Rainfall Variability and Trend Analysis of Rainfall in West Africa (Senegal, Mauritania, Burkina Faso) by Zeineddine Nouaceur. This subject area in meteorology/climatology is called "rainfall variability." Crop production in the Fanteakwa District is predominantly rainfed, exposing this major livelihood activity to the variability or change in rainfall pattern. Utilization of GIS spatial-interpolation techniques such as inverse distance weighted (IDW), Spline, and Kriging interpolation techniques are some of the ArcGIS application tools essential for data reconstruction. variability definition: 1. the quality or fact of being variable (= likely to change often): 2. the quality or fact of…. Exceedance (%): probability of exceedance (%) and Return (P): return period (years). 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]. 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 . : Spatial and temporal variability of rainfall It is timely to review recent progress in understanding of interactions between rainfall spatial and temporal resolution, variability of catchment properties and their representation in hydrological models. Rainfall Variability . 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… Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region. It has a population density of 82 persons per km2 with an average farm size less than 5.0 ha per household. Subsequently the probability that a dry-spell may be equal to or longer than days was given by (5). According to [15], the region has experienced drastic declines in its productivity potential rendering most farmers poor. The stepwise methodology is summarized in Figure 2. [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. The degree to which rainfall amounts vary across an area or over time is called 'rainfall variability'. 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]. A majority of the largely agriculture-based households participating — at research sites in eight countries, in Asia (Bangladesh, India, Thailand, Viet Nam), Africa (Ghana, Tanzania) and Latin America (Guatemala, Peru) — reported that rainfall variability is already negatively affecting production and adding to food and livelihood insecurity. 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. Many studies on rainfall variability had been used data at relatively in all resolutions, either global climate models (GCMs; e.g. This could be attributed to the persistence of intermediate warming scenarios in parts of equatorial East Africa [21, 26]. The net potential effect of severe changes in rainfall pattern is the disruption in crop production leading to food insecurity, joblessness, and poverty. Average January rainfall for stations across California. [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. An intrastation seasonal comparison showed that SRs in Embu were less variable but more drier compared to LR seasons. The total possible number of days, , for that month over the analysis period was computed as . (a) Homogeneity test for the rainfall dailies from study stations for the period between 2000 and 2013. In tandem with this observation, findings by Hansen and Indeje (2004) and Amissah-Arthur et al. the rainfall variability in the recent past. Section 2 of this paper is dedicated Often, prolonged dry-spells are accompanied by poor distribution and low soil moisture for the plant growth during the growing season. "on all spatial and temporal scales beyond that of individual weather events." A plot of homogeneity of the average monthly rainfall daily and for all stations studied showed deviations from the zero mark of the RCDs not crossing probability lines; thus homogeneity was accepted at 99% probabilities (Figure 3). Map showing the study area and its elevation with studied point gauged rainfall data; Machang’a and Embu, Kiritiri, Kindaruma, and Kiambere. SD: standard deviation; LR: long rains; SR: short rains. A study by Tilahun [11] based on the cumulative departure index established that parts of Northern and Central Ethiopia persistently received below average rainfall for the rains received between February and August since 1970. Both temporal and areal variability of precipitation may be measured in various ways. 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. Evidently, lower eastern parts of the region received low rainfall amounts as interpolated across all the test methods (ranging from 229 to 397 mm), adequately replicating trends of the actual observed rainfall. A dry-spell was considered as sequence of dry days bracketed by wet days on both sides [18]. Ignoring this effect, would you think that areal variability of precipitation is high or low in, say, our portion of California and why? Rainfall variability in the broader tropical SPCZ region is well represented by rainfall variability at the Solomon Is-lands. K-S value: Kolmogorov-Smirnov, (K-S = 0.302. Climate Change and Variability . For this study, Kriging was extended by the regional regression for each index value for areas whose terrain or other controls could have contributed to the spatial variability of the trends, explaining its better predictability. Selected metadata of the meteorological stations used in the study. Department of Geography, Valahia University, 130001 Târgovişte, Romania * Author to whom correspondence should be addressed. The variation of rainfall amounts at various locations across a region for a specific time interval. By using our services, you agree to our use of cookies. Box 43844-00100, Nairobi, Kenya, Embu University College, P.O. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. (b) Seasonal monthly (K-S value), mean, and standard deviation and. The IPCC climate models predict, for the Maghreb countries, lower rainfall and increased aridity. (2009) studied monthly rainfall distribution in Nigeria between 1985-1994 and 1995-2004 and noticed some fluctuations in most months within the decades. Special thanks are extended to RUFORUM fiscal support. rainfall variability and a more uniform rainfall distribution than other regions. This great reservoir continuously exchanges heat, moisture, and carbon with the atmosphere, driving our weather patterns and influencing the slow, subtle changes in our climate. The coefficient of variance (coefficient of variation) statistics were utilized to test the level of mean variations in LR and SR seasonal rainfall, number of rainy days (RD) and rainfall amounts (RA), and -test statistic to evaluate the significance of variation. Performance of the different interpolation techniques was varied. This calls for use of data reconstruction through interpolation. In this study, CRU data (CRU_TS 4.01) was used to investigate spatiotemporal variability of rainfall at 33 sub-basins of the Niger Central Hydrological Area (NCHA), Nigeria, over 105 years (1911–2015).). 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]. 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. This subject area in meteorology/climatology is called "rainfall variability." 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,”. Understanding spatiotemporal rainfall patterns has been directly implicated to combating extreme poverty and hunger through agricultural enhancement and natural resource management [1]. (2009) also investigated the seasonal rainfall variability in Guinea savannah part of Nigeria and concluded that rainfall variability continues Rainfall variability & change. Current observations in the three countries of central Maghreb (Morocco, Algeria, and Tunisia) are not consistent with these predictions. These results imply that there is a need for GHG emission reduction in the near future to avoid more extreme tropical rainfall during El Niño. The variability of rainfall in both annual and seasonal scales were evaluated using coefficient of variation (CV), standardized rainfall anomaly, precipitation concentration index (PCI), and standardized precipitation index. Results showed neither station nor season with persistent near average (RAI = 0) rainfall especially from stations in the subhumid region. 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. 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. Would the following charts be used to illustrate areal or temporal variability of precipitation? Variability was equally high in the number of rainy days (RD), for example, CV = 0.51 and 0.49 in Kiritiri and Kiambere, respectively. 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)). Rescaled cumulative deviations for seasonal months and studied rainfall stations for the period between 2000 and 2013. 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. Rainfall is the most important natural factor that determines the agricultural production in Bangladesh. MATLAB (Matrix Laboratory), “The Empirical Cumulative Distribution Function: ECDF,” 2013, F. A. Huff and S. A. Changnon Jr., “Precipitation modification by major urban areas,”, N. Bayazit, “A comprehensive theory of participation in planning and design (P&D),”, C. A. Shisanya, “The 1983-1984 drought in Kenya,”, M. Hulme, “Climatic perspectives on SSA desiccation: 1973–1998,”, A. Amissah-Arthur, S. Jagtap, and C. Rosen-Zweig, “Spatio-temporal effects of, A. Anyamba, C. J. Tucker, and J. R. Eastman, “NDVI anomaly Pattern over Africa during the 1997/98, A. Araya and L. Stroosnijder, “Assessing drought risk and irrigation need in Northern Ethiopia,”, G. W. Heine, “A controlled study of some two-dimensional interpolation methods,”. 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. Seasonal variability was computed in tandem with annual averages for both positive (3) and negative (4) anomalies using RAI;where is mean of the total length of record, is mean of 10 highest values of rainfall of the period of record, and is the lowest 10 values of rainfall of the period of record. Generally, stations in subhumid areas of Mbeere subcounty recorded more negative anomalies in rainfall amount received compared to Embu. It is especially important to farmers planting crops on the basis of future rain and not drought. A larger range for the monthly spatial variation was observed in the west coast region. Scientists determine the climate of a geographic location by compiling statistics over an extended time … Conversely, Embu received more rainfall during LRs with April accounting for about 52% of total rainfall received. To redress problems of inadequate, missing, and inconsistent point data especially for ungauged areas within the study area, this study sought to further evaluate the efficacy of geostatistical and/or deterministic interpolation techniques in daily rainfall data reconstruction. (2009) studied monthly rainfall distribution 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,”. In Embu, the highest positive anomalies (+5.0) were recorded in 2002, 2005, and 2007 during LRs (Figure 4). For instance, in Machang’a, the wettest LRs were recorded in 2010 (RAI = +4) while wettest SRs were recorded in 2001 (RAI = +4), 2006 (RAI = +3.8), and 2011 (RAI = +4) (Figure 4). [7]). Dry-spells during cropping months are quite common which often trigger reduced harvests or even complete crop failures, in the study region. Rainfall being a prime input and requirement for plant life in rain-fed agriculture, the occurrence of dry-spells has particular relevance to rain-fed agricultural productivity (Belachew, 2002; Rockstrom et al., 2002). 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. 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. Variability in rainfall amounts and number of rainy days during seasonal months for studied stations for the period between 2000 and 2013. However, understanding the average amount of rain per rainy day and the mean duration between successive rain events aids in understanding long-term variability and patterns [13]. It is equally important that they schedule supplementary irrigation, only based on timely, regular, and accurate dissemination daily monthly and seasonal forecasts by the Kenya Meteorological Department. Some climate models indicate that rainfall variability is likely to increase, pointing to more frequent and intense droughts. 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. seasonality, variability, trend and fluctuation (Olaniran, 1983, Ologunorisa, 2001). In this study, we investigate the covariability of rainfall across the IS and the TP on intraseasonal time scales and its impact on interannual variability of regional rainfall. A portion of warm-season rainfall variability may be attributed to the agricultural lands and irrigation practices in OKC’s surrounding areas (Niyogi et al. 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. This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. These results indicate high variability of rainfall received across all AEZs in the study area, further evidenced by massive rainfall anomalies reported earlier by this study. This study was conducted on 27 Sahelian climatic stations in three countries (Burkina Faso, Mauritania, and Senegal). Embu represent a densely populated high potential humid area with Humic Nitosols soils and generally annual rainfall above 800 mm. It is especially important to farmers planting crops on the basis of future rain and not drought. There was notable high interseasonal variability and temporal anomalies in rainfall between 2001 and 2013. Generally, Kriging spatial interpolation capability for rainfall amounts was found to be high (predicting 670–742 mm for observed 800 mm) (Figure 7). Sourced from both what is rainfall variability Kenya Meteorology Department and research sites with primary recording stations within the study was conducted 27... 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