Top PDF Proposition of an Index for the Study of the Variability of Space-Temporal Rainfall in Brazil

Proposition of an Index for the Study of the Variability of Space-Temporal Rainfall in Brazil

Proposition of an Index for the Study of the Variability of Space-Temporal Rainfall in Brazil

O IPE foi aqui expresso por um único número para cada quadrante na resolução anual, ou doze números para resolução mensal. Assim, não nos preocupamos em iden- tificar tendências temporais do IPE para a série temporal, ou mesmo identificar rupturas, à exemplo do que fizeram Villar et al. (2009) e Fetter et al. (2016) para séries pluvio- métricas. Obviamente, tais análises são factíveis mediante algumas adaptações nos procedimentos computacionais ora apresentados, sendo razoável supor que processos his- tóricos, como o desmatamento, a conurbação ou mesmo o alagamento provocado por grandes reservatórios e com- plexos hidrelétricos, possam mostrar tendências ou ruptu- ras nos valores de IPE. Neste sentido, é fundamental que se avalie a compatibilidade do processo gerador de uma su- posta mudança diante das resoluções espacial e temporal das chuvas. Essa é uma questão que poderia ser inves- tigada, por exemplo, pela aplicação do IPE a distintas séries temporais (janelas temporais), como em escala de dias ou horas. Isso possibilitaria distinguir o efeito dos fatores estáticos, como o relevo, frente aos dinâmicos, como as mudanças de uso da terra ou o aquecimento da baixa tro- posfera observado nas últimas décadas, ou mesmo, a im- portância de sistemas de tempo efêmeros, responsáveis por catástrofes e danos na esfera socioeconômica e ecológica. Trata-se de adequar o grupo de dados em investigações futuras, visando melhor analisar o fenômeno de interesse. Iniciativas desta alçada ajudariam a complementar a avalia- ção do IPE. Por outro lado, estabeleceria um desafio de esforço computacional consideravelmente maior.
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Spatio-temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador)

Spatio-temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador)

RAINFALL VARIABILITY IN THE AMAZON BASIN 1577 extended average rainfall P j is carried out. The first one considers that the extended average of a station is cal- culated using the mean observed values, after deleting outliers, i.e. data differing most from those of nearby sta- tions for a particular year. The second one considers that the extended average of a station is calculated on the basis of the most frequent values (the mode) in accordance with the neighbouring stations. Therefore, there is no need to eliminate the data that differ considerably from the aver- age, as it is carried out in the first method. In this study, Brunet-Moret’s method has been applied, and the com- parison with the other method has not yielded noticeable differences. On basis of these concepts, it is possible to analyse the data following an iterative process of station selection within a specific climatic region. The selection is supported by climatological maps and the description of rainfall regimes, as reported in previous studies. The iterative process calculates the vector, revises the results, separates inconsistent stations, calculates the vector once more, etc. Rejected stations close to the border of a region may present the behaviour of a neighbouring region. As a result, they are taken into account to calculate the vec- tor of a new climatic region. Each resulting region is associated with a ‘regional vector’ that represents the interannual pluviometric variability in the region, and it is also similar to the behaviour of all the stations which are part of this region. Consequently, this vector is a good indicator of the climatic variability in the region. Thus for each year, this index requires data in at least five stations, to find the longest analysis periods per region. The application of the RVM in the AB led to 756 stations (52% of the total) with data lasting more than 5-year con- tinuous periods, and less probabilities of errors in their series (Figure 1). On average, the data availability period is from 1975 to 2003, but, in the Andean countries, the
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Spatial and temporal analysis of rainfall concentration using the Gini index and PCI

Spatial and temporal analysis of rainfall concentration using the Gini index and PCI

As previously mentioned, hydrological variables are sensitive to rainfall variability and/or change, which has recently generated major natural disasters, and commonly linked to changes in rainfall concentration. In this context, Westra et al. [ 33 ] analyzed the relationship between maximum daily rainfall and the global near-surface temperature, finding a positive relationship between both variables. Similarly, worldwide studies done by O’gorman [ 34 ] and Asadieh & Krakauer [ 35 ], found that the maximum daily precipitations are growing faster than the average annual precipitation, implying that rainfall intensity is generally increasing, which is similar to what Sarricolea & Martin-Vide [ 27 ] and Sarricolea et al. [ 28 ] found in Chile, through a CI analysis. Therefore, the objective of this study was to characterize in time and space the behavior and concentration of daily and monthly rainfall in two climatic zones (arid–semiarid and humid–subhumid) of the country.
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Spatial-temporal Variability Of Uv-absorbing Aerosol Index Over Egypt

Spatial-temporal Variability Of Uv-absorbing Aerosol Index Over Egypt

12-years (2005 to 2016) Spatial-Temporal Variability of OMI-AI over Egypt (22°–31°N, 24°–36°E) were analyzed. Daily time series OMI-AI variations reflect increase with the time, daily average variation has generally high values during hot seasons, and low values during cold seasons. Daily OMI-AI average spatial distribution over Egypt is mapped onto a grid of 1° in longitude by 1° in latitude. Notice that a significant match in spatial distribution. So, in order to analyze the OMI-AI east-to-west gradient over Egypt; we study the longitudinal variation (at lat. 27.5°N) of the OMI-AI values. OMI-AI does not depend on longitude or latitude, but only the areas which are active dust sources. So, we examining longitudinal trends to present it in a scientific paper these detailed correlations are not wrong, but of minor importance; nobody can use such equations in order to estimate OMI-AI in a specific area.
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Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980–2010

Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980–2010

entries with name of the station; latitude, longitude and alti- tude of the location; climatic variables for which data is avail- able and start and end years in the record. The metadata in- cluded more than 900 stations around the country for which the required data is available. Using the metadata information, we have selected 120 stations for which 30 or more years ’ rainfall and maximum and minimum temperature data are available. From this, 99 stations were finally selected as suit- able for trend and variability analysis based on the length of continuous record (at least 30 years) and less than 10% miss- ing data. However, the spatial coverage of the stations is not uniform with many stations located over the highlands of cen- tral Ethiopia leaving lowland areas underrepresented. A posi- tive aspect of this skewed distribution is that most of the sta- tions are located over the regions where the spatial variability of rainfall is the highest. Station distribution over the lowland areas is extremely sparse with no stations along the border with Somali in the east and with Kenya in the south and along the border with South Sudan and Sudan in the north-west. In order to generate homogeneous time series data over Ethiopia at a spatial resolution of 50 × 50 km and to fill the gaps in the observed data, we used the bias-corrected AgMERRA (Rienecker et al. 2011 ) climate forcing data sets created for the Agricultural Model Inter-comparison and Improvement Project (AgMIP). Daily meteorological data (rainfall, maxi- mum and minimum temperatures) for the period 1980–2010 were developed from the closest AgMERRA grid data after bias correction with the station data. Finally, a data set with 374 station/point data that are uniformly distributed across the country was developed and used in this study. As most sta- tions have continuous data with little or no missing data for the period 1980 to 2010, this period was used to investigate the trends in rainfall and associated variables. Efforts were made to extend the time series of rainfall data; however, due to civil conflict during 1970 and 1980s, data for most stations during this period is either missing or incomplete with gaps for ex- tended periods.
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Spatial and temporal variability of Standardized Precipitation Index over Indochina Peninsula

Spatial and temporal variability of Standardized Precipitation Index over Indochina Peninsula

The majority of the populations in Indochina Peninsula depends on the agriculture as the main source of livelihood income. Among three countries, Vietnam is the world’s third rice exporter as well as world’s second coffee exporter. It is, therefore, important to investigate the drought and wetness over the region because crops are vulnerable to the extreme climatic condition. Most of the studies only investigated drought characteristics within a river basin; however, it is worth to study both dry and wet spell trends within the river basins for optimal use of available water resources. In this study, we investigated the spatial-temporal patterns of drought and wet spells using SPI for Indochina Peninsula. Models, data and methodology are introduced in section 2. Results are discussed in section 3 and conclusions are drawn in section 4.
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Relationship Analysis Of Vegetation Cover And Rainfall Over Ghana Based On Their Spatio-Temporal Variability

Relationship Analysis Of Vegetation Cover And Rainfall Over Ghana Based On Their Spatio-Temporal Variability

Figure 11: Yearly spatial correlation between Rainfall and Vegetation for 2005 (a), 2010 (b) and 2015 (c) The annual spatial correlation analysis produced results suggesting very high positive correlation between rainfall and vegetation in the northern part of Ghana. The northern part of the country recorded correlation coefficients between 0.6 and 0.8. This relationship between Rainfall and Vegetation is evident throughout the study period (from 2005 to 2015) as represented in Figure 11 a, b and c. This strong positive correlation between rainfall and vegetation in the northern part of Ghana can be attributed to the dominant savannah vegetation found in that part of the country. The savannah vegetation is highly responsive to water (rainfall), therefore considering that part of the country as an arid region, vegetation greenness is greatly dependent on water (rainfall).
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Temporal and spatial assessment of four satellite rainfall estimates over French Guiana and North Brazil

Temporal and spatial assessment of four satellite rainfall estimates over French Guiana and North Brazil

Abstract: Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.
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Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

Considering that the applied fertilizer and the Spearman  index  accounted  for  soil  and  SYE,  it  was  hypothesized that seasons with tough economic conditions for growers should show a higher correlation between soil and SYE. However, when the economy  is favorable to the sugarcane business, less correlation between  soil  and  SYE  is  expected,  since  fertilizer  application reduces the fertility deficiencies in poorer  soils, masking soil spatial variability.

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Space-time variability of the rainfall over the western Mediterranean region: A statistical analysis

Space-time variability of the rainfall over the western Mediterranean region: A statistical analysis

3.2. Scaling Properties and Meteorological Processes [17] The main objective of this section is to relate empiri- cal scaling behavior of rainfall to meteorological processes. [18] First, we have to overcome some difficulties related to rainfall behavior. Indeed, the rainfall is very often inter- mittent. It means that the measured signal is the superposi- tion of the rain signal and the so-called support (meaning the combination of rain and no rain). These two components of the spectrum cannot be easily separated. The only way to get the intrinsic statistical properties of the rain is to extract and to analyze the rainfall signal only for periods of continuous rain. If we do not, the support impacts the spectra as shown by de Montera et al. [2009] and Verrier et al. [2011]. In par- ticular, these authors showed that dry periods in time series of rain tend to decrease spectral slope. These dry periods are governed by physical processes. For instance, gravity waves can produce periodic patterns of rain/no rain areas. Obvi- ously, this has a signature on the support. Thus, the support itself is related to the underlying physical processes. In this study we kept the full time series including the zero rain data, i.e., both rain and support signature.
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Orographic effects on convective precipitation and space-time rainfall variability: preliminary results

Orographic effects on convective precipitation and space-time rainfall variability: preliminary results

averaged precipitation to come into statistical equilibrium. Atmospheric flow reached a stable configuration as displayed by the stable horizontally-averaged vertical profiles of potential temperature, relative humidity and entropy-related variables such as moist static energy (not shown). After a spin-up time, depending on the rate of radiative cooling applied, the system became a statistically equilibrated convective atmosphere where shallow and deep clouds form, grow and die continuously. Therefore, at equilibrium a moving average of rainfall intensity should be close to the surface evaporation rate, although individual fluctuations around the precipitation mean can be large, corresponding to the time-scale of the ensemble of cumulus clouds. Figure 2 shows the horizontally-averaged precipitation, the most variable horizontally-averaged quantity, where the initial oscillation of the rainfall rate corresponds to a spin-up process, the time needed for the system to reach a balance (three days in this particular experiment). The instantaneous rain rate of many clouds in the domain is shown in Fig. 3a), with the corresponding vertical velocity field w at the ground in Fig. 3b). Updraughts and downdraughts show evidence of clustering in a three- dimensional system, where the convection is organised in rainbands (squall-lines) with no apparent external forcing, if the minimum surface wind requirement for surface fluxes is neglected.
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Orographic effects on convective precipitation and space time rainfall variability: preliminary results

Orographic effects on convective precipitation and space time rainfall variability: preliminary results

level, U is the uniform wind speed and hm is the amplitude of the mountain. The analytical value of the vertical profile of horizontal momentum transport by gravity wave processes has to be compared with the vertical profiles obtained by the numerical integration of the linear wave equation for the bell-shaped mountain case, where curves (b), (c), (d) and (e) in Fig. 6 are profiles obtained at different times and scaled with the same analytical value. Given this normalisation, the analytical value corresponds to a value of unity, while the simulated profiles are almost equal to unity at the surface and very close to it at later times (curve (e) below the Raleigh damping layer (15.5 km)). This sponge layer absorbing waves above the tropopause has been added to the numerical scheme of the ARPS model to absorb the waves excited by the orographic perturbation so that they will have negligible amplitude when they reach the top of it. This ensures that reflections of energy are minimised and a correct flux of vertical momentum results. This absorber of waves was needed in the experiment because the BC changes the flow and convective activity, since convective motion is able to produce gravity waves even without mean wind, as is implied by expression for D for the horizontal momentum for these dry mountain wave experiments. The effect of the sponge layer is that the numerical solution provided by the model absorbs waves without changing the solution appreciably and, after the initial adjustment, resolves the flow with a very high degree of approximation. This experiment shows that the mountain introduces, in the wind and thermodynamic fields, a dynamic perturbation which depends strongly on the height of the ridge; this choice affects both linear and non-linear perturbation of the wind field. Therefore, given the size of the domain in the vertical (20 km) and its resolution in space and time, the height of the mountain (corresponding to the amplitude h m in the wave
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Spatial and Temporal Variability of Rainfall at Seasonal and Annual Time Scales at Tekeze River Basin, Ethiopia

Spatial and Temporal Variability of Rainfall at Seasonal and Annual Time Scales at Tekeze River Basin, Ethiopia

Keywords: Seasonal Rainfall, Annual Rainfall, Temporal variability, spatial variability Introduction The use of implementing expensive and elaborate rainfall monitoring networks at a basin is to capture and understand the spatial and temporal variability of rainfall. Rainfall is the most important hydrological variable and it varies considerably over space and time. This variability makes it a major source of risk for agricultural production especially for a country like Ethiopia whose economy is dependent on rain-fed agriculture. This sector is highly sensitive to the spatial and temporal variability of rainfall and much below normal rainfall years in the country resulted in low agricultural production and as a consequence it affected millions of people in the country (Wolde-Mariam, 1984, Degefu, 1987, Hurni, 1993, Camberlin, 1997 and Aredo and Seleshi, 2003) The spatial and temporal variability of water resources is also affected due to rainfall variability. Rainfall variability has greater impact on hydrology and water resources.(Novtny and Stefan,2007). The study of rainfall variability in time and space over long period of time is basic for water resources management and decision making strategies. According to (Michaelides, 2009) understanding rainfall variability in time and space helps greatly for agricultural planning, rainfall-runoff modeling, water resources assessments and climate change and environmental impact assessments.
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Effect of rainfall variability and land use on runoff and sediment in the pipiripau river basin, in the Distrito Federal, Brazil

Effect of rainfall variability and land use on runoff and sediment in the pipiripau river basin, in the Distrito Federal, Brazil

CHAVES, H.M.L.; BRAGA, B.; DOMINGUES, A.F. & SANTOS, D.G. Quantificação dos benefícios ambientais e compensações financeiras do ”Programa do Produtor de Água (ANA)”. I. Teoria. R. Bras. Rec. Hídricos, 9:5-14, 2004. CHAVES, H.M.L.; SOUZA. E.; SILVA, C. & OLIVEIRA, C. Reliability of the NRCS (SCS) equation in the estimation of runoff in small ungaged basins in Brazil. In: IAHS SCIENTIFIC ASSEMBLY, 7., Symposium S1- Sediment Budgets Abstracts. Foz do Iguaçu, 2005. (meio magnético) CHOW, V.T.; MAIDMENT, D.R. & MAYS, L.W. Applied

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Rangeland water requirement satisfaction index under rainfall variability and predicting future rainfall scenarios: implication for availability of feed resources

Rangeland water requirement satisfaction index under rainfall variability and predicting future rainfall scenarios: implication for availability of feed resources

The climate record in our study contains the latitude, longitude, and elevation of the location and monthly values of rainfall, daily average temperature, and daily average temperature variation (Jones and Thornton 2013). It also included the temporal phase angle, that is, the degree by which the climate record is “rotated” in date. This rotation is done to eliminate timing differ- ences in climate events, such as the seasons in the Northern and Southern hemispheres, so that analysis can be done on standardized climate data to identify and account for the lag phase between climate cycles. The climate record is rotated to a standard date, using the 12-point Fast Fourier transform, on the basis of the first phase angle which is calculated using both rainfall and temperature values. We used MarkSim software, which is suited for future weather generator and a program for pattern scaling of future climate simulations in Mieso, Jigjiga, and Shinile districts. The MarkSim-GCM soft- ware used latitude, longitude, and elevation of the study districts (Jones and Thornton 2013). For each district, data of future rainfall and temperature (2020–2099) were downscaled during our study. Moreover, future rainfall and temperature changes were analyzed for three-time slot-centered between 2020–2049, 2040– 2069, and 2070–2099, and these values were compared with the trend and variability of the base period rainfall data (1984 – 2015).
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Identification of rainfall temporal patterns

Identification of rainfall temporal patterns

As rainstorm pattern varies among events, it is useful to categorize them into several representative types so that individual rainfall patterns within each type are similar to one another, but not necessarily identical, whereas individual rainfall patterns between different types are dissimilar. Euclidean distance based on K-means clustering method by MacQueen (1967) is adopted herein to classify pattern typically occurred in Johor. Hence, the study used dimensionless rainfall mass curves ordinates denoted as F-based ordinates as the attributes in statistical cluster analysis to identify representative rainfall patterns in Johor. The representative of rainfall pattern can be determined after the rainfall event data is classified into 3,4,5 and 6 group. To remove the scale effects of the attributes used in the cluster analysis, attributes are standardized so that they have a zero mean and a unit standard deviation. The proper number of rainfall patterns can be determined by identifying the appropriate number of group that results from the k-means clustering analysis. Since the appropriate number of group to represent rainfall pattern is not known in advanced, it is commonly determined by a trial-and-error process of visually examining the averaged dimensionless mass curve for each group resulting from the cluster analysis. The better clustering is when the similarity within a group is greater and the difference between groups is greater. The K-means clustering algorithm is performed by using the three steps until its convergence. The three steps are (1) determine the centroid coordinate and by using k=3, 4, 5 and 6; (2) determine the distance of each object to the centroids and (3) group the object based on minimum distance. Repeat the three steps above until convergence or no object move group.
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Temporal analysis of the relationship between leptospirosis, rainfall levels and seasonality, Santa Catarina, Brazil, 2005-2015

Temporal analysis of the relationship between leptospirosis, rainfall levels and seasonality, Santa Catarina, Brazil, 2005-2015

A variability pattern in rainfall volume and occurrence of leptospirosis cases defines two seasonal periods. The lowest average rainfall (140.95 mm/month) and a lower number of diagnosed cases (1,620) were evidenced from April to September. From October to March, a higher average rainfall (176.41 mm/month) was detected as well as a higher number of leptospirosis cases (3,654). When comparing the two seasonal periods, the leptospirosis rates ranged, negatively, from 6.07 to 4.56/100,000, from April to September (24.9%), and positively, from 4.71 to 11.84 thousand, from October to March (an increase of 151.3%). The months of January totaled the highest number of leptospirosis cases (n=770) and the highest average in the monthly rainfall rates (213.20 mm), representing the largest proportional contribution in the number of leptospirosis cases (14.6%) and the highest average contributions in the rain volume (11.20%). The months of January and February accounted for almost 30% of the cases and 20% of rainfall in the period studied. Still, the highest differences concerning the incidence rates, comparing all the months, taking as reference the values of the month with the lowest rates (August), showed an incidence of leptospirosis to more than triple (RR>3.0; p<0.001) in the period from December to March. In the same way, December to March showed the highest ratio of leptospirosis cases generated for an average amount of rain, with approximately twice the ratio cases/precipitation index for any one of these months compared to the remainder months of the period. An average elevation was detected in leptospirosis cases from October until March, followed by a decline, from April on, corresponding to a period of decline until the beginning of August.
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Weather. Trends in Spatio-Temporal Variability in Annual Rainfall in Ghana, ( ) Waylen, Peter; University of Florida, Geography

Weather. Trends in Spatio-Temporal Variability in Annual Rainfall in Ghana, ( ) Waylen, Peter; University of Florida, Geography

For Peer Review Rainfall variability in humid West Africa (south of 8 o N) is less studied than that of the Sahelian zone, responsible for the general consensus of a downward rainfall trend in West Africa over the last few decades. Such a generalization may not be useful for model l ing and planning purposes. Rainfall mechanisms in the drier Sahel and the humid Guinea Coast region differ; the former region has a single rainfall peak (in summer) but the later region has a bi-modal seasonal distribution. It is therefore not necessarily true that a failure in the rainfall regime, and its subsequent impact on agriculture and livelihood, in one zone means the same for the other. In fact, it has been observed that a warmer South Atlantic is associated with more rainfall in the Guinea Coast region and less rainfall in the Sahel (Gu and Adler 2003). It is also suggested that the El Niño- Southern Oscillation phenomena (ENSO) may be more strongly associated with Sahelian rainfall than with that of the Guinea Coast region (Ward et al. 2004). Therefore there is a need to investigate rainfall trends and variability in the humid areas to better inform agricultural decision - making. Agricultural productivity has decreased in recent decades following declining rainfall since the early 1970s, with the rainfall showing signs of improvement after 2000.
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Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

In Africa, climate variability can limit development and deepen poverty [1][2]. For example, banks are unlikely to lend to farmers if the crop failure caused by drought will result in defaults, as many farmers are likely to default in the same year [3]. In the last 10 years, a new type of insurance has been designed to mitigate climate-related risk. Weather index-based insurance (WII) is linked to a weather-based index such as rainfall, rather than to a physical outcome, such as a low yield [4]. WII has the potential to be cheap to administer and transparent to operate. In principle, WII provides a means of insuring smallholder farmers throughout Africa against perils, including drought.
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Variability of annual rainfall in Tasmania

Variability of annual rainfall in Tasmania

Moreover, although in general the familiar principle that the lower the rainfall the greater the variability applies to much of Tasmania, the highest percentages of mean deviation for th[r]

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