This study has tried to develop the modified DRASTIC Model by supplying the parameters, such as structural lineament density and landuse, into conventional DRASTIC model, and to predict the potential of groundwater contamination using GIS in Whanam 2 Area, Gyeonggi Province, Korea. Since the aquifers in Korea is generally through the joints of rock-mass in hydrogeological environment, lineament density affects to the behavior of groundwater and contaminated plumes directly, and land-use reflect the effect of point or non-point source of contamination indirectly. For the statistical analysis, lattice layers of each parameter were generated, and then level of confidence was assessed by analyzing each correlation coefficient. Composite contamination map was achieved as a final result by comparing modified DRASTIC potential and the amount of generation load of several contaminant sources logically. The result could suggest the predictability of the area of contamination potential on the respects of hydrogeological aspect and water quality.
Soil erosion is influenced from a variety of factors such as rainfall distribution, soil type, land use, etc. This paper is aimed at analyzing the soil erosion hazard zone in cropland. RUSLE was used for an analysis of soil erosion amount, and for the spatial data of basin, soil erosion amount was calculated by extracting the respect topography space related factors of RUSLE using DEM, Landuse, Soil map as base map. This paper is targeting at the watershed of Gyeongan stream in Gyeonggi-do The result of an analysis of soil erosion amount showed that soil erosion occurred in the order of crop field(1210) planting area, orchard(1220), non-adjusted paddy fields(1120), and adjusted paddy fields(1110), and also the average soil erosion in these planting areas has the most amount in crop field planting area. As a result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes using the result of that result of analysis on the amount of soil erosion, in case of Class 5 in which the hazard of soil erosion is the highest, approximately 72.5ha that corresponds to 2.4% of the total farm land was decided as erosion hazard zone. For this erosion hazard zone, it was analyzed that dry field crop planting area was 72.4ha and orchard was 0.1ha, and Class 5 hazard zone did not appear in other farming areas. Also, it showed that Class II(1~50ton/ha/yr) area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state. According to the result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes, the Class V has the highest soil erosion hazard, approximately 72.5ha that corresponds to 2.4% of the total farm land was estimated as an erosion hazard zone. This erosion hazard shows 72.4ha in dry field crop planting area, 0.1ha in an orchard, but the highest hazard zone, the Class V was not shown in other farming areas. Also, it showed that Class II area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state.
Parent material, climate, topography, biological factors, and time are considered five soil forming factors. This study was conducted to elucidate the effects of several environment factors on soil distribution using quantitative analysis method, called soil series estimation algorithm in the soils of Jeju Island. We selected environment factors including mean temperature, annual precipitation, surface geology, altitude, slope, aspect, altitude difference within 1 $km^2$ area, topographic wetness index, distance from the shore, distance from the mountain peak, and landuse for a quantitative analysis. We analyzed the ranges of environment factors for each soil series and calculated probabilities of possible-soil series for certain locations using estimation algorithm. The algorithm can predicted exact soil series on the soil map with correctness of 33% on $1^{st}$ ranking, 62% within $2^{nd}$ ranking, 74% within $5^{th}$ ranking after estimating using randomly extracted environment factors. In predicted soil map, soil sequences of Entisols-Alfisols-Andisols on northern area and Alfisols-Ultisols-Andisols on western area can be suggested along increasing altitude. More modeling studies will be needed for the genesis process of soils in Jeju Island.
Proceedings of the Korea Water Resources Association Conference
/
2008.05a
/
pp.194-198
/
2008
Significant soil erosion and water quality degradation issues are occurring at highland agricultural areas of Kangwon province because of agronomic and topographical specialities of the region. Thus spatial and temporal modeling techniques are often utilized to analyze soil erosion and sediment behaviors at watershed scale. The Soil and Water Assessment Tool (SWAT) model is one of the watershed scale models that have been widely used for these ends in Korea. In most cases, the SWAT users tend to use the readily available input dataset, such as the Ministry of Environment (MOE) land cover data ignoring temporal and spatial changes in land cover. Spatial and temporal resolutions of the MOE land cover data are not good enough to reflect field condition for accurate assesment of soil erosion and sediment behaviors. Especially accelerated soil erosion is occurring from agricultural fields, which is sometimes not possible to identify with low-resolution MOD land cover data. Thus new land cover data is prepared with cadastral map and high spatial resolution images of the Doam-dam watershed. The SWAT model was calibrated and validated with this land cover data. The EI values were 0.79 and 0.85 for streamflow calibration and validation, respectively. The EI were 0.79 and 0.86 for sediment calibration and validation, respectively. These EI values were greater than those with MOE land cover data. With newly prepared land cover dataset for the Doam-dam watershed, the SWAT model better predicts hydrologic and sediment behaviors. The number of HRUs with new land cover data increased by 70.2% compared with that with the MOE land cover, indicating better representation of small-sized agricultural field boundaries. The SWAT estimated annual average sediment yield with the MOE land cover data was 61.8 ton/ha/year for the Doam-dam watershed, while 36.2 ton/ha/year (70.7% difference) of annual sediment yield with new land cover data. Especially the most significant difference in estimated sediment yield was 548.0% for the subwatershed #2 (165.9 ton/ha/year with the MOE land cover data and 25.6 ton/ha/year with new land cover data developed in this study). The results obtained in this study implies that the use of MOE land cover data in SWAT sediment simulation for the Doam-dam watershed could results in 70.7% differences in overall sediment estimation and incorrect identification of sediment hot spot areas (such as subwatershed #2) for effective sediment management. Therefore it is recommended that one needs to carefully validate land cover for the study watershed for accurate hydrologic and sediment simulation with the SWAT model.
Journal of the Korean Association of Geographic Information Studies
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v.6
no.1
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pp.119-131
/
2003
Rainfall-runoff process is under the control of hydrologic parameters having temporal and spatial variety. Accordingly, it is difficult to efficiently deal them since many parameters and various information are required to perform hydrologic simulation. So the purposes of this study is to estimate the runoff volume by frequency using GIS techniques and NRCS method. The analysis of frequency rainfall is analyzed using FARD 2002 program and the result of goodness of fit test show that Log-pearson type III is suitable distribute type for the applied area. TOPAZ program used for the analysis of DEM data examining into geological characteristic. NRCS curve numbers estimated using landuse map and soil map for the estimation of effective rain fall in the basin. The storm Type II and Type III were used as the type for the application of NRCS. The result of application show that the runoff volumes above 80 years frequency in return period have similar patterns regardless of Type II and Type III. In addition, the results of comparison with runoff volumes by frequency in the report of river improvement master plan show that it have similar volumes as the relative errors for them of 80, 100 years frequency are each 7.65%, 5.33%.
Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.
In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.
Journal of the Korean Association of Geographic Information Studies
/
v.16
no.3
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pp.54-67
/
2013
This study deals with the characteristics of view landscape in modern Daegu city which were analysed employing geographic information system(GIS). The view landscape analysis was performed by using GIS that enables to overlap land use map with the map of range of visibility, and the 3-D simulation. The results are as follows; First of all, the ratio of forest is enormously high in the range of visibility. The distribution of landscape components allows the dwellers to obtain a clear view towards forests from anywhere. The landscape components include west eroded lowlands, east open rolling lands, east eroded lowlands, and high mountain areas: Apsan(Mt.) in the south; Waryoungsan(Mt.) in the west; and Hamjisan(Mt.) and Hakbong(Mt.) in the north. On the tops of those, people are able to secure a clear vision from the viewpoint towards the surrounding mountains because of the rural areas continuing from the viewpoint to the mountains. A continuous view landscape has been formed by these natural environmental factors. Finally, there are multiple view targets with relatively high altitude that are covered with forests in the space between the urban area and the outer mountains that are higher than the view targets, which provides a scenery of mountains overlapped by higher mountains.
This study aims to classify the biotope types based on the vegetation community in built-up areas by different land use and to map the plant communities. By classifying biotopes according to a taxonomic system, the characteristics of a biological community can be well-represented. The biotope classification indexes for the target area include human behavioral factors such as land use intensity, land-use patterns and land-cover types. The type classification was divided into four hierarchic ranks starting with Biotope Class, next by Biotope Group and Biotope Type and lastly by Biotope Sub-Type. The Biotope Class was first divided into two areas: the areas improved by humans and the areas unimproved by humans. The improved areas were again divided into permeable and non-permeable regions on the Biotope Group level. In the Biotope Type level, permeable paving areas were divided into areas with wide gap pavers and those with narrow gap pavers. The differential species of each biotope type are Lindera glauca, Conyza canadensis, Mazus pumilus, Vicia tetrasperma, Crepidiastrum sonchifolium, Zoysis japonica, Potentilla supina and Festuca arundinacea. The results of this study suggest that the biotope classification methodology, using a subjective phytosociological approach, is a useful and valuable tool and the results also suggest the possibility of applying more objective and scientific methods in mapping and classifying various environments.
Recently, agricultural lands decrease sharply, which was caused by urbanization, land consolidation, road construction, and innovation city construction, etc. In particular, Goyang, Chenan and Wonju city were had severe land use change. Therefore, we analyzed changes of land use, soil properties, and soil information in order to provide the basic soil information and soil management practice in these cities. The results are summarized as follows. The area of crop cultivated land in Korea (2011) was reduced to 17.3ha compared to ones from the previous year (2009). The paddy field decreased by 24.2 ha but, upland field increased by 7.0 ha. The reasons for the reduction of the paddy field were converting paddy field to upland (20.7 ha) > public facilities (3.2) ${\geq}$ building (3.2) > idle land (1.3) > and others (0.9). Other reasons for reduction in the upland field were switching upland to paddy field, (20.7 ha) > land developed (4.5) > and restoration (0.3) respectively. The main reason of converting paddy field to upland was changing from rice to more profitable upland or greenhouse crops. The cropland area (paddy fields, upland, orchard) of Goyang, Cheonan, and Wonju city were reduced to 1,466 ha, 9,708 ha and 6,980 ha respectively. The ratio of cropland area in each city was reduced by 45~25% dramatically compared to upland soil survey project in Korea (1995~1999). These data were compared with MiFAFF statistics data to use for land use cover map of Ministry of environment. But they were differences significantly. Therefore, intensive investigation should be advised throughout the utilization plan. The paddy fields located in small valley in Wonju city were changed into upland or orchard. The drainage classes of soil have been deteriorated because the flows of water were intercepted by road construction and other disturbance to water flows. In particular, paddy fields have been changed to not only upland, orchard, greenhouse cultivation but also to fallow and soil dressing on paddy in Wonju city. The soil suitability classes of paddy field in Wonju innovation city were the 3rd grade for 70.8% of the area and the 4th grade for 29.2%. The soil suitability classes of upland was the 4th grade for 88.7% of the area. Fortunately, good soil suitability classes were not belong to innovation city in Wonju. So, the good farm land should be conserved and revise the related law.
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