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.

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.

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.

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.

An assessment of a daily rainfall erosivity model for New South Wales

Soil Research ◽  
1996 ◽  
Vol 34 (1) ◽  
pp. 139 ◽  
Author(s):  
B Yu ◽  
CJ Rosewell
Keyword(s):  
New South ◽  
New South Wales ◽  
Daily Rainfall ◽  
Climate Change Impacts ◽  
Seasonal Distribution ◽  
Coefficient Of Determination ◽  
Rainfall Erosivity ◽  
South Wales ◽  
Parameter Values ◽  
Variance Explained

A rainfall erosivity model using daily rainfall amounts to estimate rainfall erosivity was tested for 29 sites in New South Wales to see whether such a model could adequately describe the temporal variation and seasonal distribution of rainfall erosivity. The coefficient of determination varied from 0.57 to 0.97 and the average discrepancy between actual and estimated seasonal distribution was no more than 3%. A set of parameter values for sites without pluviograph data was recommended for New South Wales. With this set of recommended parameter values, the percentage of total variance explained was decreased to 44%–89% for the 29 sites. Large errors, however, can occur when estimating extreme storm erosivity with large return periods. The daily erosivity model could be used for determining the seasonal distribution of rainfall erosivity or for simulating changes to rainfall erosivity as part of climate change impacts assessment.

Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA

2020 ◽  
Author(s):  
Maria Nezi ◽  
Ioannis Tsoukalas ◽  
Charalampos Ntigkakis ◽  
Andreas Efstratiadis
Keyword(s):  
Soil Moisture ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Rainfall ◽  
Event Analysis ◽  
Antecedent Soil Moisture ◽  
Daily Streamflow ◽  
Accumulated Rainfall ◽  
Moisture Conditions ◽  
Parameter Values

<p>Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.</p>

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.

scholarly journals Estimation of Temporal R-factor Based on Monthly Precipitation Data

2021 ◽  
Vol 2070 (1) ◽  
pp. 012210
Author(s):  
Narendra Kumar Maurya ◽  
Prakash Singh Tanwar
Keyword(s):  
Heavy Precipitation ◽  
Rainfall Amount ◽  
Annual Rainfall ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
R Factor ◽  
R Value ◽  
Monthly Precipitation Data ◽  
Precipitation Events

Abstract This study assesses temporal variation in rainfall erosivity of Gurushikhar, Rajasthan, (India) on a monthly precipitation basis in the form of the USLE/RUSLE R-factor. The objective of the paper is to theoretically calculate rainfall erosivity when the unavailability of high temporal resolution pluviographic rainfall data such as Indian condition. In the study, the rainfall erosivity has been calculated using the Modified Fourier Index. The results show that the annual rainfall erosivity factor (R) value highest in the year 2017 and lowest in 1974. Conferring to an examination through NASA, earth’s global superficial temperatures in 2017 ranked as second warmest since 1880. Therefore, the rainfall amount was more in 2017 compared to past years, and also rainfall erosivity value suddenly increased in 2017, achieved the highest value. They concluded that the heavy precipitation events in the year are lead to an increase in rainfall erosivity value and risk of soil erosion.

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