scholarly journals Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

2015 ◽  
Vol 12 (5) ◽  
pp. 4965-4996 ◽  
Author(s):  
S. Yin ◽  
Y. Xie ◽  
B. Liu ◽  
M. A. Nearing
Keyword(s):  
Daily Rainfall ◽  
Rainfall Amount ◽  
Rainfall Data ◽  
Data Availability ◽  
Rainfall Erosivity ◽  
Data Prediction ◽  
Yearly Average ◽  
Model Efficiency ◽  
Erosivity Index ◽  
Better Than

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event, EI30, is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models that relate more commonly available rainfall data resolutions, such as daily or monthly totals, to rainfall erosivity. Eleven stations with one-minute temporal resolution rainfall data collected from 1961 through 2000 in the eastern water-erosion areas of China were used to develop and calibrate 21 models. Seven independent stations, also with one-minute data, were utilized to validate those models, together with 20 previously published equations. Results showed that models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Prediction capabilities, as determined using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, were demonstrated for the 41 models including those for estimating erosivity at event, daily, monthly, yearly, average monthly and average annual time scales. Prediction capabilities were generally better using higher resolution rainfall data as inputs. For example, models with rainfall amount and maximum 60 min rainfall amount as inputs performed better than models with rainfall amount and maximum daily rainfall amount, which performed better than those with only rainfall amount. Recommendations are made for choosing the appropriate estimation equation, which depend on objectives and data availability.

scholarly journals Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

2015 ◽  
Vol 19 (10) ◽  
pp. 4113-4126 ◽  
Author(s):  
S. Yin ◽  
Y. Xie ◽  
B. Liu ◽  
M. A. Nearing
Keyword(s):  
Energy Intensity ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
The World ◽  
Model Efficiency ◽  
Erosivity Index ◽  
Soil Erosion By Water ◽  
Event Energy ◽  
Eastern Half

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values – EI30) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

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 Erosive rainfall in Rio do Peixe Valley in Santa Catarina, Brazil: Part I - Determination of the erosivity index

2017 ◽  
Vol 21 (11) ◽  
pp. 774-779
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Rain Gauge ◽  
The State ◽  
Rainfall Data ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Santa Catarina ◽  
R Factor ◽  
Erosivity Index ◽  
Erosive Rainfall

ABSTRACT This study aimed to determine the rainfall erosivity index in the Valley of Rio do Peixe, in the state of Santa Catarina, Brazil. The data series of three rain gauge stations in the cities of Campos Novos, Videira, and Caçador were used to determine the rainfall erosivity based on the EI30 index and to adjust the equations in order to estimate the EI30 value from the rainfall coefficient. On average, it was observed that erosive rains represents 81.4-88.5% of the annual precipitation. The adjusted equations can be used to estimate rainfall erosivity in locations with only rainfall data. The regional equation specified for the erosivity estimation is EI30 = 74.23 Rc0.8087. The R factor is 8,704.8; 7,340.8; and 6,387.1 MJ mm ha-1 h-1 year-1 for Campos Novos, Videira, and Caçador, respectively. In Campos Novos and Videira, the erosivity was classified as high, while in Caçador, it was classified as average.

scholarly journals Erosivity of rainfall in Lages, Santa Catarina, Brazil

2014 ◽  
Vol 38 (6) ◽  
pp. 1890-1905 ◽  
Author(s):  
Jefferson Schick ◽  
Ildegardis Bertol ◽  
Neroli Pedro Cogo ◽  
Antonio Paz González
Keyword(s):  
Temporal Distribution ◽  
Daily Rainfall ◽  
Rainfall Erosivity ◽  
Santa Catarina ◽  
Time Period ◽  
Erosivity Index ◽  
Annual Total ◽  
Erosive Rainfall ◽  
Management And Conservation ◽  
Erosive Capacity

The erosive capacity of rainfall can be expressed by an index and knowing it allows recommendation of soil management and conservation practices to reduce water erosion. The objective of this study was to calculate various indices of rainfall erosivity in Lages, Santa Catarina, Brazil, identify the best one, and discover its temporal distribution. The study was conducted at the Center of Agricultural and Veterinary Sciences, Lages, Santa Catarina, using daily rainfall charts from 1989 to 2012. Using the computer program Chuveros , 107 erosivity indices were obtained, which were based on maximum intensity in 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, 100, 110, 120, 135, 150, 165, 180, 210, and 240 min of duration and on the combination of these intensities with the kinetic energy obtained by the equations of Brown & Foster, Wagner & Massambani, and Wischmeier & Smith. The indices of the time period from 1993 to 2012 were correlated with the respective soil losses from the standard plot of the Universal Soil Loss Equation (USLE) in order to select the erosivity index for the region. Erosive rainfall accounted for 83 % of the mean annual total volume of 1,533 mm. The erosivity index (R factor) of rainfall recommended for Lages is the EI30, whose mean annual value is 5,033 MJ mm ha-1 h-1, and of this value, 66 % occurs from September to February. Mean annual erosivity has a return period estimated at two years with a 50 % probability of occurrence.

Daily rainfall data to identify trends in rainfall amount and rainfall-induced agricultural events in the Nuba Mountains of Sudan

2015 ◽  
Vol 122 ◽  
pp. 16-26 ◽  
Author(s):  
Sven Goenster ◽  
Martin Wiehle ◽  
Jens Gebauer ◽  
Abdalla Mohamed Ali ◽  
Roger D. Stern ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Amount ◽  
Rainfall Data ◽  
Daily Rainfall Data

scholarly journals Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area

2016 ◽  
Author(s):  
Y. Liu ◽  
L. Liu
Keyword(s):  
Cluster Analysis ◽  
Feature Extraction ◽  
Back Propagation ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Data Availability ◽  
Three Gorges Reservoir Area ◽  
Displacement Prediction ◽  
Deformation And Failure ◽  
Landslide Displacement Prediction

Abstract. Rainfall is one of the most important factors controlling landslide deformation and failure. State-of-art rainfall data collection is a common practice in modern landslide research world-wide. Nevertheless, in spite of the availability of high-accuracy rainfall data, it is not a trivial process to diligently incorporate rainfall data in predicting landslide stability due to large quantity, tremendous variety, and wealth multiplicity of rainfall data. Up to date, most of the pre-process procedure of rainfall data only use mean value, maxima and minima to characterize the rainfall feature. This practice significantly overlooks many important and intrinsic features contained in the rainfall data. In this paper, we employ cluster analysis (CA)-based feature analysis to rainfall data for rainfall feature extraction. This method effectively extracts the most significant features of a rainfall sequence and greatly reduced rainfall data quantities. Meanwhile it also improves rainfall data availability. For showing the efficiency of using the CA characterized rainfall data input, we present three schemes to input rainfall data in back propagation (BP) neural network to forecast landslide displacement. These three schemes are: the original daily rainfall, monthly rainfall, and CA extracted rainfall features. Based on the examination of the root mean square error (RMSE) of the landslide displacement prediction, it is clear that using the CA extracted rainfall features input significantly improve the ability of accurate landslide prediction.

scholarly journals Relationships between Rainy Days, Mean Daily Intensity, and Seasonal Rainfall over the Koyna Catchment during 1961–2005

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Nandargi ◽  
S. S. Mulye
Keyword(s):  
Linear Relationship ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Rainfall ◽  
Frequency Changes ◽  
Logarithmic Relationship ◽  
The Relationship ◽  
Rainfall Climatology ◽  
Rainy Days ◽  
Better Than

There are limitations in using monthly rainfall totals in studies of rainfall climatology as well as in hydrological and agricultural investigations. Variations in rainfall may be considered to result from frequency changes in the daily rainfall of the respective regime. In the present study, daily rainfall data of the stations inside the Koyna catchment has been analysed for the period of 1961–2005 to understand the relationship between the rain and rainy days, mean daily intensity (MDI) and seasonal rainfall over the catchment on monthly as well as seasonal scale. Considering the topographical location of the catchment, analysis of seasonal rainfall data of 8 stations suggests that a linear relationship fits better than the logarithmic relationship in the case of seasonal rainfall versus mean daily intensity. So far as seasonal rainfall versus number of rainy days is considered, the logarithmic relationship is found to be better.

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.

A clonal selection algorithm model for daily rainfall data prediction

2014 ◽  
Vol 70 (10) ◽  
pp. 1641-1647
Author(s):  
N. S. Noor Rodi ◽  
M. A. Malek ◽  
Amelia Ritahani Ismail ◽  
Sie Chun Ting ◽  
Chao-Wei Tang
Keyword(s):  
Clonal Selection ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Immune Systems ◽  
Daily Rainfall Data ◽  
Data Prediction ◽  
Testing Stage

This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.

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