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.

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.

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.

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 Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

2021 ◽  
Vol 13 (18) ◽  
pp. 3631
Author(s):  
Austin Madson ◽  
Yongwei Sheng
Keyword(s):  
United States ◽  
Water Level ◽  
Cluster Computing ◽  
Mean Squared Error ◽  
The United States ◽  
United States Geological Survey ◽  
Continental Scale ◽  
Water Level Changes ◽  
Lakes And Reservoirs ◽  
The Mean

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

scholarly journals Impact Du Changement Climatique Sur L’évolution De L'érosivité Des Pluies Dans Le Rif Occidental (Nord Du Maroc)

2016 ◽  
Vol 12 (32) ◽  
pp. 79 ◽  
Author(s):  
Fatiha Choukri ◽  
Mohamed Chikhaoui ◽  
Mustapha Naimi ◽  
Damien Raclot ◽  
Yannick Pepin ◽  
...  
Keyword(s):  
Water Conservation ◽  
Soil Loss ◽  
Agricultural Practices ◽  
Daily Rainfall ◽  
Soil Protection ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Protection Measures ◽  
Pronounced Increase ◽  
R Factor

The rainfall erosivity factor (R factor in Universal Soil Loss Equation), denoting rain energy, is a key factor for soil loss modeling. Its present and future estimation is thus significant for any action related to soil and water conservation and planning. The extended series of precipitations at high temporal resolution, essential to its evaluation, are not readily available in Morocco. The objective of this study is to predict the evolution of rainfall erosivity by 2080 in the Western Rif, based on predictions of daily rain provided by the General Climatic Models (GCMs). To reflect the seasonal variability of rainfall, and thus of R factor, a series of instantaneous rain measured over 35 consecutive years was used to monthly calibrate a model to calculate erosivity based of daily rainfall. The application of this model to the predictions of different GCMs and for various scenarios of climate evolution in Western Rif shows a weak evolution of erosivity on an annual timescale but a very strong evolution of the latter according to seasons with a reduction in R factor during winter and spring, and a pronounced increase during summer and autumn. This discernable change of the seasonality of rainfall erosivity is very useful for adjusting the evolution of agricultural practices and for selecting appropriate soil protection measures.

scholarly journals Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xihua Yang ◽  
Xiaojin Xie ◽  
De Li Liu ◽  
Fei Ji ◽  
Lin Wang
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Climate Impact ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Local Climate ◽  
Erosion Risk ◽  
Daily Rainfall Data ◽  
Interpolation Techniques

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

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.

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 Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain)

2015 ◽  
Vol 63 (1) ◽  
pp. 55-62 ◽  
Author(s):  
David Hernando ◽  
Manuel G. Romana
Keyword(s):  
Mean Squared Error ◽  
Rain Gauge ◽  
Accurate Estimate ◽  
Coefficient Of Determination ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
R Factor ◽  
Record Length

Abstract The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (Rfactor) of the Universal Soil Loss Equation in regions without good spatial and temporal data coverage. In particular, the R-factor is only known at 16 rain gauge stations in the Madrid Region (Spain). The objectives of this study were to identify a readily available estimate of the R-factor for the Madrid Region and to evaluate the effect of rainfall record length on estimate precision and accuracy. Five estimators based on monthly precipitation were considered: total annual rainfall (P), Fournier index (F), modified Fournier index (MFI), precipitation concentration index (PCI) and a regression equation provided by the Spanish Nature Conservation Institute (RICONA). Regression results from 8 calibration stations showed that MFI was the best estimator in terms of coefficient of determination and root mean squared error, closely followed by P. Analysis of the effect of record length indicated that little improvement was obtained for MFI and P over 5- year intervals. Finally, validation in 8 additional stations supported that the equation R = 1.05·MFI computed for a record length of 5 years provided a simple, precise and accurate estimate of the R-factor in the Madrid Region.

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