Spatial distribution and periodicity of mean annual precipitation south of the Sahara

1978 ◽  
Vol 26 (1) ◽  
pp. 17-27 ◽  
Author(s):  
D. Klaus
Keyword(s):  
Spatial Distribution ◽  
Annual Precipitation ◽  
Mean Annual Precipitation

scholarly journals Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal

2021 ◽  
Author(s):  
Alexandru Antal ◽  
Pedro M. P. Guerreiro ◽  
Sorin Cheval
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Polynomial Interpolation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Distribution ◽  
Interpolation Methods ◽  
Mainland Portugal ◽  
The Mean ◽  
Spatial Interpolation Methods

Abstract Precipitation has a strong and constant impact on different economic sectors, environment, and social activities all over the world. An increasing interest for monitoring and estimating the precipitation characteristics can be claimed in the last decades. However, in some areas the ground-based network is still sparse and the spatial data coverage insufficiently addresses the needs. In the last decades, different interpolation methods provide an efficient response for describing the spatial distribution of precipitation. In this study, we compare the performance of seven interpolation methods used for retrieving the mean annual precipitation over the mainland Portugal, as follows: local polynomial interpolation (LPI), global polynomial interpolation (GPI), radial basis function (RBF), inverse distance weighted (IDW), ordinary cokriging (OCK), universal cokriging (UCK) and empirical Bayesian kriging regression (EBKR). We generate the mean annual precipitation distribution using data from 128 rain gauge stations covering the period 1991 to 2000. The interpolation results were evaluated using cross-validation techniques and the performance of each method was evaluated using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (R) and Taylor diagram. The results indicate that EBKR performs the best spatial distribution. In order to determine the accuracy of spatial distribution generated by the spatial interpolation methods, we calculate the prediction standard error (PSE). The PSE result of EBKR prediction over mainland Portugal increases form south to north.

Applying support vector machines optimized by genetic algorithm for estimating the spatial distribution of mean annual precipitation

2021 ◽  
Vol 14 (8) ◽  
Author(s):  
Mohammed Achite ◽  
Paraskevas Tsangaratos ◽  
Ioanna Ilia ◽  
Abderrezak Kamel Toubal
Keyword(s):  
Genetic Algorithm ◽  
Spatial Distribution ◽  
Support Vector Machines ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Support Vector ◽  
Vector Machines

scholarly journals Elevation Dependency of Precipitation over Southern Slope of Central Himalaya

Jalawaayu ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Shankar Sharma ◽  
Nitesh Khadka ◽  
Bikash Nepal ◽  
Shravan Kumar Ghimire ◽  
Nirajan Luintel ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Precipitation Amount ◽  
Winter Season ◽  
Annual Precipitation ◽  
Spatial And Temporal Variation ◽  
Mean Annual Precipitation ◽  
Eastern Region ◽  
Monthly Precipitation ◽  
Monsoon Precipitation ◽  
Post Monsoon

Precipitation plays vital roles in the global water cycle, knowledge of the spatial and temporal variation of the precipitation is essential to understanding extreme environmental phenomena such as floods, landslides, and drought. In this paper, the integrated characteristics of precipitation during 1980–2016 over Nepal along with the seasonal elevation dependency of precipitation were examined for three different regions over the country using Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. The spatial distribution of mean annual precipitation varies significantly with the highest (lowest) precipitation of ~5500 (~100) mm/year in the Arun valley (Manang and Mustang). The precipitation regime of the country is determined by the contribution of the monthly precipitation amount with distinct spatial gradients between the eastern and the western sides during pre-monsoon, post-monsoon, and winter seasons. On the contrary, the spatial distribution of monsoon precipitation tends to more heterogeneous with visible differences between the lowland, midland, and highlands as similar to the annual one. Further, elevation dependency of seasonal precipitation revealed that the winter and post-monsoon precipitation distribution in western and central are very similar, whereas post-monsoon precipitation was found slightly higher than winter season in the eastern region. The highest precipitation areas in eastern and central region are located between 2000-2500 m, which is between 500 and 1000 m in the western region of the country. Overall, the pre-monsoon, summer monsoon and annual precipitation increases gradually with elevation upto 2500 m and then decreases with increasing elevation, whereas winter and post-monsoon precipitation are almost identical to each elevation interval of 500 m.

scholarly journals Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 830
Author(s):  
Gabriele Buttafuoco ◽  
Massimo Conforti
Keyword(s):  
Spatial Distribution ◽  
Ordinary Kriging ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Block Kriging ◽  
Kriging With External Drift ◽  
Elevation Data ◽  
Support Size ◽  
Ordinary Cokriging ◽  
External Drift

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates.

Spatial Simulation of Precipitation Resources in Chongqing

2012 ◽  
Vol 518-523 ◽  
pp. 4261-4265
Author(s):  
Xiao Song Lin ◽  
Sha Sha Yu ◽  
Hai Yan Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Regional Difference ◽  
Interpolation Method ◽  
Annual Precipitation ◽  
Regression Equations ◽  
Spatial Simulation ◽  
Simulation Data ◽  
Spatial Interpolation Method ◽  
Precise Simulation

Years’ precipitation data of Chongqing from 101 metrological stations has been adopted in the paper and the regression equations between annual precipitation and altitude, longitude, and height have been obtained by the use of SPSS, then elaborate simulation of Chongqing’s precipitation resources based on regression analysis was completed through the 1km×1km grid system and fitted equation. Elaborated simulation of precipitation resources was realized by best spatial interpolation method with the support of GIS; then the results of two different simulation methods were coupled in the form of linear combination to obtain the coupling simulation of spatial distribution of Chongqing’s precipitation resources, finally the precipitation resources were summed up and distributed according to different administration areas at county level and thus obtain precise simulation data of precipitation resources in each county of Chongqing. The results showed that there is a remarkable regional difference in the spatial distribution of precipitation resources of Chongqing, and it decreases from the southeast to the northwest in general, with the annual precipitation higher than 1270mm in southeast and lower than 1080mm in northwest.

Ecological Niche Modeling Predicting the Potential Distribution of African Horse Sickness Virus from 2020 - 2060

2021 ◽  
Author(s):  
Ayalew Assefa ◽  
Abebe Tibebu ◽  
Amare Bihon ◽  
Alemu Dagnachew ◽  
Yimer Muktar
Keyword(s):  
Solar Radiation ◽  
Ecological Niche ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Mean Annual Precipitation ◽  
African Horse Sickness ◽  
Diurnal Range ◽  
Annual Minimum Temperature ◽  
Precipitation Seasonality ◽  
Vector Borne

Abstract African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African Horse Sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its fatality rate is high, trade ban and disease control costs. In planning of vectors and vector borne diseases, the application of Ecological niche models (ENM) used an enormous contribution in exactly delineating the suitable habitats of the vector. We developed an ENM with the objective of delineating the global suitability of AHSv outbreaks retrospective based on data records from 2005–2019. The model was developed in R software program using Biomod2 package with an Ensemble modeling technique. Predictive environmental variables like mean diurnal range, mean precipitation of driest month(mm), precipitation seasonality (cv), mean annual maximum temperature (oc), mean annual minimum temperature (oc) mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm) mean annual precipitation (mm), solar radiation (kj /day), elevation/altitude (m), wind speed (m/s) were used to develop the model. From these variables, solar radiation, mean maximum temperature, average annual precipitation, altitude and precipitation seasonality contributed 36.83%, 17.1%, 14.34%, 7.61%, and 6.4%, respectively. The model depicted the sub-Sahara African continent as the most suitable area for the virus. Mainly Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar and Malawi are African countries identified as highly suitable countries for the virus. Besides, OIE-listed disease-free countries like India, Australia, Brazil, Paraguay and Bolivia have been found suitable for the virusThis model can be used as an epidemiological tool in planning control and surveillance of diseases nationally or internationally.

Spatial Distribution of a Daily, Monthly and Annual Precipitation Concentration Index

2019 ◽  
pp. 1-27
Author(s):  
Farshad Ahmadi ◽  
Mohammad Nazeri ◽  
Rasoul Mirabbasi ◽  
Keivan Khalili ◽  
Deepak Jhajharia
Keyword(s):  
Spatial Distribution ◽  
Concentration Index ◽  
Annual Precipitation ◽  
Precipitation Concentration Index ◽  
Precipitation Concentration

scholarly journals Temporal and spatial distributions of precipitation on the Huang-Huai-Hai Plain during 1960–2019, China

2021 ◽  
Author(s):  
Minhua Ling ◽  
Hongbao Han ◽  
Xingling Wei ◽  
Cuimei Lv
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Kendall Test ◽  
Winter Precipitation ◽  
Annual Precipitation ◽  
Temporal And Spatial Distribution ◽  
Global Pattern ◽  
The North ◽  
Variance Contribution ◽  
Temporal And Spatial

Abstract The Huang-Huai-Hai Plain is an important commercial grain production base in China. Understanding the temporal and spatial variations in precipitation can help prevent drought and flood disasters and ensure food security. Based on the precipitation data for the Huang-Huai-Hai Plain from 1960 to 2019, this study analysed the spatiotemporal distribution of total precipitation at different time scales using the Mann–Kendall test, the wavelet analysis, the empirical orthogonal function (EOF), and the centre-of-gravity model. The results were as follows: (1) The winter precipitation showed a significant upward trend on the Huang-Huai-Hai Plain, while other seasonal trends were not significant. (2) The precipitation on the Huang-Huai-Hai Plain shows a zonal decreasing distribution from southeast to northwest. (3) The application of the EOF method revealed the temporal and spatial distribution characteristics of the precipitation field. The cumulative variance contribution rate of the first two eigenvectors reached 51.5%, revealing two typical distribution fields, namely a ‘global pattern’ and a ‘north-south pattern’. The ‘global pattern’ is the decisive mode, indicating that precipitation on the Huang-Huai-Hai Plain is affected by large-scale weather systems. (4) The annual precipitation barycentres on the Huang-Huai-Hai Plain were located in Jining city and Taian city, Shandong Province, and the spatial distribution pattern was north-south. The annual precipitation barycentres tended to move southwest, but the trend was not obvious. The annual precipitation barycentre is expected to continue to shift to the north in 2020.

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