Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
2 School of Social Development and Environmental Studies, Faculty of Social Science and Humanities, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Abstract: The TasikChiniCatchment, located at the southeast region of Pahang, Malaysia is experiencing soilerosion problems which are of environmental concern. So a study was conducted that involved the integration of the Revised Universal Soil Loss Equation (RUSLE) with the Geographic Information System (GIS) to estimate potential soil loss and identify erosion risk areas. Values for the model on rainfall erosivity (R), topographic factors (LS), land cover (C) and management factors (P) were calculated from rainfall data, together with the use of topographic and land use maps. Soil was analyzed for obtaining the soil erodibility factor (K). Physical properties such as particle size distribution, texture, hydraulic conductivity and organic matter content (OM) were analyzed to support the erosionrate analysis. The mean soil erodibility factors varied from 0.03 to 0.30 Mg h MJ -1 mm -1 . From a total of eleven soil series studied, soilerosion results showed that the five soil series with low rate of soil loss were: Tebok, Lating, Bungor, Kekura and Gong Chenak. Two soil series with moderate soil loss were Serdang and Prang. Two soil series with moderately high rate of soil loss were Kuala Brang and Rasau. The Malacca soil series had high erosionrate. The worst-case scenario was the Kedah soil series. The soilerosion potential zones were classified into five classes namely very low, low, moderately high, high and very high soil loss. The results indicated that 71.54% of the study area lay within the very low erosion risk class, 2.94% in the low erosion risk class, 3.38% in the moderately high erosion risk class, 1.45% in the high erosion risk class and 13.25% in the very high erosion risk class. This high erosionrate is expected to generate high sediment yield influx into the water bodies of TasikChini making the lake shallower and perhaps even non existent in the near future if precautionary measures are not taken.
A quantitative assessment is needed to infer on the extent and magnitude of soilerosion problems so that sound management strategies can be developed on a regional basis with the help of field measurements . Researchers have developed many tools for estimating soil loss, such as the Soil and Water Assessment Tool (SWAT), the Water Erosion Prediction Project (WEPP), the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), etc. . Among them, USLE is widely used for the study of soilerosion by water because of its simplicity, despite some inconveniences due to its extensive requirement for input data [12,13]. The USLE method predicts the long term average annual rate of erosion on a field-based rainfall pattern, soil type, topography, crop system and management practices. The major purpose of the soil loss equation is to guide methodical decision making in conservation planning on a site basis. Using conventional methods to assess soilerosion risk is expensive and time consuming. Hunri (1985)  conducted conventional study on soilerosion for the highest areas in Ethiopia focusing on various soilerosion factors.
________________________________________________________________________________________________________ Abstract:- Soilerosion causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. In the present study, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in T.G. Halli Watershed, Karnataka, India. Remote Sensing provided the base information’s such as, land use/land cover, soil, hydro- geomorphology, slope and other aspects. GIS was used for database creation and analysis purposes. Morphometric analysis was carried out for the entire watershed and also at the sub-watershed level. Drainage density was estimated to be 1.39 Km/Sq.km for the entire watershed. Soil loss was estimated using Universal Soil Loss Equation (RUSLE). The weighted soilerosion for the entire subwatershed was estimated to be 16.40t/ha/year. In GIS platform, the overlay of rainfall-runoff erosivity factor, soil erodibility factor, slope length factor, slope steepness factor, cover and management factor, support and conservation practices factor results that the high amount of soil loss is significantly low and occupies 0.11% of the entire study area. High soil loss in upstream of the basin has a close relation to LS and K factor and drainage density. As a result of soil loss in the upper catchment areas, reservoir capacity has been depleted both in dead and live storage space.
Thus, timely and accurate estimation of soilerosion loss or evaluation of risk has become imperative for many countries. It is also useful to make estimate of how fast the soil is being eroded before affecting any conservation strategies. Due to the nature of the erosion process, erosion control requires a quantifiable and qualitative evaluation of potential soilerosion on a specific site, and the knowledge of terrain, cropping system, soils, and management practices. Many researchers involved in soilerosion research for quiet long time, and effort was put in understanding the mechanism of soilerosion, predicting the rate of soilerosion and soil loss both at catchment scale or plot (Fu et al., 2004; Fu et al., 2005; Kang et al., 2001), and at a regional scale. Several sediment transport and soilerosion models have been developed around the world to estimate rates of sediment and nutrient transport under different land use systems. There are three categories of model: the empirical models, the conceptual models and physically-based models as suggested by Merritt et al., (2003). These include the USLE and GIS based USLE, WEPP, AGNPS, LISEM and EUROSEM models. These models, however, vary significantly in their complexity, inputs and requirements, the processes represent and the manner in which these processes are represented, the scale of intended use and the types of output information they provide (Ismail and Ravichandran, 2008; Merritt et al., 2003).
63.5 t/hm 2 using Morgan approach integrated with GIS and RS and it was estimated that the rate of soilerosion mainly depends on the nature of vegetation cover, overland flow and texture of the soil  . It was estimated that 75% area of the Dhrabi River Catchment (DRC) is impacted by soilerosion with mean rates of 82 t/hm 2 using the Revised Universal Soil Loss Equation (RUSLE)  and Water Erosion Prediction Project (WEPP) [18,19] models  . This results in a high variability of soil fertility and productivity within the area, which diminishes the storage and filtering function of the soil  . Soilerosion is a three step process involving detachment, transportation and deposition which causes onsite as well as offsite problems. Onsite effects are the removal of organic matter and soil nutrients which reduce agriculture production. Offsite problems are often more severe include river silting, impaired water quality of reservoirs, reduced reservoir storage, and exacerbation of floods and landslides  . Due to these effects the soil and water resources are under threat and the productivity of land is decreasing which ultimately leads to a reduction in agricultural production. The accelerated soilerosion is a serious agro-environmental threat to food security and agriculture sustainability worldwide [3,22,23] .
of potential soil loss in the area studied was very severe, especially on northwest and southeast regions, where the main sources of soil loss were come from agricultural, new settlements and mining areas. Close proximity of these activities may contribute to further deterioration of TasikChini water body through accelerated soil loss if no precaution measure was employed. To conclude, estimation of soilerosionmodelusing remotely sensed data can be used to build sustainable development strategy within TasikChiniCatchment in the future.
Predicting and estimating the potential of soilerosion is extremely important to watershed management .The advanced technology of geomatics as Geographic Information System (GIS) and Remote sensing (RS) become a valuable source of assistance to estimate soil loss at a large area, in faster manner, and with a consistent level of reliability. The first objective of this work is to quantify water-soilerosion in the Beht watershed upstream of Ouljat Sultan dams, by the Revised Universal Soil loss Equation (RUSLE), using (GIS) and (RS). The second objective is to elaborate the vulnerability map of soil to the erosion for a future use in the priorities of fight against erosion in this study area. Thereafter; a statistical analysis of results will be preceded. The results obtained shows that the watershed of Beht is subject to high erosion, with an average of (21.36 t/ha/year) and with an extreme value exceeding (500t/ha/year).
Environmental Geosciences, University of Basel, Basel, Switzerland; b Forest Soils and Biogeochemistry, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
This study presents the ﬁrst mapping of soilerosion risk modelling based on the Revised Universal Soil Loss Equation (RUSLE) at a sub-annual (monthly) temporal resolution and national scale (100 m spatial resolution). The monthly maps show highest water erosion rates on Swiss grasslands in August (1.25 t ha –1 month –1 ). In summer, the mean monthly soil loss by water erosion is 48 times higher than the mean soil loss in winter. Considering the annual average fraction of green vegetation cover of 54%, the predicted soilerosionrate for the Swiss national grassland area would add up to a total eroded soil mass of 5.26 Mt yr –1 . The RUSLE application with an intact 100% vegetation cover would largely reduce the soil loss to an average annual rate of 0.14 t ha –1 year –1 . These ﬁndings clearly highlight the importance to consider and maintain the current status of the vegetation cover for soilerosion prediction and soil conservation, respectively.
Soilerosion is a process which refers to the destruction, sep- aration, removal and sedimentation of the earth’s surface soil and its parent material caused by hydraulic, wind, freezing and thawing, gravity and other external forces (Meyer, 1984). Soilerosion has caused a set of ecological and environmen- tal problems such as land degradation, soil fertility loss, river siltation, making it a global research focus. Since the 1950s, to quantify soil loss and determine its risk, a number of soilerosion models were established based on measured data or the results of previous studies, and numerous research re- sults were obtained using Geographical Information Systems (GIS) and remote sensing (RS). One of the most applied models to estimate soilerosion is the Universal Soil Loss Equation (USLE) and its modified version the Revised Uni- versal Soil Loss Equation (RUSLE). Lu et al. (2001) used GIS and the RUSLEmodel to map and quantitatively predict patch and gully erosion in Australia. Liu (2002) established China’s SoilErosion Prediction Equation (CSLE) by the study of a slope erosion prediction model. Gao et al. (2015) calculated the soilerosion modulus in the Loess Plateau of
The main constraint encountered in the operation of GIS is calculation of the slope length factor (L). The RUSLEmodel does not allow to calculate the entire length of the slopes in the any form of the slopes in the catchment areas, while the factor of slope (S) can be obtained by usingGIS data (As-syakur, 2008). LS factor in GIS applications can be obtained from multiple data, among others: Digital Elevation Model (DEM), Shuttle Radar Topography Mission (SRTM), and the contours from RBI map. The research about the model of erosionusingRUSLE models has been done by several researchers (Demirci and Karaburun, 2012; Aroussi et al., 2013; Kumar and Kushwaha, 2013), but validation of model of RUSLE with direct measurements in the field has never been performed.
From the analysis it can be concluded that, the drainage density of the catchment is 3.31km/km 2 showing that the basin exhibits slow hydrological response, highly susceptible for gully erosion and the surface run-off is not rapidly removed from the watershed. The terrain exhibits dendritic drainage pattern, where the soil can be eroded equally easily in all directions. The pattern of the basin has coarse drainage texture. It is nearly circular, have impermeable surface which results in high peak flows for a shorter duration. It is a flat land with a low slope and less disturbed by geological structures. catchment exhibits less infiltration capacity as there are less number of drains. The erosion is likely to take place in the sub-catchments containing of greater volume of drains. An area of about 5.42km 2 is likely to susceptible to erosion causing sedimentation in the reservoir. Therefore, necessary measures are to be taken to arrest the flow of soil thereby mitigating the siltation in the reservoir
The soilerosion process is a complicated system con- trolled by a multitude of factors comprising soil char- acteristics, local climatic conditions, nature of terrain features, ground cover, land use types, conservation prac- tices, and interaction between them. Hence, both quan- titative and qualitative methods were employed to take advantage of their complementarities and counterbalance inevitable weaknesses of each approach. With the aim of triangulation, digital and non-digital data were collected from many sources including field inspection (Table 1). Most input factor of RUSLEmodel was estimated using selected methodologies or obtained from literature that have been developed specifically for Ethiopian context. For each factor considered in the RUSLEmodel, a respec- tive file was built in the GIS environment and finally merged together in the model to generate final map that indicates soil loss rate of the watershed. The data inputs pertaining perception and experiences of local com- munities were collected through one-on-one scheduled interviews with sample household’s heads, focus group discussions, in-depth key informant interview, and observations of plots and its environs with sample house- hold heads. The overall processes are depicted at Fig. 6.
study area, mainly corresponding to Sierra del Tentzo and the valleys. The highest values for the estimated soil loss range from 114 to 234 t/ha∙year was found in 4.17% of the study area and was marked as an ex- treme erosion risk. The soil loss results from human activity human activity, such as the cleaning of the agricultural land by fire and mining activities carried out on areas that are sensitive to erosion. A closer look at Figure 4a shows that the severe erosion rates are associated with a higher slope gradient factor, with coverage of the holm oak forest, deciduous forest, and rosetophilous desert. Areas of bare land or degraded grassland and shrubs are characterised by an extreme soilerosionrate, this correlates with the steepness of the slope that facilitates the transport of particles with a lower amount of energy and rain intensity, due to the topographic conditions and soil coverage. Im- mediate conservation measures are needed in such places. Figure 4b shows the correlation between the slope of the study area and the rate of erosion; it can be easily observed that the slope has a pronounced effect on the rate of the extreme soilerosion.
where A is the average annual soil loss per unit area (t ha -1 yr -1 ), an estimate of the average annual sheet plus rill erosion from rainstorms for field size upland area; R is the rainfall-runoff erosive factor for a specific location, usually expressed as average annual erosion index units (MJ-mm-ha -1 -h -1 ); K is the soil erodibility factor for a specific soil horizon, expressed as soil loss per unit of area per unit of R for a unit plot (t ha h ha -1 MJ -1 ); L is the dimensionless slope-length factor, and not the actual slope length, expressed as the ratio of soil loss from a given slope length to that of from a 22.13 meter slope length under same conditions; S is a dimensionless slope-steepness factor and not the actual slope steepness expressed as the ratio of soil loss from a given slope steepness to that of from a 9 percent slope under the same conditions; C is a dimensionless cover and management or cropping factor, expressed as a ratio of the soil loss from the condition of intersects to that of from tilled continuous fallow; P is a dimensionless conservation practice factor, expressed as a ratio of the soil loss with practices, such as contouring, strip cropping, or terracing to that of farming from a up-and-down slope. To determine the spatial distribution of average annual soil loss in the Dikrong river basin, cell- based USLE parameters in the specified 100 m × 100 m cells were multiplied for each year separately, i.e. 1988 to 2004. The annual soil losses were grouped into different scales of priority (Singh et al., 1992).
it is a hot and humid tropical climate, characterized by a long rainy season lasting up to eight months and often in- terrupted by short dry periods between January and Febru- ary (Kayembe and Wolff, 2009). The temperature varies dur- ing the year from 20 to 32 ◦ C and infrequently falls under 19 or above 34 ◦ C. Kinshasa soil types constitute Dystric Regosols, Ferralic Arenosols, and Orthic Greyzems. The tex- ture is generally sand, sandy loam, and coarse grained sands (FAO, 1997). The vegetation is generally savanna dotted with shrubs combined with gallery forests at low density areas. Semi deciduous sub-equatorial secondary forests and shrub lands of Guinean are observed in the study area (Kifukieto et al., 2014).
To understand the laws of runoff processes in a catchment, hydrologists are faced with many problems, especially in respect of ungauged catchment where hydrological data are rarely available. Many approximate formulae, usually crude empirical statements, have been developed to relate precipitation with flow. For ungauged basins it has been the endeavor of many hydrologists and earth scientists to quantify and relate geomorphological parameters of naturally shaped river basins to its hydrologic response characteristics.
Soilerosion is an issue which is still under debate in the newspapers or any electronic media, especially in area like Ranau, Sabah. Triggering factor of soilerosion is often associated with agricultural, deforestation and development activities that did not consider of environmental sustainability. Ranau, Sabah have been used as experimental laboratories for Revised Universal Soil Loss Equation (RUSLE) study. The main objective of this study is to determine the annual soil loss rate (A) value by the average annual of soil loss rate (RKLSCP). There are six factors parameter maps were considered in RUSLE as rainfall erosivity factor (R), soil erodibility factor (K), slope length factor (L), slope steepness factor (S), crop and management factor (C), and conservation supporting practices factor (P). Analyses results indicate that the influence of the C and P are important in the determination of the soilerosionrate for an area. All findings showed that integration of GIS can be used for spatial analysis in a large scale. Production of A total value maps can be applied to particular development planning areas especially for housing and agriculture developments.