Long-term Average Annual Soil Loss Research Articles

Assessment of soil erosion hazard and its relation to land use land cover changes: Case study from alage watershed, central Rift Valley of Ethiopia

Soil erosion by water and wind is among the most crucial land degradation processes in Ethiopia. This is also the case for Alage watershed located in the cental Rift Valley system. This study aimed at assessment of soil erosion hazard and its relation to land use land cover change in the watershed during the period from 1984 to 2016 for a better land management. The study is based on application of Remote Sensing (RS) and Geographical Information System (GIS) to extract inputs factor values for the Revised Universal Soil Loss Equation (RUSLE). Time-series satellite imageries of Landsat TM 1984, ETM+ 2000 and OLI 2016 were used for land use land cover change detection and determination of cover management (C) factor of the RUSLE. Biophysical data such as rainfall, soil properties, land management practices including soil and water conservation measures within the watershed were collected using field survey and secondary data sources. Slope steepness and slope length factors were derived using Digital Elevaition Model (DEM). Long-term average annual soil loss rates were estimated by the RUSLE integrated with GIS for 1984, 2000 and 2016. Using satellite imageries, the land use land cover and changes within the watershed during the three periods were obtained through a supervised classification with maximum likelihood algorithim. The results of land use land cover change indicated that the proportion of rain-fed cropland, bare land and built up areas increased by 17.4%, 5.9% and 2.9% respectively over the three study period. In contrast the proportion of bush/shrub land, irrigated cropland, grass land, forested areas and waterbodies decresaed by 15.5%, 4.7%, 3.4%, 2.3% and 0.3% respectively during the same period. Estimated average annual soil loss rates showed an increasing trend from 24.3 ton ha−1 yr−1 in 1984 to 38 ton ha−1 yr−1 in 2016. Increasing trends of average annual soil loss rate is attributed to increased proportion of cropland, bare land and built up areas during those periods leading to decreased protective vegetation cover. Hotspot areas within the watershed require implementation of land management practices to prevent further degradation and expansion of gullies. This study is relevant to demonstrate environmental implication of land use land cover change for future land management practices and land use policy in the Rift Valley of central Ethiopia.

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP

ABSTRACTThe long-term average annual soil loss (A) and sediment yield (SY) in a tropical monsoon-dominated river basin in the southern Western Ghats, India (Muthirapuzha River Basin, MRB; area: 271.75 km2), were predicted by coupling the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models. Moreover, the study also delineated soil erosion risk zones based on the soil erosion potential index (SEPI) using the analytic hierarchy process (AHP) technique. Mean A of the basin is 14.36 t ha−1 year−1, while mean SY is only 3.65 t ha−1 year−1. Although the land use/land cover types with human interference show relatively lower A compared to natural vegetation, their higher SDR values reflect the significance of anthropogenic activities in accelerated soil erosion. The soil erosion risk in the MRB is strongly controlled by slope, land use/land cover and relative relief, compared to geomorphology, drainage density, stream frequency and lineament frequency.

Role of GIS in Soil Erosion

Soil erosion is defined as the physical degradation of the landscape over time. The process is initiated when soil particles are detached from its original configuration by erosive forces such as rainfall. The soil particles may then be transported by overland flow into nearby rivers and oceans. Current developments in geographic information systems (GIS) make it possible to model complex spatial information. A GIS is used in this project to determine how soil erosion potential varies throughout a watershed. A method was developed in this research which combines the Universal Soil Loss Equation (USLE). The USLE calculates long-term average annual soil loss by multiplying six specific factors which describe the watershed characteristics such as rainfall, soil types, slope, and vegetation cover. The GIS is used to store the USLE factors as individual digital layers and multiplied together to create a soil erosion potential map. This combination provides a way to assess soil erosion potential of an area with existing data sources.

Simulation of field-measured soil loss in Mediterranean hilly areas (Chianti, Italy) with RUSLE

The issue of soil erosion is considered highly important by local administrators of the Chianti region. Thus, a methodology for predicting the long-term average annual soil loss, by using the Revised Universal Soil Loss Equation (RUSLE) in a Geographical Information System framework was developed and assessed. The rainfall and runoff erosivity factor was calculated using 35 raingauges with an acquisition interval of 15min in the period from 1996 to 2010. The soil erodibility factor was estimated using a soil map at a scale of 1:50,000. The topographic factor was calculated from a 10m digital elevation model. Moreover, a methodology was proposed that took into account the vineyard row direction and slope. Soil loss field data (566 field sites) were measured over 6years using a topographical approach, and used to validate the model results. The statistical indices for the evaluation of the model were, respectively, the mean percent error (M%E), −0.1%, the ratio of the RMSE to the standard deviation of the observations (RSR), 23.7%; and the Nash–Sutcliffe coefficient (NCS), 0.9. Statistics indicated good predictions of long-term average annual soil losses at a field scale. The average annual soil loss for the study area was predicted up to 6.4t·ha−1·y−1. Approximately 13% of the study area was classified as high erosion (≥22t·ha−1·y−1). The identification of areas with the greatest erosion risk supports the possibility of using the model for land-use and land-management planning purposes. Moreover, this assists in the identification of those areas towards which conservation measures can be directed.

Climate change impact on soil erosion in\xa0the Mandakini River Basin, North India

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

Development of a Soil Erosion Classification System for Cut and Fill Slopes

The Universal Soil Loss Equation (USLE) has been widely used to predict the long-term average annual soil loss associated with sheet and rill erosion caused by rainfall and runoff. Initially developed for agricultural purposes, it was later modified and extended for estimating soil loss on cut and fill slopes. In addition to this valuable equation, management of cut and fill slopes also requires a classification system in order to prioritize erosion control measures and maintenance operations based on estimated soil loss. Currently available erosion classifications (focused on soil productivity and sustainable agriculture) may not be relevant to transportation infrastructure slopes, in which soil loss rates are dramatically higher and primarily concern operational service conditions. Therefore, the objective of this study was to develop a classification system based on soil loss rates computed by the USLE model that can be valuable for management of cut and fill slopes. It was assumed that erosion classifications developed for agriculture could be used in cut and fill slopes as long as the ratio between respective soil loss rates was applied. It was found that the topographic factor, which accounts for the length and steepness of the slope, may explain most of the difference in respective soil loss rates. For typical values of the topographic factor, soil loss rates on cut and fill slopes were found to be roughly ten times greater than those on agriculture. Based on this finding, a new classification with six erosion levels was developed. Finally, validation analysis showed that the proposed classification successfully ranked soil loss rates reported in the literature into different categories.

The Impact of Farming and Land Ownership on Soil Erosion

The aim of this paper was to compare two methods of farming, especially their effect on water soil erosion. The examined methods were (1) large-scale farming, where more than 50% of the land was leased, and (2) small-scale farming, where the land was almost exclusively privately owned. The research area was 8 cadastres in the district of Hodonín, South Moravia, Czech Republic. In these cadastres 48 land blocks representing both large-scale and small-scale farming (i.e. owners and tenants) were chosen. The long-term average annual soil loss caused by water erosion (G) was calculated using the erosion model USLE 2D and ArcGIS 10.1. The nonparametric Mann-Whitney test was used for the statistical evaluation of the data. The difference between the soil loss (G) on land blocks farmed by small producers (owners) and large producers (tenants) was significant (p < 0.05). Differences between the values of the cropping-management factor (C) were not statistically significant (p = 0.054). Based on the analysis of other variables in the USLE equation it can be stated that a continuous slope length, conditioned by the size of land blocks, played an important role in the amount of soil loss caused by water erosion. Above all, to protect the soil from erosion and maintain soil quality it is necessary to reduce the size of land blocks farmed by tenants and improve the crop rotation systems.

ANALÝZA EROZNÍCH POMĚRŮ POVODÍ VODÁRENSKÉ NÁDRŽE ŠANCE: SROVNÁNÍ VÝSLEDKŮ METOD HODNOTÍCÍCH EROZNÍ ČINNOSTI

Soil erosion is still an issue in forestry and in human water-resources activities connected with landscape management and the protection of surface waters. The methods recently assessing the water erosion include: monitoring of suspended sediments in water, monitoring of dynamics of soil pro­per­ties, assessing the inclination of soil towards soil erosion, monitoring of effectiveness of erosion control measures, erosion processes modeling etc. (Buzek, 1981; Buzek, 1983; Holý, 1994; Jařabáč, Belský, 2008).The river basin of the water tank Šance is very important as a source of potable water and this importance is advanced, when water tank is clogged up by suspended sediments.Erosion was assessed by two methods in ArcMap 9.3 working with original data: the first method is Universal equation calculating an average annual soil loss from surface (USLE) (Wischmeier, Smith, cit. in Janeček, 2002); the second method assesses the potential erosion (MPE), using specific soil properties as factors, evaluating the rate of the intensity of erosion (Kučera, Palíková; 2009).Each method uses different ways for the description of the erodibility: USLE describes a long-term average annual soil loss as a consequence of surface erosion. It gives exact values of sediments in t . ha−1 . year−1, but from the other point of view, this method is primarily created for an agricultural land. Compared with USLE, MPE solves potential erosion and gives relative values of the erosion tendency of an environment. PME could give a new point of view on the assessing of the erosion.The river basin Ostravice above water tank Šance was used to compare these two methods. As a control measure, dates of the assessment of the water sediments regime (Buzek, 2001) were used. This observation was pursued in waters of the gagin station ČHMÚ Ostrava in Staré Hamry in according Stehlík (1969). This 25-year process of measuring shows the value of 2.47 t . ha−1 . year−1 in water tank upper Ostravice (with surface 72.96 km2). USLE shows much lower values of suspended sediments (0.41 t . ha−1 . year−1 using a continuous longitude of slopes or 0.11 t . ha−1 . year−1, with regards to the interruption of slopes by the forest roads). This difference is caused by the construction of USLE, which doesn’t respect increased values of sediments after the crossing of fords by the forestry mechanization, rill erosion or inputs of sediments from watercourse and near ravines. Therefore, the high values of suspended sediments are mainly caused by forest management (Buzek, 2001).

Testing and evaluation of the agricultural non-point source pollution model (AGNPS) on Augucho catchment, western Hararghe, Ethiopia

The annual soil erosion on the Ethiopian highlands is estimated at 1.5 billion tons. The poor soil management and land use practices are the causes of the high soil erosion rate. Assessment of soil erosion is the basis for planning soil conservation work. There are currently erosion models ranging from empirical ones like the Universal Soil Loss Equation (USLE) which provide long-term average annual soil loss estimates to process-based models such as the agricultural non-point source pollution model (AGNPS), which provide the spatial and temporal variations of soil erosion. This study was conducted to test and evaluate the AGNPS model on Augucho catchment. The AGNPS model was calibrated and validated for the study area with observed data of 8–10 years. Data year 1988 was used for validation while data year 1990 was used for calibration of the model. The model was evaluated at 100 and 200 m grid cells. GIS was employed to derive some of the parameters in addition to the primary and secondary data collection techniques. Correlation coefficients, coefficient of efficiency and homogeneity test of the correlation coefficients were used to evaluate the two grid cells and the overall model performance. The validation result indicated that the correlation coefficients were 0.59 and 0.58 for runoff, 0.96 and 0.95 for peak runoff rate, and 0.97 and 0.97 for the 100 and 200 m grid cells, respectively. Only the runoff event was non-significant. The coefficients for sediment yield and peak runoff rate were highly significant ( p≥0.01) and the pair of correlation coefficients for the same event for the two grid cells were homogeneous. The coefficients of efficiency were −1.0286 and −1.006 for runoff, 0.75 and 0.74 for peak runoff rate, and 0.656 and 0.654 for sediment yield for the 100 and 200 m grid cells, respectively. With the exception of the runoff event, the peak runoff rate and sediment yield are well predicted. There was, however, no significant difference in the output between the 100 and 200 m grid runs. For the average year of 1991 and for the 100 m grid cell run, the model estimated an average soil loss of 22 t ha −1 per year, which is much greater than the rate of soil formation (1 t ha −1 per year). This showed that appropriate soil erosion control measures should be taken in the catchment. There is a potential for the model to be used for planning and management of agricultural watersheds under the Ethiopian highland conditions.

Uncertainty assessment of soil erodibility factor for revised universal soil loss equation

Soil erodibility accounts for the influence of the intrinsic soil properties on soil erosion and is one of six factors in the Revised Universal Soil Loss Equation (RUSLE), a most widely used model to predict long-term average annual soil loss. In a traditional soil survey, each of the soil types (classes) is assigned with a soil erodibility value that is assumed to be constant over time. However, heterogeneity of soil in time and in space tends to support the concept that soil erodibility depends dynamically and spatially on the set of properties of a specific soil. This study statistically compared the published soil erodibility values with those from a set of soil samples in terms of their differences. The published values tend to underestimate soil erodibility. This feature is also supported by the uncertainty assessment in difference maps of the published K values versus those from soil samples. Spatial prediction and uncertainty analysis of the soil erodibility from the set of soil samples was carried out using a sequential Gaussian simulation. The results show that the simulation produces a reliable prediction map of soil erodibility and can be recommended as a monitoring strategy to spatially update soil erodibility.

EVALUATION OF ANNAGNPS ON MISSISSIPPI DELTA MSEA WATERSHEDS

Sediment and its associated pollutants entering a water body can be very destructive to the health of that system.Best Management Practices (BMPs) can be used to reduce these pollutants, but understanding the most effective practicesis very difficult. Watershed models are the most costeffective tools to aid in the decisionmaking process of selecting the BMPthat is most effective in reducing the pollutant loadings. The Annualized Agricultural NonPoint Source Pollutant Loadingmodel (AnnAGNPS) is one such tool. The objectives of this study were to assemble all necessary data from the MississippiDelta Management System Evaluation Area (MDMSEA) Deep Hollow watershed to validate AnnAGNPS, and to use thevalidated AnnAGNPS to evaluate the effectiveness of BMPs for sediment reduction.In this study, AnnAGNPS predictions were compared with three years of field observations from the MDMSEA DeepHollow watershed. Using no calibrated parameters, AnnAGNPS underestimated observed runoff for extreme events, but therelationship between simulated and observed runoff on an event basis was significant (R2 = 0.9). In contrast, the lower R2of 0.5 for event comparison of predicted and observed sediment yields demonstrated that the model was not best suited forshortterm individual event sediment prediction. This may be due to the use of Revised Universal Soil Loss Equation (RUSLE)within AnnAGNPS, and parameters associated with determining soil loss were derived from longterm average annual soilloss estimates. The agreement between monthly average predicted sediment yield and monthly average observed sedimentyield had an R2 of 0.7. Threeyear predicted total runoff was 89% of observed total runoff, and threeyear predicted totalsediment yield was 104% of observed total sediment yield. Alternative scenario simulations showed that winter cover cropsand impoundments are promising BMPs for sediment reduction.

Crop selection and implications for profits and wind erosion in a semi-arid environment

A daily crop growth simulation model was applied to four dryland cropping systems to estimate the profit distributions for each of four price series under stochastic weather conditions on the Southern High Plains of Texas. Stochastic dominance with respect to a function was utilized to rank each crop rotation for different risk-averse intervals. Solutions from the model indicate that long-term average annual soil loss due to wind erosion was a function of the producer's risk aversion, price expectation, and discount rate which affect the optimal crop rotation selection.