scholarly journals Spatio-temporal Evolution Analysis of Rainfall Erosivity During 1901-2017 in Beijing, China

2021 ◽  
Author(s):  
Yanlin Li ◽  
Yi He ◽  
Yaru Zhang ◽  
Liping Jia
Keyword(s):  
Soil Erosion ◽  
Future Trend ◽  
Test Method ◽  
Annual Rainfall ◽  
Trend Test ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Average Value ◽  
Pettitt Test ◽  
Variation Characteristics

Abstract Rainfall erosivity is regarded as one of the main factors affected soil erosion. Based on the 117 a monthly precipitation data of Beijing from 1901 to 2017, the temporal and spatial variation characteristics of rainfall erosivity in Beijing were analyzed by using Theil-Sen median analysis (Sen) and the Mann–Kendall (MK) trend test, R/S analysis method, cumulative anomaly method , MK mutation test method, Pettitt test, and wavelet analysis. The results showed that the average annual rainfall erosivity in Beijing ranged from 1080.6 to 6432.78 MJ • mm / (hm2 • h • a), with an average value of 3465.06 MJ • mm / (hm2 • h • a), showing a gradual decrease from southeast to northwest. In the seasonal distribution, 86% of rainfall erosivity was mainly concentrated in summer. In the past 117 years, the annual rainfall erosivity in most areas of Beijing had shown a downward trend, but its future trend also showed an increasing trend, indicating that Beijing, especially the northern part, was facing greater potential pressure of soil erosion. Through the cross validation of various methods, the abrupt change interval of rainfall erosivity in Beijing from 1901 to 2017 was from 1994 to 1997. The change of rainfall erosivity in Beijing has strong oscillation in 32 years and small periodic change in 15 and 7 years. The results will provide decision-making basis for soil erosion control and water/soil conservation planning. Additionally, they will be benefited to ensure the national agricultural and food security.

scholarly journals Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 200 ◽  
Author(s):  
Zhijia Gu ◽  
Detai Feng ◽  
Xingwu Duan ◽  
Kuifang Gong ◽  
Yawen Li ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Tibetan Plateau ◽  
Global Climate ◽  
Summer Rainfall ◽  
Annual Rainfall ◽  
Trend Test ◽  
Rainfall Erosivity ◽  
The Tibetan Plateau ◽  
Spatial And Temporal Patterns ◽  
Daily Precipitation Data

The Tibetan Plateau is influenced by global climate change which results in frequent melting of glaciers and snow, and in heavy rainfalls. These conditions may increase the risk of soil erosion, but prediction is not feasible due to scarcity of rainfall data in the high altitudes of the region. In this study, daily precipitation data from 1 January 1981 to 31 December 2015 were selected for 38 meteorological stations in the Tibetan Plateau, and annual and seasonal rainfall erosivity were calculated for each station. Additionally, we used the Mann–Kendall trend test, Sen’s slope, trend coefficient, and climate tendency rate indicators to detect the temporal variation trend of rainfall erosivity. The results showed that the spatial distribution of rainfall erosivity in the Tibetan Plateau exhibited a significant decreasing trend from southeast to northwest. The average annual rainfall erosivity is 714 MJ·mm·ha−1·h−1, and varies from 61 to 1776 MJ·mm·ha−1·h−1. Rainfall erosivity was mainly concentrated in summer and autumn, accounting for 67.5% and 18.5%, respectively. In addition, annual, spring, and summer rainfall erosivity were increasing, with spring rainfall erosivity highly significant. Temporal and spatial patterns of rainfall erosivity indicated that the risk of soil erosion was relatively high in the Hengduan mountains in the eastern Tibetan Plateau, as well as in the Yarlung Zangbo River Valley and its vicinity.

scholarly journals Spatiotemporal Variation of Drought and Its Correlation With Plant Species of Yunnan Province, China

2021 ◽  
Vol 50 (4) ◽  
pp. 1133-1142
Author(s):  
Xinhui Xu ◽  
Xingyu Zhou ◽  
Zhenqiang Liu ◽  
Xiaoqing Zhao
Keyword(s):  
Yunnan Province ◽  
Interpolation Method ◽  
Middle Part ◽  
Spatiotemporal Variation ◽  
Test Method ◽  
Trend Test ◽  
Severe Drought ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Variation Characteristics

Drought is the main natural disaster in Yunnan Province, China. In the present paper monthly precipitation observation data from Yunnan Province durign the period of 1966 - 2015 were used. From the data, the selected percentage of precipitation anomalies was used as drought index. By applying the ArcGIS inverse distance interpolation method and Mann Kendall non parametric trend test method the spatiotemporal variation characteristics of drought in Yunnan province were analyzed. Results show that the drought in Yunnan Province has a slightly upward trend. In spring and winter, there is a tendency to become wet but in summer and autumn, the tendency is shown by dry condition. It was observed that the studied area is prone to a severe drought in winter, and there will be more droughts in the east part, the northwest part, and the southwest part of Yunnan province when it is autumn. In other periods, severe doughts usually attack the middle part of Yunnan province, which can be proved by the characteristics of vegetation distribution. Bangladesh J. Bot. 50(4): 1133-1142, 2021 (December)

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

A framework of abrupt changes and trends detection for rainfall erosivity

2020 ◽  
Author(s):  
Feng Qian ◽  
Bo Hu ◽  
Honghu Liu ◽  
Jingjun Liu
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Soil Loss ◽  
Abrupt Change ◽  
Test Method ◽  
Trend Detection ◽  
Annual Rainfall ◽  
Climate Index ◽  
Rainfall Erosivity ◽  
Soil And Water

<p>Rainfall erosivity (R factor), in the Universal Soil Loss Equation (USLE) , a climate index, is used worldwide to assess and predict the potential of rainfall to cause erosion. The temporal variation in rainfall erosivity, informs of abrupt change and trend, are critical for soil loss prediction. To find a simple and effective method for accurate detection of abrupt change and trend has implication for soil and water conservation planning. In this paper, a four-step framework is proposed to detect abrupt change and trend in rainfall erosivity time series, i.e., evaluate the significance of variation in rainfall erosivity time series at three levels: no, weak and strong, abrupt change and trend detection for rainfall erosivity,  estimation of correlation coefficient between the variation component and rainfall erosivity series, remove the variation component with the largest correlation coefficient from the rainfall erosivity series, repeat the above steps for the new series until variance coefficient was insignificance. The first step is based on an index of Hurst coefficient. The trend detection is implemented using both Spearman rank and Kendall rank correlation test. For abrupt change ,three kinds of methods (Mann-Kendall, Moving T and Bayesian test) are employed.  This framework is applied to the annual rainfall erosivity series of the Three Gorges Reservoir , China. There was a large uncertainty in detecting variability with a single test method. Application of the proposed framework can reduce uncertainty  associated with soil erosion assessment and achieve more accurate regional soil and water management. </p>

The Relationship of Debris Flow Hazards and Rainfall Erosivity in the Bailong River Basin of Southern Gansu Province, China

2014 ◽  
Vol 1073-1076 ◽  
pp. 1614-1619
Author(s):  
Peng Zhang ◽  
He Ping Shu ◽  
Jin Zhu Ma ◽  
Gang Wang ◽  
Li Ming Tian
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Debris Flows ◽  
Temporal Distribution ◽  
Gansu Province ◽  
Annual Precipitation ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Erosive Rainfall ◽  
Bailong River Basin

Rainfall is one of the main factors that drive soil erosion, leading to environmental problems such as increased frequency and severity of debris flows, and ecosystem damage. Rainfall erosivity represents the potential of rainfall to cause soil erosion, and is determined by a combination of rainfall intensity. The spatial and temporal distribution of rainfall erosivity was analyzed to get its relationship with the debris flows in the Bailong River Basin in China's Gansu Province. The mean annual amount of erosive rainfall accounts for 36.0-47.1% of annual precipitation. The annual mean rainfall erosivity amounts to 798.8 MJ mm ha-1 h-1 yr-1 in the Bailong River Basin. A positive correlation between annual precipitation and annual rainfall erosivity was demonstrated at all 18 rainfall stations. However, further research is required to reveal the key factors that explain soil erosion and debris flows.

scholarly journals Effects of Tillage Methods and Vegetation Coverage on Reducing Runoff and Soil Erosion

2020 ◽  
Vol 194 ◽  
pp. 04039
Author(s):  
Ma Changchen ◽  
Wang Ran ◽  
Li Qingyuan ◽  
Lu Fangchun
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Vegetation Coverage ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Positive Correlation ◽  
Runoff Depth ◽  
Set Up ◽  
Tillage Methods ◽  
Runoff Plot

To study the characteristics of runoff and soil erosion of natural rainfall conditions, five standard runoff plots were set up in our experiment, and different tillage methods and vegetation coverage types were set up. The 58-month monthly precipitation data and 44-month runoff plot observation data from 2013 to 2017 were analysed. The results show that: 1) The monthly precipitation fluctuates significantly, ranging from 13mm to 683.5mm. The precipitation is unevenly distributed over the year. The largest average monthly precipitation is in June and the smallest is in January. Rainfall is mainly concentrated in the spring and summer. The precipitation from March to June accounts for 58.0% of the annual rainfall. 2) There is a positive correlation between runoff depth and precipitation in each runoff plot (R2= 0. 5101~0. 6676, Sig.<0.01); 3) There is also a positive correlation between soil loss and precipitation (R2=0. 424~0. 558, Sig.<0.01); 4) The amount of soil loss and the runoff depth increase with increasing rainfall. The runoff plot without any vegetation cover or farming measures increase the most. While the one with horizontal steps and shrubs, or a combination of arbor and grass increase the slowest, indicating that they have the best effect of reducing runoff and soil loss.

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 Soil erosion vulnerability in the verde river basin, southern minas gerais

2014 ◽  
Vol 38 (3) ◽  
pp. 262-269 ◽  
Author(s):  
Vinícius Augusto de Oliveira ◽  
Carlos Rogério de Mello ◽  
Matheus Fonseca Durães ◽  
Antônio Marciano da Silva
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Minas Gerais ◽  
Annual Rainfall ◽  
Southeastern Brazil ◽  
Rainfall Erosivity ◽  
Verde River ◽  
Gis Environment ◽  
Elevation Model ◽  
Very High

Soil erosion is one of the most significant environmental degradation processes. Mapping and assessment of soil erosion vulnerability is an important tool for planning and management of the natural resources. The objective of the present study was to apply the Revised Universal Soil Loss Equation (RUSLE) using GIS tools to the Verde River Basin (VRB), southern Minas Gerais, in order to assess soil erosion vulnerability. A annual rainfall erosivity map was derived from the geographical model adjusted for Southeastern Brazil, calculating an annual value for each pixel. The maps of soil erodibility (K), topographic factor (LS), and use and management of soils (C) were developed from soils and their uses map and the digital elevation model (DEM) developed for the basin. In a GIS environment, the layers of the factors were combined to create the soil erosion vulnerability map according to RUSLE. The results showed that, in general, the soils of the VRB present a very high vulnerability to water erosion, with 58.68% of soil losses classified as "High" and "Extremely High" classes. In the headwater region of VRB, the predominant classes were "Very High" and "Extremely High" where there is predominance of Cambisols associated with extensive pastures. Furthermore, the integration of RUSLE/GIS showed an efficient tool for spatial characterization of soil erosion vulnerability in this important basin of the Minas Gerais state.

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 Spatial Distribution and Temporal Trends of Rainfall Erosivity in Three Gorges Reservoir Area of China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huiying Liu ◽  
Guanhua Zhang ◽  
Pingcang Zhang ◽  
Shengnan Zhu
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Three Gorges Reservoir ◽  
Temporal Trends ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Three Gorges Reservoir Area ◽  
Rainfall Erosivity ◽  
Three Gorges

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha−1·h−1 with an average value of 6108.1 MJ·mm·ha−1·h−1. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha−1·h−1, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha−1·h−1 during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha−1·h−1 in 1966 and highest value of 8896.0 MJ·mm·ha−1·h−1 in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.

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