To optimize agricultural productivity in the region, there was need to quantify rainfall variability at a local and seasonal level as a first step of combating extreme effects of persistent dry-spells/droughts and crop failure. Crop production in the Fanteakwa District is predominantly rainfed, exposing this major livelihood activity to the variability or change in rainfall pattern. J. Mzezewa, T. Misi, and L. Ransburg, “Characterization of Rainfall at a Semi-arid Ecotope in the Limpopo Province (South Africa) and its Implications for Sustainable Crop Production,” 2013. This calls for use of data reconstruction through interpolation. There was notable high interseasonal variability and temporal anomalies in rainfall between 2001 and 2013. On average, the total amount of rainfall received in all stations was below 900 mm (subhumid stations) and 1400 mm (humid) per annum. To demonstrate this new trend, a detailed regional analysis of rainfall evolution is conducted. It is thus essential to match the crop phenology with dry-spell lengths based days after sowing to meet the crop water demands during the sensitive stages of crop growth. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. The choice of rainfall stations used depended on availability of the station, the agroecological zones, and the percentage of missing data (less than 10% for a given year as required by the world meteorological organization (WMO). rainfall variability and a more uniform rainfall distribution than other regions. Generally, high variability (often attributed to La Nina, El Nino, and Sea Surface Temperatures) could occasion rainfall failures leading to declines in total seasonal rainfall in the study area. [7] noted that most studies do not provide information on the much-needed character of within-season variability despite its critical influence on soil-water distribution and productivity. Rainfall is the most important natural factor that determines the agricultural production in Bangladesh. Results showed neither station nor season with persistent near average (RAI = 0) rainfall especially from stations in the subhumid region. An intrastation seasonal comparison showed that SRs in Embu were less variable but more drier compared to LR seasons. A dry-spell was considered as sequence of dry days bracketed by wet days on both sides [18]. Climate variability is the term to describe variations in the mean state and other characteristics of climate (such as chances or possibility of extreme weather, etc.) This significantly affects the cropping calendar in rain-fed agricultural productivity of the region. 1 and . Several ways of quantifying the variability of rainfall are discussed in Note 10.L. However, Mbeere subcounty continues to experience population pressure occasioned by the influx of immigrants from the overpopulated high potential areas. On the other hand, Kriging is a geostatistical method, which is based on statistical models that include autocorrelation, which underpins the statistical relationships among the measured and predicted data points [32]. It is against this background this study focuses on examining selected climate variables and their impacts on maize yields. Before frequency analysis of the rainfall data is done, various transformations are essential for the data to follow particular probability distribution patters. Often, prolonged dry-spells are accompanied by poor distribution and low soil moisture for the plant growth during the growing season. Conversely, Embu received more rainfall during LRs with April accounting for about 52% of total rainfall received. 2,* 1. The efficacy of interpolation techniques was assessed using mean absolute errors (MAE) (9) and root mean square errors (RMSE) (10) statistics plus validation using gauged rainfall data:where and are the predicted and observed or measured rainfall values. Embu represent a densely populated high potential humid area with Humic Nitosols soils and generally annual rainfall above 800 mm. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Richard and Poccard 1998; Landman et al. [21]. Conversely, the incumbent study showed that the decade between 2000 and 2013 experienced marked increases in SRs and a decrease in LRs. Low values of and would indicate that data was homogeneous:where is maximum (max) of and in the range of and Min is Minimum. 1. define and distinguish between climate variability and climate change, 2. understand the enhanced greenhouse effect and its consequences on climate, 3. understand climate change scenarios for Bangladesh, and 4. analyse climate change uncertainties in drought-prone areas. Predictions in Machang’a recorded high values of best-fit compared to Kiambere which could be attributed to high missing data in the raw rainfall dailies in the latter station (Figure 8). These account for close to 90% of total rainfall received annually; implying that smaller proportions of rainy days supplied much of the total amounts of rainfall received in the region. These areas represent Kenya’s central highlands and those of East Africa, predominant of smallholder rain-fed, nonmechanized agriculture and diminutive use of external inputs. The consecutive dry days were prepared from historical data. Climate is defined as long-term averages and variations in weather measured over a period of several decades. (2003). 2015, Article ID 380404, 16 pages, 2015., 1Department of Environmental Science, Kenyatta University, P.O. K. F. Ngetich, M. Mucheru-Muna, J. N. Mugwe, C. A. Shisanya, J. Diels, and D. N. Mugendi, “Length of growing season, rainfall temporal distribution, onset and cessation dates in the Kenyan highlands,”, M. R. Jury, “Economic impacts of climate variability in South Africa and development of resource prediction models,”, A. N. Micheni, F. M. Kihanda, G. P. Warren, and M. E. Probert, “Testing the APSIM model with experiment data from the long term manure experiment at Machang’a (Embu), Kenya,” in, S. K. Kimani, S. M. Nandwa, D. N. Mugendi et al., “Principles of integrated soil fertility management,” in, G. A. Meehl, T. F. Stocker, W. D. Collins et al., “Global climate projections,” in, C. Recha, G. Makokha, P. S. Traoré, C. Shisanya, and A. Sako, “Determination of seasonal rainfall variability, onset and cessation in semi-arid Tharaka district, Kenya,”, E. M. Mugalavai, E. C. Kipkorir, D. Raes, and M. S. Rao, “Analysis of rainfall onset, cessation and length of growing season for western Kenya,”, M. V. K. Sivakumar, “Empirical-analysis of dry spells for agricultural applications in SSA Africa,”, Y. Seleshi and U. Zanke, “Recent changes in rainfall and rainy days in Ethiopia,”, K. Tilahun, “Analysis of rainfall climate and evapo-transpiration in arid and semi-arid regions of Ethiopia using data over the last half a century,”, J. Barron, J. Rockstrom, F. Gichuki, and N. Hatibu, “Dry spell analysis and maize yields for two semi-arid locations in east Africa,”, P. B. I. Akponikpè, K. Michels, and C. L. Bielders, “Integrated nutrient management of pearl millet in the sahel using combined application of cattle manure, crop residues and mineral fertilizer,”. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. This subject area in meteorology/climatology is called "rainfall variability." Conversely, probabilities of monthly rainfall during cropping seasons exceeding cropping threshold were equally low, for example, 5% probability to exceed 419 mm in April and 331 mm in November (Table 4(b)). Mbeere region appeared to have experienced pronounced declines in rainfall amounts especially those received during LRs. Climate Change and Variability . The term "climate" is a term that is used to describe the average mix of meteorological conditions in a geographical location over the long term. Run-off collection and general confinement of rain-water within the crop’s rooting zone could enhance rain-water use efficiency as demonstrated by Botha et al. According to Shisanya [25], La Nina events significantly contributed to the occurrence of persistent droughts and unpredictable weather patterns during LRs in Kenya. The main objective of this paper is to assess the rainfall variability and to identify the relationship between paddy production and rainfall, by means of statistical analysis. Regionally, findings of Seleshi and Zanke [10] further showed that annual and seasonal rainfall (Kiremt and Belg seasons) in Ethiopia were highly variable with CV values ranging between 0.10 and 0.50. Adefolalu (1986) studied the rainfall trends for periods of 1911–1980 over 28 meteorological stations in Nigeria with 40 years moving average showing appearance of declining rainfall. Based on these findings, it is apparent that farmers in the lower eastern Mbeere region are encouraged to intensify cropping during SRs as compared to LRs. It has an annual mean temperature ranging from 17.4 to 24.5°C and average annual rainfall of  700 to 900 mm. were adequately predicted in Kriging and IDW when compared to Spline prediction (Figure 7). This replicates high chances that soil moisture could be lost by evaporation bearing in mind the high chances (81%) that the same dry-spells exceeding 15 days could reoccur during the cropping season. Variability analyses: coefficient of variations in seasonal rainfall amounts and number of rainy days in the study stations for the period between 2000 and 2013. Many studies on rainfall variability had been used data at relatively in all resolutions, either global climate models (GCMs; e.g. The amount of rainfall received during LRs and SRs varied significantly in Embu but not in Machang’a. Noticeably, Embu appeared to be receiving more near average rainfall during SRs (2002, 2003, 2007, and 2011) contrary to the trends observed in Mbeere region (especially in Kindaruma and Kiambere) (Figure 4). Example: Annual average precipitation, Northern California, The variation of rainfall amounts at a given location across a time interval. Assorted arguments regarding the varied performances of the different interpolation techniques could explain the results of this study. For instance, rainfalls in Victoria during the period 1913-76 tended to be about 10 per cent less than average each 2.1 years or so. [7]). The and are the respective means of these values and is the number of observations. In addition, it was evident that the amount of rainfall and number of rainy days received in the past decade in most stations were more consistent (temporally) in April and November but highly unpredictable in March (onset) and December (cessation). Many translated example sentences containing "high rainfall variability" – French-English dictionary and search engine for French translations. [27] observed that SRs constituted the main growing season in the drier parts of SSA and Great Horne of Africa for crops such as maize, sorghum, green grams, and finger millet. The method for frequency analysis of dry-spells was adapted from Belachew [19] as follows: in the years of records, the number of times that a dry-spell of duration days occurs was counted on a monthly basis. Probabilities that the region would experience dry-spells exceeding 15 days during a cropping season were equally high, for example, 46% in Embu and 87% in Machang’a. In most arid and semiarid regions, soil moisture availability is primarily dictated by the extent and persistency of dry-spells. Daily rainfall data were sourced from both the Kenya Meteorology Department and research sites with primary recording stations within the study area. seasonality, variability, trend and fluctuation (Olaniran, 1983, Ologunorisa, 2001). In Embu, the highest positive anomalies (+5.0) were recorded in 2002, 2005, and 2007 during LRs (Figure 4). 2. The stepwise methodology is summarized in Figure 2. In general, a map of relative variability (Figure 10.9) is the inverse of a map of annual rainfall (Figure 10.3), being higher in drier regions. A larger range for the monthly spatial variation was observed in the west coast region. The inherently fertile Nitosols are the reasons for high-potential productivity while lower and erratic rainfall, less fertile, shallow, and sandy Ferralsols, and high drought frequency explain predominant crop failures [14]. Comparison between recorded and ArCGIS Kriging predicted average decadal rainfall amount across study stations: error bars denote standard deviation of observed means, Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya, Department of Environmental Science, Kenyatta University, P.O. Results showed that there was at least 90% chance of rainfall exceeding 141.5 mm (lowest) and 258.1 mm (highest) during LRs in Kindaruma and Embu, respectively, within a return period of about 1 year (Table 4). Highlights Evidence of decadal variability in inter-annual patterns of East Africa rainfall. Figure 8 shows the scatter plots of recorded versus predicted (interpolated) decadal average rainfall across the study stations based on Kriging interpolation technique. Studies by Sivakumar [9], Seleshi and Zanke [10], and Tilahun [11] noted high variations in annual and seasonal rainfall totals and rainy days in Ethiopia and Sudano-Sahelian regions. Evidently, probabilities that seasonal rainfall amounts would exceed the threshold for cropping (500–800 mm) were quite low (10%) in all stations. This variability ranges over many time and space scales such as localized thunderstorms and tornadoes, to larger-scale storms, to droughts, to multi-year, multi-decade and even multi-century time scales. By using our services, you agree to our use of cookies. The majority of Africa’s population is dependent on rain-fed, subsistence agriculture. Would the following charts be used to illustrate areal or temporal variability of precipitation? These decisions can be optimized if the probability of dry-spells is computed after successful (effective) planting dates. A study by Mzezewa et al. Homogeneous seasonal rainfall totals for both seasons were then subjected to trend and variability analyses based on rainfall anomaly index (RAI) as described in [11]. One feature of rainfall variability is the evidence from many places, including south-east Australia, that years tend to be alternately wet and dry, especially in the tropics. The fluctuations comprising climate variability can influence patterns of … The Kolmogorov-Smirnov (K-S value) Test values, -Square for the seasonal rainfall, and the values of the average rainfall means for rainfall months are summarized in Tables 3(a) and 3(b). In cases of high data gaps (unrecorded or missing), multiple imputations were utilized to fill in missing daily data through creation of several copies of datasets with different possible estimates. Results showed that available rainfall data series from study station are homogenous implying that the time series were a record of one population. Both the inverse distance weighted (IDW) and Spline methods are deterministic methods since their predictions are directly based on the surrounding measured values or on specified mathematical formulas [31]. Box 43844-00100, Nairobi, Kenya, Embu University College, P.O. It has two components viz. Knowledge of lengths of dry-spells and the probability of their occurrence can also aid in planning for supplementary risk aversion strategies through prediction of high water demand spells. As a major concern to food production in Ghana, this study seeks to show the relationship between the production of major crops and rainfall distribution pattern in the Worobong Agroecological Area (WAA) relative to foo… Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. The method used to determine the modes of this variability and the trends of rainfall is the chronological graphic method of information processing (MGCTI) of the “Bertin Matrix” and continuous wavelets transform (CWT). Rainfall variability & change. The degree to which rainfall amounts vary across an area or over time is called 'rainfall variability'. Some climate models indicate that rainfall variability is likely to increase, pointing to more frequent and intense droughts. Like in most other places, the rainfall data within in the drier parts of Embu county and the neighbouring stations are scarce with missing data making their utilization quite intricate. P: predicted precipitation; O: Observed precipitation; SD: standard deviation; MAE: mean absolute error; RMSE: root mean square error; IDW: inverse weighted mean. Variability in the number of rainy days (CV-RD) for each seasonal month was equally high in the two study stations. Rainfall Variability . Kolmogorov-Smirnov values (one-sided sample K-S test) showed K-S values (0.15 to 0.23) consistently lower than the K-S table value (0.302) for at probability indicating that an exponential, continuous distribution of the studied datasets was statistically acceptable, based on the empirical cumulative distribution function (ECDF) derived from the largest vertical difference between the extracted (observed K-S value) and the table value [20–22]. Reliable rainfall is rain providing water when and where expected. during both SR and LR seasons. Scientists determine the climate of a geographic location by compiling statistics over an extended time … For instance, probabilities of having dry-spells exceeding 15 days are relatively high (63%, 80%, 91%, 93%, and 57% for Machang’a, Kiritiri, Kiambere, Kindaruma, and Embu, resp.) To aid in understanding spatiotemporal occurrence and patterns agro-climatic variables (e.g., rainfall) and accurate and inexpensive quantitative approaches such as GIS modelling and availabil-ity of long-term data are essential. The variation of rainfall amounts at various locations across a region for a specific time interval. The Weibull method was used to estimate probabilities while the maximum likelihood method (MOM) was utilized as a parameter estimation statistic. [21] established that seasonal rainfall amount greater than 450 mm is indicative of a successful growing season and described it as a threshold rainfall amount. Box 43844-00100, Nairobi, Kenya, 2Department of Agricultural Resource Management, Kenyatta University, P.O. In this regard, the choice of crop variety and type should be based on the degree of its tolerance to drought. Various agricultural studies have been carried out in the region hence the rationale behind its selection. There are two types (or components) of rainfall variability, areal and temporal. Daily primary and secondary rainfall time series were captured into MS Excel spread-sheet where seasonal rainfall totals for both Short Rains (SR) and Long Rains (LR) that is, March-April-May (MAM) and October-November-December (OND), respectively—annual average and number of rainy days were computed. It was observed that lowest probabilities of occurrence of dry-spells of all durations were recorded in the month of April (during LRs) and November (during SRs). A comparison of the predicted and recorded rainfall amounts showed further best-fit performance of the Kriging interpolation technique in ArcGIS. Generally, Kriging spatial interpolation capability for rainfall amounts was found to be high (predicting 670–742 mm for observed 800 mm) (Figure 7). Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region. Mzezewa et al. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall … [12]. Section 2 of this paper is dedicated Rainfall data are a vital meteorological input to agricultural modelling systems and water resources planning and management studies. Geographic information systems (GIS) and modeling have become critical tools in agricultural research and natural resource management (NRM) yet their utilization in the study area is quite minimal and inadequate. Results showed that the probability of occurrence of dry-spells of various durations varied from month to month of the growing season. On the other hand, it was apparent that SRs recorded consistent above-average trends during this study, indicating possibilities of a reliable growing season especially for the drier Machang’a region. Many aspects of the global climate are changing rapidly, and the primary drivers of that change are human in origin. Weibull method for estimating probabilities and method of moment (MOM) parameter estimation methods proved to be sufficient for the task, in evaluating data series homogeneity and frequency. Assorted studies have cited unpredictability of LR seasonal rainfall patterns and farmers’ reliance on SRs (e.g., Cohen, 1987; [25]; Hutchinson, 1996; and Recha et al. The probability that a dry-spell not longer than does not occur at a certain day in a growing season was computed by (6); probability that a dry-spell longer than days will occur in a growing season was calculated by (7) and probability that a dry-spell exceeding days would occur within a growing season was computed by (8) as shown in the following:ArcGIS software tool combined with the digital elevation model (DEM) to generate average spatial rainfall and maps using various interpolation techniques was utilized for data reconstruction purposes. Some of the variability does not appear to be caused systematically and occurs at random times. It was also noted that drylands were characterized by high rainfall variability and unpredictable droughts. (a) Homogeneity test for the rainfall dailies from study stations for the period between 2000 and 2013. Trends of high variability in seasonal monthly rainfall reported by this study have also been cited by Mzezewa et al. On the other hand, the probabilities that dry-spells would exceed these day durations were equally high (Figure 6). [21] observed 47% chance of seasonal rainfall exceeding 580 mm but 0% (no increase) of exceeding total annual rainfall for a 5-year return period in the semiarid Ecotope of Limpopo, South Africa. (Time does not vary. B. Hornet, and C. A. Shisanya, J. Mugwe, D. Mugendi, M. Mucheru-Muna, D. Odee, and F. Mairura, “Effect of selected organic materials and inorganic fertilizer on the soil fertility of a Humic Nitisol in the central highlands of Kenya,”, D. Raes, P. Willems, and F. Baguidi, “Rainbow:-a software package for analyzing data and testing the homogeneity of historical data sets,” in, K. K. Kumar and T. V. R. Rao, “Dry and wet spellsat Campina Grande-PB,”, J. J. Botha, J. J. Anderson, D. C. Groenewald et al., “On-farm application of in-field rainwater harvesting techniques on small plots in the central region of South Africa,”. (2009) studied monthly rainfall distribution The net potential effect of severe changes in rainfall pattern is the disruption in crop production leading to food insecurity, joblessness, and poverty. Low SD values indicated the restriction of variations (rescaled cumulative deviations, RCD) around mean rainfall amounts thus high homogeneity (Table 2). Rainfall Variability over Thailand Related to the El Nino-Southern Oscillation (ENSO) @article{Kirtphaiboon2014RainfallVO, title={Rainfall Variability over Thailand Related to the El Nino-Southern Oscillation (ENSO)}, author={Sarinya Kirtphaiboon and P. Wongwises and A. Limsakul and S. Sooktawee and U. Humphries}, journal={Journal of Sustainable Energy and Environment}, year={2014}, … Decadal rainfall trends showed that both long rains (LRs) and average annual rainfall have decreased in the past 13 years in the region. The variability of rainfall and the pattern of extreme high or low precipitation are very important for the agriculture as well as the economy of the country. Decadal rainfall anomaly index for both LR_MAM and SR_OND in Embu, Machang’a, Kiritiri, Kindaruma, and Kiambere; RAI: rainfall anomaly index. This method was preferred to single imputation and regression imputation as it appropriately adjusted the standard error for missing data yielding complete data sets for analysis [16]. (2009) also investigated the seasonal rainfall variability in Guinea savannah part of Nigeria and concluded that rainfall variability continues Better prediction of the Kriging method established in this study could be attributed to its capability of producing a prediction surface, thus providing a measure of the certainty or accuracy of the predictions. Kriging and Spline techniques reported more representative values of observed rainfall when compared to the IDW method. Nonetheless, meteorological stations in the region which are sole sources of climatic data are only limited to single locations spatially. In Machang’a, Kiritiri, and Kindaruma, rainfall amounts during LRs were highly variable (CV = 0.41, 0.39, and 0.47, resp.) This region lies in the lower midlands 3, 4, and 5 (LM 3, LM 4, and LM 5), upper midlands 1, 2, 3, and 4 (UM 1, UM 2, UM 3, and UM 4), and inner lowland 5 (IL 5) [14] at an altitude of approximately 500 m to 1800 m above sea level (a.s.l) (Figure 1). The probabilities of occurrence of consecutive dry days were estimated by taking into account the number of days in a given month . semi-arid climatic conditions affecting large areas, seasonal droughts, very high rainfall variability and sudden and high-intensity rainfall; Eurlex2019 The specific natural feature is the hilly area of Maramureș, causing a certain reduction in temperatures and increased rainfall variability . Box 30677-00100, Nairobi, Kenya, Conversely, areas of the subhumid Mbeere subcounty are emblematic of a low agricultural potential with less fertile and low soil-water-holding Ferralsols, frequent droughts, and annual rainfall of less than 600 mm [14]. Frequency analyses of meteorological data require that the time series be homogenous in order to gain in-depth and representative understanding of the trends over time [17]. General high probabilities of persistent dry-spells in SSA have been reported by Hulme [26], Dai et al. Thus, deficit is likely to prevail throughout the rain seasons as observed in other SSA regions (Li et al., 2006). Generally, stations in subhumid areas of Mbeere subcounty recorded more negative anomalies in rainfall amount received compared to Embu. Box 30677-00100, Nairobi, Kenya. Box 43844-00100, Nairobi, Kenya, 3Embu University College, P.O. Often, nonhomogeneity and lack of exponential distributions between datasets indicate gradual changes in the natural environment and thus trigger variability, which corresponds to changes in agricultural production [23, 24].