the hypothesis of dregs conveyance proportion by applying suitable method .In precipitation and catchment heterogeneity, both soil disintegration and silt transport procedures are spatially fluctuated because of the spatial variety. Such irregularity has animated the utilization of information concentrated dispersed technique for the estimation of catchment disintegration and residue yield by discretizing a catchment into sub-ranges every having around homogeneous attributes and steady precipitation dissemination (Young et al., 1987; Beven, 1989).To outline the spatial contrast of the parameters like geography, soil and area use in a watershed, the utilization of Geographical Information System (GIS) system is well suitable. The discretization of the catchment into little matrix cells and for the calculation of such physical attributes of these cells as slant, area utilize and soil sort, by utilization of GIS methods, the all of which influence the courses of soil disintegration and testimony in the diverse sub-ranges of a catchment. Various distinctive models (both test and procedure based) have been built up to decipher soil misfortune information in light of GIS. Utilizing the USLE parameter to gauge the precipitation based disintegration and the vehicle of non-point source contamination stacks on upperMahanadicatchment in Raijm gaging station. They have utilized exact relationship between Delivery Ratio (DR) and catchment region keeping in mind the end goal to register residue load. Jain et al. (2003) made a count of dregs yield for the upperMahanadi stream bowl at Raijm gaging station: (I) relationship between suspended residue load and release and (II) exact relationship. The sediment– discharge relationship was produced utilizing day by day information. For estimation of the silt yield utilizing the test relationship, different land parameters, for example, area utilization and geology were produced utilizing Geographic Information System (GIS) system. They likewise used trial comparison to gauge residue conveyance proportion keeping in mind the end goal to compute dregs yield at catchment outlet. By utilizing. GIS, Remote Sensing (RS) with UniversalSoilLossEquation (USLE) to distinguish the basic disintegration inclined ranges of watershed for positioning reasons.
Abstract. High soil erosion and excessive sediment load are serious problems in several Himalayan river basins. To ap- ply mitigation procedures, precise estimation of soil erosion and sedimentyield with associated uncertainties are needed. Here, the revised universalsoillossequation (RUSLE) and the sediment delivery ratio (SDR) equations are used to estimate the spatial pattern of soil erosion (SE) and sed- iment yield (SY) in the Garra River basin, a small Hi- malayan tributary of the River Ganga. A methodology is proposed for quantifying and propagating uncertainties in SE, SDR and SY estimates. Expressions for uncertainty propagation are derived by first-order uncertainty analysis, making the method viable even for large river basins. The methodology is applied to investigate the relative impor- tance of different RUSLE factors in estimating the mag- nitude and uncertainties in SE over two distinct morpho- climatic regimes of the Garra River basin, namely the up- per mountainous region and the lower alluvial plains. Our results suggest that average SE in the basin is very high (23 ± 4.7 t ha −1 yr −1 ) with higher values in the upper moun- tainous region (92 ± 15.2 t ha −1 yr −1 ) compared to the lower alluvial plains (19.3 ± 4 t ha −1 yr −1 ). Furthermore, the topo- graphic steepness (LS) and crop practice (CP) factors ex- hibit higher uncertainties than other RUSLE factors. The an- nual average SY is estimated at two locations in the basin – Nanak Sagar Dam (NSD) for the period 1962–2008 and Husepur gauging station (HGS) for 1987–2002. The SY at NSD and HGS are estimated to be 6.9 ± 1.2 × 10 5 t yr −1 and 6.7 ± 1.4 × 10 6 t yr −1 , respectively, and the estimated 90 % interval contains the observed values of 6.4 × 10 5 t yr −1 and
destroyer to land cover management and resources of the Upper-Helmand river basin catchment. The Upper- Helmand river basin catchment covers an area of 46,793 square kilometers. In the present study, UniversalSoilLossEquation (USLE) model with Remote Sensing and Geographical Information System (GIS) techniques have been used to estimate soil erosion risks and sedimentyield at the Upper-Helmand catchment outlet (Kajki reservoir). Potential soil erosion and magnitude are determined in the catchment. Using USLE model, soil erosion map has been prepared and presented, which will be helpful for conservational and management practices to reduce soil erosion and its yield into the reservoir. It is also found that the average soil erosion from the catchment is 4.48ton/ha/year and corresponding sedimentyield trapped at the Kajaki reservoir.
11 International Journal in Physical and Applied Sciences http://ijmr.net.in, Email: email@example.com events due to the abrupt relief, inappropriate land use and land cover patterns. Agricultural lands without vegetation cover are more vulnerable to erosion threats. The erosion models coupled withDigital Elevation Model (DEM) along with Geographical Information System (GIS) have been proved to be an effective tool for estimating and quantitatively assessing the magnitude and spatial distribution of erosion so that effective management strategies as well as soil conservation programmescan be developed and applied on a regional basis with the help of field measurements. Several methods for erosion intensity and associated sedimentyield assessment have been developedcategorized into empirical, conceptual and physically based models with varying accuracy and complexity. The two of the most scientifically accepted and widely applied empirical based models which estimate long-term average annual soilloss and sedimentyield by sheet and rill erosion are the Revised UniversalSoilLossEquation (RUSLE) (Wischmeier et al. 1978, Renard et al. 1997) and Erosion Potential Model (EPM) (Gavrilovic 1988) predicting the erosion potential on a cell-by- cell basis in regions where measurements are completely absent. These empirical models are worldwide applied due to the ease of use, the low input data requirements,the simplicity, the computation demands, the time-consuming and the low implementation costas well. The utmost objective of this study was to spatially assess the annual soil erosion rate and develop a soil erosion map for the three sub-catchments of Atalanti river basin in Central Greece, namely Alarginos, Karagkiozis and Ag. Ioannis by using erosion models and GIS techniques.RUSLE is an erosion estimation model by overland flow comprising six factors, namely, rain
Sedimentation in lentic and lotic water resources is the outcome of the land erosion in their catchment area. Land erosion ultimately affects the physical and chemical properties of soils and resulting on-site nutrient loss and off-site sedimentation and nutrients enrichment of water resources. The off-site effects of erosion in the form of sedimentation and nutrients enrichment are usually more pricey and severe than the on-site effects on land resources. Many empirical equations and procedures have been developed for estimating sedimentyield at the outlet of a catchment. These regression equations for estimation of annual sedimentyield are linked with catchment area, land use patterns, meteorological conditions and runoff generated within the catchment. These equations are widely accepted and used for prediction of sedimentyield from the un-gauged catchment area. In the present studyUpper Lake, Bhopal and its catchment area is taken as a test case and entire study was aimed with two main objectives, first, to estimate and compare the annul sedimentation yield using different empirical equations and second, to determine the sediment characteristics deposited in the bottom of the Upper Lake. The study results revealed that significant annual sedimentation yield were observed which were found in between 0.22-5.6 Mcum/year. As far as, sediment characteristic is concern, it was also found rich in nutrient and organic loads which may be the significant nutrient contributors to hypo- limnetic lake environment. Therefore, an integrated catchment area plan is imperative which can manage on-site effect of soil erosion that could reduce the risk and negative impacts on downstream Upper Lake ecosystem.
Abstract Elevated suspended sediment concentrations in fluvial environments have important implications for system ecology and even small concentrations may have serious consequences for sensitive ecosystems or organisms, such as freshwater pearl mussels (Margaritifera margaritifera). Informed decision making is therefore required for land managers to understand and control soil erosion and sediment delivery to the river network. However, given that monitoring of sediment fluxes requires financial and human resources which are often limited at a national scale, sediment mobilisation and delivery models are commonly used for sedimentyieldestimation and management. The Revised UniversalSoilLossEquation (RUSLE) is the most widely used model for overland flow erosion and can, when combined with a sediment delivery ratio (SDR), provide reasonable sediment load estimations for a catchment. This paper presents RUSLE factors established from extant GIS and rainfall datasets that are incorporated into a flexible catchment modelling approach. We believe that this is the first time that results from a RUSLE application at a national scale are tested against measured sedimentyield values available from Ireland. An initial assessment of RUSLE applied to Irish conditions indicates an overestimation of modelled sedimentyield values for most of the selected catchments. Improved methods for model and SDR factors estimation are needed to account for Irish conditions and catchment characteristics. Nonetheless, validation and testing of the model in this study using observed values is an important step towards more effective sedimentyield modelling tools for nationwide applications.
The original and modified forms of the USLE, is widely used model to assess soilloss from a catchment area . USLE model has involved number of parameters, such as rainfall erosivity factor (R), erodibility factor (K), topographic parameters (LS), vegetative cover (C) and soil conservation practice factor (P). In the present study, UniversalSoilLossEquation (USLE) is being used to assess potential soil erosion from Upper-Helmand catchment and its impact on Kajaki reservoir. Arc-GIS 10.3 software is being used for the generation and development of input digital data for the USLE model to estimate the soil erosion form the catchment and generation of output maps.
Parveen, R., & Kumar, U. (2012). Integrated Approach of UniversalSoilLossEquation (USLE) and Geographical Information System (GIS) for SoilLoss Risk Assessment in Upper South Koel Basin, Jharkhand.
Prasannakumar, V., Vijith, H., Geetha, N., & Shiny, R. (2011). Regional scale erosion assessment of a sub-tropical highland segment in the Western Ghats of Kerala, South India. Water resources management, 25(14), 3715-3727.
and the model’s inability to account for types of soil erosion other than rill or inter-rill erosion. Lastly, the paper outlined some key future directions for (R)USLE research: incorpo- rating soilloss from other types of soil erosion, importance of estimating soilloss at sub-annual scales and recommended equations, validation of soilloss estimates, and consistency in reporting units in the future literature. To represent gully erosion, the Compound Topographic Index (CTI) was briefly discussed, while the sediment delivery ratio (SDR) was also presented to account for linking soilloss to sediment deliv- ery to streams. The ability to predict sub-annual soilloss or seasonal erosion modelling is important in study areas hav- ing high temporal variation of rainfall throughout the year, and/or having varying crop growth and tillage cycles, both being factors that can impact potential soilloss. Land man- agement policy and decisions might be more robust if they consider modelling scenarios that test the effect of different types of crop and support practices on soil erosion mitiga- tion. These scenarios can include a myriad of options: ex- panded urban areas or development, changing crop rotation cycles, or applying support practices in steep or upland ar- eas. Further, seasonal soil erosion has implications on water quality, and understanding the extent of the problem can help local government address potential sources of sediment de- livery and be more proactive in land management. Validation of soilloss estimates is important in understanding the ac- curacy of the (R)USLE application, and future work could involve compiling an extensive global database of soilloss estimates derived from observations and models, including those models more complex than (R)USLE. This database would be useful for future researchers in comparing their re- sults and assess the accuracy of model applications. Greater transparency in reporting the sub-factor units, sub-factor val- ues, and soilloss estimates is important to decrease uncer- tainty when future (R)USLE applications borrow sub-factor equations and values from previous studies. The limitations section addresses the fourth objective of this review.
3.3 Model calibration and validation
The first step in the calibration and validation process in SWAT is the determination of the most sensitive parameters for a given watershed or subwatershed. Sensitivity analysis is the process of determining the rateof change in model output with respect to changes in model inputs (parameters). The second step is validation for the component of interest (stream flow, sediment yields, etc.). Validation involves running a model using parameters that were determined during the calibration process and comparing the predictions to observed data not used in the calibration . The observed daily runoff and sedimentyield data at the outlet of the watershed were obtained from the Department of Hydrology. These data are required for calibration and validation of the SWAT model.
relocates within the same field instead of being delivered beyond the field edge. On the other hand, an enrichment factor is necessary to account for the increase of P concentration in fine-textured clay-enriched sediment due to particle selectivity during the deposition process (Wischmeier and Smith, 1978; Ongley, 1982; Foster et al., 1985). The overall effect of receiving slope on the reduction of particulate P loss is represented in the edge of field delivery ratio (EFDR). There are also many examples of effective sediment-control and P-reduction practices, including vegetative or tree/shrub riparian buffers (Peterjohn and Correll, 1984; Cooper and Gilliam, 1987; Daniels and Gilliam, 1996), sediment trapping ponds or basins (Bhaduri et al., 1995; Borden et al., 1998; Verstraeten and Poesen, 2002), as well as reduced tillage practices (Beasley et al., 1985; Gaynor and Findlay, 1995; Andraski et al., 2003). These optional practices, whether implemented in the field or beyond the field edge, can often efficiently control soil erosion and sedimentyield, and therefore reduce particulate P loss (Thaxton, 2005). In PLAT, these factors are represented by the buffer delivery ratio (BDR) and sediment trap delivery ratio (STDR). In addition, PLAT also uses an Fe-P factor to account for the release of Fe-bound P as sediments are reduced in anaerobic environments (Shelton and Coleman, 1968). Other possible practices, but not evaluated in this component of PLAT, include crop rotation, contour cultivation, strip cropping, terraces, grass waterways, and diversion structures.
The scenario results can all be explained by physical reasoning. In addition they match the analysis by Lorente et al. (2002) of debris flow occurrence in the flysch sector containing the Ijuez catchment. This analysis found the distribution of debris flows to be higher in reforested areas (31%), shrublands (24%) and areas with natural pine forest (20%) compared with meadow lands (7%). The non- cultivated area contained 68% of all the debris flows compared with 30% in sloping fields. The results also match the general observation that the number of landslides increases after tree removal. On the other hand, they do not agree with observations that suggest an accompanying increase in sedimentyield. A possible reason for this could be the absence of overland flow, which means that the increase in hillslope erosion (from landslides and because of reduced protection against raindrop impact) is not converted into an increased supply to the river system. Wetter conditions could produce a different result.
conditions, topography, lithology and land use. Most of these variables were also used in previous studies aiming to identify the factors controlling SY (e.g. Syvitski and Milliman, 2007; de Vente et al., 2013; Vanmaercke et al., 2014). To quantify the overall degree of seismic activity, the spatial average of the expected Peak Ground Acceleration with a recurrence interval of 100 years (PGA; Lungu et al., 2004) was calculated for each catchment. The relative importance of these characteristics in explaining spatial variation in SY was explored by means of correlation analyses. To account for potential inter-correlations, partial correlation analyses were also conducted. Partial correlation measures the degree of association between two variables, with the effect of other controlling variables removed (Fisher, 1924; Steel and Torrie, 1960).
Abstract: Water is one of the essential natural resource for the very survival of life on the planet Earth. Demand for water is increasing day by day, with the ever increasing population, resulted severe water crisis. We need water for agriculture, industry, human and cattle consumption. The available water is also affected by problem of pollution and contamination. Therefore it is very important to manage this very essential resource in a sustainable manner. Hence, we need proper management and development plan to conserve, restore or recharge water, where soilloss is very high due to various topographical conditions. The USLE (UniversalSoilLossEquation) method is one of the significant RS-GIS tools for prioritization of micro watersheds. A watershed is an ideal unit for study and to implement any model of water management towards achieving sustainable development. The significant factors for the planning and development of a watershed are its physiography, drainage, geomorphology, soil, land use/land cover and available water resources. In the current study, the micro-watershed priority fixation has been adopted under USLE model using Remote Sensing data. SRTM DEM, rainfall data and soil maps have been used to derive various thematic layers. The study area (Simlapal, W.B.) was subjected to USLE model of classifying and prioritizing the micro watersheds. The study area is divided into 22 sub-watersheds with areas ranging from 25 to 30 sq. km from the drainage map. Again each sub-watershed is divided into micro-watersheds with areas ranging from 5to10 sq. km. Thus 77 micro-watersheds were delineated for the present study area, considering all the controlling factors. Based on the results the 77 micro- watersheds could be prioritized in to five ranges viz very high, high, medium, low and very low.
Consequently, there is need for models capable of ef- ficiently forecasting water levels and discharge rates. In this regard application of ANN is more effective. Earlier the works of Bruen and Yang , Campolo et al. [4,5], Coulibaly et al. , Dawson and Wilby , Imrie et al. , Lekkas et al. , Lohani , Minns and Hall , Muhamad and Hassan , Mukerji et al. , Solo- mantine and Xue , Solomantine and Price , Zea- land et al.  emphasized the application of artificial neural networks over other methods. In flood forecasting both the peak and travel time has equal significance. The travel time is also of great importance in prediction of the stage. It varies with stages and channel condition. Travel time generally reduces when water approaches the top of the bank. As the river overflows flooding over the flood plain the travel time may begin to increase again due to the relatively rough surfaces lying in the overbank stages. In order to give justification for different types of peaks with respect to travel time clustering method is adopted in this study. Earlier, Zhang and Hall  have applied clustering methods in regional flood frequency analysis and Xiongrui et al.  in establishing rainfall- runoff relationships. In view of the above an attempt has been made in this study to apply both statistical method and ANN based approach for forecasting the peak dis- charge and travel time in the downstream reach of the
Soil erosion is still a serious problem in the Western Ghats of Kothagiri Taluk, and attempting different methods to evaluate soilloss at the watershed scale is necessary for sustainable land use and comprehensive Talukal development. RUSLE is often used to estimate average annual soilloss from an area. RUSLE model in GIS environment is a relatively simple soil erosion assessment method. ArcGIS 9.1 software was used to generate the spatial distribution of the RUSLE factors. The four factor layers (R, K, LS, and C) were all converted into grids using a 20-m data set of the Kothagiri taluk in the same reference system. Subsequently, these grids were multiplied in the GIS as described by the RUSLE function. Thus, the annual soilloss was estimated on a pixel-by-pixel basis, and the spatial distribution of the soil erosion in the studied taluk was obtained. To adopt the RUSLE, large sets of data starting from rainfall, soil, slope, crop, and land management are needed in detail. In developing countries all the necessary data are often not available or require ample time, money, and effort to prepare such data sets. RUSLE is a straightforward and empirically based model that has the ability to predict long term average annual rate of soil erosion on slopes using data on rainfall pattern, soil type, topography, crop system and management practices. In the present research, annual soil erosion rate map was generated for Kothagiri Taluk, a mountainous area, which represents most of the terrain characteristics of Western Ghats. Soil erosion mapping was modeled within Kothagiri Taluk, integrating the RUSLE with GIS. To predict average annual soilloss caused by sheet and rill erosion from the study, the parameters used in the RUSLE equation depend on soil characteristics, topography and landuse of the area. Based on this analysis, the amount of soilloss
Gully erosion is a serious geo-environmental problem globally, the areas in Orlu Senatorial Zone of Imo State, Nigeria has experienced varying degrees of erosion menace leading to land degradation and loss of valuable properties. Identification of gully locations from satellite images, development of erosion risk maps and calculation of rate of soilloss are important advancement in GIS technologies. Gullies in the study area were identified using Earth Explorer and Sas Planet remote sensing software. Investigations were carried out to distinguish gullies from other existing open cavities. Gully heads were picked as points using Global Positioning System (GPS) receivers. Soil erosion risk map was prepared based on rate of soilloss, determined using the Revised UniversalSoilLossEquation (RUSLE). The RUSLE factors of soil erosion (R, C, LS, K and P) were computed from collected rainfall data, landsat imagery, soil analysis and Digital Elevation Model to develop the soil erosion risk map. A total of 91gully erosion sites were identified out of which 80 were active. The resultant erosion map was compared to the satellite remote sensing based maps of identified gully sites. The erosion risk showed dominance of gullies and high annual soilloss in the northern part of the study area respectively.
Results: The average heavy metals content in the studied cultivated soils and Nile sediments are above the acceptable levels. Generally, Nile sediments and cultivated soils at Aswan and Luxor were unpolluted to moderately polluted with heavy metals. Pollution indices indicated that the studied Nile sediments were at considerably ecological risk from Cd (Er = 138.89) and Zn (Er = 140.52). In contrast, the cultivated soil was at very high ecological risk from Cd (Er = 295.24). Conclusions: The current research revealed that the soil and sediments in the Upper Egypt are less polluted than Lower Egypt. Thus, the concentrations of toxic elements are increased from south to north direction in Egypt along the Nile River. The sources of the toxic metals may possibly be natural or anthropogenic in the studied area. The anthropogenic source is resulting from paper, pulp, ferrosilicon factories, and phosphate mining at Edfu. In addition, there are some polluting industries such as sand quarry, shale mining, and the nitrogen fertilizer factory at Aswan. On the other hand, the natural sources of toxic waste are the drains during the seasonal flash floods.
The present study was carried out for a period of one year 2014-15. Water samples were collected from 12 different places from local stations of Mahanadi river (Table) in pre-monsoon (March-June-2014), monsoon (July-October-2014) and post monsoon (November-February-2015) periods at a regular interval of once in a month. Water samples were collected in acid- washed plastic bottles of one l tr. capacity having double stopper facilities to its full capacity without entrapping air bubbles inside. Two bottles of water was collected from each station i.e. one for analysis of physico-chemical parameters and other for heavy metals. About two ml. of concentrated HNO 3 was added to the second bottle of each station to preserve the heavy metals present in the