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1.
Siruvani watershed with a surface area of 205.54 km2 (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h−1 year−1 and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the high-erosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.  相似文献   

2.
Soil erosion and associated sedimentation are a threat to the sustainable use of surface water resources through the loss of volume storage capacity and conveyance of pollutants to receiving water bodies. The RUSLE2 empirical model and isotopic sediment core analyses were used to evaluate watershed erosion and reservoir sediment accumulation rates for Lake Anna, in Central Virginia. A sediment flux rate of 66,000 Mg/year was estimated from the upper basin and land use was determined to be the primary factor contributing to soil erosion. Barren lands and agricultural activities were estimated to contribute the most sediment (>20 Mg/ha/year), whereas forested and herbaceous landscapes were less likely to erode (<0.3 Mg/ha/year). Eleven separate 210Pb-based estimates of sediment accumulation were used to construct reservoir-scale sedimentation rates. Sedimentation rates in the upper reaches of the reservoir were variable, ranging from 2.3 to 100 Mg/ha/year, with a median rate of 8.4 Mg/ha/year. Historical sedimentation showed an increase in annual accumulation from 1972 to present. Based on these data the reservoir has experienced a 2% loss of volume storage capacity since impoundment in 1972. Results from this study indicate that Lake Anna is not currently experiencing excessive sedimentation and erosion problems. However, the predominance of highly erosive soils (soil erodibility factor >0.30) within the watershed makes this system highly vulnerable to future anthropogenic stressors.  相似文献   

3.
Estimation of soil erosion using RUSLE in Caijiamiao watershed,China   总被引：4，自引：1，他引：3  
Jinghu Pan  Yan Wen 《Natural Hazards》2014,71(3):2187-2205
Soil erosion is a serious environmental and production problem in China. In particular, natural conditions and human impact have made the Chinese Loess Plateau particularly prone to intense soil erosion area. To decrease the risk on environmental impacts, there is an increasing demand for sound, and readily applicable techniques for soil conservation planning in this area. This work aims at the assessment of soil erosion and its spatial distribution in hilly Loess Plateau watershed (northwestern China) with a surface area of approximately 416.31 km2. This study was conducted at the Caijiamiao watershed to determine the erosion hazard in the area and target locations for appropriate initiation of conservation measures using the revised universal soil loss equation (RUSLE). The erosion factors of RUSLE were collected and processed through a geographic information system (GIS)-based approach. The soil erosion parameters were evaluated in different ways: The R-factor map was developed from the rainfall data, the K-factor map was obtained from the soil map, the C-factor map was generated based on Landsat-5 Thematic Mapper image and spectral mixture analysis, and a digital elevation model with a spatial resolution of 25 m was derived from topographic map at the scale of 1:50,000 to develop the LS-factor map. Support practice P factor was from terraces that exist on slopes where crops are grown. By integrating the six-factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the study area was obtained by the RUSLE model. The results showed that spatial average soil erosion at the watershed was 78.78 ton ha?1 year?1 in 2002 and 70.58 ton ha?1 year?1 in 2010, while the estimated sediment yield was found to be 327.96 × 104 and 293.85 × 104 ton, respectively. Soil erosion is serious, respectively, from 15 to 35 of slope degree, elevation area from 1,126 to 1,395 m, in the particular area of soil and water loss prevention. As far as land use is concerned, soil losses are highest in barren land and those in waste grassland areas are second. The results of the study provide useful information for decision maker and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a river watershed scale on a cell basis in Chinese Loess Plateau and for planning of conservation practices.  相似文献   

4.
Assessment and inventory on soil erosion hazard are essential for the formulation of successful hazard mitigation plans and sustainable development. The objective of this study was to assess and map soil erosion hazard in Lesser Himalaya with a case study. The Dabka watershed constitutes a part of the Kosi Basin in the Lesser Himalaya, India, in district Nainital has been selected for the case illustration. The average rate of erosion hazard is 0.68 mm/year or 224 tons/km2/year. Anthropogenic and geo-environmental factors have together significantly accelerated the rate of erosion. This reconnaissance study estimates the erosion rate over the period of 3 years (2006–2008) as 1.21 mm/year (398 tons/km2/year) in the barren land having geological background of diamictite, siltstone and shale rocks, 0.92 mm/year (302 tons/km2/year) in the agricultural land with lithology of diamictite, slates, siltstone, limestone rocks, while in the forest land, it varies between 0.20 mm/year (66 tons/km2/year) under dense forest land having the geology of quartzwacke and quartrenite rocks and 0.40 mm/year (132 tons/km2/year) under open forest/shrubs land having geological setup of shale, dolomite and gypsum rocks. Compared to the intensity of erosion in the least disturbed dense forest, the erosion rate is about 5–6 times higher in the most disturbed agricultural land and barren land, respectively. The erosion hazard zones delineated following scalogram modelling approach. Integrated scalogram modelling approach resulted in severe classes of soil erosion hazard in the study area with numerical values of Erosion Hazard Index (EHI) ranging between 01 (very low hazard) and 5 (very high hazard).  相似文献   

5.
Due to the existence of fragile karst geo-ecological environments, such as environments with extremely poor soil cover, low soil-forming velocity, and fragmentized terrain and physiognomy, as well as inappropriate and intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China; evaluation of soil loss and spatial distribution for conservation planning is urgently needed. This study integrated the revised universal soil loss equation (RUSLE) with a GIS to assess soil loss and identify risk erosion areas in the Maotiao River watershed of Guizhou. Current land use/cover and management practices were evaluated to determine their effects on average annual soil loss and future soil conservation practices were discussed. Data used to generate the RUSLE factors included a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and precipitation data. The results of the study compare well with the other studies and local data, and provide useful information for decision makers and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a larger watershed scale in Guizhou.  相似文献   

6.
http://www.sciencedirect.com/science/article/pii/S1674987111001034   总被引：10，自引：0，他引：10  
A comprehensive methodology that integrates Revised Universal Soil Loss Equation（RUSLE） model and Geographic Information System（GIS） techniques was adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala,India.The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method.GIS data layers including,rainfall erosivity（R）,soil erodability（K）,slope length and steepness（LS）,cover management （C） and conservation practice（P） factors were computed to determine their effects on average annual soil loss in the area.The resultant map of annual soil erosion shows a maximum soil loss of 17.73 t h-1 y-1 with a close relation to grass land areas,degraded forests and deciduous forests on the steep side-slopes（with high LS ）.The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.  相似文献   

7.
A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha−1 h−1) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk.  相似文献   

8.
Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.  相似文献   

9.
Predicting soil erosion change is an important strategy in watershed management. The objective of this research was to evaluate land use change effects on soil erosion in the north of Iran using five land use scenarios. Three land use maps were created for a period of 25 years (1986–2010) to investigate land use transition and to simulate land use for the year 2030. Additionally, the RUSLE model was used to estimate erosion and the effect of land use change. The results showed that CLUE-s is suitable for modeling future land use transition using ROC curve. The median soil loss in the basis period was 104.52 t ha?1 years?1. Results indicate that the range of soil loss change is 2–32% in simulated period and soil loss value was higher than basis period in all scenarios. Thirty percent decrease in demand scenario has the lowest soil loss in simulated period, and the soil loss value under this scenario will be only 2% more than the basis period. Thus, the soil conversion effects resulted from the demand of each land use.  相似文献   

10.
This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha−1 ya−1 in 2005, and the area of 47.5 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.  相似文献   

11.
The installation of a rural settlement complex in the watershed stream Indaiá has promoted changes in land-use and vegetation cover dynamics; however, the effects of intensive agriculture and cattle farming in rural settlements on soil loss rates are not well known. Predictive models implemented in geographic information systems have proven to be effective tools for estimating erosive processes. The erosion predictive model Revised Universal Soil Loss Equation (RUSLE) is a useful tool for analyzing, establishing and managing soil erosion. RUSLE has been widely used to estimate annual averages of soil loss, by both interrill and rill erosion, worldwide. Therefore, the aim of this work was to estimate the soil loss in the watershed stream Indaiá, using the RUSLE model and geoprocessing techniques. To estimate soil loss, the following factors were spatialized: erosivity (R), erodibility (K), topography (LS), land-use and management (C) and conservation practices (P); the annual soil loss values were calculated using the RUSLE model equation. The estimated value of soil loss in the hydrographic basin ranged from 0 to 4082.16 Mg ha?1 year?1 and had an average value of 47.81 Mg ha?1 year?1. These results have demonstrated that 68.16 % of the study area showed little or no soil loss based on the Food and Agriculture Organization’s (FAO 1980) classification. When comparing the average value of soil loss obtained using the RUSLE model with the Natural Potential for Erosion, a 16-fold reduction in soil was found, which highlighted the fact that vegetation cover (C factor) has a greater influence than other factors (R, K and LS) on soil loss prediction attenuation. These results lead to the conclusion that soil loss occurs by different methods in each settlement in the basin and that erosive processes modeled by geoprocessing have the potential to contribute to an orderly land management process.  相似文献   

12.
The present comparative study is multi-temporal in nature. The Revised Universal Soil Loss Equation (RUSLE), remote sensing, and GIS were used to model the soil loss estimation for soil conservation and vegetation rehabilitation in Nun Nadi watershed for the years 2000 and 2009. The estimated mean soil loss for the year 2000 and 2009 is 3,283.11 and 1,419.39 Mg?ha?1 year?1, respectively. The study finds that about 80 % area has low or least risk of erosion and about 7 % is exposed to high or very high risk which indicates the improvement in terms of soil loss if we compare the data of both the time periods. The findings show that the rainfall, LULC change, and elevation are the main responsible factors for the soil loss in Nun Nadi watershed. Conservation measures have been adopted; however, the problem still remains serious and demands urgent attention.  相似文献   

13.
Soil organic carbon (SOC) is one of the key components for assessing soil quality. Meanwhile, the changes in the stocks SOC may have large potential impact on global climate. It is increasingly important to estimate the SOC stock precisely and to investigate its variability. In this study, Yangjuangou watershed was selected to investigate the SOC distribution under different land uses. We found that SOC concentration decreased with increasing soil depth under all land uses and was significantly different across the vertical soil profile (P < 0.01). However, considering effect of land use on SOC, it is only significant (P < 0.01) in the topsoil (0-5 cm) layer. This indicated that land use has a large effect on the stocks of SOC in the surface soil. The stratification ratio of SOC > 1.2 may mean that soil quality is improving. The order of the SOC density (0-30 cm) under different land uses is forestland > orchard land > grassland > immature forestland > terraced cropland. The SOC stock is found to be as large as 2.67 × 103 t (0-30 cm) in this watershed. Considering time effect of restoration, the slope cropland just abandoned is more efficient for SOC accumulation than trees planted in the semi-arid hilly loess area.  相似文献   

14.
In highlands of semiarid Turkey, ecosystems have been significantly transformed through human actions, and today changes are taking place very rapidly, causing harmful consequences such as soil degradation. This paper examines two neighboring land use types in Indagi Mountain Pass, Cankiri, Turkey, to determine effects of the conversion of Blackpine (Pinus nigra Arn. subsp. pallasiana) plantation from grassland 40 years ago on soil organic carbon (SOC) and soil erodibility (USLE-K). For this purpose, a total of 302 disturbed and undisturbed soil samples were taken at irregular intervals from two sites and from two soil depths of 0–10 cm (D1) and 10–20 cm (D2). In terms of SOC, conversion did not make any statistical difference between grassland and plantation; however, there were statistically significant differences with soil depth within each land use, and SOC contents significantly decreased with the soil depth (P < 0.05) and mostly accumulated in D1. SOC values were 2.4 and 1.8% for grassland and 2.8 and 1.6% for plantation, respectively, at D1 and D2. USLE-K values also statistically differed significantly with the land use, and in contrast to the statistics of SOC, there was no change in USLE-K with the soil depth. Since USLE-K was estimated using SOC, hydraulic conductivity (HC) and soil textural composition––sand (S), silt (Si), and clay (C) contents of soils––as well as SOC did not change with the land use, we ascribed the changes of USLE-K with the land uses to the differences in the HC as strongly affected by the interactions between SOC and contents of S, Si, and C. On an average, the soil of the grassland (USLE-K = 0.161 t ha h ha−1 MJ−1 mm−1) was more erodible than those of the plantation (USLE-K = 0.126 t ha h ha−1 MJ−1 mm−1). Additionally, topographic factors, such as aspect and slope, were statistically effective on spatial distribution of the USLE-K and SOC.  相似文献   

15.
Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.  相似文献   

16.
In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.  相似文献   

17.
In 2003, chlorofluorocarbon (CFC) apparent ages, major ion chemistry and C isotopes were determined in nine springs from Sagehen Basin, a high elevation watershed in the eastern Sierra Nevada. Springs with similar apparent ages, which ranged between 15 and 45 a, had very similar chemistry despite being found in different areas of the watershed. In agreement with earlier studies, concentrations of rock-derived cations (Ca2+ and Na+), conductivity, temperature and pH increase with apparent age, documenting the chemical evolution of this groundwater system. In contrast with the cation data, δ13C and ΣCO2 show no correlation with apparent age. δ13C displays a strong linear relationship with 1/ΣCO2 (R2 = 0.91). This is consistent with results from a previously developed soil respiration/diffusion model. Spring radiocarbon content ranged between 85 and 110 pmc and varied with apparent age, whereby the youngest groundwater has the highest radiocarbon values. The spring radiocarbon is set by the soil pCO2 and its trend can be best described assuming the soil CO2 is composed of a mixture of 50–66% fast- (15–25 a) and 33–50% slow- (4 ka) cycling components. These results are consistent with previous soil C studies. The C isotope data indicate that in Sagehen Basin the groundwater ΣCO2 is inherited from the soil zone with little, if any, contribution from the dissolution of disseminated calcite.  相似文献   

18.
This study was aimed at predicting soil erosion risk in the Buyukcekmece Lake watershed located in the western part of Istanbul, Turkey, by using Revised Universal Soil Loss Equation (RUSLE) model in a GIS framework. The factors used in RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps, and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area using spatial analyst tool of ArcGIS 9.3. In the study, soil loss rate below 1 t/ha/year was defined as low erosion, while those >10 t/ha/year were defined as severe erosion. The values between low and severe erosion were further classified as slight, moderate, and high erosion areas. The study provided a reliable prediction of soil erosion rates and delineation of erosion-prone areas within the watershed. As the study revealed, soil erosion risk is low in more than half of the study area (54%) with soil loss <1 t/ha/year. Around one-fifth of the study area (19%) has slight erosion risk with values between 1 and 3 t/ha/year. Only 11% of the study area was found to be under high erosion risk with soil loss between 5 and 10 t/ha/year. The severe erosion risk is seen only in 5% of the study area with soil loss more than 10 t/ha/year. As the study revealed, nearly half of the Buyukcekmece Lake watershed requires implementation of effective soil conservation measures to reduce soil erosion risk.  相似文献   

19.
The 18O and 2H (HDO) compositions are summarized for sampled springs (n = 81) within the Elwha watershed (≈692 km2) on the northern Olympic Peninsula. Samples, collected during 2001–2009, of springs (n = 158), precipitation (n = 520), streams (n = 176), and firn (n = 3) assisted the determinations for meteoric composition of recharge waters. The local mean water line (LMWL) is defined as δ2H = 8.2δ18O − 9.3 for the watershed. Recharge history is surmised from groundwater ages ranging from 5 ± 3 years (apparent 85Kr) to 9,490 ± 420 14C cal years BP. About 56% of the springs were recharged over the last 1,000 years while 13% of springs were recharged over 5,000 years ago. Spring HDO values fluctuate between −11.8 to −15.6‰ δ18O and −90.9 to −119.4‰ δ2H. Deuterium excess values predominate around 4–6‰. The HDO proxy records from springs suggest a pronounced paleoclimate shift in air masses near 5,000 year BP on the Peninsula.  相似文献   

20.
 Three dolines (sinkholes), each representing different land uses (crop, grass, and forest) in a karst area in East Tennesse, were selected to determine soil erosional and depositional rates. Three methods were used to estimate the rates: fallout radiocesium (137Cs) redistribution, buried surface soil horizons (Ab horizon), and the revised universal soil loss equation (RUSLE). When 137Cs redistribution was examined, the average soil erosion rates were calculated to be 27 t ha–1 yr–1 at the cropland, 3 t ha–1 yr–1 at the grassland, and 2 t ha–1 yr–1 at the forest. By comparison, cropland erosion rate of 2.6 t ha–1 yr–1, a grassland rate of 0.6 t ha–1 yr–1, and a forest rate of 0.2 t ha–1 yr–1 were estimated by RUSLE. The 137Cs method expressed higher rates than RUSLE because RUSLE tends to overestimate low erosion rates and does not account for deposition. The buried surface horizons method resulted in deposition rates that were 8 t ha–1 yr–1 (during 480 yr) at the cropland, 12 t ha–1 yr–1 (during 980 yr) at the grassland, and 4 t ha–1 yr–1 (during 101 yr) at the forest site. By examining 137Cs redistribution, soil deposition rates were found to be 23 t ha–1 yr–1 at the cropland, 20 t ha–1 yr–1 at the grassland, and 16 t ha–1 yr–1 at the forest site. The variability in deposition rates was accounted for by temporal differences;137Cs expressed deposition during the last 38 yr, whereas Ab horizons represented deposition during hundreds of years. In most cases, land use affected both erosion and deposition rates – the highest rates of soil redistribution usually representing the cropland and the lowest, the forest. When this was not true, differences in the rates were attributed to differences in the size, shape, and closure of the dolines. Received: 10 October 1995 · Accepted: 13 October 1995  相似文献   

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