scholarly journals Assessing the sources of uncertainty associated with the calculation of rainfall kinetic energy and the erosivity <i>R</i> factor. Application to the Upper Llobregat Basin, NE Spain

2010 ◽  
Vol 7 (3) ◽  
pp. 3453-3479
Author(s):  
G. Catari ◽  
J. Latron ◽  
F. Gallart
Keyword(s):  
Kinetic Energy ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Sources Of Uncertainty ◽  
R Factor

Abstract. The sources of uncertainty associated with the calculation of rainfall kinetic energy and rainfall erosivity were investigated when the USLE R factor was operationally calculated for a mountainous river basin (504 km2) in the Southeastern Pyrenees. Rainfall kinetic energy was first obtained at the scale of the rainfall event by means of sub-hourly precipitation tipping-bucket rain gauge records and updates of the Kinnell (1981) equation. Annual erosivity values for the nearby pluviometric stations were then derived from the linear regressions between daily rainfall erosivity and daily precipitation, obtained for two different seasons. Finally, maps for rainfall erosivity estimates were obtained from the station values with Thiessen polygons. The sources of uncertainty analysed were i) the tipping-bucket instrumental errors, ii) the efficiency of the Kinnell (1981) equation, iii) the efficiency of the regressions between daily precipitation and kinetic energy, iv) the temporal variability of annual rainfall erosivity values, and the spatial variability of v) annual rainfall erosivity values and vi) long-term R factor values. The results showed that the uncertainty associated with the calculation of rainfall kinetic energy from rainfall intensity at the event and station scales is highly relevant and must be taken into account for experimental or modelling purposes; for longer temporal scales, the relevance of this source of uncertainty remains high if there is a low variability of the types of rain. Temporal variability of precipitation at wider spatial scales is the main source of uncertainty when rainfall erosivity is to be calculated on an annual basis, whereas the uncertainty associated with the long-term R factor is rather low and less important than the uncertainty associated with the other RUSLE factors when operationally used for long-term soil erosion modelling.

scholarly journals Assessing the sources of uncertainty associated with the calculation of rainfall kinetic energy and erosivity – application to the Upper Llobregat Basin, NE Spain

2011 ◽  
Vol 15 (3) ◽  
pp. 679-688 ◽  
Author(s):  
G. Catari ◽  
J. Latron ◽  
F. Gallart
Keyword(s):  
Kinetic Energy ◽  
Temporal Variability ◽  
Rainfall Intensity ◽  
Spatial Scales ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Sources Of Uncertainty ◽  
Eastern Pyrenees ◽  
Erosion Modelling

Abstract. The diverse sources of uncertainty associated with the calculation of rainfall kinetic energy and rainfall erosivity, calculated from precipitation data, were investigated at a range of temporal and spatial scales in a mountainous river basin (504 km2) in the south-eastern Pyrenees. The sources of uncertainty analysed included both methodological and local sources of uncertainty and were (i) tipping-bucket rainfall gauge instrumental errors, (ii) the efficiency of the customary equation used to derive rainfall kinetic energy from intensity, (iii) the efficiency of the regressions obtained between daily precipitation and rainfall erosivity, (iv) the temporal variability of annual rainfall erosivity values, and the spatial variability of (v) annual rainfall erosivity values and (vi) long-term erosivity values. The differentiation between systematic (accuracy) and random (precision) errors was taken into account in diverse steps of the analysis. The results showed that the uncertainty associated with the calculation of rainfall kinetic energy from rainfall intensity at the event and station scales was as high as 30%, because of insufficient information on rainfall drop size distribution. This methodological limitation must be taken into account for experimental or modelling purposes when rainfall kinetic energy is derived solely from rainfall intensity data. For longer temporal scales, the relevance of this source of uncertainty remained high if low variability in the types of rain was supposed. Temporal variability of precipitation at wider spatial scales was the main source of uncertainty when rainfall erosivity was calculated on an annual basis, whereas the uncertainty associated with long-term erosivity was rather low and less important than the uncertainty associated with other model factors such as those in the RUSLE, when operationally used for long-term soil erosion modelling.

scholarly journals Spatio-temporal variability of erosivity in Mato Grosso, Brazil

2018 ◽  
Vol 13 (6) ◽  
pp. 1
Author(s):  
Luis Augusto Di Loreto Di Raimo ◽  
Ricardo Santos Silva Amorim ◽  
Eduardo Guimarães Couto ◽  
Rodolfo Luiz Bezerra Nóbrega ◽  
Gilmar Nunes Torres ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Vegetation Change ◽  
Rain Gauge ◽  
Mean Value ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
Mato Grosso ◽  
North West ◽  
R Factor ◽  
The Impact

The impact of rainfall on surfaces lacking vegetal cover can dissociate soil particles, thereby initiating the erosion process. This is known as rainfall erosivity and is expressed by the R factor in the Universal Soil Loss Equation. Agricultural areas often show seasonally erosion susceptibility throughout the year due to oscillations of the soil exposure rate and the vegetation change. Considering that approximately 30 million ha of the Mato Grosso State in Brazil is used for agriculture, this study aimed to predict and map the spatial and temporal variability of its territory. We evaluated the monthly (EI30) and annual (R) erosivity for 158 rain gauge stations and spatialized the values of EI30 and R by the Kriging method. It was observed that R values ranked as very high in the north, and high and medium-high in the south of Mato Grosso state. The mean value is 8835 MJ mm ha-1 h-1 year-1, considered high. Ninety-one percent of the annual erosivity was concentrated in the period between October and April, corresponding to the rainy season. The highest R factor values were found in the macro-regions of the northwest, north, west and medium-north of Mato Grosso State.

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.

scholarly journals Values of rainfall erosivity factor for the Czech Republic

2013 ◽  
Vol 61 (2) ◽  
pp. 97-102 ◽  
Author(s):  
Miloslav Janeček ◽  
Vít Květoň ◽  
Eliška Kubátová ◽  
Dominika Kobzová ◽  
Michaela Vošmerová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Water Erosion ◽  
Rainfall Erosivity ◽  
The Czech Republic ◽  
Average Value ◽  
R Factor ◽  
The Usa ◽  
Time Division ◽  
R Factors

Abstract The processing of ombrographic data from 29 meteorological stations of the Czech Hydrometeorological Institute (CHMI), according to the terms of the Universal Soil Loss Equation for calculating long term loss of soil through water erosion, erosion hazard rains and their occurrence have been selected, with their relative amount and erosiveness - R-Factors determined for each month and years. By comparing the value of the time division of the R-Factor in the area of the Czech Republic and in selected areas of the USA it has been demonstrated that this division may be applied in the conditions of the Czech Republic. For the Czech Republic it is recommended to use the average value R = 40 based on the original evaluation.

scholarly journals Thunderstorms and thunderstorm precipitations in southern Poland

2014 ◽  
Vol 2 (3) ◽  
pp. 33-46
Author(s):  
Zuzanna Bielec-Bąkowska
Keyword(s):  
Temporal Variability ◽  
Daily Precipitation ◽  
Overwhelming Majority ◽  
Spatial And Temporal Variability ◽  
Southern Poland ◽  
Clear Cut ◽  
Term Change ◽  
Atmospheric Fronts ◽  
Precipitation Events

AbstractThis paper addresses spatial and temporal variability in the occurrence of thunderstorms and related precipitation in southern Poland between 1951 and 2010. The analysis was based on thunderstorm observations and daily precipitation totals (broken down into the few ranges) from 15 meteorological stations. It was found that precipitation accompanied an overwhelming majority of thunderstorms. The most frequent range of thunderstorm precipitation totals was 0.1–10.0 mm which accounted for 60% of all values while precipitation higher than 20.0 mm accounted only for ca. 8%. During the study period, long-term change in the number of days with thunderstorm precipitation within a certain range displayed no clear-cut trends. Exceptions included: 1) an increase in the number of days with thunderstorm precipitation in the lowest range of totals (0.1–10.0 mm) at Katowice, Tarnów, Rzeszów and Lesko and decrease at Mt. Kasprowy Wierch, 2) an increase in the range 10.1–20.0 mm at Zakopane and 20.1–30.0 mm at Opole, 3) a decrease of the top range (more than 30.0 mm) at Mt. Śnieżka. It was found that the heaviest thunderstorm precipitation events, i.e. totalling more than 30 mm, and those events that covered all or most of the study area, occurred at the time of air advection from the southern or eastern sectors and a passage of atmospheric fronts.

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 THE CREATION OF BIOCLIMATIC VEGETATION MAP TO DEVELOP SUSTAINABLE AGRO FORESTRY IN BA AND KONE RIVER BASIN, VIETNAM

2021 ◽  
pp. 54-62
Author(s):  
Nguyen Huu Xuan ◽  
◽  
Nguyen Khanh Van ◽  
Hoang Thi Kieu Oanh ◽  
Vuong Van Vu ◽  
...  
Keyword(s):  
River Basin ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Zonal Vegetation ◽  
South Central ◽  
Bioclimatic Variables ◽  
The Government ◽  
Coast Of Vietnam ◽  
Agro Forestry

Bioclimate and natural vegetation have a long - term relationship that identify the potential vegetation distribution at different areas. For that reason, bioclimatic classification system was applied to the territory of Ba and Kone river basin, Vietnam. The precipitation and temperature dataset of Ba and Kone river basin was collected from 17 climate, hydrology, rain gauge stations which allowed to create a bioclimatic map at a scale of 1:250.000. Three bioclimatic factors of thermal-moisture basic conditions such as annual temperature (TN), annual rainfall (RN), length of dry season (n) are selected as criteria system of Ba and Kone river basin’s bioclimate. In order to describe the relationships between bioclimatic variables and zonal vegetation units, the resulting map presented 12 bioclimatic units corresponding distribution of vegetation from low to high altitudes. By building bioclimatology map in Ba and Kone river basin, the government can develop sustainable agro forestry in Central Highlands and South Central Coast of Vietnam.

scholarly journals Estimating Rainfall Erosivity Factor Using Future Climate Projection in the Myjava Region (Slovakia)

2021 ◽  
Vol 24 (s1) ◽  
pp. 31-36
Author(s):  
Peter Valent ◽  
Roman Výleta
Keyword(s):  
Soil Erosion ◽  
Climate Models ◽  
Regional Climate ◽  
Multiple Linear Regression Model ◽  
Rain Gauge ◽  
Rainfall Erosivity ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
R Factor ◽  
Geostatistical Approach

Abstract Rainfall erosivity factor (R) of the USLE model is one of the most popular indicators of areas potentially susceptible to soil erosion. Its value is influenced by the number and intensity of extreme rainfall events. Since the regional climate models expect that the intensity of heavy rainfall events will increase in the future, the currently used R-factor values are expected to change as well. This study investigates possible changes in the values of R-factor due to climate change in the Myjava region in Slovakia that is severely affected by soil erosion. Two rain gauge stations with high-resolution 1-minute data were used to build a multiple linear regression model (r 2 = 0.98) between monthly EI 30 values and other monthly rainfall characteristics derived from low-resolution daily data. The model was used to estimate at-site R-values in 13 additional rain gauge stations homogeneously dispersed over the whole region for four periods (1981–2010, 2011–2040, 2041–2070, 2071–2100). The at-site estimates were used to create R-factor maps using a geostatistical approach. The results showed that the mean R-factor values in the region might change from 429 to as much as 520 MJ.mm.ha−1.h−1.yr−1 in the second half of the 21st century representing a 20.5% increase.

scholarly journals Comparing Rainfall Erosivity Estimation Methods Using Weather Radar Data for the State of Hesse (Germany)

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1424 ◽  
Author(s):  
Jennifer Kreklow ◽  
Bastian Steinhoff-Knopp ◽  
Klaus Friedrich ◽  
Björn Tetzlaff
Keyword(s):  
Climate Change ◽  
Rain Gauge ◽  
The State ◽  
Estimation Methods ◽  
Rainfall Erosivity ◽  
Spatiotemporal Resolution ◽  
Rain Gauge Data ◽  
R Factor ◽  
Gauge Data ◽  
Erosive Rainfall

Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.

Water Balance of an Australian Subtropical Rainforest at Altitude: the Ecological and Physiological Significance of Intercepted Cloud and Fog

1997 ◽  
Vol 45 (2) ◽  
pp. 311 ◽  
Author(s):  
Lindsay B. Hutley ◽  
David Doley ◽  
David J. Yates ◽  
Arthorn Boonsaner
Keyword(s):  
Water Balance ◽  
Vegetation Type ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Balance Study ◽  
Term Survival ◽  
Tree Transpiration ◽  
Fog Deposition ◽  
Dry Seasons

A water balance study of a small subtropical rainforest catchment (10 ha, 1000 m altitude) was conducted at Gambubal State Forest, near the headwaters of the Condamine River, 200 km south-west of Brisbane, south-eastern Queensland. Mean annual rainfall of the site is approximately 1125 mm, but is variable and often less than 900 mm. Tree transpiration rates are low and depletion of the large soil moisture reserves enables extraction for lengthy periods of time, permitting survival during extended dry seasons (May–November). Fog deposition to the forest provides the equivalent of an additional 40% of rainfall to the site as measured using a conventional rain gauge. A frequently wet canopy results in reduced transpiration rates and direct foliar absorption of moisture alleviates water deficits of the upper crown leaves and branches during the dry season. These features of this vegetation type may enable long-term survival at what could be considered to be a marginal rainforest site.

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