Mapping monthly rainfall erosivity for the Lazio Region (Italy)

2020 ◽  
Author(s):  
C. Mineo ◽  
E. Ridolfi ◽  
B. Moccia ◽  
F. Napolitano
Keyword(s):  
Monthly Rainfall ◽  
Rainfall Erosivity ◽  
Lazio Region

scholarly journals Comparison of Different Rainfall Erosion Estimation Methods for the Island of Crete

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 67 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Manolis Grillakis
Keyword(s):  
Temporal Resolution ◽  
Total Duration ◽  
Power Estimation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Daily Data

Interactions between soil and rainfall plays a vital role in ecological, hydrological and biogeochemical cycles of land. Among those interactions, the phenomenon of rainfall induced soil erosion is crucial to the soil functions, as it affects the soil structure and organic matter content that subsequently affects soil ability to hold moisture and nutrients. The erosive power of a specific rainfall event is regulated by its intensity and total duration. Various methodologies have been developed and tested to estimate the rainfall erosivity in different hydroclimatic regions and using different rainfall measuring timescales. Studies have shown that high temporal resolution measurements provide a more robust erosivity estimation. Nonetheless the sparsity and scarcity of such high temporal resolution data make the accurate estimation of rainfall erosivity difficult. Here, we compare different erosion power estimation methods based on different rainfall timescales for the island of Crete. Sub-daily (30-min) rainfall data based estimation is used as the basis for the assessment of a daily data based estimation methodology and two different methods that use monthly rainfall data. Modified Fournier Index (MFI) is incorporated in the study through different literature approaches and a regression equation is developed between rainfall erosivity power and MFI index for Crete. Results indicate that the use of daily data in the rainfall erosive power estimation is a good approximation of the sub-daily estimation, while formulas based on monthly rainfall data tend to exhibit larger deviations.

scholarly journals Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

Water ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 119 ◽  
Author(s):  
Panos Panagos ◽  
Pasquale Borrelli ◽  
Jonathan Spinoni ◽  
Cristiano Ballabio ◽  
Katrin Meusburger ◽  
...  
Keyword(s):  
Monthly Rainfall ◽  
Rainfall Erosivity ◽  
Conversion Factors

scholarly journals Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

2017 ◽  
Vol 41 (0) ◽  
Author(s):  
Lucas Machado Pontes ◽  
Marx Leandro Naves Silva ◽  
Diêgo Faustolo Alves Bispo ◽  
Fabio Arnaldo Pomar Avalos ◽  
Marcelo Silva de Oliveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
River Basin ◽  
Monthly Rainfall ◽  
Rainfall Erosivity

scholarly journals Validasi Nilai Erosivitas Hujan Dari Data Penginderaan Jauh TRMM 3B42 Di Bali Selatan

1970 ◽  
Vol 16 (1) ◽  
Author(s):  
I Wayan Sandi Adnyana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Rain Gauge ◽  
Monthly Rainfall ◽  
Bias Error ◽  
Rainfall Erosivity ◽  
Rain Gauge Data ◽  
Point Analysis ◽  
Gauge Data ◽  
High Bias ◽  
Precipitation Analysis ◽  
Very High

Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energydetermines the erosivity and is in turn greatly dependent on rainfall intensity. Research hasbeen conducted to validate monthly rainfall erosivity derived from the Tropical Rainfall MeasuringMission (TRMM) Multisatellite Precipitation Analysis(TMPA)3B43 version 7 usingraingauge data analysis from 2003 to 2012. Rain gauge located in the south Bali regions wereemployedto monitor erosivity value from two different methods that are base on Bols (1978)andAbdurachman(1989). Therelationship of erosivity and their other factor from TRMM3B43andrain gauge data statistical analysis measures consisted of the linear correlation coefficient,themean bias error (MBE), and the root mean square error (RMSE). Data validation wasconductedwith point-by-point analysis. The results of these analyses indicate that satellitedatahave lower values than the gauge estimation values. The point-by-point analysis indicatedsatellite data values of high to very high correlation, while values of MBE and RMSEtendedto indicate underestimations with high square errors. Moreover,monthly rainfall erosivityderived from TRMM give high correlation from both methods, with has high bias androot-mean-squareerror. In general, the data from TRMM3B43 version 7 are potentially usabletoreplace rain gauge data based on erosivity estimation, but after inconsistencies and errorsaretaken into account.

scholarly journals Spatial-Temporal Trend Analysis of Rainfall Erosivity and Erosivity Density of Tropical Area in Air Bengkulu Watershed, Indonesia

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Supriyono Supriyono ◽  
Sugeng Utaya ◽  
Didik Taryana ◽  
Budi Handoyo
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Anthropogenic Activities ◽  
Monthly Rainfall ◽  
Rainfall Erosivity ◽  
Tropical Country ◽  
Distance Weighted ◽  
Average Monthly Rainfall ◽  
Research Findings ◽  
Parametric Statistics

Abstract There have been many studies on rainfall erosivity and erosivity density (ED). However, it was not widely developed in Indonesia as a tropical country and has unique precipitation patterns. They are indicators for assessing the potential risk of soil erosion. The Air Bengkulu Watershed is undergoing severe land degradation due to soil erosion. This study aimed to analyze spatial-temporal in rainfall erosivity and ED based on monthly rainfall data (mm). The data used consisted of 19 weather stations during the period 2006–2020 and which are sparsely distributed over the watershed. The analysis was done by using Arnold's equation. Then, the trend was tested using parametric and non-parametric statistics, and analysed with linear regression equation, and Spearman's Rho and Mann Kendall's tests. The spatial distribution of both algorithms was analysed using the inverse distance weighted (IDW) method based on the geographic information system (GIS). Unlike previous research findings, The long-term average monthly rainfall erosivity and ED revealed a general increase and decreasing trend, whereas it was found to be non-significant when both indices were observed. However, these results indicate a range from 840.94 MJ · mm−1 · ha−1 · h−1 · a−1, 552.42 MJ · mm−1 · ha−1 · h−1 · a−1 to 472.09 MJ · mm−1 · ha−1 · h−1 · a−1 in that November month followed by December and April are the most susceptible months for soil erosion. Therefore, The upstream area of the region shows that various anthropogenic activities must be managed properly by taking into account the rainfall erosivity on the environment and that more stringent measures should be followed in soil and water conservation activities.

scholarly journals Regionalization of monthly rainfall erosivity patterns in Switzerland

2016 ◽  
Vol 20 (10) ◽  
pp. 4359-4373 ◽  
Author(s):  
Simon Schmidt ◽  
Christine Alewell ◽  
Panos Panagos ◽  
Katrin Meusburger
Keyword(s):  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Spatial And Temporal Patterns ◽  
Erosion Risk ◽  
Explanatory Variables ◽  
R Factor ◽  
June July ◽  
Leave One Out

Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression–kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June–September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.

scholarly journals Mapping monthly rainfall erosivity in Europe

2017 ◽  
Vol 579 ◽  
pp. 1298-1315 ◽  
Author(s):  
Cristiano Ballabio ◽  
Pasquale Borrelli ◽  
Jonathan Spinoni ◽  
Katrin Meusburger ◽  
Silas Michaelides ◽  
...  
Keyword(s):  
Monthly Rainfall ◽  
Rainfall Erosivity

ASSESSMENT OF SOIL EROSION USING FOURNIER INDEXES TO ESTIMATE RAINFALL EROSIVITY

2019 ◽  
Vol 18 (8) ◽  
pp. 1739-1745 ◽  
Author(s):  
Gabriel Lazar ◽  
Alina Maria Coman ◽  
Georgiana Lacatusu ◽  
Ana Maria Macsim
Keyword(s):  
Soil Erosion ◽  
Rainfall Erosivity
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