Modelling of Recent Erosion Rates in a Lake Catchment in the North-Western Siwalik Himalayas

Semwal, Poonam; Khobragade, S. D.; Nainwal, H. C.

doi:10.1007/s40710-017-0234-y

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…The rainfall data were collected from the Indian Meteorological Department (IMD) for four stations and one local station at a place called Borgaon. Inverse distance weighted (IDW) interpolation technique is widely used for rainfall studies using ArcGIS 10 (Magesh and Chandrasekar 2016; Mushtaq and Lala 2017;Semwal et al 2017). The equation is as follows:…”

Section: Rainfall Erosivity Factor (R Factor)mentioning

confidence: 99%

“…Soil erosion is although a natural process resulting from loss and transportation of top soil layer by the agents like wind and water, some anthropogenic activities also enhance the rate of the soil loss (Parveen and Kumar 2012). Water induced soil displacement is have several negative effects, where topsoil removal can decrease the productivity of soil and adversely restrict agricultural crop rooting depth (Oldeman 1994).Soil erosion has been considered as a hazard contributing factor which leads adverse impacts on watershed by causing land degradation, increase floods and disturbing the river flow (Nag et al 2020).Soil detachment and transportation and siltation into water bodies like a reservoir cause lowering in its storage capacity and disrupt aquatic life (Semwal et al 2017). On-site soil erosion due to improper land management, consequently affects the nutrient availability, water holding capacity, organic matter, reduces soil carbon stock, raises CO 2 emission and hamper soil biodiversity (FAO 2011).For the careful management of soil and water resources comprehensive knowledge of the behavioral process taking place in the watershed is essential (Jasrotia and Singh 2006).…”

Section: Introductionmentioning

confidence: 99%

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Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Bagwan

Gavali

2020

Model. Earth Syst. Environ.

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Soil loss by water erosion is a common type of land degradation issue in the hilly regions of the world. The present study investigates the soil erosion risk due to change in Land Use Land Cover (LULC) brought in by the construction of dam in a hilly watershed of the River Urmodi embracing Kaas Plateau, a world heritage site. The Revised Universal Soil Loss Equation (RUSLE) model was used with change detection analysis for soil erosion estimation. Thematic layers required for the computation of factors required in RUSLE were gathered using remote sensing (RS) and Geographical Information Science techniques. The changes in LULC affect the soil erosion phenomena and therefore, the present study analyzes drastic alteration in the hydrological nature of the watershed brought by the construction of reservoir through damming the Urmodi River. For assessment of the impact of the dam we have used RS data with 30 m spatial resolution, which includes Digital Elevation Model and Landsat TM and Landsat 8 bands. Using RUSLE, soil risk zone was mapped and the Change Detection in zones were computed. The very simple logic of 'soil erosion class as a type of land cover' was successfully applied to perform the change transition for the Urmodi river watershed. Confusion matrix technique used for analyzing the change, has the ability to show the previous and next condition of zone type very affectively with good precision. Along with Change Detection technique, soil erosion class transition operation was also performed to assess the conversion of zones. This gave the important output regarding increased conversion from very slight to very severe risk zone by 70.22 km 2 . The investigation concludes that there was extensive change in very severe (> 80 ton ha −1 year −1 ) soil vulnerable risk zone in the last 17 years with the rise of 14.87%. The conversion of lower risk category areas to higher risk category areas of soil erosion was detected all over the watershed. Undertaking such studies revealed the changes brought in the soil erosion susceptible zones by building of dams. This study will be a crucial help to policy and decision makers for proper planning of watershed with consideration to LULC in the event of dam construction on the rivers flowing in hilly terrain.

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Section: Rainfall Erosivity Factor (R Factor)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Bagwan

Gavali

2020

Model. Earth Syst. Environ.

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“…In general, C-factor values close to 0 are assigned to areas of a high vegetation cover while those close to 1 correspond to bare land [SEMWAL et al 2017]. In the current study, data related to the normalized difference vegetation index (NDVI) (acquisition date: 9 March 2018, acquired from Landsat 8 satellite imagery with a spatial resolution of 30 m) were used to assess the C-factor and thus interpret the impact of different vegetation layers on the soil loss in the study area (Fig.…”

Section: Stonementioning

confidence: 99%

Journal of Water and Land Development

Melalih,

Mazour

2020

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The loss of soil quality due to erosion is a global problem, particularly affecting natural resources and agricultural production in Algeria. In this study, the Revised Universal Soil Loss Equation (RUSLE) is applied to estimate the risk of water erosion in the Ain Sefra arid watershed (Algeria). The coupling of this equation with Geographic Information Systems (GIS) allows to assess and map the soil loss rates. The land erosion is influenced by many control variables, such as the topographic factor of the terrain and the length of slope (LS factor), rainfall erosivity (R factor), sensitivity of soil to erosion (K factor), presence of vegetation (C factor) and the anti-erosion cultivation techniques (P factor). To calculate the average annual soil loss, these five factors were considered and multiplied in the RUSLE Equation. The result shows that the average rate of soil loss is estimated at about 5.2 t•ha -1 •y -1 over the whole watershed. This study is the first of its kind in the region and aims to assess the soil loss caused by water erosion processes in this arid zone. Consequently, it is essential to take real intervention measures in these upstream areas in order to combat this scourge, based on priorities ensuring the sustainable management of natural resources in the study area.

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“…In this domain, limited research work in regards to the calculation of biodiversity indexes of microbes found in Sukhna and Dhanas lakes is reported. Work related to the quality of water 8,9 , catchment characteristics 10,11 , consequences of climatic changes 12 of these water bodies have few citations in contemporary literature.…”

Section: Introductionmentioning

confidence: 99%

Estimating Microbial Diversity via Morphological Based Microscopic Image Analysis: Methods and Metrics

Dhindsa¹,

Bhatia²,

Agrawal³

et al. 2020

J Pure Appl Microbiol

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To accelerate the monitoring and counting of biodiversity of various species, there is a need for automating the process of computing biodiversity. The calculations of the alpha and beta biodiversity indexes are fundamental for the analysis of ecological and biodiversity studies. Sukhna and Dhanas lakes, India are critical for the maintenance of the health of the residents and aquatic life thriving in them. Both lakes are prone to pollution. Due to these factors, there is a need for building digitized infrastructure for monitoring the health of these lakes. Hence in this research work, an automated algorithm has been devised for the computation of biodiversity of microorganisms. The work focuses on the surface water of both these lakes. The automation of biodiversity computation is done with image processing algorithms and is applied to the primary data collected. From this study, it is apparent that the counting of microorganisms using image processing algorithms is an easier and efficient way for biodiversity studies as compared to the manual process of estimating the population of microbes. The results show that the species richness of Dhanas Lake is more as compared to Sukhna Lake. The dissimilarity between the two lakes is five species as per the primary data collected. This shows that the biodiversity of Dhanas Lake is better than the Sukhna Lake but it is prone to harmful algal blooms. This may be attributed to the fact that Dhanas Lake may have multiple sources of pollution that need to be identified.

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Modelling of Recent Erosion Rates in a Lake Catchment in the North-Western Siwalik Himalayas

Cited by 11 publications

References 24 publications

Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Journal of Water and Land Development

Estimating Microbial Diversity via Morphological Based Microscopic Image Analysis: Methods and Metrics

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