Kenya Rainfall Data 2021 Download

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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. 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. 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. Most meteorological data in the study area are inconsistent, unrecorded, or missing, leading to more discrete and unreliable data for analysis besides the main stations themselves being several kilometres from the target area. This calls for use of data reconstruction through interpolation.

Kenya Rainfall Data Download

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On the other hand, the much-needed information on inter-/intraseasonal variability of rainfall in the region is still inadequate despite its critical implication on soil-water distribution, water use efficiency (WUE), nutrient use efficiency (NUE), and final crop yield. 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. Since rainfall which is heterogeneous, in particular, is the most critical factor determining rain-fed agriculture, knowledge of its statistical properties derived from long-term observation could be utilized in developing optimal mitigation strategies in the area. 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.

The maximum and the range of the rescaled cumulative deviations from the mean were evaluated based on number of Nil values, non-Nil values, and mean and standard deviations as well as K-S values ((2) to test homogeneity. Low values of and would indicate that data was homogeneous:where is maximum (max) of and in the range of and Min is Minimum.

The frequency analyses were based on lognormal probability distribution with log10 transformation using cumulative distribution function (CDF) for both LR and SR rainfall amounts. The Weibull method was used to estimate probabilities while the maximum likelihood method (MOM) was utilized as a parameter estimation statistic. 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].

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.

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.

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 and are the respective means of these values and is the number of observations.

Homogeneity analyses had no Nil-values (values below threshold) but 100% non-Nil values (above threshold) showing high homogeneity. The standard deviations (SD) of the normalized means for both LR and SR rainfall amounts were low, for example, lowest SD = 0.1 (in Embu and Kiritiri during SRs), and highest SD = 0.9 (in Embu and Kindaruma) during LRs. Low SD values indicated the restriction of variations (rescaled cumulative deviations, RCD) around mean rainfall amounts thus high homogeneity (Table 2).

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).

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. According to Shisanya [25], La Nina events significantly contributed to the occurrence of persistent droughts and unpredictable weather patterns during LRs in Kenya. In contrast, El Nino events (of 1997 and 1998) have been cited as the key inputs of the positive anomalies in SR seasonal rainfall in the ASALs of Eastern Kenya [27, 28].

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. Evaluation of variability based on coefficient of variation (CV) in rainfall amount (RA) and number of rainy days (RD) showed that most stations received highly variable rainfall.

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.

Assorted arguments regarding the varied performances of the different interpolation techniques could explain the results of this study. 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]. 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]. 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. In this study, the resultant patterns of spatial distribution for each map were an outcome of the generated patterns from the mapping of the index value (the mean annual precipitation) and as influenced by the spatial local conditions (elevation) including the nonexistence of altitudinal variability of the parameters of the distribution function and the interpolation methods used. Statistically, the spatial distribution of quantiles is theoretically better underpinned in Kriging method than in the other methods tested. 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.

Uncertainty in rainfall pattern has put rain-fed agriculture in jeopardy, even for the regions considered high rainfall potential like the Central Highlands of Kenya (CHK). The rainfall pattern in the CHK is spatially and temporally variable in terms of onset and cessation dates, frequency and occurrence of dry spells, and seasonal distribution. Appraisal of the variability is further confounded by the lack of sufficient observational data that can enable accurate characterisation of the rainfall pattern in the region. We, therefore, explored the utilisation of satellite daily rainfall estimates from the National Aeronautics and Space Administration (NASA) for rainfall pattern characterisation in the CHK. Observed daily rainfall data sourced from Kenya meteorological department were used as a reference point. The observation period was from 1997 to 2015. Rainfall in the CHK was highly variable, fairly distributed and with low intensity in all the seasons. Onset dates ranged between mid-February to mid-March and mid-August to mid-October for long rains (LR) and short rains (SR) seasons, respectively. Cessation dates ranged from late May to mid-June and mid-December to late December for the LR and SR, respectively. There was a high probability (93%) of dry spell occurrence. More research needs to be done on efficient use of the available soil moisture and on drought tolerant crop varieties to reduce the impact of drought on crop productivity. Comparison between satellite and observed rain gauge data showed close agreement at monthly scale than at daily scale, with general agreement between the two datasets. Hence, we concluded that, given the availability, accessibility, frequency of estimation and spatial resolution, satellite estimates can complement observed rain gauge data. Stakeholders in the fields of agriculture, natural resource management, environment among others, can utilise the findings of this study in planning to reduce rainfall-related risks and enhance food security.

Where Xm is the missing record at station X, X is the long-term mean for the station with the missing data in specific year and month, Y is the long-term mean of the station with complete data, and Ym is the corresponding records of the station Y having complete data.

Where xi is the annual rainfall records and x the mean. The cumulative deviation, Sk should fluctuate around zero for the homogenous rainfall series. The initial value of Sk = 0 and last value Sk = n are all equal to zero. e24fc04721