Geophysical evaluation of soils and soil loss estimation in a semiarid region of Maharashtra using revised universal soil loss equation (RUSLE) and GIS methods

Maury, Shrikant; Gholkar, Madhav; Jadhav, Ajit; Rane, Nilkanth

doi:10.1007/s12665-019-8137-z

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…The C and P-factors reflect the impact of vegetation coverage and agricultural practices (e.g., contour farming, tillage, terracing, stone walls, grass margins, etc. ), respectively, on the erosion (Maury, Gholkar, Jadhav, & Rane, 2019;Phinzi & Ngetar, 2019). The values of both factors range from 0 to 1 with those close to 0 being attributed to areas with high vegetation coverage and presence of support practices respectively, whereas those close to 1 to bare lands and areas with none practice respectively.…”

Section: Usped Modellingmentioning

confidence: 99%

Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece

et al. 2021

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Among the environmental threats, the intensification of natural hazards, such as soil erosion may threaten the integrity and value of cultural heritage sites. In this framework, the present study's main objective was to identify archaeological sites susceptible by soil erosion, taking the case study of Chania prefecture in Crete Island. Remotely sensed and other available geospatial datasets were analyzed in a GIS-based empirical model, namely Unit Stream Power Erosion and Deposition (USPED), to estimate the average annual soil loss and deposition rates due to waterinduced erosion in the study area. The resultant erosion map was then intersected with the locations and surrounding zones of the known archaeological sites for identifying the sites and the portions of their vicinity being at risk. The results revealed that Chania prefecture and its cultural heritage are significantly affected by both soil loss and deposition processes. Between the two processes, soil loss was found to be more intensive, influencing a larger part of the prefecture (especially to the west) as well as a higher amount of archaeological sites. The extreme and high soil loss classes were also detected to cover the most considerable portion of the sites' surrounding area. The identification of the archaeological sites being most exposed to soil erosion hazard can constitute a basis for cultural heritage managers in order to take preventive preservation measures and develop specific risk mitigation strategies.

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Section: Usped Modellingmentioning

confidence: 99%

Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece

et al. 2021

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“…Defined as the ratio of soil loss with a specific conservation support practice to the corresponding loss with upslope and downslope cultivation, support practice factor reflects the effects of such practices (e.g., contour farming, tillage, terracing, stone walls, grass margins, etc.) aiming to reduce the eroding power of rainfall-runoff and, thus, reduce the amount of soil loss [51]. The most common approach to produce the P-factor is based on field observations and visual image interpretation.…”

Section: Revised Universal Soil Loss Equation (Rusle)mentioning

confidence: 99%

“…where ρ NIR and ρ RED are the reflectance values at NIR and Red spectral bands, respectively. As an indicator of the energy reflected from the earth's surface, NDVI has been widely used to represent the various vegetation coverage conditions of several regions [18,49,51]. Its values range between −1 and 1 indicating a lack of vegetation or dense vegetation, respectively.…”

Section: Implementation Of Ruslementioning

confidence: 99%

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

et al. 2020

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Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.

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“…However, the main processes of soil nitrogen loss are vegetation consumption and soil loss [11][12][13], and the final status of the comprehensive process of soil nitrogen gains and losses can be reflected in the soil nitrogen storage [14,15]. At present, relevant studies have focused separately on the spatiotemporal variation in soil nitrogen [16,17] and the loss of soil sediment [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China

et al. 2022

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To explore the spatial heterogeneity of nitrogen supply from human activities to soil in coastal areas, we established a soil nitrogen net replenishment index (A-SNNRI). We applied the Revised Universal Soil Loss Equation (RUSLE) model for soil loss risk calculation and geostatistical analysis for process simulation. A case study in the Yellow River Delta (YRD) showed that the A-SNNRI worked well. During the summer crop-growing season, population and land use presented significant influences on the soil total nitrogen (STN) status. Urban villages and arable land both had the largest summary STN and variety. There was a negative correlation between STN change and soil loss. The east coast held both the largest A-SNNRIs and soil loss risks. There were significant positive correlations between A-SNNRIs and population and GDP. Therefore, to control and reduce soil-source nitrogen exports in the YRD, we need to reduce nitrogen emissions from urban villages, agriculture, industry, and aquaculture and determine the main risk locations along the east coast and in the main city.

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Geophysical evaluation of soils and soil loss estimation in a semiarid region of Maharashtra using revised universal soil loss equation (RUSLE) and GIS methods

Cited by 11 publications

References 34 publications

Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece

Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China

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