Determination of Soil Erosion Risk in the Mustafakemalpasa River Basin, Turkey, Using the Revised Universal Soil Loss Equation, Geographic Information System, and Remote Sensing

Sediment transport from steep slopes and agricultural lands into the Uluabat Lake (a RAMSAR site) by the Mustafakemalpasa (MKP) River is a serious problem within the river basin. Predictive erosion models are useful tools for evaluating soil erosion and establishing soil erosion management plans. The Revised Universal Soil Loss Equation (RUSLE) function is a commonly used erosion model for this purpose in Turkey and the rest of the world. This research integrates the RUSLE within a geographic information system environment to investigate the spatial distribution of annual soil loss potential in the MKP River Basin. The rainfall erosivity factor was developed from local annual precipitation data using a modified Fournier index: The topographic factor was developed from a digital elevation model; the K factor was determined from a combination of the soil map and the geological map; and the land cover factor was generated from Landsat-7 Enhanced Thematic Mapper (ETM) images. According to the model, the total soil loss potential of the MKP River Basin from erosion by water was 11,296,063?Mg?year(-1) with an average soil loss of 11.2?Mg?year(-1). The RUSLE produces only local erosion values and cannot be used to estimate the sediment yield for a watershed. To estimate the sediment yield, sediment-delivery ratio equations were used and compared with the sediment-monitoring reports of the Dolluk stream gauging station on the MKP River, which collected data for >41?years (1964-2005). This station observes the overall efficiency of the sediment yield coming from the Orhaneli and Emet Rivers. The measured sediment in the Emet and Orhaneli sub-basins is 1,082,010?Mg?year(-1) and was estimated to be 1,640,947?Mg?year(-1) for the same two sub-basins. The measured sediment yield of the gauge station is 127.6?Mg?km(-2)?year(-1) but was estimated to be 170.2?Mg?km(-2) year(-1). The close match between the sediment amounts estimated using the RUSLE-geographic information system (GIS) combination and the measured values from the Dolluk sediment gauge station shows that the potential soil erosion risk of the MKP River Basin can be estimated correctly and reliably using the RUSLE function generated in a GIS environment.     

Suitability of different erosivity models used in RUSLE2 for the South West Indian region

RUSLE2 is the most used soil erosion model in practice. The rainfall-erosivity factor (R) is one of the six factors that is taken into consideration while estimating soil loss at a hill slope profile. R is determined using rainfall data collected from any region making use of basic rainstorm kinetic energy versus rainfall intensity relationships, which are variable for different geographic regions. Indian researchers used a specific erosivity model for building an iso-erosivity map for India. Many other erosivity models around the world are now available. However, it is not clear whether one can replace RUSLE2 recommended model by the ones derived in other geographic regions for using in Indian soil erosion studies. This has been examined here on south-western Indian data. Various models derived in diverse places were analyzed and compared with the RUSLE2 recommended relationship; and found that, a few could very well replace the usual RUSLE2 recommended expression.     

An Approach for Estimating Soil Carbon Using the National Nutrient Loss Database

Agricultural lands have the potential to contribute to greenhouse gas mitigation by sequestering organic carbon within the soil. Credible and consistent estimates will be necessary to design programs and policies to encourage management practices that increase carbon sequestration. Because a nationwide survey of soil carbon by the wide range of natural resources and management conditions of the United States is prohibitively expensive, a simulation modeling approach must be used. The National Nutrient Loss Database (NNLD) is a modeling and database system designed and built jointly by the USDA– Natural Resources Conservation Service (NRCS) and Texas A&M University to provide science-based inferences on environmental impacts from changes in agricultural management practices and programs at the regional and national level. Currently, the NNLD simulates 16 crops and covers 1.35 × 10⁸ ha. For estimating soil carbon sequestration, the database will be populated with 1.5 × 10⁶ field-level model runs using the EPIC (Environmental Policy Impact Calculator) model, which includes newly incorporated carbon equations consistent with those in the Century model. Each run will represent a unique situation defined by state, crop, climate, soil, irrigation type, conservation practice, tillage system, and nutrient management treatment (nutrient rate, application frequency, application timing, and manure category). Results are to be assigned to specific National Resource Inventory points (NRI) to simulate regional and national baselines. In this article we present the modeling approach and discuss the strengths and limitations. Published online     

Impact of Vegetative Cover on Runoff and Soil Erosion at Hillslope Scale in Lanjaron, Spain

Soil loss and surface runoff patterns over a four-year period (1997–2000) were studied in erosion plots from three hillslopes under different vegetative covers (Rosmarinus officinalis, Triticum aestivum and natural-spontaneous vegetation) in Lanjaron (Alpujarras) on the south flank of the Sierra Nevada of southeast Spain. The erosion plots were located on the hillslopes at 35.5% incline, at 1,480 m in altitude and with 41.8 m² (21 m×1.9 m) in area. The vegetative covers were tested for effectiveness in controlling the surface runoff and soil loss production. The highest runoff and erosion values, ranging from 114.1 to 1.7 mmyr^–1 and from 14,564.3 to 6.6 kgha^–1yr^–1, respectively, over the entire study period, were measured under the Triticum aestivum. In the Rosmarinus officinalis, runoff ranged from 7.9 to 1.3 mmyr^–1 and erosion from 156.4 to 2.3 kgha^–1yr^–1, while on the hillslope under natural-spontaneous vegetation, runoff ranged from 4.4 to 0.9 mmyr^–1 and erosion from 322.3 to 2.2 kgha^–1yr^–1. According to the results the vegetative covers of Rosmarinus officinalis and natural-spontaneous vegetation reduced the soil losses by 99 and 98%, with respect to the Triticum aestivum, and the runoff losses by 94 and 96%, respectively. Also, the Rosmarinus officinalis and natural-spontaneous plants influenced infiltration by intercepting much of the rainfall water respect to the Triticum aestivum. Monitoring allowed more direct linkages to be made between management practices and their impacts on runoff and soil erosion, thereby enabling to identify problems and take appropriate preventive measures to improve the management practices.     

Uncertainties associated with the assessment of vegetation

The Environmental Impact Statement (EIS) attempts to portray the current character of a given ecosystem. This provides a Baseline against which future possible changes can be assessed. Ecosystems, however, are dynamic and in a constant state of change. Consequently, data representing a single year reflect conditions only for that year and can be misleading if compared against any other year. In addition to this temporal variation, considerable spacial variation exists in species composition and cover. Ample sampling is required to compensate for this heterogeneity. Variation, or background noise, can be minimized by the use of similarity indexes and sampling over a period of at least two years.     

Toxicity studies and environmental impact assessment

There is abundant evidence that many factors can influence the toxicity of a particular pollutant including environmental fluctuations, season of the year, stage in life cycle, size, and sex. All of these factors should be assessed before making a judgment of the effect on natural populations. Such an assessment can be conceptualized using a simple population model through whichcontrol gates operate as functions of 1). the direct self-maintainance feedback from existing adult population biomass and 2). the recruitment of new individuals due to the maturation of larvae. By extracting general principles of organismic response to pollutants it is possible to incorporate the information into large-scale ecosystem models which would serve as working tools for answering environmental decision-making problems.     

Seasonality of Soil Erosion Under Mediterranean Conditions at the Alqueva Dam Watershed

The Alqueva reservoir created the largest artificial lake of Western Europe in 2010. Since then, the region has faced challenges due to land-use changes that may increase the risk of erosion and shorten the lifetime of the reservoir, increasing the need to promote land management sustainability. This paper investigates the aspect of seasonality of soil erosion using a comprehensive methodology that integrates the Revised Universal Soil Loss Equation (RUSLE) approach, geographic information systems, geostatistics, and remote-sensing. An experimental agro-silvo pastoral area (typical land-use) was used for the RUSLE factors update. The study confirmed the effect of seasonality on soil erosion rates under Mediterranean conditions. The highest rainfall erosivity values occurred during the autumn season (433.6 MJ mm ha^?1 h^?1), when vegetation cover is reduced after the long dry season. As a result, the autumn season showed the highest predicted erosion (9.9 t ha^?1), contributing 65 % of the total annual erosion. The predicted soil erosion for winter was low (1.1 t ha^?1) despite the high rainfall erosivity during that season (196.6 MJ mm ha^?1 h^?1). The predicted annual soil loss was 15.1 t ha^?1, and the sediment amount delivery was 4,314 × 10³ kg. Knowledge of seasonal variation would be essential to outline sustainable land management practices. This model will be integrated with World Overview of Conservation Approaches and Technologies methods to support decision-making in that watershed, and it will involve collaboration with both local people and governmental institutions.     

A Conceptual Model for Defining and Assessing Land Management Units Using a Fuzzy Modeling Approach in GIS Environment

Appropriate land management decisions are important for current and future use of the land to ensure its sustainability. This requires that land management units (LMUs) be specified to enable the identification of specific parameters employed in decision making processes. This paper presents the development of a conceptual model, within geographic information systems (GIS), for defining and assessing LMUs from available biophysical information. The model consists of two main components (sub-models): land quality-based suitability analysis and soil erosion estimation. Using a fuzzy set methodology, the first sub-model was constructed to derive a land suitability index (LSI) for a cropping land utilization type. The LSI thus highlights the suitability grades of every pixel in the study area on a continuous basis. A sub-model of soil erosion was established based on the Revised Universal Soil Loss Equation (RUSLE) utilising the same spatial data bases employed for structuring the LSI. Using a soil loss tolerance principle, a fuzzy membership function of average annual soil loss (called soil loss index, SLI) was established, leading to compatibility between LSI and SLI for data integration. LMUs were then derived from various combinations of LSI and SLI. The methodology developed shows the significance of the model for refining available land suitability evaluation systems, which take no account of expected land degradation (from erosion) due to a nominated land use. It also provides a valuable guideline for cost-effective GIS applications in the identification and assessment of homogeneous land units, using available spatial information sets, at a finer scale.     

Bioremediation of Soil Degraded by Sewage Sludge: Effects on Soil Properties and Erosion Losses

Soils in the Mediterranean area are very prone to erosion due to the loss of organic matter and the consequent lack of protective vegetation. In this experiment a Mediterranean degraded soil with a 15% slope was amended at a rate of 250 t ha^–1 wet weight with sewage sludge and with a mixture of sewage sludge and barley straw (70% carbon from sewage sludge and 30% from the straw) in order to study their influence on soil structure recovery and hence the soilss resistance to erosion processes. Both types of organic amendment led to an improvement in several soil properties (physical, biological, and microbiological) as a result of the spontaneous growth plant covering that became evident three months after amendment. This vegetation remained throughout the two years of the experiment and prevented the water erosion processes that normally precede soil degradation. Amendment by sewage sludge alone reduced soil loss by 80% compared with the control soil, while the mixture that included both sewage sludge and barley straw reduced losses by 84%, both reducing runoff by 57%. The amended soils showed increases in the percentage of stable aggregates, the levels of the total and water-soluble C fractions, microbial biomass C, basal respiration, and the activity of the different enzymes involved in the biogeochemical cycles of C, N, and P. The results confirm the usefulness of sewage sludge as an organic amendment for recovering damaged soils.     

Stochastic modeling of diffuse pesticide losses from a small agricultural catchment

The objective of this study was to identify the main sources of variation in pesticide losses at field and catchment scales using the dual permeability model MACRO. Stochastic simulations of the leaching of the herbicide MCPA (4-chloro-2-methylphenoxyacetic acid) were compared with seven years of measured concentrations in a stream draining a small agricultural catchment and one year of measured concentrations at the outlet of a field located within the catchment. MACRO was parameterized from measured probability distributions accounting for spatial variability of soil properties and local pedotransfer functions derived from information gathered in field- and catchment-scale soil surveys. At the field scale, a single deterministic simulation using the means of the input distributions was also performed. The deterministic run failed to reproduce the summer outflows when most leaching occurred, and greatly underestimated pesticide leaching. In contrast, the stochastic simulations successfully predicted the hydrologic response of the field and catchment and there was a good resemblance between the simulations and measured MCPA concentrations at the field outlet. At the catchment scale, the stochastic approach underestimated the concentrations of MCPA in the stream, probably mostly due to point sources, but perhaps also because the distributions used for the input variables did not accurately reflect conditions in the catchment. Sensitivity analyses showed that the most important factors affecting MACRO modeled diffuse MCPA losses from this catchment were soil properties controlling macropore flow, precipitation following application, and organic carbon content.     

Investment in human capital in Africa

Summary The author analyses the problems currently faced by the African continent, recognises six factors which he believes are important in influencing the analysis, and argues that the only secure, renewable asset any country or continent has, is its people. He maintains that people development, rather than the classical economic forms of encouraging development, offers Africa a viable way forward. He then identifies a number of specific issues within such a strategy of investment in human capital.Brian Walker is the President of the International Institute for Environment and Development (IIED), and this address was given at the inaugural meeting of the International Year of Shelter for the Homeless, in London, on 18th April, 1985. A second, earlier, associated address was published inThe Environmentalist 5(3) 167–170.     

Conservation strategies for effective land management of protected areas using an erosion prediction information system (EPIS)

This research demonstrates the predictive modeling capabilities of a geographic information system (GIS)-based soil erosion potential model to assess the effects of implementing land use change within a tropical watershed. The Revised Universal Soil Loss Equation (RUSLE) was integrated with a GIS to produce an Erosion Prediction Information System (EPIS) and modified to reflect conditions found in the mountainous tropics. Research was conducted in the Zenzontia subcatchment of the Río Ayuquíla, located within the Sierra de Manantlán Biosphere Reserve (SMBR), México. Expanding agricultural activities within this area will accentuate the already high rate of soil erosion and resultant sediment loading occurring in the Río Ayuquíla. Two land-use change scenarios are modeled with the EPIS: (1) implementation of soil conservation practices in erosion prone locations; and (2) selection of sites for agricultural expansion which minimize potential soil loss. Confronted with limited financial resources and the necessity for expedient action, managers of the SMBR can draw upon the predictive capacity of the EPIS to facilitate rapid and informed land-use planning decisions.     

Managing Uncertainty in Runoff Estimation with the U.S. Environmental Protection Agency National Stormwater Calculator

The U.S. Environmental Protection Agency National Stormwater Calculator (NSWC) simplifies the task of estimating runoff through a straightforward simulation process based on the EPA Stormwater Management Model. The NSWC accesses localized climate and soil hydrology data, and options to experiment with low‐impact development (LID) features for parcels up to 5 ha in size. We discuss how the NSWC treats the urban hydrologic cycle and focus on the estimation uncertainty in soil hydrology and its impact on runoff simulation by comparing field‐measured soil hydrologic data from 12 cities to corresponding NSWC estimates in three case studies. The default NSWC hydraulic conductivity is 10.1 mm/h, which underestimates conductivity measurements for New Orleans, Louisiana (95 ± 27 mm/h) and overestimates that for Omaha, Nebraska (3.0 ± 1.0 mm/h). Across all cities, the NSWC prediction, on average, underestimated hydraulic conductivity by 10.5 mm/h compared to corresponding measured values. In evaluating how LID interact with soil hydrology and runoff response, we found direct hydrologic interaction with pre‐existing soil shows high sensitivity in runoff prediction, whereas LID isolated from soils show less impact. Simulations with LID on higher permeability soils indicate that nearly all of pre‐LID runoff is treated; while features interacting with less‐permeable soils treat only 50%. We highlight the NSWC as a screening‐level tool for site runoff dynamics and its suitability in stormwater management.     

Lead removal from contaminated water using mineral adsorbents

This study records experiments undertaken to determine the suitable conditions for the use of naturally occurring minerals (talc, chalcopyrite and barite) as an adsorbent for the removal of lead ions from liquid wastes. The adsorption of lead ions from solutions containing different initial lead concentrations (50, 100, 200, 400, 600, 800 and 1000 mg l^–1 Pb as lead nitrate) using different size fractions (<63 m, 63–150 m) of talc, chalcopyrite and barite at different pH (3, 5, 7 and 9) and different adsorption times (24, 48, 72 and 96 hr) was examined. The results revealed that of the studied minerals, the chalcopyrite fraction at 63–150 m showed the highest adsorption capacity. The adsorption data of Pb ions was also analyzed with the help of the Langmuir and Freundlich models to evaluate the mechanistic parameters associated with the adsorption process. The adsorption isotherms obtained from the Langmuir and Freundlich equations were generally linear and the adsorption of Pb by the studied minerals was correlated with the adsorption maximum and binding energy constant of the Langmuir equation and equilibrium partition constant and binding partition coefficient of the Freundlich equation. It was concluded that the equilibrium time of adsorption was 72 hr at an optimum pH from 7 to 9. This technique might be successfully used for the removal of lead ions from liquid industrial wastes and wastewater.     

Selected landslide types in the Central Himalaya: their relation to geological structure and anthropogenic activities

In a region that is highly sensitive to tectonic instability, the fragile nature of the Himalayas becomes further adversely affected by anthropogenic intervention. In the present study observations indicate that the landsliding process occurs along various tectonic zones where it is assisted by human activities. Bedding and joint plane dip slopes, high joint and joint set frequencies, low vegetation cover, high monsoonal rainfall, thin soil cover and anthropogenic activities were found to be the main causative factors of the landslides. Anthropogenic activities include local path, canal and road construction, mining and quarrying, overgrazing, deforestation and unscientific agricultural practices, such as tilling steeper slopes (>30) without contour benches and without provision of drainage ditches, and overcropping without giving rest to the overtaxed soils. Where slope conditions are critical human activities should be controlled so as to minimise the slope failure processes. Various recommendations are proposed.     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

A Baseline Study on Water Resources of the Tourist Island, Pulau Perhentian, Peninsular Malaysia, from an Ecological Perspective

Ambient water quality and bacterial populations in the water resources of Pulau Perhentian, a Marine Park of Malaysia, were studied during a peak tourist arrival, off-monsoon period. The overall water and bacteriological quality of swimming/boating sites in the island is at an acceptable condition, although the conductivity (0.88–60 mS m^–1) due to high dissolved solid concentrations is slightly high, when compared with the recommended standard and published guidelines for the protection/maintenance of recreation water and its aesthetic enjoyment (Universiti Malaya, Department of Environment, Malaysia, 1986). However, the bacteriological quality of the drinking/bathing water wells is poor, with faecal coliform counts (2 up to 1,600 MPN per 100 ml sample water) exceeding the permissible levels defined for raw water supplies (with or without treatment). This suggests that the groundwater, seepage and run-off into the wells is contaminated. Contrary to the belief of the water users being interviewed in a social survey, the untreated drinking/bathing water is not bacteria-free. The excessive counts are attributable to deoxygenated conditions (12–42 percent corresponding to 0.9–3.3 mg l^–1) prevailing in the wells, high total suspended solids (up to 202 mg l^–1) which promote bacterial growth, lack of adequate sewerage systems to receive effluents from lavatories of dwellings, chalets and resorts, as well as the lack of centralised water treatment plants and storage tanks to cope with water scarcity and waterway contamination.     

Emissions from burning biofuels in metal cookstoves

Promoting stoves that burn wood and other biofuels more efficiently is one of the means to reduce fuel consumption, but such efficient stoves may also emit more carbon monoxide and total suspended particulates. In an earlier study, a standard chamber method was proposed to estimate emission factors from burning fuelwood (Acacia nilotica). Here that methodology is extended to measure emission factors from burning of dungcakes and crop residues (Brassica or mustard stalks)—common fuels in many developing countries. The amounts of carbon monoxide (CO) and total suspended particulates (TSP) emitted by four different models of stoves, when using each of the three biofuels, are measured.The CO emission factors range from 13–68 (g/kg) for fuelwood to 26–67 g/kg for dungcakes and 20–114 g/kg for crop residues, for particulates they range from 1.1–3.8 to 4.1–7.8 and 2.1–12.0 g/kg for the three fuels, respectively. On a per unit heat delivered basis, the emissions of CO and TSP from both dungcakes and crop residues are two to three times higher compared to those from fuelwood. While for some improved stove-fuel combinations, the increase in emission factors was offset by the increase in thermal efficiency, this was not always so and causes a dilemma. The more efficient stoves are found to have higher emission factors of both CO and TSP for all three fuels. Emissions per standard task (i.e, on a unit heat delivered basis) is proposed as a criterion to evaluate cookstoves.     

Evaluation of soil erosion risk using Analytic Network Process and GIS: A case study from Spanish mountain olive plantations

The study presents an approach that combined objective information such as sampling or experimental data with subjective information such as expert opinions. This combined approach was based on the Analytic Network Process method. It was applied to evaluate soil erosion risk and overcomes one of the drawbacks of USLE/RUSLE soil erosion models, namely that they do not consider interactions among soil erosion factors. Another advantage of this method is that it can be used if there are insufficient experimental data. The lack of experimental data can be compensated for through the use of expert evaluations. As an example of the proposed approach, the risk of soil erosion was evaluated in olive groves in Southern Spain, showing the potential of the ANP method for modelling a complex physical process like soil erosion.     

The effect of model structure and data in modeling land conditions in disturbed complex ecosystems

Off-road vehicles increase soil erosion by reducing vegetation cover and other types of ground cover, and by changing the structure of soil. The investigation of the relationship between disturbance from off-road vehicles and the intensity of the activities that involve use of vehicles is essential for water and soil conservation and facility management. Models have been developed in a previous study to predict disturbance caused by off-road vehicles. However, the effect of data on model quality and model performance, and the appropriate structure of models have not been previously investigated. In order to improve the quality and performance of disturbance models, this study was designed to investigate the effects of model structure and data. The experiment considered and tested: (1) two measures of disturbance based on the Vegetation Cover Factor (C Factor) of the Revised Universal Soil Loss Equation (RUSLE) and Disturbance Intensity; (2) model structure using two modeling approaches; and (3) three subsets of data. The adjusted R-square and residuals from validation data are used to represent model quality and performance, respectively. Analysis of variance (ANOVA) is used to identify factors which have significant effects on model quality and performance. The results of the ANOVA show that subsets of data have significant effects on both model quality and performance for both measures of disturbance. The ANOVA also detected that the C Factor models have higher quality and performance than the Disturbance models. Although modeling approaches are not a significant factor based on the ANOVA tests, models containing interaction terms can increase the adjusted R-squares for nearly all tested conditions and the maximum improvement can reach 31%.