Rainfall erosivity and erosivity density in Eastern Ghats Highland of east India

The rainfall erosivity (R-factor in USLE) is the long-term average of the sum of the product of rainfall kinetic energy and its maximum 30-min intensity. Therefore, at most 30-min time intervals pluviograph records are required to calculate R-factor. But, such high-resolution data are scarce in many parts of the world and require lengthy processing period. In this study, R-factor was correlated with daily, monthly and annual rainfall, and its spatial variability in Eastern Ghats Highland of east India was mapped. The result showed that power regression models predicted satisfactorily the daily, monthly and annual R-factor, of which annual R-factor model performed best (model efficiency 0.93). Mean monsoon season R-factor was 15.6 and 10.0 times higher than the pre- and post-monsoon season R-factor, and thus remained highly critical with respect to erosion. Annual R-factor values ranged from 3040 to 10,127 MJ mm ha^?1 h^?1 year^?1, with standard deviation of 1981 MJ mm ha^?1 h^?1 year^?1. Rainfall intensity was positively correlated with erosivity density, and numerical value of rainfall intensity was almost double of the erosivity density value. The combination of rainfall and erosivity density was used to identify flood, erosion and landslide-prone areas. The developed iso-erosivity, erosivity density and risk maps can be opted as a tool for policy makers to take suitable measures against natural hazards in Eastern Ghats Highland of east India and elsewhere with similar rainfall characteristics.

     

Spatial prediction of soil erosion risk by remote sensing,GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley,Kerala, India

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.     

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework,Chania, Northwestern Crete,Greece

Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year⁻¹) for some watersheds showing extended erosion and demanding the attention of local administrators.     

Soil erosion risk assessment of the Keiskamma catchment,South Africa using GIS and remote sensing

This paper examines the soil loss spatial patterns in the Keiskamma catchment using the GIS-based Sediment Assessment Tool for Effective Erosion Control (SATEEC) to assess the soil erosion risk of the catchment. SATEEC estimates soil loss and sediment yield within river catchments using the Revised Universal Soil Loss Equation (RUSLE) and a spatially distributed sediment delivery ratio. Vegetation cover in protected areas has a significant effect in curtailing soil loss. The effect of rainfall was noted as two pronged, higher rainfall amounts received in the escarpment promote vegetation growth and vigour in the Amatole mountain range which in turn positively provides a protective cover to shield the soil from soil loss. The negative aspect of high rainfall is that it increases the rainfall erosivity. The Keiskamma catchment is predisposed to excessive rates of soil loss due to high soil erodibility, steep slopes, poor conservation practices and low vegetation cover. This soil erosion risk assessment shows that 35% of the catchment is prone to high to extremely high soil losses higher than 25 ton ha⁻¹ year⁻¹ whilst 65% still experience very low to moderate levels of soil loss of less than 25 ton ha⁻¹ year⁻¹. Object based classification highlighted the occurrence of enriched valley infill which flourishes in sediment laden ephemeral stream channels. This occurrence increases gully erosion due to overgrazing within ephemeral stream channels. Measures to curb further degradation in the catchment should thrive to strengthen the role of local institutions in controlling conservation practice.     

Regional soil erosion risk mapping using RUSLE,GIS, and remote sensing: a case study in Miyun Watershed,North China

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.     

Soil erosion prediction at the basin scale using the revised universal soil loss equation (RUSLE) in a catchment of Sicily (southern Italy)

Soil erosion by water is a serious problem in southern Italy, particularly in Sicily which is one of the Italian administrative regions prone to desertification. Soil erosion not only affects soil quality, in terms of agricultural productivity, but also reduces the availability of water in reservoirs. This study was conducted in the Comunelli catchment in south-central Sicily, to predict potential annual soil loss using the revised universal soil loss equation (RUSLE) and to test the reliability of this methodology to predict reservoirs siltation. The RUSLE factors were calculated for the catchment using survey data and rain gauge measurement data. The R-factor was calculated from daily, monthly and annual precipitation data. The K-factor was calculated from soil samples collected in May and November 2004. The LS topographic factor was calculated from a 20 m digital elevation model. The C- and P-factors, in absence of detailed data, were set to 1. The results were compared with those obtained from another soil loss estimation method based on ¹³⁷Cs and with the soil loss estimated from the sediment volume stored in the Comunelli reservoir between 1968 and 2004.     

Evaluation of rainfall erosivity and its temporal variation in the Yanhe River catchment of the Chinese Loess Plateau

The potential of rain to generate soil erosion is known as the rainfall erosivity (R), and its estimation is fundamental for a better understanding of the erosive ability of certain rainfall events. In this paper, we investigated the temporal variations of rainfall erosivity using common daily rainfall data from four meteorological stations during 1956 to 1989 and 2008 to 2010 periods in the Yanhe River catchment of the Chinese Loess Plateau. The adaptability of several simplified calculation models for R was evaluated and compared with the results of previous studies. An exponential model based on the modified Fournier index (MFI) was considered as the optimum for our study area. By considering the monthly distribution and coefficient of variation of annual precipitation, equations based on two indices, the MFI and its modification F _F , produced a higher calculation accuracy than mean annual precipitation. The rainfall erosivity in the Yanhe River catchment has a remarkable interannual difference, with a seasonality index ranging from 0.69 to 1.05 and a precipitation concentration index from 14.51 to 27.46. In addition to the annual rainfall amounts, the extreme wave of monthly rainfall distribution also has an effect on the magnitude and temporal variation of rainfall erosivity, especially interannual variation. For long time series of rainfall erosivity, a trend coefficient r of ?0.07 indicated a slight decline in erosivity in the Yanhe River catchment from 1956 to 2010.     

Modeling soil erosion using RUSLE and GIS in a watershed occupied by rural settlement in the Brazilian Cerrado

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.     

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^–1yr^?1.     

Conditional simulation of USLE/RUSLE soil erodibility factor by geostatistics in a Mediterranean Catchment,Turkey

Soil erosion is a major environmental problem that threatens the sustainability and productivity of agricultural areas. Assessment and mapping of soil erosion are extremely important in the management and conservation of natural resources. The universal soil loss equation (USLE/RUSLE) is an erosion model that predicts soil loss as a function of soil erodibility (K-factor), as well as topographic, rainfall, cover, and management factors. The traditional approach assumes that one soil erodibility value represents the entire area of each soil series. Therefore, that approach does not account for spatial variability of soil series. This study was carried out to evaluate the use of the sequential Gaussian simulation (SGS) for mapping soil erodibility factor of the USLE/RUSLE methodology. Five hundred and forty-four surface soil samples (0–20 cm) were collected from the study area to determine the soil erodibility. A simulation procedure was carried out on 300 realizations, and histogram and semivariogram of the simulation were compared to the observed values. The results showed that the summary statistics, histogram, and semivariogram of the simulation results were close to the observed values. In contrary to the traditional approach and kriging, 95% confidence interval of the simulated realizations was formed in order to determine uncertainty standard deviation map, and the uncertainty was explained numerically. The SGS produced a more reliable soil erodibility map and it can be more successfully used for monitoring and improving effective strategies to prevent erosion hazards especially to improve site specific management plans.     

Soil erosion determinations by using 137Cs technique in the agricultural regions of Gediz Basin, Western Turkey

Gediz Basin is one of the regions where intense agricultural activities take place in Western Turkey. Erosion and soil degradation have long been causing serious problems to cultivated fields in the basin. This work describes the application of two different ¹³⁷Cs models for estimating soil erosion rates in cultivated sites of the region. Soil samples were collected from five distinct cultivated regions subject to soil erosion. The variations of ¹³⁷Cs concentrations with depth in soil profiles were investigated. Soil loss rates were calculated from ¹³⁷Cs inventories of the samples using both proportional model (PM) and simplified mass balance model (SMBM). When PM was used, erosion and deposition rates varied from −15 to −28 t ha⁻¹ year⁻¹ and from +5 to +41 t ha⁻¹ year⁻¹, respectively; they varied from −16 to −33 t ha⁻¹ year⁻¹ and from +5 to +55 t ha⁻¹ year⁻¹ with SMBM. A good agreement was observed between the results of two models up to 30 t ha⁻¹ year⁻¹ soil loss and gain in the study area. Ulukent, a small representative agricultural field, was selected to compare the present data of ¹³⁷Cs techniques with the results obtained by universal soil loss equation (USLE) applied in the area before.     

Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran

Water erosion is a serious and continuous environmental problem in many parts of the world. The need to quantify the amount of erosion, sediment delivery, and sediment yield in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts. In this study, an effort to predict potential annual soil loss and sediment yield is conducted by using the Revised Universal Soil Loss Equation (RUSLE) model with adaptation in a geographic information system (GIS). The rainfall erosivity, soil erosivity, slope length, steepness, plant cover, and management practice and conservation support practice factors are among the basic factors that are obtained from monthly and annual rainfall data, soil map of the region, 50-m digital elevation model, remote sensing (RS) techniques (with use of Normalized Difference Vegetation Index), and GIS, respectively. The Ilam dam watershed which is located southeast part of Ilam province in western Iran is considered as study area. The study indicates that the slope length and steepness of the RUSLE model are the most effective factors controlling soil erosion in the region. The mean annual soil loss and sediment yield are also predicted. Moreover, the results indicated that 45.25%, 12.18%, 12.44%, 10.79%, and 19.34% of the study area are under minimal, low, moderate, high, and extreme actual erosion risks, respectively. Since 30.13% of the region is under high and extreme erosion risk, adoption of suitable conservation measures seems to be inevitable. So, the RUSLE model integrated with RS and GIS techniques has a great potential for producing accurate and inexpensive erosion and sediment yield risk maps in Iran.     

Spatial and isotopic analysis of watershed soil loss and reservoir sediment accumulation rates in Lake Anna, Virginia, USA

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.     

An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS

Remote sensing data and Geographical Information System (GIS) has been integrated with the weighted index overlay (WIO) method and E ₃₀ model for the identification and delineation of soil erosion susceptibility zones and the assessment of rate of soil erosion in the mountainous sub-watershed of River Manimala in Kerala (India). Soil erosion is identified as the one of the most serious environmental problems in the human altered mountainous environment. The reliability of estimated soil erosion susceptibility and soil loss is based on how accurately the different factors were estimated or prepared. In the present analysis, factors that are considered to be influence the soil erosion are: land use/land cover, NDVI, landform, drainage density, drainage frequency, lineament frequency, slope, and relative relief. By the WIO analysis, the area is divided into zones representing low (33.30%), moderate (33.70%), and high (33%) erosion proneness. The annual soil erosion rate of the area under investigation was calculated by carefully determining its various parameters and erosion for each of the pixels were estimated individually. The spatial pattern thus created for the area indicates that the average annual rate of soil erosion in the area was ranging from 0.04 mm yr⁻¹ to 61.80 mm yr⁻¹. The high soil erosion probability and maximum erosion rate was observed in areas with high terrain alteration, high relief and slopes with the intensity and duration of heavy precipitation during the monsoons.     

Application of the <Superscript>137</Superscript>Cs tracer technique to study soil erosion of alpine meadows in the headwater region of the Yellow River

The ¹³⁷Cs tracer technique was used to study soil erosion of alpine meadow grassland in two small river basins in the headwater region of the Yellow River. The results show that the levels of ¹³⁷Cs in soil samples from this alpine meadow vegetation zone exhibit an exponential distribution, generally within a depth of approximately 20 cm. Due to strong winds, freeze-thaw cycles and water, soil erosion was found to be stronger on the upper slope than on the lower slope, and except for the slope crest, the intensity of soil erosion at other sites was as follows: upslope < midslope < downslope. There was a significant negative correlation between the intensity of soil erosion and the extent of alpine meadow vegetation cover (P < 0.01). The mean soil erosion modulus exhibited a linear reduction trend with an increase in vegetation cover, and the correlation coefficient R ² was ≥ 0.997. The higher the degradation degree of the alpine meadow grassland, the greater is the soil erosion. The mean erosion modulus in the severely degraded meadow zone was 2.23 times greater than the one in the slightly degraded zone, and the maximum erosion modulus reached 2.96 × 10⁶ kg/km²/a.     

The RUSLE erosion index as a proxy indicator for debris flow susceptibility

Debris flows represent dangerous occurrences in many parts of the world. Several disasters are documented due to this type of fast-moving landslides; therefore, natural-hazard assessment of debris flows is crucial for safety of life and property. To this aim, much current work is being directed toward developing geotechnical-hydraulic models for the evaluation of debris flow susceptibility. A common base for such current models is parameterization of background predisposing and triggering factors such as inherent characteristics of geo-materials, topography, landscape and vegetation cover, rainfall regime, human activities, etc. which influence the occurrence of these processes on slopes. The same factors are also taken into account in soil erosion prediction models. Consequently, it seems worth investigating the effectiveness of the soil erosion index as debris flows susceptibility indicator. To this aim, a logistic regression analysis was carried out between the erosion index assessed by means of the Revised Universal Soil Loss Equation (RUSLE) model and the inventory of debris flows that have occurred in an area in Sicily (Southern Italy). Model assumptions were verified and validated by means of a series of statistical tools. Different possible scenarios were also evaluated by considering hypothetical changes in soil erosion rate under different rain erosivity conditions. Notwithstanding the rough approximations in model data collection, the outcomes appear encouraging.     

Observations of surface radon in Central Italy

Two years of in situ radon concentration measurements in the atmospheric surface layer have been collected in a central Italy town (L’Aquila), located in the Aterno river valley. These data have been analyzed in order to study the controlling mechanisms of surface radon abundance; observations of coincident meteorological parameters confirmed the role of dynamics on the local removal rate of this tracer. The relatively high negative correlation of hourly data of surface wind speed and radon activity concentration (R = −0.54, on annual scale) suggests that dynamical removal of radon is one of the most important controlling processes of the tracer accumulation in the atmospheric surface layer. An attempt is made to quantify the precipitation impact on radon soil fluxes. No anticorrelation of radon and precipitation comes out from the data (R = −0.15), as in previous studies. However, since the main physical parameter affecting the ground radon release is expected to be the soil accumulation of water, snow or ice, the emission flux has also been correlated with soil moisture; in this way a much clearer anticorrelation is found (R = −0.54).     

Estimation of soil erosion using RUSLE in Caijiamiao watershed,China

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.     

Implantation of recoiling radionuclides of U and Th radioactive series applied to estimation of surficial erosion of CaCO3 materials

A method to detect the extent of rainfall-dependent erosion of CaCO₃ materials, based on implantation of recoiling nuclei of U–Th disequilibrium series (UTHIRN) is outlined here. Radon chambers provided natural implantation of short and medium-lived isotopes (²¹²Pb and ²¹⁰Pb) on surfaces of natural varieties of CaCO₃. Equal thicknesses of CaCO₃ were dissolved in order to measure the distribution of the recoiled nuclei inside the target material. The measured distribution was found to be consistent with the theoretical distribution expected for mechanisms of nuclear stopping. The ²¹⁶Po half-recoil range (18 nm) obtained from the experimental curve was slightly higher than the theoretical one (16 nm).The ratio between residual and initial activity, monitored by non-destructive methods, was then used to calculate the eroded thickness of CaCO₃ tablets. The ‘chemical’ and ‘mechanical’ contributions of the surficial erosive process were evaluated by leaching experiments and by simulated rainfall, respectively. Successively, the erosion rates produced during single rainfall events were compared with the experimentally obtained rates, using ternary diagrams (pH/ mm of rainfall/eroded thickness). The difference between the total eroded thickness measured by the proposed method (22.5 nm) and the total eroded thickness predicted by the theoretical model (16.9 nm) for the considered rainfalls was about 25%. The temporal resolution of the erosion rate, the extension of monitored area and the detail of the surficial eroded thickness (up to ∼40 nm) are notably improved by the UTHIRN method.     

The impact of acid rain on phosphorus leaching from a sandy loam calcareous soil of western Iran

Simulated acidic precipitation (1:1 equivalent basis H₂SO₄:HNO₃) at pH values of 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 and 7.0 were conducted using column leaching to determine impacts of simulated acid rain on phosphorus (P) leaching from a calcareous sandy loam soil over a 40-day period. Soil columns were irrigated every day to make a total of 1,061 mm, equivalent to 3.5 years of rainfall (based on average annual rainfall). Leachates were collected and analyzed for anions and cations. There was significant nonlinear correlation between the amount of P leached and the simulated acid rain (R ² = 0.61). Losses of P from the pH 2.5 and 7.0 treatments were 1.23, and 1.32 mg kg⁻¹, respectively. The results showed that the amount of P leached from pH 4 (1.46 mg kg⁻¹) and 5 treatments (1.52 mg⁻¹ kg⁻¹) were significantly larger than other treatments. Linear equation adequately described leaching of P in different treatments. The slope (mg kg⁻¹ day⁻¹) in the linear equation was defined as the leaching rate and for the pH 2.5 was 0.0354, and 0.0382 and 0.0406 for pH 4.5 and 7.0, respectively. The geochemical code Visual MINTEQ was used to calculate saturation indices. Leaching of P in different treatments was controlled by rate-limited dissolution of hydroxyapatite, β-tricalcium phosphate and to some extent octacalcium phosphate. The results indicate that acid rain in calcareous sandy loam soils may pose a risk in terms of groundwater contamination with P.