Rainfall erosivity factor for India using modified Fourier index
02 Jul 2016-Journal of Applied Water Engineering and Research (Taylor & Francis)-Vol. 4, Iss: 2, pp 83-91
TL;DR: In this article, a spatio-temporal average rainfall erosivity factor map has been generated for India, which makes use of 101 years monthly rainfall data and 52 spatial points.
Abstract: Spatio-temporal average rainfall erosivity factor map has been generated for India. This study on rainfall erosivity makes use of 101 years monthly rainfall data and 52 spatial points. The results presented here provide the much needed guidance to remove ambiguities regarding the rainfall erosivity factor in the Indian context. This study has a variety of applications in erosion prediction technology, such as Universal Soil Loss Equation or Revised Universal Soil Loss Equation, or in rainfall-data-deficient regions. In-depth observations can develop a deeper understanding of rainfall variation to estimate the erosivity factor. Rainfall erosivity factor map can facilitate agriculturists and soil conservationists to identify rainfall erosivity potential at diverse locations, and thereby apply obligatory safety measures to minimize soil erosion. The rainfall erosivity factor map has been used to provide a more rational value of the average rainfall erosivity factor covering India, in regions where rainfall d...
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TL;DR: In this paper, the authors present an attempt to prepare a flood hazard susceptibility map, which is used to assess the risk of a major natural disaster due to their devastating effects that lead to socio-economic losses.
Abstract: Floods are considered as a major natural disaster due to their devastating effects that lead to socio-economic losses. The present study is an attempt to prepare a flood hazard susceptibility map o...
155 citations
Cites methods from "Rainfall erosivity factor for India..."
...The rainfall intensity map was calculated using the modified fournier index methodology (Tiwari et al. 2016) (Equation (1)):...
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TL;DR: In this article, the MIROC5 model of RCP 2.6, 4.5, 6.0 and 8.5 scenarios have been used to estimate the future period precipitation in storm rainfall event.
87 citations
TL;DR: A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil, and land cover classes for the base line (1961-1990) and future climate scenarios (2071-2100) as discussed by the authors.
Abstract: This study is an attempt to quantify the impact of climate change on the hydrology of Armur watershed in Godavari river basin, India. A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil and land cover classes for the base line (1961–1990) and future climate scenarios (2071–2100). Sensitivity analysis of the model has been performed to identify the most critical parameters of the watershed. Average monthly calibration (1987–1994) and validation (1995–2000) have been performed using the observed discharge data. Coefficient of determination (
$$R^{2}$$
), Nash–Sutcliffe efficiency (ENS) and root mean square error (RMSE) were used to evaluate the model performance. Calibrated SWAT setup has been used to evaluate the changes in water balance components of future projection over the study area. HadRM3, a regional climatic data, have been used as input of the hydrological model for climate change impact studies. In results, it was found that changes in average annual temperature (+3.25 °C), average annual rainfall (+28 %), evapotranspiration (28 %) and water yield (49 %) increased for GHG scenarios with respect to the base line scenario.
46 citations
Cites background from "Rainfall erosivity factor for India..."
...For example, larger reservoir spillways and drainage waterways will be required where runoff is expected to increase, and higher water supply storage needed where runoff is expected to decrease (Sethi et al. 2015; Tiwari and Rai 2015; Tiwari et al. 2015)....
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TL;DR: In this article, the authors used SDSM (Statistical DownScaling Model) after calibration and validation using two GCMs (general circulation model) data of HadCM3 (A2 and B2 scenario) and CGCM3(A1B and A2 scenario).
40 citations
TL;DR: In this article, the average annual soil erosion of the Arkosa watershed ranges from 6 t/ha/year to 5 t/6 t/a/year and very high soil loss areas are found in the southern, south-eastern, and eastern part of the watershed.
Abstract: Soil is one of the most important natural resources; therefore, there is an urgent need to estimate soil erosion. The subtropical monsoon-dominated region also faces a comparatively greater problem due to heavy rainfall with high intensity in a very short time and the presence of longer dry seasons and shorter wet seasons. The Arkosa watershed faces the problem of extreme land degradation in the form of soil erosion; therefore, the rate of soil erosion needs to be estimated according to appropriate models. GCM (general circulation model) data such as MIROC5 (Model for Interdisciplinary Climate Research) of CMIP5 (Coupled Model Intercomparison Project Phase 5) have been used to project future storm rainfall and soil erosion rates following the revised universal soil loss equation (RUSLE) in various influential time frames. Apart from that, different satellite data and relevant primary field-based data for future prediction were considered. The average annual soil erosion of Arkosa watershed ranges from 6 t/ha/year. The very high (> 6 t/ha/year) and high (5–6 t/ha/year) soil loss areas are found in the southern, south-eastern, and eastern part of the watershed. Apart from this, low (1–2 t/ha/year) and very low (< 1 t/ha/year) soil loss areas are associated with the western, northern, southern, and major portion of the watershed. Extreme precipitation rates with high kinetic energy due to climate change are favorable to soil erosion susceptibility. The results of this research will help to implement management strategies to minimize soil erosion by keeping authorities and researchers at risk for future erosion and vulnerability.
39 citations
References
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TL;DR: A doubling in global food demand projected for the next 50 years poses huge challenges for the sustainability both of food production and of terrestrial and aquatic ecosystems and the services they provide to society.
Abstract: A doubling in global food demand projected for the next 50 years poses huge challenges for the sustainability both of food production and of terrestrial and aquatic ecosystems and the services they provide to society. Agriculturalists are the principal managers of global useable lands and will shape, perhaps irreversibly, the surface of the Earth in the coming decades. New incentives and policies for ensuring the sustainability of agriculture and ecosystem services will be crucial if we are to meet the demands of improving yields without compromising environmental integrity or public health.
6,569 citations
"Rainfall erosivity factor for India..." refers background in this paper
...Soil loss is the most prominent aspect in land degradation and influences environmental setback globally (Singh et al. 1992; Dhawan 1995; Balpande et al. 1996; Yadav 1996; Scherr 1999; Tilman et al. 2002; Chatterjee et al. 2014)....
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TL;DR: The methods used to calculate both the Revised Universal Soil Loss Equation (RUSLE) erosivity factor (R) and the 10 year frequency storm erosion index value (EI10) are presented in this paper.
853 citations
"Rainfall erosivity factor for India..." refers background in this paper
...The ambiguities regarding the unit of R factor has been discussed and detailed in numerous literatures (FAO 1977; Renard & Freimund 1994)....
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...Probability difference between R and P shows high coherence (Figure 8) which leads to the empirical nonlinear model carried out linearly by Rambabu (Narayana & Babu 1983; Singh et al. 1992; Renard & Freimund 1994)....
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TL;DR: In this paper, the best indicator of the capacity of a storm to erode soil was found to be the variable whose value is the product of the rainfall energy and maximum 30-minute intensity of the storm, which explained from 72 to 97% of the variation in individual-storm erosion from tilled continuous fallow on 6 soils.
Abstract: Extensive regression analyses of basic soil-loss data were designed to determine the best indicator of the capacity of a storm to erode soil. The rainstorm characteristic found to be outstanding as such an indicator is the variable whose value is the product of the rainfall energy and maximum 30-minute intensity of the storm (designated as EI). This variable explained from 72 to 97% of the variation in individual-storm erosion from tilled continuous fallow on 6 soils. Seasonal rainfall erosion index values computed by adding the EI values of storms > 0.5 inch explained as high as 94% of the yearly deviation in total soil loss from fallow during the summer season. Tested against data from plots in continuous row crop for 10 or more years at each of four widely separated locations, the summed EI values explained from 72 to 85% of the yearly variation in soil loss within corresponding cover periods. Expected annual values of the index and seasonal distribution of the erosion potential may be readily computed from local rainfall records. This has been done for 60 locations in the 31 Eastern States. About 8,000 plot-years of basic erosion data are being analyzed to evaluate factors for a universal soil-loss equation for which the erosion potential of expected local rainfall serves as the base. Tests show that estimates of average erosion losses computed in this manner are sufficiently accurate to serve as sound bases for conservation farm planning.
511 citations
TL;DR: In this article, the Revised Universal Soil Loss Equation (RUSLE) was integrated with a Geographic Information System (GIS) to model erosion potential for soil conservation planning within the Sierra de Manantlan Biosphere Reserve (SMBR), Mexico.
Abstract: This research integrates the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) to model erosion potential for soil conservation planning within the Sierra de Manantlan Biosphere Reserve (SMBR), Mexico. Mountainous topography and a tropical uni-modal precipitation regime characterize this region. These unique climatic and topographic characteristics required a modification of the standard RUSLE factors and their derivation. The resulting RUSLE–GIS model provides a robust soil conservation planning tool readily transferable and accessible to other land managers in similar environments. Future pressure to expand agriculture and grazing operations within the SMBR will unquestionably accentuate the already high rate of soil erosion and resultant sediment loading of watercourses occurring in this region. Until recently there did not exist a reliable or financially viable means to model and map soil erosion within large remote areas. An increase in the reliability and resolution of remote sensing techniques, modifications and advancements in watershed scale soil erosion modelling techniques, and advances in GIS, represent significantly improved tools that can be applied to both monitoring and modelling the effects of land use on soil erosion potential. Data used in this study to generate the RUSLE variables include a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and tabular precipitation data. Soil erosion potential was modelled within Zenzontla, a sub-catchment of the Rio Ayuquila, located in the SMBR, and the results are presented as geo-referenced maps for each of the wet and dry precipitation seasons. These maps confirm that high and extreme areas of soil loss occur within the Zenzontla sub-catchment, and that erosion potential differs significantly between wet and dry seasons.
417 citations
TL;DR: In this article, a comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adopted to determine the soil erosion vulnerability of a forested mountainous subwatershed in Kerala, India.
Abstract: A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala, India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method. GIS data layers including, rainfall erosivity ( R ), soil erodability ( K ), slope length and steepness ( LS ), cover management ( C ) and conservation practice ( P ) factors were computed to determine their effects on average annual soil loss in the area. The resultant map of annual soil erosion shows a maximum soil loss of 17.73 t h -1 y -1 with a close relation to grass land areas, degraded forests and deciduous forests on the steep side-slopes (with high LS ). The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.
417 citations
"Rainfall erosivity factor for India..." refers background or methods in this paper
...The average rainfall erosivity factor (R) for the years 2004–2008 was considered as 151.466 MJ cm ha−1 h−1 yr−1 for Kerala state in India (Prasannakumar et al. 2012)....
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...466 MJ cm ha−1 h−1 yr−1 for Kerala state in India (Prasannakumar et al. 2012)....
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...Due to unavailability of hourly rainfall data in Indian conditions, R-factor is computed based on Fourier Index (FI); more precisely on Modified Fourier Index (MFI) (Pandey et al. 2007, 2009; Dabral et al. 2008; Prasannakumar et al. 2011, 2012; Ghosh 2013)....
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