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.     

Assessment of sand encroachment in Kuwait using GIS

Assessment of sand encroachment in Kuwait using Geographical Information System (GIS) technology has been formulated as a Multi-Criteria Decision Making problem. The Delphi method and Analytical Hierarchy Process were adopted as evaluating techniques, in which experts’ judgments were analyzed for objectively estimating and weighting control factors. Seven triggering factors, depicted in the form of maps, were identified and ordered according to their priority. These factors are (1) wind energy; (2) surface sediment; (3) vegetation density; (4) land use; (5) drainage density; (6) topographic change and (7) vegetation type. The factor maps were digitized, converted to raster data and overlaid to determine their possible spatial relationships. Applying a susceptibility model, a map of sand encroachment susceptibility in Kuwait was developed. The map showed that the areas of very high and high sand encroachment susceptibility are located within the main corridor of sand pathway that coincides with the northwesterly dominant wind direction.     

Evaluation of watershed soil erosion hazard using combination weight and GIS: a case study from eroded soil in Southern China

Natural Hazards - On September 20, 2017, Maria, the eleventh-most intense Atlantic hurricane ever recorded, made landfall at 6:15 am local time, the second category 5 hurricane to strike the island...     

Estimation of spatial patterns of soil erosion using remote sensing and GIS: a case study of Indravati catchment

Soil erosion is a serious environmental problem in Indravati catchment. It carries the highest amount of sediments compared with other catchments in India. This catchment spreading an area of 41,285 km² is drained by river Indravati, which is one of the northern tributaries of the river Godavari in its lower reach. In the present study, USLE is used to estimate potential soil erosion from river Indravati catchment. Both magnitude and spatial distribution of potential soil erosion in the catchment is determined. The derived soil loss map from USLE model is classified into six categories ranging from slight to very severe risk depending on the calculated soil erosion amount. The soil erosion map is linked to elevation and slope maps to identify the area for conservation practice in order to reduce the soil loss. From the model output predictions, it is found that average erosion rate predicted is 18.00 tons/ha/year and sediment yield at the out let of the catchment is 22.30 Million tons per annum. The predicted sediment yield verified with the observed data.     

Chemical evolution of an inland sabkha: a case study from Sabkha Matti,Saudi Arabia

Hydrogeology Journal - Sabkha Matti is a potential discharge point for regional groundwater systems in the Rub’ al Khali topographic basin of Saudi Arabia. The hydrogeochemical evolution of...     

Spatial modeling of groundwater quality based on using Schoeller diagram in GIS base: a case study of Khorramabad,Iran

Regulating the water supply for a specified district needs comprehensive quality information about the nearest aquifer. There are many methods to investigate the water quality, but in most cases, they involve time series study and do not consider space dimension. The application of advanced qualitative assessments such as geographical information systems (GIS) could be a reasonable choice. In addition, the classic Schoeller diagram (CSD) is one of the diverse drinking water assessments in which aquifer quality is distinguished according to major ions concentrations. However, the results of this diagram are limited to one point, and there is no possibility of qualitative classification of the surrounding area. Because of this, in this investigation, a new procedure, called the Schoeller-GIS (S-GIS) approach, is presented in order to apply CSD onto a district through GIS tools. For this project, the quality information of 105 wells in the study area (near Khorramabad, Iran) has been collected, and a quality assessment of the aquifer has been conducted based on both classic and novel approaches. Results indicated that, according to the CSD method, all qualitative parameters of the aquifer except Ca and Mg were located within the Good range, whereas the results of S-GIS approach categorized the study area into Good (55%), Permissible (36%), and Moderately suitable (8%). This indicates that the latest method may be more accurate by about 30% which could lead to more efficient management of water resources.     

Groundwater control for construction purposes: a case study from Kuwait

One of the major rehabilitation projects in Kuwait during the 1990s was improvement of the wastewater treatment plant at Jahra, a town 30 km north of Kuwait City. The project incorporates construction of a pumping station to collect the sewage via a network of sewer lines throughout the city. Groundwater occurs 4 m below the ground surface and construction specifications required lowering the groundwater table by 16 m to the foundation grade, 20 m below the ground surface. Open excavation with four stage well point system of dewatering was selected to ensure dry foundation conditions; but the system failed to lower the groundwater table down to the desired depth. Review of site investigation records and recalculation of field hydraulic conductivity resulted in design changes, augmenting the well point system with a number of deep wells and sumps to lower the water table down to the foundation grade. The paper discusses subsurface conditions and presents an analysis of the cause of failure of the well point system. The actual hydraulic conductivity proved to be several folds higher than the calculated one that was determined using Hazen’s formula. It was found that use of Hazen’s formula led to serious underestimation of field hydraulic conductivity. It is advised not to use such formulas without thorough investigation and proper interpretation of borehole data.     

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.     

Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh

This work developed models to identify optimal spatial distribution of emergency evacuation centers(EECs) such as schools, colleges, hospitals, and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh.The use of location-allocation models(LAMs) for evacuation in regard to flood victims is essential to minimize disaster risk.In the first step, flood susceptibility maps were developed using machine learning models(MLMs), including: Levenberg–Marquardt back propagation(LM-BP) neural network and decision trees(DT) and multi-criteria decision making(MCDM) method.Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic(AUROC) curve.Mathematical approaches in a geographic information system(GIS) for four well-known LAM problems affecting emergency rescue time are proposed: maximal covering location problem(MCLP), the maximize attendance(MA), p-median problem(PMP), and the location set covering problem(LSCP).The results showed that existing EECs were not optimally distributed, and that some areas were not adequately served by EECs(i.e., not all demand points could be reached within a 60-min travel time).We concluded that the proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation.     

Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed,northwest Turkey

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

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.     

基于包气带剖面研究黄土地下水补给

正地下水补给是含水层从外界获得水量的过程,是地下水系统形成演化的基础和水文地质学科研究的核心问题(庞忠和等,2018)。在黄土高原地区,地下水是生产生活的重要水源,查明黄土高原地下水补给来源和补给量对合理开发利用地下水资源意义重大。前人对此开展了较多研究并取得丰富成果,但由于黄土高原包气带厚度较大,地下水补给受多因素影响,其时空变化较为复杂(赵文智等,2017)。本次选择小型黄土残塬,通过两组包气带剖     

Landslide susceptibility mapping using GIS and weighted overlay method: a case study from North Jordan

Road instability along the Jerash–Amman highway was assessed using the weighted overlay method in Geographic Information System environment. The landslide susceptibility map was developed from nine contributing parameters. The map of landslide susceptibility was classified into five zones: very low (very stable), low (stable), moderate (moderately stable), high (unstable), and very high (highly unstable). The very high susceptibility and high susceptibility zones covered 15.14% and 31.81% of the study area, respectively. The main factors that made most parts of study area prone to landslides include excessive drainage channels, road cuts, and unfavorable rock strata such as marl and friable sandstone intercalated with clay and highly fractured limestone. Fracture zones are a major player in land instability. The moderate and high susceptibility zones are the most common in urban (e.g., Salhoub and Gaza camp) and agricultural areas. About 34% of the urban areas and 28.82% of the agricultural areas are characterized by the high susceptibility zone. Twenty percent of the Jerash–Amman highway length and 58% of the overall highway length are located in the very high susceptibility zone. The landslide susceptibility map was validated by the recorded landslides. More than 80 of the inventoried landslides are in unstable zones, which indicate that the selected causative factors are relevant and the model performs properly.     

Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.     

GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach: a case study in Macau

Rainfall-runoff models play an important role in urban water resource management. The storm water management model (SWMM) developed by the US Environmental Protection Agency (EPA) is a widely used dynamic rainfall-runoff model for analyzing quantity and quality problems associated with urban drainage systems. In an ideal situation, the SWMM model would be designed and analyzed using a collection of catchment modeling systems. Traditionally, catchment discretization for rainfall-runoff modeling is performed manually on watershed maps, a time-consuming job with less-than-accurate results. An alternative approach to catchment discretization based on geographic information system (GIS) is proposed in this paper. The automatic discretization approach was successfully applied to rainfall-runoff modeling in Macau using a SWMM model. The results showed that the proposed approach outperformed the conventional catchment-discretization method in terms of producing meaningful parameters and avoiding most of the tedious preliminary tasks.

     

Predictive modeling for preventive Archaeology: overview and case study

The use of GIS and Spatial Analysis for predictive models is an important topic in preventive archaeology. Both of these tools play an important role in the Support Decision System (SDS) for archaeological research and for providing information useful to reduce archaeological risk. Over the years, a number of predictive models in the GIS environment have been developed and proposed. The existing models substantially differ from each other in methodological approaches and parameters used for performing the analysis. Until now, only few works consider spatial autocorrelation, which can provide more effective results. This paper provides a brief review of the existing predictive models, and then proposes a new methodological approach, applied to the neolithic sites in the Apulian Tavoliere (Southern Italy), that combines traditional techniques with methods that allow us to include spatial autocorrelation analysis to take into account the spatial relationships among the diverse sites.     

Climate change and soil moisture: A case study

An important aspect of regional climate change is alteration in soil moisture availability. The EPIC (Erosion Productivity Impact Calculator — Sharpley and Williams, 1990) model is applied to estimate soil hydrology consequences in a representative subcatchment (24 km²) of Lake Balaton, Hungary. The study is based on the soil hydrology parameters of the EPIC model, which exhibit a relatively fast response to the climate variations. To specify the regional climate scenario for Hungary in semi-annual time resolution, a statistical approach computing regression between regional and hemispherical mean climate characteristics is employed. The semi-annual scenarios are further refined by applying the principle of geographical analogy. This difference corresponds to a 0.5 °K increase of the hemispherical mean temperature, i.e. the climate after 20–40 years of monotonous warming (IPCC, 1996a). Diurnal statistical parameters which correspond to present (base-line) and future climates of the investigated site are finally introduced into the built-in weather generator of the EPIC model. A supplementary factor of variation in soil hydrology is crop-rotation, responsible for half of the standard deviations, as compared to the hypothetical maize monoculture experiment. Climate generated differences in transpiration, soil moisture content, and crop-available water during water stress days. In connection with the assumed warming, there is a general trend towards drought during the vegetation growing period.     

Soil erosion modeling of a Himalayan watershed using RS and GIS

Employing the remote sensing (RS) and geographical information system (GIS), an assessment of sediment yield from Dikrong river basin of Arunachal Pradesh (India) has been presented in this paper. For prediction of soil erosion, the Morgan-Morgan and Finney (MMF) model and the universal soil loss equation (USLE) have been utilized at a spatial grid scale of 100 m × 100 m, an operational unit. The average annual soil loss from the Dikrong river basin is estimated as 75.66 and 57.06 t ha⁻¹ year⁻¹ using MMF and USLE models, respectively. The watershed area falling under the identified very high, severe, and very severe zones of soil erosion need immediate attention for soil conservation.