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.

scholarly journals Seasonal Dynamics of Rainfall Erosivity in Switzerland

2016 ◽  
Author(s):  
Simon Schmidt ◽  
Christine Alewell ◽  
Panos Panagos ◽  
Katrin Meusburger
Keyword(s):  
Kriging Interpolation ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Erosion Risk ◽  
Explanatory Variables ◽  
R Factor ◽  
Monthly Pattern ◽  
June July ◽  
Leave One Out

Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified by the kinetic energy of a rainfall event and its maximum 30-min intensity. 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 dynamic rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a characteristic regional and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modelling approach to assess simultaneously spatial and monthly pattern 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-minute temporal resolution to calculate long-term monthly mean R-factors. Stepwise regression and Leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal pattern of the R-factor for each month across Switzerland. The monthly R-factor is mapped by its specific regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like 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 highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June to September). Highest erosion risk can be expected for July where not only rainfall erosivity but also erosivity density is high. Additionally 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 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 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 Trends, change points and spatial variability in extreme precipitation events from 1961 to 2017 in China

2020 ◽  
Vol 51 (3) ◽  
pp. 484-504 ◽  
Author(s):  
Linchao Li ◽  
Yufeng Zou ◽  
Yi Li ◽  
Haixia Lin ◽  
De Li Liu ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Extreme Event ◽  
Heavy Precipitation ◽  
Kendall Test ◽  
Temporal Variations ◽  
Change Points ◽  
Monthly Precipitation ◽  
Flood Peak ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Extreme precipitation events vary with different sub-regions, sites and years and show complex characteristics. In this study, the temporal variations, trends with significance and change points in the annual time series of 10 extreme precipitation indices (EPIs) at 552 sites and in seven sub-regions were analyzed using the modified Mann–Kendall test and sequential Mann–Kendall analysis. Three representative (extremely wet, normal and extremely dry) years from 1961 to 2017 were selected by the largest, 50%, and smallest empirical frequency values in China. The spatiotemporal changes in the EPIs during the three representative years were analyzed in detail. The results showed that during 1961–2017, both the consecutive wet or dry days decreased significantly, while the number of heavy precipitation days had no significant trend, and the other seven wet EPIs increased insignificantly. The abrupt change years of the 10 EPIs occurred 32 and 40 times from 1963 to 1978 and from 1990 to 2016, respectively, regardless of sub-region. The extremely dry (or wet) events mainly occurred in western (or southwestern) China, implying a higher extreme event risk. The extremely wet, normal and extremely dry events from 1961 to 2017 occurred in 2016, 1997 and 2011 with empirical frequencies of 1.7%, 50% and 98.3%, respectively. In addition, 1998 was the second-most extremely wet year (empirical frequency was 3.7%). The monthly precipitation values were larger from February to August in 1998, forming a much earlier flood peak than that of 2016. The 10 EPIs had close connections with Normalized Difference Vegetation Indexes during the 12 months of 1998 and 2016. This study provides useful references for disaster prevention in China.

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 rainfall erosivity factor in the Czech Republic and its uncertainty

2016 ◽  
Vol 20 (10) ◽  
pp. 4307-4322 ◽  
Author(s):  
Martin Hanel ◽  
Petr Máca ◽  
Petr Bašta ◽  
Radek Vlnas ◽  
Pavel Pech
Keyword(s):  
Czech Republic ◽  
Spatial Interpolation ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
The Czech Republic ◽  
Interpolation Methods ◽  
R Factor ◽  
Record Length ◽  
Spatial Coverage ◽  
Spatial Interpolation Methods

Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640 MJ ha−1 mm h−1, with values for the individual stations ranging between 320 and 1520 MJ ha−1 mm h−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

Global warming decreases rainfall but increases short-duration rain-rates during heavy precipitation events in the eastern Mediterranean

2021 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Chaim Garfinkel ◽  
Dorita Rostkier-Edelstein ◽  
Ori Adam ◽  
...  
Keyword(s):  
Global Warming ◽  
Short Duration ◽  
Eastern Mediterranean ◽  
Heavy Precipitation ◽  
Annual Rainfall ◽  
Cmip5 Models ◽  
The Future ◽  
Rain Rates ◽  
The Impact ◽  
Precipitation Events

<p>Heavy precipitation events (HPEs) in the densely populated eastern Mediterranean trigger natural hazards, such as flash floods and urban flooding. However, they also supply critical amounts of fresh water to this desert-bounded region. The impact of global warming on such events is thus vital to the inhabitants of the region. HPEs are poorly represented in global climate models, leading to large uncertainty in their sensitivity to climate change. Is total rainfall in HPEs decreasing, as projected for the mean annual rainfall? Are short duration rain rates decreasing, or rather increasing as expected from the higher atmospheric moisture content? Where are the changes more pronounced, near the sea or farther inland towards the desert? To answer these questions, we have identified 41 historical HPEs from a long weather radar record (1990-2014) and simulated them in the same resolution (1 km<sup>2</sup>) using the convection-permitting weather research and forecasting (WRF) model. Results were validated versus the radar data, and served as a control group to simulations of the same events under ‘pseudo global warming’ (PGW) conditions. The PGW methodology we use imposes results from the ensemble mean of 29 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for the end of the century on the initial and boundary conditions of each event simulated. The results indicate that HPEs in the future may become more temporally focused: they are 6% shorter and exhibit maximum local short-duration rain rates which are ~20% higher on average, with larger values over the sea and the wetter part of the region, and smaller over the desert. However, they are also much drier; total precipitation during the future-simulated HPEs decreases substantially (~-20%) throughout the eastern Mediterranean. The meteorological factors leading to this decrease include shallower cyclones and the projected differential land-sea warming, which causes reduced relative humidity over land. These changing rainfall patterns are expected to amplify water scarcity – a known nexus of conflict and strife in the region – highlighting the urgent need for deeper knowledge, and the implementation of adaptation and mitigation strategies.</p>

Utilization of fire brigade data in the impact analysis of extreme precipitation events over Berlin

2020 ◽  
Author(s):  
Alexander Pasternack ◽  
Ines Langer ◽  
Henning Rust ◽  
Uwe Ulbrich
Keyword(s):  
Impact Analysis ◽  
Global Climate ◽  
Urban Climate ◽  
Heavy Precipitation ◽  
High Temporal Resolution ◽  
Fire Brigade ◽  
Extreme Precipitation Events ◽  
Multiple Data Sets ◽  
The Impact ◽  
Precipitation Events

<p>Large cities and urban regions are highly sensitive to impacts caused by extreme events, e.g. heavy rainfall, since they cause fatalities and economic damages. Moreover, due to regional consequences of global climate change, problems caused by hazardous atmospheric events are expected to intensify in future. Thus adequate adaptation planning of urban infrastructure not only requires further research on potential impacts under changing precipitation patterns, but also practical feasibility for end users like insurances or fire brigades.</p><p>According to this we relate heavy precipitation events over Berlin to the available data on time and location of the respective fire brigade operations, within the research program “Urban Climate Under Change” ([UC]<sup>2</sup>) funded by the BMBF. For this purpose multiple data sets like station, radar and model  based data with a high temporal resolution will be used.  Thus an improved assessment of the spatial and temporal evolution of severe precipitation events can be made,  which is consequently also of aid in the investigation of a connection to related impacts in the urban area.</p>

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.

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