Temporal soil erosion risk evaluation: a CORINE methodology application at Elmalı dam watershed,Istanbul

Geographical information systems (GIS)-based soil erosion risk assessment models continue to play an important role in soil conservation planning. In the present study, soil erosion risk of Istanbul–Elmalı dam watershed was determined within GIS-based COoRdination of INformation on the Environment (CORINE) soil erosion risk assessment method. Initially soil texture, soil depth, and surface stoniness maps were created and were intersected in GIS environment in order to generating erodibility map. Then, Fournier precipitation and Bagnouls–Gaussen drought indices determined based on meteorological data and erosivity were calculated. The composed erodibility map was co-evaluated within erosivity value and slope map of the site for composing potential erosion risk map. At the final step, the previous yearly land use maps which belong to years 1984, 1992, and 2003 intersected with potential erosion risk maps and temporal actual erosion risk alteration were assessed. In conclusion, according to our results in Elmalı watershed dam in 1984 there have been low, medium, and high erosion risks at rates 29.67, 52.49, and 17.84%, respectively, whereas in 2003 the erosion risk values have changed from low to high as 26.43, 46.57, and 27.00%, respectively. Inter-year comparison alteration to the advantage of the high erosion risk could have resulted from over degradation and high exposure to anthropogenic activities.     

Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India (N=370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India (N=36) and an arid region of USA (N=1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested ‘established’ PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from ‘established’ PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.     

Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran)

Soil texture is a key variable that reflect a number of soil properties such as soil permeability, water holding capacity, nutrient storage and availability, and soil erosion. The main objective of this study was to produce the kriged maps of soils of the Shahrekord region, central Iran. One hundred four soil samples were collected on a 375-m² sampling grid from the depths of 0–30, 30–60, and 60–100 centimeter, and their particle sizes were determined using hydrometer method. The results showed a moderately spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, increasing clay and therewith observation of heavier soil textures is evident from surface to subsurface layers of the soils in the studied area due to rainfall and/or irrigation agriculture. These findings indicated that study of the soil texture variation with depth can be used as a clue for site-specific management and precision agriculture. Moreover, we suggest further analysis by using other data layers like topographical parameters, land use, parent material, soil erosion, and any other information which might influence the spatial distribution of soil texture.     

Spatial prediction of soil properties in a watershed scale through maximum likelihood approach

Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km² may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach.     

Evaluation of the seismic risk of the unreinforced masonry building stock in Antioquia,Colombia

This work focuses on the exploitation of very high-resolution (VHR) satellite imagery coupled with multi-criteria analysis (MCA) to produce flood hazard maps. The methodology was tested over a portion of the Yialias river watershed basin (Nicosia, Cyprus). The MCA methodology was performed selecting five flood-conditioning factors: slope, distance to channels, drainage texture, geology and land cover. Among MCA methods, the analytic hierarchy process technique was chosen to derive the weight of each criterion in the computation of the flood hazard index (FHI). The required information layers were obtained by processing a VHR GeoEye-1 image and a digital elevation model. The satellite image was classified using an object-based technique to extract land use/cover data, while GIS geoprocessing of the DEM provided slope, stream network and drainage texture data. Using the FHI, the study area was finally classified into seven hazard categories ranging from very low to very high in order to generate an easily readable map. The hazard seems to be severe, in particular, in some urban areas, where extensive anthropogenic interventions can be observed. This work confirms the benefits of using remote sensing data coupled with MCA approach to provide fast and cost-effective information concerning the hazard assessment, especially when reliable data are not available.     

Effect of different soil data on hydrological process modeling in Weihe River basin of Northwest China

Hydrological process modeling depends on the soil data spatial resolution of the watershed. Especially, in a large-scale watershed, could a higher resolution of soil data contribute to a more accurate result? In this study, two soil datasets with different classification systems FAO (World Reference Base) and GSCC (the Genetic Soil Classification of China) were used as inputs for the SWAT model to study the effects of soil datasets on hydrological process modeling in Weihe River basin, China. Results show that the discharge simulated using FAO soil data was better than one simulated using GSCC soil data before model calibration, which indicates that FAO soil data needed less effort to calibrate. After model calibration, discharges were simulated better by both of FAO and GSCC soil data but statistical parameters demonstrate that we can make a relatively more accurate estimation of discharge using the GSCC rather than FAO soil data. Soil water content (SW) simulated using GSCC soil data was statistically significantly higher than those simulated using FAO soil data. However, variations in other hydrological components (surface runoff (SURQ), actual evapotranspiration (ET), and water yield (WYLD) were not statistically significant. This might be because SW is more sensitive to soil properties. For studies aiming to simulate or compare SW, merely calibrating and validating models using river discharge observations is not enough. The hydrological modelers need to identify the key hydrological components intrinsic to their study and weigh the advantages and disadvantages before selecting suitable soil data.     

Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed for soil conservation in Bago river basin of Lower Myanmar

Bago River is an important river in Myanmar. Although shorter than other rivers, it has its own river system, and people along the river rely heavily on it for their daily lives. The upper part of the watershed has changed rapidly from closed forest to open forest land in the 1990s. Since the recent degradation of the forest environment, annual flooding has become worse during the rainy season in Bago City. This paper aims at determining soil conservation prioritization of watershed based on soil loss due to erosion and morphometric analysis in the Bago Watershed by integrating remote sensing and geographic information system (GIS) techniques. In this study, soil erosion of the Bago watershed was determined using the Universal Soil Loss Equation. Such factormaps as rainfall, soil erodibility, slope length gradient, and crop management were compiled as input parameters for the modeling; and the soil loss from 26 sub-watersheds were estimated. Then, the soil erosion maps of the Bago watershed for 2005 were developed. The resulting Soil Loss Tolerance Map could be utilized in developing watershed management planning, forestry management planning, etc.     

Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed,Nan Province,Thailand

Land degradation is still a very common problem in the mountains of Asia because of inappropriate land use practice in steep topography. Many studies have been carried out to map shifting cultivation and areas susceptible to soil erosion. Mostly, estimated soil loss is taken as the basis to classify the level of soil loss susceptibility of area. Factors that influence soil erosion are: rainfall erosivity, soil erodibility, slope length and steepness, crop management and conservation practices. Thus the reliability of estimated soil loss is based on how accurately the different factors were estimated or prepared. As each and every small pixel of our earth surface is different from one area to another, the manner in which the study area was discretized into smaller homogenous sizes and how the most accurate and efficient technique were adopted to estimate the soil loss are very important. The purpose of this study is to produce erosion susceptibility maps for an area that has suffered because of shifting cultivation located in the mountainous regions of Northern Thailand. For this purpose, an integrated approach using RS and GIS-based methods is proposed. Data from the Upper Nam Wa Watershed, a mountainous area of the Northern Thailand were used. An Earth Resources Data Analysis System (ERDAS) imagine image processor has been used for the digital analysis of satellite data and topographical analysis of the contour data for deriving the land use/land cover and the topographical data of the watershed, respectively. ARCInfo and ARCView have been used for carrying out geographical data analysis. The watershed was discretized into hydrologically, topographically, and geographically homogeneous grid cells to capture the watershed heterogeneity. The soil erosion in each cell was calculated using the universal soil loss equation (USLE) by carefully determining its various parameters and classifying the watershed into different levels of soil erosion severity. Results show that during the time of this study most of the areas under shifting cultivation fell in the highest severity class of susceptibility.     

GIS-based lake sediment budget estimation taking into consideration land use change in an urbanizing catchment area

The objective of this study was to assess the lake sediment budget of land use changes using the Universal Soil Loss Equation (USLE), sediment delivery ratio (SDR), and trap efficiency (TE). The geographic information system was combined with the USLE to estimate the soil erosion of the Lake Asan watershed. Spatial data for each of the USLE factors were obtained from the land use, soil, and 1/25,000 scale digital contour maps. Landsat-5 TM images were selected for analyzing soil erosion changes due to land use changes. The sediment yield to Lake Asan was estimated using the SDR and TE. The estimated sediment budget was compared with observed data from the Lake Asan watershed between 1974 and 2003. The total estimated annual mean sediment budgets from Lake Asan in 1986, 1992, and 2000 were 0.267, 0.301, and 0.339 × 10⁶ ton, respectively, with an average of 0.302 × 10⁶ ton. The average measured sediment budget was 3.15 × 10⁶ ton year^?1. The average estimated value shows reasonable agreement with the observed sediment balance. The average estimated and measured sediment budgets contain uncertainties due to both the methods and the approach used by the observers. The simulated results indicated that soil erosion in the Lake Asan watershed increased at a rate of approximately 2 % per year from 1986 to 2000 due to land use change. This study may be useful for managers to identify reservoir rehabilitation management methods for stable irrigation water supply.     

石羊河流域平原区土壤入渗特性空间变异的研究

在野外原位入渗试验的基础上,建立了简化Philip入渗模型及其土壤转换函数,借助ARCVIEW软件生成了石羊河流域平原区土壤入渗特性参数的空间分布图,并分析了入渗特性的空间变异特征。研究表明,简化Philip入渗模型是点面转化中比较理想的模型,模型参数α的土壤转换函数可以简便地估算土壤入渗特性,两者的结合是研究大尺度土壤入渗空间变异的一种有效方法。     

RETRACTED ARTICLE: Assessment of soil erosion using RUSLE and GIS: a case study of the Maotiao River watershed,Guizhou Province,China

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.     

Effects of land use classification on landscape metrics based on remote sensing and GIS

Landscape metrics are important when studying landscape structure and function. This study investigated the sensitivity of two core sets of landscape metrics to different land use classification systems by analyzing two typical landscapes at a small watershed scale and a county scale on the Loess Plateau, China. The results showed that (1) eight landscape metrics, selected using Spearman’s correlation coefficient method and principle component analysis, adequately reflected the major landscape properties at both scales. (2) Land use classification had a significant influence on most of the selected landscape metrics, affecting their magnitude and the way in which they changed with increasing classification details; four general patterns were exhibited, i.e., increasing; decreasing; increasing and then decreasing; and relatively constant at the small watershed scale, while for the county scale, they were increasing; decreasing; increasing and then decreasing; and remaining relatively constant after initially increasing. (3) The general response patterns of most of the selected landscape metrics to the changing land use classification were similar among different years at both scales, which implied that the age of the data source used to generate the land use maps since 1982 had little effect on the landscape metrics, and that the underlying landscape pattern described by the metrics had not notably changed.     

Prioritization of subwatersheds based on quantitative morphometric analysis in lower Bhavani basin,Tamil Nadu,India using DEM and GIS techniques

Watershed prioritization is one of the most important processes in natural resource management system especially in areas of sustainable watershed development and planning. Morphometric characteristics are the viable entity to understand the hydrological behavior of the subwatershed. For prioritization of subwatershed, morphometric analysis was utilized by using the linear, areal, and relief aspects of the drainage basin. In this context, remote sensing and GIS has been proved to be an efficient tool to identify the morphological features. The Survey of India (SOI) topographical maps, satellite data IRS-LISS III, and Cartosat DEM data were utilized to understand the drainage pattern and also for prioritization of subwatershed areas. The prioritization of subwatershed has been attempted using novel and quantitative approaches based on compound parameter ranking for soil erosion. Lower compound factors were chosen as the most feasible for soil erosion. Based on the observation, eight subwatersheds with a higher degree of the slope were severely prone to soil erosion and remaining 21 subwatersheds occur in low-lying areas that can be developed as sustainable watersheds. The identified subwatershed requires immediate soil remediation and water conservation measures for efficient watershed planning and management. The proposed study might be helpful for resource planners, government agencies, private sectors, and other stake holders to take up soil conservation measures and fixation of water-harvesting structures for better decision making.     

Fertility of soil map units: An exploratory study in lower N'Gell Valley,Northern Nigeria

A detailed soil survey of the lower N'gell valley, N Nigeria had previously been completed and the results presented as major soil groups. This paper describes how, on the basis of seven measures of soil fertility, these were evaluated to produce soil fertility maps. The maps focus attention on those chemical properties most directly related to soil fertility and the management of crops in the field. They may be used as an interpretive classification in conjunction with the original soil map, and as guides to the factors which need urgent attention.     

Spatial variability of soil properties in relation to land use and topography in a typical small watershed of the black soil region,northeastern China

Soil degradation resulted from unreasonable land use and erosion has been a serious problem in the black soil region of northeastern China. This paper seeks to understand the relationships between topsoil properties and topography and land use for land management targeting at improving soil quality in this region. A total of 292 soil samples and 81 volumetric rings were taken from a typical small watershed of the region in June 2005 for examining total carbon (TC), total nitrogen (TN), soil texture (classified into gravel, sand, silt, and clay), and bulk density (ρ _b), respectively. Spatial variability of these soil properties was evaluated with classical statistics and geostatistics methods. The results of classical statistics indicated that TC, TN, sand, silt, clay content, and ρ _b were moderate variables while gravel had great variability. Soil properties were mainly correlated to slope position, elevation and land types. Geostatistical analyses showed that the spatial autocorrelation for TC, TN, and silt was weak, strong for clay and moderate for and ρ _b sand, respectively. The spatial variations of soil properties are affected comprehensively by topographic factors, land use, erosion, and erosion control in this watershed. Past erosion, however, is the most important component to induce change of soil properties. In this small watershed, current soil and water conservation measures play an important role in controlling soil loss. But the restoration of soil properties was unsatisfactory. Comparing with untilled soil of this region, TC, TN, silt content are excessively low; whereas ρ _b, sand and clay content are excessively high; gravel appears at most sampling locations. It is necessary for improving soil properties to protect forest and grassland and change cultivation system of farmlands.     

流域尺度的岩溶区土壤pH值空间变异研究——以云南小江流域为例

以云南小江流域为例,利用地统计学与GIS相结合研究流域尺度上岩溶区土壤pH的空间变异.结果表明,流域土壤pH平均值为6.74,变异系数为9.9%;半方差函数分析表明,流域土壤pH的空间变异符合指数模型,其变程为12 km,反映了流域尺度上岩溶区土壤的连续性较差.纯块金方差与基台值的比值为50%,土壤pH具有中等的空问相关性,由空问自相关部分引起的空问变异性的程度较大;全局趋势分析和Kriging插值分析结果表明,流域土壤pH整体明显地呈条带状分布,但在流域中西部地区为斑块状分布,空间变异明显,土壤pH值在东西、南北方向上呈明显的倒U形;地质背景的异质性和地形起伏以及复杂的土地利用方式是土壤pH空间变异较大的主要因素.      

Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

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.     

Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

Soil salinity has been known to be problematic to land productivity and environment in the lower Yellow River Delta due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity has gained increasing importance for the demand of management and sustainable utilization of arable land in this area. Apparent electrical conductivity, as measured by electromagnetic induction instrument in a fairly quick manner, has succeeded in mapping soil salinity and many other soil physical and chemical properties from field to regional scales. This was done based on the correlation that existed between apparent electrical conductivity and many other soil properties. In this paper, four spatial prediction methods, i.e., local polynomial, inverse distance weighed, ordinary kriging and universal kriging, were employed to estimate field-scale apparent electrical conductivity with the aid of an electromagnetic induction instrument (type EM38). The spatial patterns estimated by the four methods using EM38 survey datasets of various sample sizes were compared with those generated by each method using the entire sample size. Spatial similarity was evaluated using difference index (DI) between the maps created using various sample sizes (i.e., target maps) and the maps generated with the entire sample size (i.e., the reference map). The results indicated that universal kriging had the best performance owing to the inclusion of residuals and spatial detrending in the kriging system. DI showed that spatial similarity between the target and reference maps of apparent electrical conductivity decreased with the reduction in sample size for each prediction method. Under the same reduction in sample size, the method retaining the most spatial similarity was universal kriging, followed by ordinary kriging, inverse distance weighed, and local polynomial. Approximately, 70 % of total survey data essentially met the need for retaining 90 % details of the reference map for universal kriging and ordinary kriging methods. This conclusion was that OK and UK were two most appropriate methods for spatial estimation of apparent electrical conductivity as they were robust with the reduction in sample size.     

Approximate relationship between frequency-dependent skin depth resolved from geoelectromagnetic pedotransfer function and depth of investigation resolved from geoelectrical measurements: A case study of coastal formation,southern Nigeria

The task involved in the interpretation of Vertical Electrical Sounding (VES) data is how to get unique results in the absence/limited number of borehole information, which is usually limited to information on the spot. Geological and geochemical mapping of electrical properties are usually limited to direct observations on the surface and therefore, conclusions and extrapolations that can be drawn about the system electrical characteristics and possible underlying structures may be masked as geology changes with positions. The electrical resistivity study pedotransfer functions (PTFs) have been linked with the electromagnetic (EM) resolved PTFs at chosen frequencies of skin/penetration depth corresponding to the VES resolved investigation depth in order to determine the local geological attributes of hydrogeological repository in the coastal formation dominated with fine sand. The illustrative application of effective skin depth depicts that effective skin depth has direct relation with the EM response of the local source over the layered earth and thus, can be linked to the direct current earth response functions as an aid for estimating the optimum depth and electrical parameters through comparative analysis. Though the VES and EM resolved depths of investigation at appropriate effective and theoretical frequencies have wide gaps, diagnostic relations characterising the subsurface depth of interest have been established. The determining factors of skin effect have been found to include frequency/period, resistivity/conductivity, absorption/attenuation coefficient and energy loss factor. The novel diagnostic relations and their corresponding constants between 1-D resistivity data and EM skin depth are robust PTFs necessary for checking the accuracy associated with the non-unique interpretations that characterise the 1-D resistivity data, mostly when lithostratigraphic data are not available.