Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape

Gupta, Surya; Kumar, Suresh

doi:10.1007/s12040-017-0823-1

Cited by 81 publications

(36 citation statements)

References 59 publications

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“…Good correlations were obtained when using a value of 2 for α and 1 for β [42]. This particular equation has been used in several studies worldwide to calculate C values [4,17,[43][44][45][46]. Since the equation was developed using daily images by comparing against monthly C factor values, it allows us to calculate monthly (C ndviM ), and annual C values (C ndvi ) by aggregating the average values of the scenes accordingly.…”

Section: Factor Value Estimationmentioning

confidence: 99%

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

et al. 2020

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The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cndvi) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cndvi often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cndvi values and compare the values with those of literature values (Clit) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cndvi and Clit improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cndvi values were generally higher compared with Clit values. Consequently, modelling erosion using Cndvi resulted in two times higher rates than modelling with Clit. The Cndvi values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cndvi values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cndvi estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cndvi values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

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Section: Factor Value Estimationmentioning

confidence: 99%

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

et al. 2020

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“…However, numerous studies have established a statistical regression equation between R and variable rainfall, such as annual rainfall [4,[23][24][25]. More recently, there has been evidence of the influence of climate change on rainfall erosivity in various parts of the globe [7,17,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

Duulatov

Chen

Amanambu

et al. 2019

Water

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Climate change-induced precipitation variability is the leading cause of rainfall erosivity that leads to excessive soil losses in most countries of the world. In this paper, four global climate models (GCMs) were used to characterize the spatiotemporal prediction of rainfall erosivity and assess the effect of variations of rainfall erosivity in Central Asia. The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR) were statistically downscaled using the delta method under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for two time periods: “Near” and “Far” future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity and its projected changes over Central Asia. WorldClim data was used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine rainfall erosivity. The results show an increase in the future periods of the annual rainfall erosivity compared to the baseline. For all GCMs, with an average change in rainfall erosivity of about 5.6% (424.49 MJ mm ha−1 h−1 year−1) in 2030s and 9.6% (440.57 MJ mm ha−1 h−1 year−1) in 2070s as compared to the baseline of 402 MJ mm ha−1 h−1 year−1. The magnitude of the change varies with the GCMs, with the largest change being 26.6% (508.85 MJ mm ha−1 h−1 year−1), occurring in the MIROC-5 RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.

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“…Current technology has made it possible to obtain satellite images easily and to develop computational tools to extract and process the information needed to calculate some biophysical properties at the Earth's surface; for example, the Absorbed Photosynthetically Active Radiation (APAR), the net CO 2 exchange in a local ecosystem (NEE CO 2 ) [10], or the primary net production [11]. In particular, many methodologies have been developed to identify vegetation coverings, for example: spectral classification methods [12][13][14], fractional vegetation cover methods [15], and vegetation indices [16][17][18][19][20][21][22]. However, most of these methods have been designed to focus mainly on green vegetation, and not on dry or dead vegetation, which are required by erosion models to assess the vegetation cover factor [4].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation

et al. 2019

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Vegetation Indices (VIs) represent a useful method for extracting vegetation information from satellite images. Erosion models like the Revised Universal Soil Loss Equation (RUSLE), employ VIs as an input to determine the RUSLE soil Cover factor (C). From the standpoint of soil conservation planning, the C factor is one of the most important RUSLE parameters because it measures the combined effect of all interrelated cover and management variables. Despite its importance, the results are generally incomplete because most indices recognize healthy or green vegetation, but not senescent, dry or dead vegetation, which can also be an important contributor to C. The aim of this research is to propose a novel approach for calculating new VIs that are better correlated with C, using field and satellite information. The approach followed by this research is to state the generation of new VIs in terms of a computer optimization problem and then applying a machine learning technique, named Genetic Programming (GP), which builds new indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of this approach to estimate the C factor and the erosion rates for two watersheds in Baja California, Mexico, and Zaragoza, Spain. The synthetic indices calculated using this methodology produce better approximation to the C factor from field data, when compared with state-of-the-art indices, like NDVI and EVI.

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Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape

Cited by 81 publications

References 59 publications

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation

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