p) is a process like: yt =α+βt1yt-1+ β2yt-2 +Єt or Rainfall= α +β1T+β2H+ random Error ……….. (2.18) Where α is the constant and β1and β2 are the coefficients of temperature and humidity. This look like multiple regression model, but yt is regressed on past values of yt rather than on separate predictor variables, this explains the prefix “auto”. This model describes the time series, plus a random error in the process. A random error (Єt) is assumed to be independently and identically distributed normally (Gaussian) with mean 0 and constant variance, is denoted by Єt. The simplest model is the Autoregressive model of order 1[AR (1) model], which uses only lag 1 observation, defined as Yt = αyt-1+ Єt ……….. (2.19)
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Where Yt is the current observation, Yt-1 is the previous observation, α the parameter to be estimated, known as AR (1) parameter. This process is sometimes called the Markov process, after the Russian A .A Markov. The parameter in this model (α) should lies between +1 and -1; otherwise there are problems with model. If the parameter estimate is close to +1, then one should be considering the model of the form Yt=yt-1+ Єt or Yt - yt-1= Єt ………………… (2.20) Thus one should be modeling not the raw data, but differences between the data. One can use more than one log; therefore the general form of the model is AR (p) model, which uses p- lags of the data (i.e. forecasting yt from yt-1; yt-2; …; yt-p). For most data series found in practice, lag -2 is the highest order required, and for such complex models, the parameters do not always lie between +1 and -1. Thus the model for AR (2) is given by Yt =α1yt-1+ α2yt-2 +Єt …. (2.21) Generally, in the discussion above, the model has been written as if the data were zero average; of course data do not have a zero mean, but some other value. Therefore, the model for AR (1) which including the mean becomes Yt =μ+ αyt-1+ Єt …………… (2.22) Practically, the first model to be tested on the stationary series consists solely of an Autoregressive term with lag 1. Therefore, the autocorrelation and partial autocorrelation patterns will be examined for significant autocorrelation to see whether the error coefficients are uncorrelated. That is the coefficient Values are zero within 95% confidence limits and without apparent pattern. When fitted values as close as possible to the original series values are obtained, the sum of the squared residuals will be minimized, a technique called least squares estimation. Alternative models are comparing the progress of these factors, favoring models which use as few parameters as possible. Finally, when a satisfactory model has been established a forecast procedure is applied.
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2.6.12 Prediction interval Prediction interval in regression analysis it is a range of values that estimate the value of the dependent variable for given values of one or more independent variables. Comparing prediction intervals with confidence intervals: i. Prediction intervals estimate a random value, while confidence intervals estimate population parameters. ii. A prediction interval is an estimate of an interval in which future observations will fall, with certain probability, given what has already been observed. It usually consists of an upper and a lower limit between which the future value is expected to lie with prescribed probability (1- α) %. As a result a methodology for outlier detection involves in the rule that an observation is an outlier if it falls outside the prediction interval computed.
2.6.13 Forecasting One of the main objectives in investigating a time series is forecasting. This can be using through the simplest model which adequately describes the behavior of the observed variable and the required forecast. Besides, in most complex model the current value of the variable can depend on past events, to forecast future data points before they are measured. Forecasting is designed to help decision making and planning in the present for the future. It empowers people because their use implies that we can modify variables now to alter (or be prepared for) the future. Therefore, prediction is an invitation to introduce change into a system. It is necessarily t to understand the current situation when there is a time lag between data collection and assessment. (Emelyne, 2013)
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CHAPIII: MATERIALS AND METHODOLOGY
In chapter III, the methods, materials and equipment used including their origin and specification in order to get information are explained in details.
3.1 SITE DESCRIPTION
After direct observation, personal interview, the researchers found that RWAMPARA Swamp located in between NYARUGENGE and KICUKIRO Districts, the swamp covers 13.7 ha and its soil is clayey silt where agriculture is carried out by the people of these surrounding sectors. Maize, beans, green peppers, carrots, beets, tomatoes, cucumbers, eggplants, and cabbages are rotated in the field.
Figure 3. 1: Culture of Rwampara swamp
The swamp meet flooding and drought problems leading to yield reduction that is why it needs rainfall prediction for managing their agricultural activities and the type crops needed according to season.
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3.1.1 Site localization
It is found that RWAMPARA Swamp is located between KICUKIRO and NYARUGENGE Districts, the swamp is bounded by three sectors of GIKONDO, NYARUGENGE and NYAMIRAMBO .It covers an area of 151ha. The swamp has not enough production yet, it has fertile soil and enough information of rainfall to minimize the cost of irrigation for best preparing the future of their crops to know where irrigation are required or not required.
3.1.2 Soil type
The soil of Rwampara is characterized by clayey silt capable to save water in short dry season of two months. This type of soil, it has natural fertility capable for cabbage, carrots, cucumber, beets, tomatoes, eggplants, green papers and beans. The moisture content in that soil is equal to sixty percent and decrease to ten percent in dry seasonal.
3.1.3 Rainfall pattern
The rainfall patterns of Rwampara is the same of all nation characterized by short rain season or short wet season beginning from September to November, short dry season starting in December to February, long rain season or long wet season starting from March to May and long dry season starting from June to August.
3.1.4 Meteo factors of study area
The climate of Rwampara is characterized by the following data in the table 3.1; these data were collected by Meteo-Rwanda, Kanombe airport station from 1972 to 2013. These climatologically data were collected at altitude of 1490, latitude of 1.96*S and longitude of 30.11 *E.
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Average rainfall, temperature, humidity, wind speed and wind from 1972 to 2013
Table 3.1: Average Meteo data collection
Monthly average /Meteo data factors
Rainfall Mm
Temperature oC
Humidity %
Wind speed m/s
Wind
January
72.5
21.2
75.5
2.4
20.4
February
91.2
21.4
75.0
2.5
20.4
March
118.0
21.2
76.9
2.6
20.4
April
151.4
21.0
81.1
2.2
20.4
May
89.1
20.9
79.8
2.4
20.4
June
21.5
20.7
69.9
2.5
20.4
July
12.5
20.9
69.4
2.7
20.4
August
31.1
21.9
64.3
3.0
20.4
September
71.5
21.8
75.6
3.0
20.4
October
101.3
21.4
79.5
5.9
20.4
November
116.4
20.7
80.8
5.5
20.4
December
85.0
20.9
79.0
8.6
20.4
Annuals Average
82.4
21.2
75.6
3.4
20.4
3.2 RESEARCH TOOLS
The national meteorological services agency, Rwanda, is the responsible organization for the collection and publishing of meteorological data. The monthly rainfall data from the period January 1972 to December 2013 of Kigali AERO station of Kigali region were taken from national meteorological service Agency (meteo Rwanda data in Appendix). Te following equipments was used to collect data on the site:
3.2.1 Digital camera
A digital camera is a camera that takes video or still photographs, or both, digitally by recording images via an electronic image sensor.
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A digital camera is used to capture the photos of plants of Rwampara swamp.
Figure 3. 2: Digital camera
3.2.2 Global Positioning System (GPS)
The Global Positioning System (GPS) is a satellite based navigation system that consists of 24 orbiting satellites, each of which makes two circuits around the Earth every 24 hours. With signals from three or more satellites, a GPS receiver can triangulate its location on the ground (i.e. longitude and latitude) from the known position of the satellites. In addition, a GPS receiver can provide on your speed and direction of travel. GPS was used as the leveling in order to determine the elevation (1396m) and area (150.8ha) of Rwampara swamp.
Figure 3. 3: GPS
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3.3 RESEARCH METHODOLOGY
3.3.1 Contour map of the study area
Figure 3. 4: Contour map of Rwampara
3.3.2 Questionnaire and interview
This research was conducted through the following steps:
Information through different visits which are made of the sites such as MASAKA swamp, RULINDO swamp, MULINDI swamp where irrigation is carried out with the purpose of getting more information concerning rainfall prediction as applied in Rwanda;
The information through the visit of Rwanda meteorology service about rainfall forecasting, factors affecting rainfall, and challenges; Production of the survey map and the contour map of the swamp showing the different features of the swamp using COVADIS and AUTOCAD and production of crop pattern of the swamp.
GIKONDO SECTOR KICUKIRO DISTRICT
NYAMIRAMBO SECTOR NYARUGENGE DISTRICT
NYARUGENGE SECTOR NYARUGENGE DISTRICT
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3.3.3 Meteo data collection
In this project, we use data collected by meteo-Rwanda Kigali AERO station from 1972 to 2013 of monthly rainfall, monthly mean temperature and monthly relative humidity. This data, we are simulating in SPSS software to predict rainfall of two years.
3.3.4 Use of Cropwat window 8.0
CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the calculation of crop water requirement, irrigation needed and irrigation scheduling according to rainfall precipitate for various crops for a range of climatologically stations worldwide. CLIMWAT 2.0 for CROPWAT is a joint publication of the water development and management unit and the climate change and Bio energy unit of FAO. Cropwat window is a program that was published by FAO (1992) penman-monteith method for calculating reference crop evapotranspiration. These estimates are used in crop water requirements calculation. Here is a briefly of how Cropwat windows operate:
Enter monthly climate (ETO) data. You can double click-check entered data by using the climate data. Table and /or the climate data graph.
If rainfall is significant, enter monthly rainfall data and select the method of effective rainfall calculation.
Enter cropping pattern data
You can see the results of crop water requirement calculations in crop water requirements;
Enter/ retrieve soil data;
Save reports of input data results as required
3.3.5 Use of SPSS window 11.0
Statistical package for social sciences (SPSS) software time series analysis and forecasting has become a major tool in hydrology, environmental management, and climatic fields. It is used to modeling and forecasting rainfall data in literatures.
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The rainfall prediction using regressive analysis is written as: Rainfall= constant+ coefficient of temperature+ coefficient of relative humidity+ standard error As written in equation: y=α+β1T+β2H+Є ……….. (3.1) Where, y: rainfall predicted, T: temperature, H: relative humidity and the constant α and the coefficients β1 and β2 Є: random error or standard error.
i. ARIMA Model
The ARIMA model is an extension of the ARMA model in the sense that by including auto- regression and moving average it has an extra function for differencing the time series. If a dataset exhibits long term variations such as trends, seasonality and cyclic components, differencing a dataset in ARIMA allows the model to deal with them. Two common process of ARIMA for identifying patterns in times series and forecasting are auto-regression and moving Average.
ii. Autoregressive process
Most series consists of elements that are serially dependent in the sense that one can estimate a coefficient or a set of coefficients that describe consecutive elements of the series from specific, time-lagged (previous) elements. Each observation of time series is made up of random error components (random shock, ἐ) and a linear combination of prior observations.
iii. Moving average process
Independent from the autoregressive process, each element in series can also affected by the past errors (or random shock) that cannot be accounted by the auto-regressive component. Each observation of the time series is made up of random error components and linear combination of prior random shocks.
iv. General form of non-seasonal and seasonal
ARIMA models are sometimes called Box-Jenkins models.
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An ARIMA model is a combination of an auto-regressive (AR) process and a moving average (MA) process applied to non- stationary data series. As such, in the general non-seasonal, ARIMA (p; d; q) model, AR (p) refers to in order of the autoregressive part, I (d) refers to degree of differencing involved and MA (q) refers to order of the moving average part .The equation for the simplest ARIMA (p; d; q) model is Seasonal ARIMA (SARIMA) is generalization and extension of the ARIMA method in which a pattern repeats seasonally over time. In addition to the non-seasonal parameters, seasonal parameters for a specified lag (established in the identification phase) need to be estimated. Analogous to simple ARIMA parameters, these are: seasonal autoregressive (P), seasonal differencing (D), and seasonal moving average parameters is usually determined during the identification phase and must explicitly specified. In addition to the non-seasonal ARIMA (p; d; q) model introduced above, we could identify SARIMA (P; D; Q) parameters for our data. The general form of the SARIMA (p; d; q) x (P; D; Q) model using backshift notation is given by: Four phases are involved in identifying patterns of time series data using non-seasonal and seasonal ARIMA .These are: model identification, parameter estimation, diagnostic checking and forecasting. The first step is to determine if the time series is stationary and if there is significant seasonality that needs to be modeled.
3.3.6 Books and e-book
In this project we used Seasonal Autoregressive Integrated Moving Average (SARIMA) model, proposed by Box and Jenkins (1976), for model building and forecasting for rainfall. The box and Jenkins methodology is powerful approach to the solution of many forecasting problems. It can provide extremely accurate forecasts of times series and offers a formal structured approach to model building and analysis. There many quantitative methods of model building and forecasting which are used in climatology and metrological studies. With the development of the statistical software packages and its available, these techniques have become easier, faster and more accurate to use. In this study, we employ seasonal adjusted series (SAS) and SPSS software packages for the statistical data analysis.
The Box-Jenkins methodology assumes that the time series is stationary and serially correlated. Thus, before modeling process, it is important to check whether the data under study meets these assumptions or not.
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CHAPITER IV: RESULTS AND DISCUSSIONS
In this chapter, the SPSS software is applied to model rainfall relationship using observed data of RWAMPARA swamp located in KIGALI CITY from METEO RWANDA Kigali AERO station. It was originally assumed that rainfall would be the best predominant factor in this swamp. However, subsequent research strongly indicates that rainfall generally was the most critical input. Numerous of runs of data were done to demonstrate the impact of various trainings data inputs. Several of those runs presented in this chapter to demonstrate the evolution of final model. For each run, an evaluation of the SPSS reliability is presented Procedure is then presented for the systematic selection of inputs variables. The SPSS is extremely versatile program offering a number of choices of data processing and error criteria. These choices are discussed and crop water requirement needed by the maize, beans, beets, cabbage and eggplant are discussed in this chapter using CLIMWAT and CROPWAT software.
4.1 SURVEY MAP AND MAIN FEATURES OF SITE
Figure 4. 1: Survey map of Rwampara
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4.2 INTERVIEW RESULTS
4.2.1 Rwampara site
We have seen that there are many characteristics of changes of precipitation due to climate changes. In that area there is many crops which has been cultivated in long dry season to avoid water pounding destroy crops caused by high quantity of rainfall in wet seasons such as carrots, eggplants, beets, cabbages, cucumbers, tomatoes, green-peppers, etc; and they applied the furrow and natural irrigation systems in that swamp, which produce high production during that dry season because it irrigate the crops rather than wet season because the crops need water regulated. So in wet season they are cultivating maize, beans and soybeans need high quantity of water. The management of that swamp is distributed by five cooperatives in order to produce high quantity of production such as TECOCOKI (Terimbere Complex Cooperative Kigarama). The management of that swamp followed three agriculture seasons, one of them is SEASON A start in October until January, the second one is season B start in February until May, the last one is season C start in June until September.
4.2.2 RWANDA meteorology agency
RWANDA meteorology agency have many rainfall forecast system used tropical models to forecast data from GITEGA station, airport station, and other four station and satellite data in hourly, daily, monthly, and season forecasting. For season forecasting, they are making it at Nairobi/ Kenya station with eastern Africa region experts to predict it. At that station has not capacity of predicting yearly prediction and also meteo Rwanda has not capacity of predicting it because of materials. For seasonal prediction has advantage to agriculture activities purpose like Rwampara swamp area and weather forecasting for aviation movement.
4.3 METEO DATA INFLUENCING RAINFALL PATTERNS AT RWAMPARA
There are many factors influencing rainfall patterns at Rwampara swamp favorite precipitation to fall down. Those factors are temperature (minimum and maximum), air pressure, wind speed, relative humidity, sunshine, wind direction, soil moisture, elevation, and population density. So in our prediction, we have forty two years ago data precipitations, temperature, and relative humidity.
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4.4 EVALUATION OF RAINFALL MODEL
4.4.1 Modeling procedures
The historical measurement of precipitation, humidity and temperature are available for RWAMPARA swamp. This is contrast to data on:
1) Soil characteristics;
2) Land use;
3) Initial soil moisture;
4) Infiltration; and
5) Groundwater characteristics those are usually scarce and limited.
A model could be developed using readily available data sources would be easy to apply in practice. Because of this, the dependent variable (rainfall) has relation with independent variables (temperature and humidity) are inputs selected for use in this model and predicted rainfall is the output. The selection of training data to represent the characteristics of swamp and meteorological patterns is critical modeling. The period of time for historical data selected was from January, 1972 through December 2013 the total of 42 years; the period was selected because of minimization of errors and increases the accuracy. It provides an adequate number of observations for SPSS as well as a reasonable of extreme predicted observations.
4.4.2 Modeling and simulation
The modeling shows the type of model used in prediction and rainfall equation modeling in the simulation of input data and analysis it in output results. The type of equation used is detected using regression system for showing model equation, after this equation, we make another simulation to select a type of model used related the results observed.
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Model coefficients
Table 4. 1: regression coefficients
Model
Unstandardized Coefficients
α and β1, 2
Std. Error
1 (Constant)
Humidity Temperature
-195.563 3.384 1.363
74.542 0.285 3.071
So these coefficients show that modeling equation is:
푹=휶+휷ퟏ푯+휷ퟐ푻+Є …………………………… (4.1)
푹=−ퟏퟗퟓ.ퟓퟔퟑ+ퟑ.ퟑퟖퟒ푯+ퟏ.ퟑퟔퟑ푻+ퟕퟒ.ퟓퟒퟐ+ퟎ.ퟐퟖퟓ푯+ퟑ.ퟎퟕퟏ푻 Є=ퟕퟒ.ퟓퟒퟐ+ퟎ.ퟐퟖퟓ푯+ퟑ.ퟎퟕퟏ푻 푹=−ퟏퟐퟏ.ퟎퟐퟏ+ퟑ.ퟔퟔퟗ푯+ퟒ.ퟒퟑퟒ푻
Where, R= rainfall forecast, H= relative humidity, T= temperature and Є= standard error. The selection of rainfall model type, we must simulate time plot stationary and calibrating the model available after transformation of different models related to the characteristics of results showed. In our software, it has three different models for each has there characteristics related to the results of previous models. Those model different models are:
1) ARIMA (Autoregressive Integrated Moving Average Model);
2) Exponential smoothing model;
3) Autoregression model; and
4) Seasonal decomposition model.
54. 39
Example of time plot of rainfall model-3
Transforms: dif ference (1)
Date
OCT 2014
JUL 2012
APR 2010
JAN 2008
OCT 2005
JUL 2003
APR 2001
JAN 1999
OCT 1996
JUL 1994
APR 1992
JAN 1990
OCT 1987
JUL 1985
APR 1983
JAN 1981
OCT 1978
JUL 1976
APR 1974
RAINFALL
300
200
100
0
-100
-200
-300
-400
Figure 4. 2: rainfall time plot model
For this type of plot we can use ARIMA Model for suitability of analyzing the results
represented by model_3 above.
Example of ARIMA model plot
i. Model description
This model represents variable (rainfall), non seasonal differencing (1), seasonal differencing
(1), and the length of seasonal cycle (12).
ii. Model parameters
This model represents different parameters from original value estimation.
AR1: Autoregressive;
MA1: Moving Average;
SMA1: Seasonal Moving Average; and
Constant
Our model has ninety five percent (95%) of confidence intervals should be generated.
55. 40
iii. Model termination criteria
This model represents termination criteria such as:
Parameter epsilon of 0.001;
Maximum Marquardt constant of 1.00E+09;
Maximum number of iterations of 10.
iv. Time plot of model_6
This plot illustrates the previous rainfall and forecast rainfall in the same plot.
Transforms: dif ference (1)
Date
OCT 2014
JUL 2012
APR 2010
JAN 2008
OCT 2005
JUL 2003
APR 2001
JAN 1999
OCT 1996
JUL 1994
APR 1992
JAN 1990
OCT 1987
JUL 1985
APR 1983
JAN 1981
OCT 1978
JUL 1976
APR 1974
300
200
100
0
-100
-200
-300
-400
RAINFALL
Fit for RAINFALL f ro
m ARIMA, MOD_13 CON
Figure 4. 3: Forecasting model
4.4.3 Level of acceptance of the model
This research, the performance of the model is measured by difference between and predicted
values of dependent variables (rainfall) or the errors. Average error is the absolute value of the
actual values minus the predicted values divided by the number of patterns. Correlation is
measure of how the actual and predicted correlate to each other in terms of direction (i.e.,
when the actual value increases, does the predicted value increase and vice).
56. 41
4.4.4 Importance of the model
Computer modeling helps in taking decisions for implementation of various projects. A model is decision support tool
It is important in predicting for future in some areas.
It is of great importance in different fields of science and engineering to develop different application and procedures for management of systems.
Modeling assists in taking measures for protection for agriculture crops
It is important in understanding the functioning of complex scientific or engineering projects.
Computer models reduce chances of failure for scientific or engineering projects. A good model was reflecting all the probable failures or successes of the project in question.
4.5 CROP WATER REQUIREMENT FOR DIFFERENT CROPS
An irrigation requirement characteristic shows in the table below for small vegetations sowing on fifteen April 2014 and harvest at eighteen July 2014.
Table 4. 2: Irrigation water requirement
Month
Decade
Stage
Kc
Etc
Etc
Eff. Rain
Irr. Req.
Coeff.
mm/day
mm/dec
mm/dec
mm/dec
April
2
Initiation
0.70
2.47
14.8
25.2
0.0
April
3
Initiation
0.70
2.46
24.6
38.0
0.0
May
1
Development
0.72
2.55
25.5
34.0
0.0
May
2
Development
0.83
2.92
29.2
30.9
0.0
May
3
Development
0.95
3.46
38.0
24.3
13.7
June
1
Mid
1.04
3.90
39.0
16.0
23.1
June
2
Mid
1.04
4.04
40.4
9.0
31.3
June
3
Mid
1.04
4.17
41.7
8.8
32.9
July
1
Late
1.03
4.23
42.3
8.4
33.8
July
2
Late
0.97
4.13
33.0
5.6
26.0
Total
328.6
200.2
160.8
57. 42
Where Kc: crop coefficient (dimensionless), ETc: crop evapotranspiration (mm/day), Eff. Rain: effective rain (mm/decade) and Irr. Req.: irrigation requirement for crops. Etc= Kc x ETo ………………… (4.3) Where ETo= reference Crop evapotranspiration (mm/decade). Note: seasonal crop coefficient (Kc) = (Kc initial season + Kc mid season + Kc end season)/3.
4.6 RAINFAL PREDICTION
4.6.1 Measurement of the accuracy
We have selected ARIMA model after checking. Now we proceed to compare their accuracy performance using the various accuracy measures. For this purpose we used observations from September 2012 to December 2013 of monthly data for calculation of forecasting error using following equation: Error = rainfall – rainfall forecast …………… (4.4)
58. 43
Table 4. 3: Error measurement
DATE
HUMIDITY
TEMPERATURE
RAINFALL
RAINFALL FORECAST
ERROR
Sep-12
75.6
22.1
61.3
81.6
-20.3
Oct-12
79.5
22.3
97.9
115.8
-17.9
Nov-12
80.8
21.2
170.6
120.3
50.3
Dec-12
79
21.7
74.3
98.3
-24
Jan-13
79.5
22.8
63.2
83.6
-20.4
Feb-13
77.4
22.2
72.4
104.3
-31.9
Mar-13
86.9
22.5
324.3
116.8
207.5
Apr-13
86.1
22.4
141.7
201.1
-59.4
May-13
81.7
21.5
35.4
120.8
-85.4
Jun-13
62.8
21.4
0
27.9
-27.9
Jul-13
52.3
22.2
0
16.3
-16.3
Aug-13
58.2
23.8
6.7
34.7
-28
Sep-13
75.6
21.7
77.4
66.9
10.5
Oct-13
79.5
23
96.2
110.9
-14.7
Nov-13
80.8
20.8
217.4
118.2
99.2
Dec-13
79
21.8
89.2
104.5
-15.3
AVERAGE
75.91875
22.0875
95.5
88.3
7.2
To measure the forecasting ability of the ARIMA model, we have estimated within sample and out of sample forecasts. If the magnitude of the difference between the forecasted and actual values is low, then the model has good forecasting performances. In this case, the seasonal ARIMA (1; 1; 1) X (0; 1; 1) model has shown better results which is evident from table 4.4. Now the final model for forecasting of historical monthly rainfall series of Kigali AERO station is as given below. The ARIMA model (1; 1; 1) x (0; 1; 1) can be written as:
푹풂풊풏풇풂풍풍=−ퟏퟗퟓ.ퟔ+ퟑ.ퟒ푯풖풎풊풅풊풕풚+ퟏ.ퟒ푻풆풎풑풆풓풂풕풖풓풆+푹풂풏풅풐풎 풆풓풓풐풓 Or 푹=휶+휷ퟏ 푯+휷ퟐ 푻+Є . ………………… (4.5)
Є=흁+∅ퟏ 푯+∅ퟐ 푻 ………… (4.6)
59. 44
Rainfall predicted table from 2014 to 2015 in the table below:
Table 4. 4: Rainfall forecasting result for two years
DATE
RAINFALL FORECAST (mm)
UCL (mm)
LCL (mm)
January 2014
88.3
181.9
0.0
February 2014
112.3
208.8
15.8
MARCH 2014
144.8
242.9
46.8
APRIL 2014
163.0
262.5
63.5
MAY 2014
107.9
208.9
7.0
JUNE 2014
35.8
138.2
0.0
JULY 2014
25.8
129.6
0.0
AUGUST 2014
45.0
150.3
0.0
September 2014
84.8
191.4
0.0
October 2014
123.1
231.1
15.0
November 2014
140.8
250.3
31.3
DECEMBER 2014
96.4
207.3
0.0
TOTAL
1168
2403.2
179.4
JANUARY 2015
90.9
204.2
0.0
February 2015
114.5
229.4
0.0
MARCH 2015
147.0
263.4
30.6
APRIL 2015
165.1
283.0
47.2
MAY 2015
110.1
229.5
0.0
JUNE 2015
38.0
158.9
0.0
JULY 2015
28.0
150.4
0.0
AUGUST 2015
47.2
171.1
0.0
September 2015
87.0
212.3
0.0
October 2015
125.3
252.1
0.0
November 2015
143.0
271.3
14.7
DECEMBER 2015
98.6
228.4
0.0
TOTAL
1194.7
2654
92.5
60. 45
4.6.2 Rainfall pattern for agriculture of Rwampara swamp
Rwampara swamp is characterized by four patterns in that they have three agriculture seasons. Those four seasons are short wet season, short dry season, long wet season, and long dry season.
Short wet season (winter) starting from September to November;
Short dry season (spring) starting from December to January;
Long wet season (autumn) starting from February to May; and
Long dry season (summer) starting from June to August.
The three agriculture seasons are:
Season A starting from October and end in January;
Season B starting from February and end in May; and
Season C starting from June and end in September.
In season A, they are cultivating maize, peppers, beets and cucumber; in season B, they are cultivating beans, soybeans, eggplants, and cabbages; then in season C they are cultivating tomatoes, carrots, lettuces, scallions, small vegetations and onions.
4.7 PLANTING CROPS AND SOWING DATE
4.7.1 Planting crops
The prediction of crop species depends on the time at which prediction is required. If for example, a prediction of national yield is required shortly before harvest time, then the agricultural statistics for the current year data may be available, and the approaches described above are applicable. One possible approach in this case is simply to assume that at a regional scale the change in land use from one year to another is negligible. Such an assumption would be reasonable for a region where single crop farming dominates and no major changes in economic or regulatory factors have occurred.
61. 46
A second possibility is to use declared intentions of farmers, where such information is available. The Rwandan agricultural ministry (MINAGRI) policies involves asking farmers to declare which crops they intend to cultivate in each field, for example: eastern province are cultivating maize, soybeans, beans etc. A minor problem here is that climatic conditions may lead to some changes in plan, for example: Bugesera district. A major difficulty is obtaining this information, which is protected by privacy laws. The information is made available in form of computer database, but this only concerns data aggregated by district and furthermore there is considerable delay before this is done.
4.7.2 Sowing date
For past data one could simply seek to obtain the sowing date for each field, but this can be very difficult for large numbers of fields. Even if one is willing to address direct inquiries to each farmer many may not respond. Information that is generally available is a recommended sowing period for each crop, each variety and each region. One also has in general climate information and statistical information about farm structure and land use.
a. Predicting sowing date
Sowing dates could be based on the recommendations that exist for each variety in each region, but within the possible sowing period the actual sowing date will depend on available manpower, the state of the soil and climate conditions. This suggests two possibility approaches, either using a fixed average sowing date or calculating a sowing date for each field based on information about farm cooperatives and climate. An example of calculation of sowing date is the SIMSEN model of sowing date proposed by Leenhardt and Lemaire (2002).
Determining possible sowing days using a soil water model: The water balance model is run at daily time step over the months of the sowing period to determine, for each soil type, which days are possible sowing days. To determine if sowing is possible, a decision rule based on soil water status and precipitation is used. The rule is :”If the soil water content (SWC) is below x% of soil available water capacity (SAWC) , and if it does not rain more than y mm this day, then the sowing can occur” Threshold values x and y were obtained, for the study of RWAMPARA swamp, after analysis of the past sowing dates.
62. 47
Determining the time required to sow crop: the other step of SIMSEM procedure is primarily based on the information given by the farm typology (a classification according to general type, especially in archaeology, psychology, or the social sciences): the type and area of various crop soil associations for each farm type, the kind and size of its livestock, and the amount of manpower available. However, complementary information (and very specific to the region considered) was provided by experts from local technical institutes: the earliest possible date for sowing the various summer crops, winter crops, autumn crops, and spring crops; the priority between crops for sowing, the time necessary to sow for various soil types, and estimations of daily working time.
b. Determination of available season and crops
Table 4. 5: Sowing date program and types of crops
year
Season
Rainfall (mm)
Sowing date prediction
Crops available per season
2014
B
420.8
February
Beans ,soybeans, eggplants, cabbages
C
156.6
June
Tomatoes, carrots, lettuces, scallions, small vegetations
A
381.7
October
Peppers, beets, cucumber, maize
2015
B
462.3
February
Beans ,soybeans, eggplants, cabbages
C
167.8
June
Tomatoes, carrots, lettuces, scallions
A
397.2
October
Peppers, beets, cucumber, maize
63. 48
CHAPTER V: CONCLUSION AND RECOMMENDATION
5.1 CONCLUSION
After the completion of this research project entailed “USING METEO DATA FOR RAINFALL PREDICTION IN RWANDA, CASE STUDY “RWAMPARA SWAMP” located in between NYARUGENGE and KICUKIRO districts, it was found that average rain water is 1181.4mm/year, the evapotranspiration of the small vegetations were 328.6mm/decade, effective rainfall was 200.2mm/decade and irrigation requirement of 160.8mm/decade for the year 2014. In this project the use of SPSS software Box-Jenkins methodology has been shown historical rainfall data. The estimation and diagnostic analysis results revealed that models‟ are adequately fitted to the historical data. In particular, the residual analysis which is important for diagnostic checking confirmed that there is no violation of assumptions in relation to model adequacy. Further comparison based on the forecasting accuracy of the models is performed with the holdout some rainfall values. The point forecast results showed a very closer match with the pattern of the actual data and better forecasting accuracy in validation period. The quality of data is also a major issue for creating rainfall forecasting model .The ARIMA or SARIMA modeling required the data be cleaned of erroneous or missing elements. To do this, every time there was a “no data available” report from any reporting station (METEO RWANDA). For this project, similarly cleaned data was used to be able to predict rainfall for the future time of two years, in order reduce the expenses of money during irrigation. Although the SPSS trained in this study can only be applied to the RWAMPARA swamp, the guidelines in the selection of the data, training criteria, and the evaluation of SPSS reliability are based on statistical rules. Therefore, they are independent of the application. These guidelines can be used in any application.