Project Abstract

<p>&nbsp;                   <b>ABSTRACT</b>&nbsp;</p><p>Estimation of rainfall for a desired return period is one of the pre-requisites for any design purpose at a particular site, which can be achieved by probabilistic approach. This study is aimed at using the statistical parameters of long-term observed rainfall data to generate monthly rainfall depth and estimate rainfall values at different return periods for hydrological and agricultural planning purposes. The daily rainfall data of 60 years from 1953 to 2012 were collected from the meteorological station situated at the Institute for Agricultural Research (IAR), Ahmadu Bello University (ABU) Zaria. In this work, the expected number of observations was 720.However, a sample size of 431 being the months with rainfall amounts was used for the study. Three probability distributions functions viz normal, log normal and exponential distribution were used for rainfall generation. The data was processed to estimate the statistical parameters of the distributions for Probability Density Functions (PDFs) selected. The two types of parameters estimated using the methods of maximum likelihood via the statistical package called EasyFitXL5.6 are the scale and location parameters. The parameters‟ estimates were further used in various PDFs equations to generate new sets of rainfall amounts. Five statistical goodness of fit test were used in order to select the best fit probability distribution and are; Anderson-Darling, Chi-square and Kolmogorov-Smirnov, ANOVA and Student t test. From the analysis of the data, it was discovered that the normal PDF predicts correctly the monthly amount of rainfall and specific return periods rainfall values followed by the log-normal and exponential PDF, the least predictor. After carefully observing and testing the three PDFs, it was discovered that the normal PDF estimated closely the monthly amounts of rainfall compared with the lognormal and exponential PDFs when considered generally for monthly and return periods rainfall values estimation. Nevertheless, the normal PDFs have been found to predict well the daily rainfall amounts in Samaru. This work should be made available to local and immediate environments to Samaru through local and regional experts on climatic information dissemination for use in planning and management of specific rainfed crop(s) production. <br></p>

Project Overview

<p> Background of the Study&nbsp;</p><p>Synthetically generated daily or monthly rainfall data are frequently necessary in data scarce environments as input into the planning and design of water resources and soil conservation projects; simulation studies of crop growth and yield; farming systems and field farm operations scheduling ( Jamaludin and Jemain 2007). Rainfall is the principal phenomenon driving many hydrological extremes such as floods, droughts, landslides, debris and mud-flows; its analysis and modelling are typical problems in applied hydrometeorology. Rainfall exhibits a strong variability in time and space. Hence, its stochastic modeling is not an easy task (De Michele and Bernardara, 2005). A good understanding of the pattern and distribution of rainfall is important for water resource management of an area. Knowledge of rainfall characteristics, its temporal and spatial distribution play a major role in the design and operation of agricultural systems, telecommunications, runoff control, erosion control, as well as water quality systems. Generated weather is needed to supplement existing weather data, provide alternative weather realizations for a particular historical record, or identify possible weather sequences for a seasonal climate forecast (Walpole and Mayers, 1989). <br></p><p> The amount and pattern of rainfall are among the most important weather characteristics which affect agriculture profoundly. In addition to their direct effects on water balance in soil, they are strongly related to other weather variables such as solar radiation, temperature, humidity and evaporation which are also important factors affecting the growth and development of crops, pests, diseases and weeds. However, rainfall data form an essential input into many climatologic studies for agriculture, wherein considerable research is focused on rainfall analysis and modelling (Nnaji, 2001). This work is concerned with the modeling of the distribution of monthly rainfall amounts in Samaru, Zaria Kaduna state. Evaluating a range of scenarios that accurately reflect precipitation variability is critical in hydrological and water resource applications. Inputs to these applications can be provided using location- and interval-specific probability distributions. These distributions make it possible to estimate the likelihood of rainfall within a specified range. In order to improve the ability of African decision makers to prepare for and deal with the consequences of precipitation anomalies, it is important to provide them with a more complete understanding of the range and likelihood of rainfall totals a location could possibly receive. Models of rainfall probability distributions over various timescales are useful tools for gaining this kind of understanding. Modelling rainfall variability in Africa presents an imposing problem for many reasons, including the need to summarize rainfall data for many years at many sites and the difficulty in finding a single method to represent such a variety of rainfall regimes.&nbsp;</p><p>Rainfall regimes across the vast African continent differ widely in terms of total accumulations, seasonal timing, and amounts of variability. Any method that is applicable across this wide range of conditions has to be quite flexible. The typical approach to gaining a better understanding of the spatial and temporal variability in precipitation starts with the acquisition of historical rainfall data. These historical data provide necessary information about accumulation amounts in both time and space for the region and form the basis for fitting and testing distribution models. When historical data is unavailable in a region, or available data is inaccurate or incomplete in a spatial or temporal sense, geophysical models can be used to „fill in‟ the missing values. These geophysical models are based on available data at other locations and times, as well as additional variables that add information to the model. Assuming the historical values, recorded or modelled – exist and are accepted as reasonably accurate, it is possible to fit parametric statistical distribution models to rainfall histories at individual locations of interest. Using a parametric distribution model allows for a more stable and extensive analysis of the rainfall probabilities than would be available using the raw data directly (Saleh,2011).The resulting distributions describe the estimated probability of different amounts of rain at a location for a select time interval such as annual, seasonal or monthly based on the historical values for that interval at that location. There are many probability distributions that could be successfully utilized to parameterize rainfall distributions. The critical component for these distributions is that they be flexible enough to represent a variety of rainfall regimes.&nbsp;</p><p>From a practical point of view, there is little difference between many of the commonly used distributions when estimating parameters based on a limited number of points, as is the case in much of the developing world. Out of these available distributions, the normal, log-normal and exponential distributions are more widely understood, making them a good choice for implementation of this work ( Rafa and Khanbilvardi,2014 ).Once the parameters of the distribution have been estimated, they can be used to describe rainfall regimes and be used in a variety of applications.&nbsp;</p><p>&nbsp; The distribution parameters might complement or even replace such common measures as the median, variance, minimum, maximum and quartile values as descriptors of the rainfall at any location. For locating potential hazard hotspots, distribution parameters may be used to identify areas with a disposition towards certain precipitation related hazards such as drought, flood, outbreak of disease, or reliability in providing adequate water for rain-fed agriculture. Monitoring of rainfall conditions may use distribution parameters as the foundation for the standardized precipitation index. <br></p><p> Combining distributions with probabilistic forecasts may result in a quantitative estimation of seasonal rainfall accumulations. More specifically, probability distribution parameters are estimated from monthly model derived historical rainfall values with a spatial resolution compatible with current agro-climatic models. One of the ways by which the hydrological parameters may be analyzed is through the use of different probability distribution models to estimate rainfall amounts for data-scarce areas/environments for agricultural planning. <br></p><p> 1.2&nbsp;<b>STATEMENT OF PROBLEMS</b></p><p><b></b>Lack of extensive rainfall data for effective agricultural planning and hydrological structures construction. It is a known fact in Nigeria that most local areas or environments are without recorded rainfall data or are data-scarce areas. Proper recorded rainfalls are often scarce to come by in those areas. Therefore in such areas, it is imperative to estimate rainfall data for agricultural planning purposes such as crop water requirement determination, irrigation requirement of crops, effective rainfall determination and other hydrological analyses. <br></p><p> Lack of quantitative forecast over small spatial scale or areas by the agricultural and forestry sector for effective planning of field operations, management of different agronomic and horticultural practices, irrigation and pest and disease management practices to avoid the weather risks during the crop growing season. With increasing demand for more food by millions of Nigerians, it has become highly necessary that all relevant hydrological parameters that directly affect food crop and animal production, water harvesting system, rate of ground water recharge and yields of crops should be collected and analyzed in such a way to make agricultural planning easier. <br></p><p> Variability in extreme events‟ dynamics for hydrological structures construction. In most cases, the return periods of interest exceed usually the periods of available records and could not be extracted directly from the recorded data. The design and construction of certain projects, such as dams and urban drainage systems, the management of water resources, and the prevention of flood damage require an adequate knowledge of extreme events of high return periods. <br></p><p> 1.3. <b>JUSTIFICATION OF THE STUDY&nbsp;</b></p><p>The result of this study should provide and Identify the appropriate model/s for monthly rainfall amount for Samaru and its environ as almost all of the climate variables are dependent on rainfall events (Deni and Jemain, 2009). Improved accuracy in seasonal rainfall calculation will help farmers to plan well and take effective approach in their agricultural activities. Also, it will facilitate the government and other relevant stakeholders in planning and effectively implementing adaptation strategies in different sectors, particularly agriculture, water and energy to lessen the impacts of drought. <br></p><p> The study should be a tool and useful guides for the designers, planners and decision maker‟s to prepare for and deal with the consequences of precipitation anomalies since models of rainfall probability distributions over various timescales can provide useful information. Agricultural and hydrological parameters have been observed to affect the rate of runoff generation, soil erosion, and the amount of water infiltration for crop production, ground water recharge and the yield of crop during the growing season (Edoga, 2007). <br></p><p> The study should give information on certain aspects of rainfall such as, the expected amount for a particular period which are valuable to agriculturists. Recent advancement in statistical methods particularly in the application of generalized linear models has much improved the range of techniques available for analysis of data that are not normally distributed. <br></p><p> 1.4. <b>OBJECTIVES OF THE STUDY</b><b></b></p><p>The general objective of this study is to generate rainfall amounts based on the statistical parameters obtained from long time recorded rainfall data for Samaru and its environs (within a distance of 50 km radius away from Samaru) for agricultural planning purposes. The specific objectives include: <br></p><p> 1. To determine the statistical parameters of mean and standard deviation of the observed rainfall data.&nbsp;</p><p>2. To calculate monthly rainfall amounts using three selected PDFs and compare with the observed values.&nbsp;</p><p>3. To calculate monthly rainfall amounts for selected return periods and compare with those determined from the observed data. <br></p>