Rainfall Coefficient Of Variation Research Articles

Understanding traditional agro-ecosystem dynamics in response to systematic transition processes and rainfall variability patterns at watershed-scale in Southern Ethiopia

Multi-temporal analysis of land cover dynamics using remote sensing can enable the determination of the spatial extent and average rate of land cover change. With the application of an appropriate change analysis method, it is also possible to distinguish whether a land cover change has occurred by the effect of a random or a systematic process. In connection with this, characterizing rainfall variability and historical meteorological drought events can allow understanding of their effects on agro-ecosystems and vegetation cover dynamics. Therefore, this study has evaluated multi-temporal land cover change in response to the possible impacts of population pressure and rainfall variability on agro-ecosystem dynamics. This was conducted on a traditional agroforestry-dominated landscape in Southern Ethiopia. Using Landsat images acquired in 1985, 2000, and 2018, a post-classification land cover change analysis approach was employed to distinguish between a systematic and random process of inter-category transitions. Assessment of drought events and rainfall variability dynamics were performed using standardized precipitation index (SPI) and rainfall coefficient of variation (CV), respectively. Mann–Kendall test was also applied for the detection of a monotonic rainfall trend. A bias-corrected Climate Hazards group Infrared Precipitation with Stations (CHIRPS) over 1981–2017 was used to calculate the SPI, CV and Mann–Kendall trend test. The analysis showed that above 41% of the landscape has experienced land cover transitions between 1985 and 2018. This has primarily resulted by a systematic and rapid expansion of agriculture, urban areas, and eucalyptus plantations, at the expense of natural vegetation ecosystems. Consequently, over the last 33 years (1985–2018), natural forest, grassland, and wetland have declined by 74.8%, 83.3%, and 78.4%, respectively. Another major land cover change identified in this study was the replacement of open-field crops by agroforestry, mainly in the western part of the catchment. Such expansion of agroforestry has appeared to be spatially correlated with a lower amount of long-term average and more variable rainfall. Perhaps, this could indicate farmers’ response to rainfall variability by diversifying agricultural production options (i.e. agroforestry system), by replacing the more risk-prone mono-cropping culture. The observed persistence and further expansion of traditional agroforestry (a combination of perennial crops and scattered trees) have implications in terms of enhancing biodiversity conservation and environmental protection. Overall, the land cover transitions that occurred over the last three decades suggest future conservation priorities for improved landscape management, with more emphasis on the most exposed natural vegetation ecosystems.

Spatiotemporal variability and trends of rainfall and its association with Pacific Ocean Sea surface temperature in West\xa0Harerge Zone, Eastern Ethiopia

BackgroundRainfall variability exceedingly affects agriculture in Ethiopia, particularly in the eastern region where rainfall is relatively scarce. Hence, understanding the spatiotemporal variability of rainfall is indispensable for planning mitigation measures during high and low rainfall seasons. This study examined the spatiotemporal variability and trends of rainfall in the West Harerge Zone, eastern Ethiopia.MethodThe coefficient of variation (CV) and standardized anomaly index (SAI) were used to analyze rainfall variability while Mann-Kendall (MK) trend test and Sen’s slop estimator were employed to examine the trend and magnitude of the rainfall changes, respectively. The association between rainfall and Pacific Ocean Sea Surface Temperature (SST) was also evaluated by Pearson correlation coefficient (r).ResultsThe annual rainfall CV during 1983–2019 periods is between 12 and 19.36% while the seasonal rainfall CV extends from 15–28.49%, 24–35.58%, and 38–75.9% for average Kiremt (June–September), Belg (February–May), and Bega (October–January) seasons, respectively (1983–2019). On the monthly basis, the trends of rainfall decreased in all months except in July, October, and November. However, the trends were not statistically significant (α = 0.05), unlike in November. On a seasonal basis, the trends of mean Kiremt and Belg seasons rainfall decreased while it increased in Bega season although it is not statistically significant. Moreover, the annual rainfall showed a non-significant decreasing trend. The findings also revealed that the correlation between rainfall and Pacific Ocean SST was negative for Kiremt while positive for Belg and Bega seasons. Besides, annual rainfall and Pacific Ocean SST was negatively correlated.ConclusionsHigh spatial and temporal rainfall variability was observed at the monthly, seasonal, and annual time scales. Seasonal rainfall has high inter-annual variability in the dry season (Bega) than other seasons. The trends in rainfall were decreased in most of the months. Besides, the trend of rainfall decreased in the annual, Belg and Kiremt season while increased in the Bega season. The study also indicated that the occurrence of droughts in the study area was associated with ENSO events like most other parts of Ethiopia and East Africa.

Determination of flood-inducing rainfall and runoff for highly urbanized area based on high-resolution radar-gauge composite rainfall data and flooded area GIS data

This study derived the Flood-Inducing-Rainfall (FIR) and Flood-Inducing-Runoff (FIRO) for urban flood warning. For this, we derived a relationship between the watershed time of concentration and accumulated rainfall depth for 261 flood events at 239 watersheds of Seoul with areas between 0.5 and 6.8 km2 during the years 2010 and 2011, based on 10-minute 1β × 1 km radar-gauge composite rainfall field. This relationship was further categorized by discrete ranges of the proportion of the flooded area in the watershed (FP) and coefficient of variation (CV) of the rainfall time series. Then, minimum possible rainfall intensity calculated for each of the classified depth-duration relationship was determined as FIR for the specified range of FP and CV. A similar methodology was applied to derive FIRO, which used runoff depths that were estimated using the NRCS Curve Number method. We found that the FIR and FIRO varied between 37–62 and 10–41 mm/h, respectively. FIR and FIRO increase with an increase in FP, suggesting that greater rainfall causes larger flooded area. As rainfall CV increases, FIR and FIRO decrease, suggesting that the temporally concentrated rainfall requires less rainfall to cause the flood. A flood warning system that checks the accumulated rainfall at each of the 239 Seoul watersheds every 10 min was developed to verify the FIR and FIRO against the 21 flood events that occurred between 2012 and 2015. When flooding was tolerated on 5 percent of the watershed area, the ratios of hit, miss, and false alarm of the warning system based on the accumulated rainfall was 44.2, 9.5, and 55.8%, respectively. The ratios of hit, miss, and false alarm of the warning system based on the runoff was 66.7, 4.7, and 33.3% respectively. The flood warning system based on gauge-only or radar-only data gives approximately 3 times as much false warnings compared with the system based on radar-gauge composite data. Also, the warning system that does not consider rainfall temporal variability gave approximately twice as much false warnings as the system that considered temporal variability.

GIS-based climate variability and drought characterization in Ethiopia over three decades

Abstract The climate variability and drought frequency over potential crop growing regions of Ethiopia during 1983−2012 were analysed. Data from 87 weather stations across the country were used for this analysis. Ethiopian agricultural activities are highly dependent on the long rainy season (June−September) rainfall, which accounts for 70% of the total annual rainfall. There was no significant change in rainfall during annual and the bimodal seasons during this period of study. However, there was significant change in the rainfall coefficient of variation. STARDEX precipitation indices provided a measure of intensity, frequency and proportion of total rainfall. Ninetieth percentile of rainfall, number of rainy days with rainfall >10 mm/day and the greatest 10 d total rainfall were increased over time at most of the stations. Among the major droughts, 1984−'85 drought was reported as the most severe drought with peak negative SPI value −3.68 in Wollo. The longest duration of drought lasted for 63 months in Borena Zone in southern Ethiopia during 1983−2012 period. Extreme maximum temperature (90th percentile) has increased over 45% of the weather stations, while, extreme minimum temperature (90th percentile) has increased 53% of the weather stations. Extreme maximum temperature events have been increasing during the seasons in Ethiopia, which is the real concern for agricultural and livestock activities, as these sectors significantly contribute to about 50% of GDP for the country. However, given the increasing response capacity of the government, as observed during 2002−'03 drought, environmental disaster is expected to be under control over time.

Climate Variability and Cocoa Production in Meme Division of Cameroon: Agricultural Development Policy options

This study examines the effect of climate variability on cocoa production which is the mainstay of the population of Meme Division. Meme Division stands as one of the highest cocoa producing divisions of the Cameroon. Field studies accompanied by the administration of 155 questionnaires (50 each for Kumba and Mbonge Subdivisions and 55 for Konye Subdivision) were employed. Information on climatic variables (temperature and rainfall) and cocoa output for 21 years (1990-2010) was also obtained. The data was analyzed with the use of the four point likert scale survey and the coefficient of variation (CV). The results showed that the CV for rainfall (15.1%) and temperature (11.0%) all exceeded the variability threshold of 10% indicating that they exhibit significant variability. Trend analysis for cocoa output shows that unusual variations were experienced in some years. This was further confirmed by the Jarque-Bera statistics of 0.68 (P- Value = 0.71) which indicates that the output of cocoa is not normally distributed over time. It was also observed that the increasing unpredictability of rainfall and temperatures tend to confuse the cocoa crop and the farmers as their traditional farming calendar have been distorted; no sooner has the cocoa crop been able to adjust with the new climatic scenario than they are confronted with a different pattern. Furthermore, farmers noted a drop in cocoa output per hectare. This has led to a reduction in incomes realised giving rise to limited possibility for expansion. It is seemingly evident that their present adaptation strategies are yet to contain this ugly situation. The study suggests a number of agricultural development planning options which the government and the local population can embark on to develop a resilience framework for cocoa farmers in Meme Division in the face of expected variability in climate.

The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics

AbstractAim The aim of this study is to introduce a structural vegetation map of the Serengeti ecosystem and, based on the map, to test the relative influences of landscape factors on the spatial heterogeneity of vegetation in the ecosystem.Location This study was conducted in the Serengeti–Maasai Mara ecosystem in northern Tanzania and southern Kenya, between 34° and 36° E longitude, and 1° and 2° S latitude.Methods The vegetation map was produced from satellite imagery using data from over 800 ground‐truthing points. Spatial characteristics of the vegetation were analysed in the resulting map using the fragstats software package. Average patch area and nearest neighbour distance (NND) were determined for grassland, shrubland and woodland vegetation types. The heterogeneity of vegetation types was estimated with Simpson’s diversity index (D). Structural equation modelling (SEM) was used to explore the relationships between the spatial characteristics of vegetation and three predictor variables: annual rainfall, coefficient of variation (CV) in annual rainfall, and topographic moisture index (TMI).Results A vegetation map is presented along with a detailed summary of the distribution of land‐cover classes and spatial heterogeneity in the ecosystem. Significant relationships were found between vegetation diversity (D) and TMI, and also between D and average rainfall. The average area of grassland patches showed significant relationships with average rainfall, with rainfall CV and with TMI. Grassland NND was positively associated with average rainfall. Woodland patch area showed a unimodal response to average rainfall and a negative linear association with TMI. Woodland NND showed a U‐shaped association with annual rainfall and a weaker positive linear association with TMI. An acceptable model that explained variation in shrubland patch characteristics could not be identified.Main conclusions The vegetation map and analysis thereof resulted in three significant causal explanatory models that demonstrate that both rainfall and topography are important contributors to the distribution of woodlands and grasslands in the Serengeti. These findings further indicate that changes in patch characteristics have a complex interaction with rainfall and with topography. Our results are concordant with recent studies suggesting that percent woody cover in African savannas receiving less than c. 650 mm year−1 is bounded by average annual rainfall.

Weather, soil conditions and the interannual variability of herbage production and nutrient uptake on annual Mediterranean grasslands

Environmental effects on the variability of herbage production and nitrogen uptake of an ungrazed annual Mediterranean sward were studied with a well-tested simulation model. Meteorological data for a 21 year period were from a semi-arid site in the northern Negev of Israel and the observed daily rainfall was multiplied by 0.5, 1.0, 1.5 and 2.0 to simulate different rainfall zones. Rainfall regime, nutrient level and soil depth accounted for 83–91% of the variation in the standard deviation of herbage production (SD h) and of nitrogen uptake (SD n), respectively, while rainfall and nutrient level alone accounted for more than 95% of the variation in the coefficient of variation (CV) of both herbage production (CV h) and nitrogen uptake (CV n). Distribution of the accountable variation in SD h was divided among the three factors (24% rainfall, 40% nutrient availability and 18% soil depth), while the variation in CV h was the result predominantly of rainfall (93%), to a much smaller extent to nutrient availability (3%) and to a negligible degree to soil depth (0.1%). Greater nutrient availability tended to increase both SD h and CV h. SD h in relation to rainfall was highest in the intermediate semi-arid zone, but relatively low in both the drier and wetter zones. On the whole, CV h and CV n decreased with higher rainfall, even though the CV of rainfall was held constant. CV h and CV n sharply distinguished between the zone with less than 264 mm rainfall and higher rainfall areas. This, indeed, is the approximate dividing line between relatively reliable conditions for arable agriculture and submarginal conditions in the region. It is shown that the lower stability of herbage production, expressed as CV h, in drier regions is not so much a consequence of greater rainfall CV as of lower absolute amounts of rainfall. Under more humid conditions, greater stability (i.e. lower CV h) can be a result of nutrient deficiency, and not only a consequence of lower rainfall CV.