Rainfall erosivity estimation using daily rainfall amounts for South Australia

Soil Research ◽  
1996 ◽  
Vol 34 (5) ◽  
pp. 721 ◽  
Author(s):  
B Yu ◽  
CJ Rosewell
Keyword(s):  
Model Performance ◽  
South Australia ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
The South ◽  
Winter Rainfall ◽  
R Factor

The rainfall erosivity model relating storm erosivity to daily rainfall amounts was tested for 4 sites in South Australia where seasonal rainfall erosivity is generally out of phase with seasonal rainfall because of the predominant winter rainfall. The model worked reasonably well, with the coefficient of efficiency varying from 0.54 to 0.77, and the average discrepancy between actual and estimated monthly distribution was no more than 3%. The model performance in the winter rainfall area is similar to that in the uniform and summer rainfall areas. A set of regional parameter values estimated using a combined dataset is recommended for other sites in the agricultural and viticultural areas of South Australia where the mean annual rainfall ranges from 300 to 500 mm. The R-factor and its seasonal distribution were estimated for 99 sites in South Australia using long-term daily rainfall data. The R-factor varies mostly between 250 and 500 MJ . mm/(ha . h . year). Rainfall erosivity peaks in winter in the southern part of the western agricultural area and the south-east corner of the State, while it peaks in summer in the inland area east of the South Flinders and Mount Lofty Ranges.

Rainfall erosivity and its estimation for Australia's tropics

Soil Research ◽  
1998 ◽  
Vol 36 (1) ◽  
pp. 143 ◽  
Author(s):  
B. Yu
Keyword(s):  
Model Performance ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Wet Season ◽  
R Factor ◽  
Different Seasons ◽  
Monthly Distribution ◽  
The Tropics ◽  
Parameter Values

Pluviograph data at 6-min intervals for 41 sites in the tropics of Australia were used to compute the rainfall and runoff factor (R-factor) for the Revised Universal Soil Loss Equation (RUSLE), and a daily rainfall erosivity model was validated for these tropical sites. Mean annual rainfall varies from about 300 mm at Jervois (015602) to about 4000 at Tully (032042). The corresponding R-factor ranges from 1080 to 33500 MJ·mm/(ha ·h·year). For these tropical sites, both rainfall and rainfall erosivity are highly seasonal with a single peak in February mostly. Summer months (November–April) typically contribute about 80% of annual rainfall and about 90% of the R-factor. The daily erosivity model performed better for the tropical sites with a marked wet season in summer in comparison to model performance in temperate regions of Australia where peak rainfall and peak rainfall erosivity may occur in different seasons. A set of regional parameters depending on seasonal rainfall was developed so that the R-factor and its seasonal distribution can be estimated for sites without pluviograph data. The prediction error using the regional parameter values is about 20% for the R-factor and 1% for its monthly distribution for these tropical sites.

scholarly journals Validation of Satellite Rainfall Products for Western Uganda

2014 ◽  
Vol 15 (5) ◽  
pp. 2030-2038 ◽  
Author(s):  
Jeremy E. Diem ◽  
Joel Hartter ◽  
Sadie J. Ryan ◽  
Michael W. Palace
Keyword(s):  
Daily Rainfall ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Boreal Summer ◽  
Time Step ◽  
African Rainfall ◽  
Rainfall Total ◽  
Mean Annual Rainfall ◽  
Western Uganda

Abstract Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations.

scholarly journals Database of rainfall erosivity factor for 141 locations in Brazil.

2021 ◽  
Vol 1 (3) ◽  
pp. 95-101
Author(s):  
Roberto Avelino Cecílio ◽  
João Paulo Bestete de Oliveira ◽  
David Bruno de Sousa Teixeira ◽  
Fernando Falco Pruski ◽  
Sidney Sara Zanetti
Keyword(s):  
Soil Erosion ◽  
Best Practices ◽  
Regression Models ◽  
Environmental Problem ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Lack Of Information ◽  
Erosivity Index

Soil erosion is a serious agricultural and environmental problem considered as a threat to sustainable development around the world. Rainfall is the primary cause of soil erosion, what leads the knowledge of its potential to cause soil erosion (rainfall erosivity – R-factor) to be a valuable tool for the design of land conservation best practices. As Brazil has a lack of information about rainfall erosivity, the present paper has determined the R-factor of 141 pluviographic stations distributed over Brazilian territory. Initially, erosive rainfalls were identified, and then the EI30 erosivity index was used to obtain the rainfall erosivity values. Regression models for the estimation of rainfall erosivity using daily rainfall data were established based on the correlation between the monthly average values of erosivity and the modified Fournier index. Results showed that the annual rainfall erosivity in the Brazilian stations analyzed ranged from 368.7 to 16,850.6 MJ mm ha-1 h-1 year-1. The results presented help to expand information about the spatial distribution of rainfall erosivity in Brazil, contributing to better conservation planning of land use.

scholarly journals Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1498 ◽  
Author(s):  
Solomon Mulugeta ◽  
Clifford Fedler ◽  
Mekonen Ayana
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Winter Rainfall ◽  
Adaptation Measures ◽  
Awash River Basin ◽  
Long Term Trends ◽  
Increasing Trends

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

Spatial and seasonal distribution of rainfall erosivity in Australia

Soil Research ◽  
2002 ◽  
Vol 40 (6) ◽  
pp. 887 ◽  
Author(s):  
Hua Lu ◽  
Bofu Yu
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Seasonal Distribution ◽  
Rainfall Erosivity ◽  
Continental Scale ◽  
R Factor ◽  
Monthly Distribution ◽  
The Mean

Spatially distributed rainfall erosivity and its seasonal distribution are needed to use the revised universal soil loss equation (RUSLE) for erosion risk assessment at large scale. An erosivity model and 20-year daily rainfall data at 0.05° resolution were used to predict the R-factor and its monthly distribution for RUSLE in Australia. Predicted R-factor values were compared with those previously calculated using pluviograph data for 132 sites around Australia. The daily erosivity model was further evaluated for 43 sites where long-term pluviograph data were available. Predicted and calculated monthly distributions of the R-factor were compared for these 43 sites. For the 132 sites where R-factor values were compiled from previous investigations, the model efficiency was 0.81 with root mean squared error (rmse) of 1832 MJ.mm/(ha.h.year), or 47.5% of the mean for the 132 sites. For the additional 43 sites, the coefficient of efficiency was 0.93 with a 12.7 mm rainfall threshold, and 0.94 when all storms were included in the calculations. The rmse was 908 MJ.mm/(ha.h.year), or 28.6% of the mean for the 43 sites with a zero rainfall threshold. The prediction error for monthly distribution of the R-factor was 2.3% with a zero threshold and 2.5% with 12.7�mm threshold. This and previous studies have shown that the daily rainfall erosivity model can be used to accurately predict the R-factor and its seasonal distribution in Australia. Digital maps were produced showing the spatial and seasonal distribution of the R-factor at 0.05° resolution in Australia. These maps have been used to assess rill and sheet erosion rate at the continental scale.

Tendencies in the geographical distribution of the genus Mariscus (Cyperaceae) in southern Africa*

Bothalia ◽  
1983 ◽  
Vol 14 (3/4) ◽  
Author(s):  
P. J. Voster
Keyword(s):  
Geographical Distribution ◽  
Southern Africa ◽  
Environmental Conditions ◽  
West Coast ◽  
Summer Rainfall ◽  
Annual Rainfall ◽  
The West ◽  
Water Requirements ◽  
Winter Rainfall ◽  
Flora Of Southern Africa

The genus Mariscus occurs over practically the whole of the Flora of Southern Africa (FSA) region, with the exception of the west coast and the Karoo region of the Cape Province. Practically no species occur under conditions of less than 250 mm of rain per annum, whereas the highest concentrations of subgenera/sections and species occur where the annual rainfall is more than 500 mm. All the species in the region are summer rainfall plants, or if they occur in the winter rainfall area they behave like summer rainfall plants. The different subgenera/sections recognized within the genus differ widely in respect of their water requirements, yet there is a general concentration of taxa in the eastern part of the country where the rainfall is higher than in the west. Nevertheless, the highest concentrations of taxa are not in the areas of highest rainfall, which suggests that topographical diversity is probably the most important factor determining concentrations of taxa. The geographical distribution of each subgenus/section in the FSA region is explained in relation to the total distribution of the subgenus/section. The possible climatic or environmental conditions which may determine the distribution of each subgenus/section are mentioned, and the concentration of species within each subgenus/section is demonstrated by means of maps.

scholarly journals Rainfall erosivity mapping over mainland China based on high density hourly rainfall records

2021 ◽  
Author(s):  
Tianyu Yue ◽  
Shuiqing Yin ◽  
Yun Xie ◽  
Bofu Yu ◽  
Baoyuan Liu
Keyword(s):  
Soil Erosion ◽  
Average Rate ◽  
Mainland China ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
R Factor ◽  
Hourly Rainfall ◽  
Hourly Data ◽  
Factor Map

Abstract. Rainfall erosivity represents the effect of rainfall and runoff on the average rate of soil erosion. Maps of rainfall erosivity are indispensable for soil erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve current erosivity maps based on daily rainfall data for mainland China, hourly rainfall data from 2381 stations for the period 1951–2018 were collected to generate the R factor and the 1-in-10-year EI30 maps (available at https://dx.doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020). Rainfall data at 1-min intervals from 62 stations (18 stations) were collected to calculate rainfall erosivities as true values to evaluate the improvement of the new R factor map (1-in-10-year EI30 map) from the current maps. Both the R factor and 1-in-10-year EI30 decreased from the southeastern to the northwestern, ranging from 0 to 25300 MJ mm ha−1 h−1 a−1 for the R factor and 0 to 11246 MJ mm ha−1 h−1 for the 1-in-10-year EI30. New maps indicated current maps existed an underestimation for most of the southeastern areas and an overestimation for most of the middle and western areas. Comparing with the current maps, the R factor map generated in this study improved the accuracy from 19.4 % to 15.9 % in the mid-western and eastern regions, from 45.2 % to 21.6 % in the western region, and the 1-in-10-year EI30 map in the mid-western and eastern regions improved the accuracy from 21.7 % to 13.0 %. The improvement of the new R factor map can be mainly contributed to the increase of data resolution from daily data to hourly data, whereas that of new 1-in-10-year EI30 map to the increase of the number of stations from 744 to 2381. The effect of increasing the number of stations to improve the interpolation seems to be not very obvious when the station density was denser than about 10 · 103 km2 1 station.

scholarly journals MULTIVARIATE MODELS FOR PREDICTING RAINFALL EROSIVITY FROM ANNUAL RAINFALL AND GEOGRAPHIVAL COORDIATES IN A REGION WITH A NON- UNIFORM PLUVIAL REGIME

2020 ◽  
Vol 51 (5) ◽  
pp. 1249-1261
Author(s):  
Keya & Karim
Keyword(s):  
Linear Models ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Storm Events ◽  
Multivariate Models ◽  
Good Correspondence ◽  
Study Region ◽  
R Factor ◽  
Unseen Data ◽  
Predicted Values

Soil erosion by water is a major land degradation problem because it threatens the farmer’s livelihood and ecosystem's integrity. Rainfall erosivity is one of the major controlling factors inducing this process. One obstacle of estimating the R-factor is the lack of detailed rainfall intensity data worldwide. To overcome the problem of data scarceness for individual analysis of storm events for developing the country with a non-uniform pluvial regime like the upper part of Iraq, multivariate models were derived for estimating annual rainfall erosivity. They were based on annual rainfall data and geographical coordinates of a group of meteorological stations distributed over the study area. A host of statistical indices were selected to evaluate adequately the model's performance. Further, the models were cross-validated using k-fold procedure and unseen data. Subsequently, four linear models were proposed for estimating the annual erosivity for the study area. Good correspondence was found between the measured and predicted values. Among the proposed models, the model with the combination of annual rainfall, latitude and longitude outperformed the remaining proposed ones.  After calculating the annual, the ArcMap software ver. 10.2 was applied to map the spatial variability of the R-factor over the study region.

scholarly journals EVALUATING HYDROLOGIC MODEL PERFORMANCE OF GLOBAL AND LOCAL WEATHER DATA INPUTS

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 265-271
Author(s):  
J. Serrano ◽  
J. M. Jamilla ◽  
B. C. Hernandez ◽  
E. Herrera
Keyword(s):  
Assessment Tool ◽  
Statistical Significance ◽  
Model Performance ◽  
Daily Rainfall ◽  
Hydrologic Model ◽  
Weather Data ◽  
Annual Rainfall ◽  
Hydrologic Models ◽  
Event Based ◽  
Local Weather

Abstract. Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.

Grasslands NuSiral white clover (Trifolium repens L.)

2002 ◽  
Vol 42 (7) ◽  
pp. 1023 ◽  
Author(s):  
J. F. Ayres ◽  
L. A. Lane ◽  
J. R. Caradus ◽  
P. T. P. Clifford
Keyword(s):  
White Clover ◽  
Trifolium Repens ◽  
Summer Rainfall ◽  
Moisture Stress ◽  
Annual Rainfall ◽  
Winter Rainfall ◽  
Mild Winter ◽  
Winter Activity ◽  
Trifolium Repens L ◽  
Cattle And Sheep

Grasslands NuSiral is a medium–large-leaf white clover (Trifolium repens L.) cultivar with intermediate growth habit (midway between open and erect and dense and prostrate), early flowering maturity and high growing point density. G. NuSiral possesses the plant-type attributes known to be desirable for broad adaptation to dryland pasture conditions in Australia and has been developed for cattle and sheep pastures in both summer rainfall and winter rainfall areas where average annual rainfall exceeds 750 mm. G. NuSiral is expected to provide enhanced stolon survival and autumn recovery in environments where summer moisture stress is present but not intense, and winter growth comparable to cv. Haifa where mild winter conditions allow the expression of G. NuSiral's winter activity.

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