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Download games sediment

Postby Kijinn В» 14.04.2019

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Soils play an important role in the maintenance of global food supplies, an ever increasingly important role as total population expands. The more recent GLASOD Global Assessment of Soil Degradation survey has indicated more than 10 9 ha of the land surface of the world are currently experiencing serious soil degradation as a result of water erosion. Erosion prediction is the most widely used and most effective tool for soil conservation planning and design.

Because it is impossible to monitor the influence of every farm and ranch management practice in all ecosystems under all weather conditions, erosion predictions are used to rank alternative practices with regard to their likely impact on erosion. These erosion predictions are thus an essential part of soil conservation programs. Assessment of soil erosion as to how fast soil is being eroded is helpful in planning conservation work. Estimates of the rate of soil loss may then be compared with what is considered acceptable and the effects of different conservation strategies can be determined.

Modeling can be an effective method of predicting soil loss under a wide range of conditions as it can provide a quantitative and consistent approach to estimating soil erosion and sediment transport. Using remote sensing and GIS to parameterize such models allows them to be applied over local, regional and global scales. Two main types of model: empirically based and process based are available for predicting soil erosion and sediment transport.

Empirically based technology means regression or lumped mathematical models, which were developed using the experimental data of plot studies on erosion by water. Zingg [ 29 ] and Musgrave [ 18 ] equations are examples of initial steps towards the empirical soil erosion models.

It is the most widely used model for soil erosion estimation because of the simplicity. It is based on the set of mathematical equations that estimate average annual soil loss from inter-rill and rill erosion. In addition, the equation combines interrelated physical and management parameters such as soil type, rainfall pattern, and topography that influence the rate of erosion. Physically or process based models are intended to represent the essential mechanisms controlling erosion and sediment transport process.

These models are the synthesis of individual component that affect the erosion and transport process. Although physically based models try to emulate the physical processes involved in soil erosion and sediment transport, the weakness of these models is numerous parameters they need for calibration and also suffer from the problem of equifinality [ 3 ].

The overall aim of the study is the modeling of soil erosion and transport processes in distributed manner so that erosion, deposition and sediment yield can be computed and verified with the observations in data limited conditions. To achieve this objective, an empirical model was framed within Geographic Information System GIS to predict soil erosion in distributed manner.

Then, the sediment delivery approach is used to predict sediment yield in this study. For the empirical approach, the revised form of the USLE model, RUSLE, is used to predict erosion potential on a cell-by-cell basis in conjunction with SEDD model to determine the catchment sediment yield by using the concept of sediment delivery ratio [ 7 ]. A very popular empirical model, known as USLE is used to estimate soil erosion in this study.

Then, sediment delivery approach is used to estimate the sediment yield which a part of eroded sediment that appears at watershed outlet. Empirical methods such as the USLE have been found to produce realistic estimates of surface erosion and also sediment yield over areas of small size [ 26 , 10 4 ].

For moderately susceptible soil in both rill and inter-rill erosion, McCool et al. The slope steepness factor S is evaluated from the following equations McCool et al. C and P factors are assigned to different grid according to land cover while K factor is estimated using the soil data. In a catchment, not all eroded soil reaches the catchment outlet but a part of the soil eroded in an overland region gets deposited within the catchment. The values of ratio of sediment yield to total surface erosion, which is termed as sediment delivery ratio D R , for an area are found to be affected by catchment physiography, sediment sources, transport system, texture of eroded material, land cover etc.

However, variables such as catchment area, land slope and land cover have been mainly used as parameters in empirical equations for D R [ 9 , 12 ]. The travel time is strongly dependent on the topographic and land cover characteristics of an area and therefore its relationship with D R is justified. Based on their studies on probability distribution of travel time, the following relationship was assumed herein for a grid cell lying in an overland region of a catchment:.

The travel time for grids located in a flow path to the nearest channel can be estimated if the lengths and velocities for the flow paths are known. The direction of flow from one cell to a neighboring cell is often ascertained by using an eight direction pour point algorithm in grid-based GIS analysis.

Once the pour point algorithm identifies the flow direction in each cell, a cell-to-cell flow path is determined to the nearest stream channel and thus to the catchment outlet.

If the flow path from cell i to the nearest channel cell traverses m cells and the flow length of the i th cell is l i which can be equal to the length of a square side or to a diagonal depending on the direction of flow in the i th cell and the velocity of flow in cell i is v i , the travel time t i from cell i to the nearest channel can be estimated by summing up the time through each of the m cells located in that flow path:.

In this study, the method of determination of the overland flow velocity proposed by the US Soil Conservation Service was chosen due to its simplicity and the availability of the information required SCS, The flow velocity v i is considered to be a function of the land surface slope and the land cover characteristics:. Introducing equations 10 and 11 into equation 9 gives. Values of the coefficient a i for different land uses were adopted from [ 8 ] and are presented in Table 4.

If S E is the amount of soil erosion produced within the i th cell of the catchment estimated using equation 1 , then the sediment yield for the catchment, S y , was obtained as follows:.

Since the D R of a cell is hypothesized as a function of travel time to the nearest channel, it implies that the gross erosion in that cell multiplied by the D R value of the cell becomes the sediment yield contribution of that cell to the nearest stream channel. The D R values for the cells marked as channel cells are assumed to be unity. The study area selected for this study is Bagmati Basin, Nepal.

The basin is chosen because of its bio-climatic diversity due to elevation differences from valley floors to mountain summits, and related land use changes having influence on soil erosion, which is considered typical for the Middle Mountains of Nepal. Bagmati is the draining river from the Kathmandu city which is the capital of Nepal. The Bagmati basin covers an area of 3, km 2 in total and drains out of Nepal across the Indian State Bihar to reach the Ganges.

The watershed can be divided into three main areas: the upper, middle and the lower Bagmati watershed areas BWA. From the Chovar gorge, the river flows into the Middle Bagmati watershed Area across the Mahabharat and Siwalik ranges. The catchment area of upper and middle Bagmati basin is about 2, km 2. The terrain of the upper and middle BWA is rugged and comprised of several steep mountains except Kathmandu valley.

The area of upper and middle Bagmati basin draining to Karmaiya is considered in the study on the basis of data availability.

In the basin, steep slope in mountainous area and land use change are the major factors of soil erosion, which is considered typical for the Middle Mountains of Nepal. Total population in the catchment is about 1. Figure 1 shows the map of the catchment along with streams and tributaries. Hydrologic data rainfall, evaporation, suspended sediment concentration for the basin are obtained from Department of Hydrology and meteorology DHM.

STRM DEM provides comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers.

The details of hydrologic data are provided in Table 1 while Table 2 contains the details about spatial data set. As observed in the DEM of the watershed Figure 2 , the elevation varies significantly from as low as m to as high as m from mean sea level. Lower part of the watershed is relatively flat compared to the upper and middle part. Kathmandu, the capital of Nepal lies in the upper part of the watershed.

The land use in the watershed is observed to be mixed type. Cultivated land is major land use pattern in the upper part of the watershed while in middle and lower part of the watershed, forest area is seen to be dominant land use type.

Majority of built-up area falls on the upper part of watershed, which represents Kathmandu. The land use pattern in the watershed is presented in Figure 4. The land use distribution in the watershed is presented in Figure 3. The most extensive soils in the area are Dystrochrepts, Hapludalfs and Haplumbrepts, which occupy most of the hilly and mountaineous land. The Dystrochrepts are also the most important soils in the inner Terai valleys.

Soil type Rhododtalfs is commonly found in the gently undulating slopes and restricted to scattered, quasi-subtropical areas in the lower Hiamlayas.

These soils are prone to severe soil erosion. The soil in the south face on the low altitude Mahabharat range is Dystrochrepts and Hapludalfs. These soils are mostly cultivated. The Haplaquepts are the dominant soils in the Terai plain as well as on paddy fields in hilly areas and elsewhere. Major soil types in the mountainous lands are Haplumbrepts and Dystrochrepts. Loamy soil texture is dominant in the watershed as demonstrated in Figure 4. Revised Universal Soil Loss Equation is one of the simplified models, which predicts soil erosion from hillslopes.

The factors such as rainfall runoff erosivity factor R associated with the model represent the effects of climatic parameters in soil erosion while soil erodibility factor K represents the nature of the soil, its characteristics and influence in soil erosion.

Topography and land use practices are other major factors incorporated in the model to account their effects in soil erosion. Out of seven rainfall stations in Bagmati basin, one station measures hourly rainfall while remaining six other stations measures daily rainfall. So, rainfall data from these seven stations are analyzed to find the correlation in the rainfall pattern. The analysis of daily, monthly and annual rainfall trends of these stations showed that the trend was similar for all these stations.

This helped to in disaggregating daily rainfall data into hourly data for the remaining six stations. Soil erodibility K factor values were assigned on grid by grid basis on the basis of soil texture [ 21 ] of the basin and assigned K values are presented in Table 3. Equation 5 was used for L factor calculation while S factor was computed using equation 8 for each cell.

Similarly, C value, which depends on land use, was obtained from different literature [ 21 ], [ 16 ]. The C values used assigned for different land use in the basin are tabulated in Table 4. In case of P factor, the value is taken 0. The SDR map for the basin is presented in Figure 5 below.

It is observed that flat areas around the south and north parts of the watershed has low sediment delivery ratios while the hilly areas within the watershed had higher values for sediment delivery ratio. This finding is consistent with the fact that steep areas are supposed to have higher sediment delivery ratio compared to flat areas.

In terms of watershed management perspective, the areas with higher values of SDR should be given higher priority compared to areas with lower SDR values for implementation of erosion control measures in this watershed.

The sediment yield data are available for only few months of the year for Bagmati basin. So, it was not possible to analyze the long term sediment yield value and thus, monthly computation is carried out. Soil erosion map and SDR map was used to compute the sediment yield value at the watershed outlet. The Observed monthly sediment yield was compared with the computed as seen in Figure 6 below. The simulated result using this approach is fairly consistent with the observed data although this methodology slightly overpredicted sediment yield for the most of the observed months.

There can be several reasons which can lead to overestimation of sediment yield values. For example, only one rainfall station had hourly measurement while remaining stations recorded daily values.

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