KR20130020225A - Systems for analyzing natural succession in riparian vegetation - Google Patents

Abstract

PURPOSE: A system for analyzing waterfront vegetation succession is provided to quantitatively analyze a vegetation change by developing/using a simulation-based waterfront vegetation succession prediction model stereotyping vegetation succession steps. CONSTITUTION: A start module inputs a starting condition and a dynamic module derives potential vegetation through a retrogression and succession process. The process predicts vegetation succession formed of a visual module indicating the derived waterfront vegetation. The starting condition is formed of information about regions, an average water level, and topography. The retrogression and succession process is analyzed through shear stress, the regions, the average water level, the topography, and flooding duration. [Reference numerals] (AA) Start module; (BB) Region(Zone); (CC,II) Average water level; (DD,JJ) Topography; (EE) Start condition; (FF) Dynamic module; (GG) Shear stress; (HH) Region; (KK) Duration of flooding; (LL) Succession; (MM) Retrogression; (NN) Input; (OO) Processor; (PP) Output; (QQ) Potential vegetation(n year); (RR) Potential vegetation(n+1 year); (SS) Output view module; (TT) Waterfront vegetation

Description

Hydrogen-proliferation analysis system {Systems for Analyzing Natural Succession in Riparian Vegetation}

The present invention relates to a hydrophysical growth analysis system and a method using the same.

The study of vegetation entering and growing due to artificial disturbances in rivers, specifically the processes of recruitment, establishment, succession, retrogression, etc. Started. Vegetation invading rivers increases flow resistance, which reduces flow velocity, increases flood level (or depth), and has a significant impact on river topography changes. The effects of vegetation on the flow are being studied through field surveys and hydraulic model experiments, and active researches are also being conducted on numerical modeling techniques to predict them. However, the impacts or interactions of stream flows on vegetation remain at the beginning.

The phenomenon of white sand disappearing and covered with grass and trees is gradually increasing in the rivers downstream of Korean dams. To analyze these problems and find possible solutions, we can understand and reproduce the causal process that white rivers turn into green rivers. It is important to develop a model that exists. This model of predicting or evaluating the interaction between stream flow and vegetation is commonly called riparian vegetation model or floodplain vegetation model.

The study of the floodplain vegetation model began in the late 1990s and attempted to simulate the vegetation migration of the vegetation in the floodplain or the keys mainly based on field surveys and test results in rivers. As an early study of this model, Mahoney and Rood (1998) proposed a Recruitment Box Model (RBM) that simulates the migration and mobilization of North American poplars in floodplains. Benjankar (2006) later developed the Dynamic Floodplain Vegetation Model (DFVM), a conceptual numerical model of hydraulic processes and ecological change.

Conventional models on the settling, growth or transition of stream vegetation have simulated the temporal evolution of trees, seedling settling, biomass changes, physiological responses, or changes in vegetation types. However, these simulation results are used for academic purposes and are difficult to apply for river management purposes.

Underground design using numerical model is widely used, but no appropriate solution is proposed in terms of artificial hydraulic environment change and biological management. In particular, the greening of rivers provides aesthetic stability but can have a significant impact on river flows, requiring management solutions. Thus, modeling integrating quantitative ecology, computer simulation, and geographic information systems is essential for long-term research in macroecology, which studies the equilibrium between landscape biodiversity and environmental changes caused by human activities. Such systematic research is still incomplete.

Numerous papers and patent documents are referenced and cited throughout this specification. The disclosures of the cited papers and patent documents are incorporated herein by reference in their entirety to better understand the state of the art to which the present invention pertains and the content of the present invention.

The present inventors have tried to develop a vegetation prediction model for river management. As a result, the present inventors developed a computer simulation-based vegetation transition prediction model that typifies the vegetation transition stage, and uses it to quantitatively analyze vegetation changes, identify characteristics of dominant species, and actually predict / analyze vegetation transition. The present invention has been completed.

It is an object of the present invention to provide a vegetation transition model for river management.

Another object of the present invention to provide a vegetation stream prediction method for river management.

Other objects and advantages of the present invention will become more apparent from the following detailed description of the invention, claims and drawings.

According to one aspect of the invention, the invention provides a recording medium storing a computer program for predicting vegetation transition for river management, comprising a program capable of executing the following process: (a) inputting a start condition; A start module; (b) a dynamic module that derives potential vegetation through a succession and retrogression process; And (c) a vegetation transition consisting of a visualization module representing riparian vegetation derived in process (b).

According to another aspect of the invention, the present invention provides a vegetation transition prediction method for river management comprising the step of analyzing the above-described vegetation transition prediction model.

The present inventors have tried to develop a vegetation prediction model for river management. As a result, the present inventors developed a computer simulation-based vegetation transition prediction model that typifies the vegetation transition stage, and quantitatively analyzes vegetation changes using the vegetation transition, and identifies the characteristics of the dominant species to substantially predict / analyze the vegetation transition. .

The present invention confirmed the validity of the model by developing a vegetation stream suitable for rivers in Korea and applying it to the Nakdong River (eg, between Songyacheon and Micheon Confluence) located in Gyepyeong-ri, Pungsan-eup, Andong-si, Gyeongsangbuk-do.

According to the model of the present invention, the present invention is composed of three modules, namely, a start module, a dynamic module and an output view module. More specifically, the start module examines a potential natural vegetation community and creates a vegetation map according to the surface level, the topography, and the space (vegetable settlement area) information. Thereafter, the shear stress and the submerged duration are inputted to the prepared vegetation to determine whether each vegetation transitions or regresses. Finally, the visualization module displays the determined transition or degeneration of the vegetation as color-coded vegetation.

According to a preferred embodiment of the present invention, the start module is implemented by inputting a start condition, and the start condition includes area, average water level, and topographic map information. The region, average water level, and topographic map information can be obtained in two ways. First, a process of correcting image information of a target section obtained based on aerial photographs provided by the National Geographic Information Institute and the Korea Forest Research Institute is carried out. Secondly, field survey is conducted to verify the validity of the acquired image data. According to a preferred embodiment of the present invention, the image data obtained according to the two methods described above was confirmed the precision through the following process (see: Figures 2 and 3). In the present invention, the coordinate system is unified to the world geodetic system and utilizes a georeferencing function. At this time, the same position is identified between the aerial photograph to which the correct coordinate value is input and the aerial photograph to input the coordinate value, and the coordinate values are assigned by overlapping the locations. The preprocessed image is verified by checking the accuracy of the boundary of roads, banks, rice fields, and fields using swipe function to look down the image left and right and up and down in the state of overlapping images, and verify the accuracy by using the field GPS survey results.

After that, the river topography is classified based on field surveys (eg, annual tree surveys of major trees) for the preprocessed image data. In other words, the main vegetation and terrain acquired from the site are investigated and mapped, and the locations of trees and herbaceous plants are identified based on this.

According to a preferred embodiment of the present invention, the transition and degeneration process is analyzed through shear stress, area, mean water level, topographic map and flood duration.

According to a preferred embodiment of the present invention, the transition process is divided into a start step, carve step, reed step, shrub step and arbor step. More specifically, each step in the present invention is configured as follows. The start-up phase consists of open four weeks where frequent flooding disturbances occur each year, with a duration of less than one year. The pioneering phase is predominantly large, with tree seedlings introduced and lasting less than two years. The reed phase is predominantly mixed with willow tree or moon root grass. Moon root grass forms a net colony or willow shrubs less than 6 years old frequently live and lasts less than 7 years. The shrub stage is dominated by willows due to disturbances and competitions such as sedimentation. Sunbursts and kingbirds less than 20 years old live together or cluster alone and last for less than 20 years. The arbor phase is dominated by the kings who have the upper hand in competition with the suns. About 90-year-old willows have been found in the current survey site and last for over 100 years.

According to a preferred embodiment of the present invention, the transition stage of the present invention can be divided into the start stage, the pioneering stage, the reed stage, the shrub stage and the arbor stage by various parameters, more preferably the smooth water level, the shear stress and the sustained year. And most preferably according to the shear stress. According to the present invention, through the image data and field survey, the ground water level of the start step, pioneer step, reed step, shrub step and arbor step of the present invention is <0.0, <0.1, 0.1-0.3, 0.3-0.7 and 0.7- 1.2, and durations are 1 year, 2-3 years, 3-5 years, 3-15 years, and 11-100 years, respectively. Most preferably, the shear stresses of the start, pioneer, reed, shrub and arbor stages of the present invention are 1, 5, 10, 15 and 15 (N / m 2), respectively.

According to a preferred embodiment of the present invention, the regression process occurs frequently in the pioneering stage.

According to a preferred embodiment of the present invention, the basic algorithm of the vegetation transition prediction model of the present invention is shown schematically in FIG. 14, more preferably, by inputting the shear stress calculated by the maximum flood amount for each year, Mock up the process. At this time, by repeatedly performing the process of inputting the year shear stress to derive a more objective simulation results. The final result is then presented as a visualization module and verified by calculating its area.

The predicted / simulated results according to the model and method of the present invention were relatively in agreement with the field survey results (see FIGS. 20 and 21). Taken together, as a result of comparing / verifying simulation results and field survey results obtained using the prediction model of the present invention, it can be seen that the prediction model and the method of the present invention are very suitable for simulating and predicting changes in rivers. .

The features and advantages of the present invention are summarized as follows:

(a) The present invention relates to a vegetation transition predictive model in which the vegetation transition stage is categorized and its use.

(b) The model of the present invention is composed of three modules, that is, a start module, a dynamic module and a visualization module, and the transition stage is divided into five stages (starting stage, pioneering stage, reed stage, shrub stage and arbor stage).

(c) The predictive model of the present invention and the predictive method using the same can be performed very simply and efficiently through quantitative analysis of vegetation changes and computer simulation using a geographic information system.

(d) Therefore, the prediction model of the present invention and the prediction method using the same are useful for analyzing the vegetation acceleration, and are very suitable for simulating and predicting the change of rivers.

1 is a map schematically showing the location of a river to be investigated.
2 is a diagram schematically showing a preprocessing process for image data of a river to be investigated.
3 is a result of dividing the transition stages of the image data of the river subject to the pre-processing process.
4 is a diagram illustrating a process of reading image data and creating a numerical model using a real-time precision GPS survey service provided by the National Geographic Information Institute.
5 is a result showing a numerical elevation model of the topographic map of Andong demonstration zone using ArcGIS.
FIG. 6 shows a three-dimensional vegetation diagram in which a vegetation distribution is created by using a GPS instrument and the position of the vegetation map is corrected based on the aerial photograph.
7 is a result of preparing the vegetation distribution of the year by image analysis.
8 is a graph showing changes in vegetation distribution for a given year by image analysis.
9 is a result showing the vegetation degree (9a) and age distribution map (9b) of the Nakdong River Danho Bridge survey group.
10 is a result schematically showing the classification of vegetation transition stage and its characteristics.
11 is a diagram showing the structure of a predictive model of the present invention.
Figure 12 shows the hydrological characteristics created based on the flow rate data analysis of the Andong water table, showing the change according to the construction of the Andong dam and Imha dam. FIG. 12A is a graph showing the hydrologic curve, and FIG. 12B is a graph showing the aging change of the maximum amount of flood per year.
FIG. 13 is a result obtained by converting the calculated shear stress to a raster file, with the annual maximum flood for 1988 (FIG. 13A), 1991 (FIG. 13B), 1995 (FIG. 13A) and 2007 (FIG. 13A). Shear stress for each year was calculated.
14 is a schematic diagram showing the modeling process of the vegetation transition model.
15 shows results of model creation using GIS.
16 is a result showing a process of visualizing the results obtained from the vegetation stream predictive model.
17 is a graph showing simulation results from 1988 to 2007.
18 is a result showing the start condition (1971) of the vegetation transition model.
19 is a result showing a simulation result (2005) obtained through the vegetation transition model.
20 is a result of comparing the field survey results and simulation results.
Figure 21a is the result of analyzing the aerial photo of the year, Figure 21b is a result showing the simulation result.

Hereinafter, the present invention will be described in more detail with reference to Examples. It is to be understood by those skilled in the art that these embodiments are only for describing the present invention in more detail and that the scope of the present invention is not limited by these embodiments in accordance with the gist of the present invention .

Example

1. River downstream of the dam Vegetation  Change analysis

1.1 Historical aerial photograph analysis

1.1.1. Acquisition of Video Material

If the vegetation survey data does not exist in the past, the vegetation survey before the dam construction can be quantitatively analyzed using aerial photographs provided by the National Geographic Information Institute and the Korea Forest Research Institute. In addition, satellite image information such as IKONOS and KOMSAT exist, but the operating period of the satellite is limited in recent years. The river to be investigated of the present invention is a Nakdong river located in Gyepyeong-ri, Pungsan-eup, Andong-si, Gyeongsangbuk-do, and is a section between Songyacheon and Micheon confluence (Fig. 1). The image information of the target site was retrieved and acquired using the search portal of the National Geographic Information Institute and the National Forest Research Institute. The image information is shown in Table 1.

Image information of the target section. year 1971 1988 1991 video

accumulation 1 / 50,000 1 / 20,000 1 / 20,000 year 1995 2003 2005 video

accumulation 1 / 20,000 1 / 50,000 1 / 20,000

1.1.2 Preprocessing of Image Data

The National Geographic Information Institute regularly acquires changes in the forest and topography of the national territory from the sky and produces and distributes them in the database at regular intervals within 10 years, but is provided in a simple image form (TIFF) for security reasons. The National Forest Research Institute provides orthodontic aerial photographs including location information, but provides only the latest images. Therefore, when the orthodontic correction is completed using the aerial photographs, the aerial photographs are corrected. However, aerial surveys alone cannot obtain plant types or topographical information.

The preprocessing of the image data is shown schematically in FIG. 2 and followed the processing provided by the Geographic Information System (ArcGIS ver. 9.3.1, ESRI Inc.). First, the coordinate system was unified to the world geodetic system in consideration of the interworking with the GPS survey result acquired in WGS 1984 using ArcToolbox's Project Raster function. In the past, georeferencing has been used to input location information into aerial photographs. This method checks the same location (main bridges, roads, etc.) with the correct aerial coordinates entered and the aerial photographs for which the coordinates are entered, and superimposes the locations to give the coordinate attribute. The error rate varies depending on the user's skill level, but it is most appropriate in the situation where the location information at the time of aerial photographing is not provided. The preprocessed image was verified by checking the accuracy of the boundary of road, bank, rice field, and field by swipe function of looking down the image left and right and up and down in the state of overlapping image and verifying the precision using the field GPS survey result.

1.1.3 Reading Video

Except in 1991, some of the aerial photographs of the past aerial photographs were classified, based on the results of field surveys such as annual tree surveys of major trees for the remaining pre-processed aerial photographs (Fig. 3). First of all, in 2009 and 2010, we surveyed major vegetation and topography using GPS in the field and map it to geographic information system. After confirming the location of the main trees in the mapped data, we compared and confirmed the locations of trees and herbaceous plants in black and white images sequentially from 2005 using the layer overlay function. The exact plant name could not be identified, so I tried to classify the vegetation transition step by step. The stages were divided into open water, start stage (open sand stock), pioneer stage (1 and 2 year herbaceous plants), reed stage (dal root grass), and arbor tree stage (oregano-tree) according to the results of the transition type survey. However, it was difficult to distinguish between the pioneering and reed stages. On the other hand, the 2005 image was analyzed by replacing the results of the 2009 field survey with only a small change in the area and the root of the moon root grass.

1.2 Field Survey

Topographic surveys and existing vegetation maps were constructed to read historical image data and create numerical models. For more accurate topographic survey and vegetation map, we used real-time precision GPS survey service provided by National Geographic Information Institute. This service refers to a virtual reference station survey, and the principle is to use three or more points of reference station (GPS) to establish a virtual reference point around the mobile station (observation point), and to observe observation error correction factors (such as ionosphere, troposphere and It is a RTK (Real Time Kinematic) GPS system in which positioning accuracy and initialization time do not depend on the distance between reference stations (GPS constant observation stations) by transmitting data obtained by removing the satellite orbit error, etc.) (FIG. 4). The GPS instrument used was Hiper-Gb from Topcon, with an error rate of 1 cm in the horizontal direction and 1.5 cm in the vertical direction. Meanwhile, the level point was acquired and corrected to find the exact elevation of the terrain.

Survey results were created using a digital elevation model of 5 m 5 m grid size using ArcGIS (Fig. 5). GPS surveying was performed precisely at 1 m intervals. TIN files were created from the acquired 3D point coordinates and then converted into a 5 m class raster file.

Existing vegetation map was obtained by obtaining the coordinates of the distribution area of the main vegetation using a GPS instrument and corrected the position of the vegetation plotted on the aerial photograph in the field (Figure 6). Three-dimensional vegetation can be a visual comparison for future vegetation change studies by representing the vegetation distribution on the actual terrain.

1.3 Vegetation  Change analysis

In the past, aerial photographs were analyzed for 1971, 1988 and 1995, and in 2009, they were plotted on the basis of 2005 aerial photographs and then corrected using GPS survey results. As a result, it was confirmed that the vegetation distribution and topography of the Nakdong River target section was greatly changed (Figs. 7 and 8). The effects of Andong Dam and Imha Dam, which were completed in 1976 and 1992, respectively, were not confirmed to settle in Saju after the construction of Andong Dam. It was judged. In addition, since the area of arable land in the exclusion area gradually increased, it was judged that the decrease of flood frequency and flood level caused by dam operation brought about stability of the terrain.

In terms of percentage change in actual area, open water and stocks reached 84% before the construction of Andong and Imha dams, but in 1995 decreased by half to 47% and maintained at 31% in 2009. As the area of open water decreased due to the decrease in the level of water, and the keys which were frequently flooded were revealed in the air, the vegetation inflow, settlement, and growth environment were improved. On the other hand, the area of arable land occupied a quarter of the total area from 0% in 1971 to 24% in 2009. In 1971, the area of reed stage was 14%, but in 1988, it was reduced to 6% due to artificial damage caused by the expansion of arable land. However, in 1995, it reached 31%, and in 2009 it was slightly reduced to 24%. This may be due to the competition with the willow tree, and was not expressed in the vegetation distribution map. However, it was determined that there will be settlement of the willow tree in 1995.

2. Vegetation  Transition stage typing and Vegetation  Characterization

2.1 River Vegetation  Transition Stage Typing

2.1.1 Study Subject

The river to be investigated is the passing section of Danho-ri Danho-ri, Namhu-myeon, Andong-si, Gyeongsangbuk-do (B point in Fig. 1). The vegetation ages were surveyed in the subject area, with open keys and large groups of 0-2 years, willow shrubs 3 to 5 years and 6 years-10 years, and willow tree arbors 11 to 19 years and 20 years. From year to year 30, the King Willow Arboretum could be divided into more than 90 years (Fig. 9). The vegetation distribution was herbaceous and large in the vicinity of the open tops with low topography, and the moon root grass community prevailed. In addition, rare willow communities were found within the open keys.

The transition stages classified by Egger et al. (2007) were corrected based on the cluster survey and the tree age survey in the subject section, and the characteristics of the main dominant species, duration and phase, were analyzed.

2.1.2 Target Zone Vegetation  Transition stage

The transition stages were divided into start, carve, reed, shrub, arbor phases (FIG. 10). The start-up phase, which is the beginning of the transition, consists of open keys and is a place where frequent flooding disturbances occur every year. The pioneering stage is dominated by large dogs, and the willow plants and moon roots are mixed. The reed stage is dominated by moon root grasses, and the moon root grasses form a net colony or willow shrubs less than 6 years old are frequently mixed. The shrub stage is a stage in which sunbursts and kingbirds less than 20 years old live together or cluster alone. The arbor phase is dominated by the kings who competed with sunbursts. About 90-year-old willows were found at the site.

Regression occurs frequently in the pioneer stage, but could not be confirmed at the reed, shrub and arbor stage. This is due to the fact that no flood occurred during the investigation period and the flow rate was controlled due to the nature of the downstream stream of the dam, so that no flow rate was found to shear the successfully settled shrubs.

2.2 Transitional Steps Dominant Vegetation  Characterization

Growth characteristics of the dominant species at the transition stages are summarized in Tables 2 and 3.

Dominant species duration and vegetation height. Plant species Transition stage Lifetime (years) Height (m) Big dog exploitation One 0.990.06 Moonroot Reed > 2 First Year 1.380.2 Next Year 2.500.16 Sunburdle Shrubs, arbors <20 H = 6.04 ln (received)-9.818
(5-20 years, R = 0.926) Willow Shrubs, arbors > 90 H = 7.578 ln (received)-12.62
(Born 7-22, R = 0.942)

Sunburst diameter and crown width of sunburls, kingburs. Plant species Chest diameter Water pipe width Sunburdle D = 12.35 (receipt)-21.33
(R = 0.821) W = 0.325 (D) + 0.130
(R = 0.898) Willow D = 9.792 (receipt)-12.64
(R = 0.915) W = 0.187 (D) + 0.611
(R = 0.951)

It is a herbaceous annual herb that grows large and spreads seeds in autumn and germinates in spring. Compared to other species, it is more sensitive to moisture condition, and if a large swollen colony is found on open keys after spring, it suggests that moisture condition was sufficient during seed germination. Therefore, in the place where the large dog germinates, it is a good environment for germination of the roots of the grass and willow seeds. Moon-root is a perennial herbaceous plant with fast growing characteristics due to trophic breeding of underground diameters. On-site observations showed that the development of underground diameter was noticeable in the first year and the development of ground diameter in the following year. Sunbers were found until the age of 20 years by the tree age survey, but did not find any age group after that. Although it was confirmed to be born until 90 years, it was judged that additional investigation is needed like sunbers because there are two 270 year old guards of 270 years old on the land 3km away from the survey site, but based on the results so far investigated in the target area It was.

3. Vegetation  Resistor module

3.1 2D Numerical Model Comparison

3.1.1 RAM2  Model Literature Research

The RAM2 model is a general-purpose two-dimensional finite element flow analysis model mounted on a RAMS (River Analysis and Modeling System) model developed in Korea through the development of technology to secure water resources continuously. The RAM2 model adopts the two-dimensional shallow water equation and considers the influence of bed friction as the Manning equation. The shape of the element is 3-node, 6-node for triangular networks, 4-node, 8-node for square networks. It is configured to be calculated and can be calculated for mixed networks. Such a computing network may be associated with DEM and TIN among the functions of the GIS. In the RAM2 model, the Newton-Raphson method is applied to solve the nonlinear equations, and the linearized equations are analyzed by frontal and modified frontal methods. As initial conditions, water level and flow rate conditions can be input, and as the upstream and downstream boundary conditions, the level flow rate and the level-flow relationship can be applied. It is possible to simulate abrupt flow rectification such as dam collapse flow analysis and embankment collapse flow analysis, and two-dimensional simulations of streams and ponds. Dry / wet simulation can be effectively interpreted by moving boundary technique, partial wetting technique, and oil weighting technique.

3.1.2 RMA2  Model Literature Research

Since the RMA2 model was first developed in 1973 by the US Corps of Engineers, hydrodynamics of rivers, reservoirs, and estuaries, including flows in river channel sections including load diagrams, flows near bridges, and stream sections including inflation and reduction sections, It has been widely used in interpretation. The dominant equation is a two-dimensional shallow water equation that is deeply integrated with the Navier-Stokes equation and the continuous equation.

The RMA2 model has a restart function that can be continuously simulated using the results of previous time steps, and can also take account of the wetting and drying processes, Coriolis force, and wind stress. Model parameters such as eddy viscosity, manning coefficient, etc. are provided in various ways. Hand structures can be considered and mass preservation can be checked by calculating the flow rate for any cross section. User input for numerical analysis, such as setting convergence conditions, is possible, and various boundary conditions can be assigned.

The digitizer used in the RMA2 model is a finite element method using the weighted residual Galerkin method. The element can be a one-dimensional line element or a triangle or square element, and a parabolic curve can be used as a side. The shape function is the second order function for the flow rate and the first order function for the water level. Gaussian integration is used for spatial integration, and nonlinear finite difference approximation is used for temporal derivatives. Digitization is a complete speech method. The nonlinear system at each time step is solved using Newton-Raphson iteration.

The selection of appropriate boundary conditions and parameters in wet / dry processing and application to flood flows is important to improve the stability of the solution for complex finite element networks. Drying / wetting processes have been proposed to address the complexity of topography discretization required for flood flow problems. The stability problem is necessary to prevent the divergence of the solution and to reflect the natural delay condition by removing the locked element from the calculation process during the iteration. In the RMA-2 model, the elemental elimination method and the Marsh Porosity method are used as the dry / wet treatment method.

3.1.3 River2d  Model Literature Research

The River2D model is a two-dimensional depth average flow model developed by the University of Alberta, funded by the Government of Canada, the Scientific Research Foundation, and the USGS, and includes a module for fish habitat based on PHABSIM in natural streams. The River2 model is a finite element model that can be considered upstream / stream transitions and dry / wet.

Since the River2 model basically assumes hydrostatic pressure distribution, it is impossible to simulate sections with steep or abrupt river slope changes. The effects of dimensional flow are not taken into account. In addition, unlike models for the simulation of flow and circulation in a wide area of sub-regions such as M2D and ADCIRC, there is a limitation that it does not consider the effects of global forcing or wind.

The River2 model introduces groundwater flow equations for the treatment of dry / wet areas. In other words, it is determined that the element has a free surface of negative in order to allow continuous calculation without changing boundary conditions, and the mass conservation equation for groundwater flow is introduced. At this time, the values of the transmissivity (T) and the storage coefficient (storativity, S), which are characteristics of the artificial aquifer, can be set by the user, and the accuracy of the dry / wet simulation may be affected by the set value. do.

The River2D model includes a simulation of the flow under the floating ice layer. The floating ice layer has a shear effect on the flow, which in turn causes resistance to flow and flow, leading to a decrease in flow rate and level rise. The River2D model simulates the flow pattern under the floating ice layer by considering the increase in the flow resistance due to the floating ice layer through a modification to the momentum equation.

3.1.4 Numerical Model Comparison

The characteristics of general purpose two-dimensional flow models are compared and reviewed. Key considerations include the basic equations of equations, the treatment of bed friction gradients, the shape of elements, the applied simulation method, the numerical integration method, the method of interpreting nonlinear equations, the method of interpreting linearized equations, the setting of initial and boundary conditions, and special circumstances. Consideration of applicability and applicability, analysis of dry / wetting phenomena, and inclusion of other functional modules is considered.

Table 4 compares the general features of each model.

2D model comparison. division RAM2 RMA-2 River2d Developing country Korea United States of America Canada Finite Element Technique SU / PG Technique Bubnov-garerkin Petrov-Galerkin Mesh construction Linear / second order Secondary Secondary Interflow analysis possible impossible possible Transition Analysis possible impossible possible Rapid Current Analysis possible impossible impossible Artificial viscosity term Do not introduce Overintroduction causes loss of physical meaning Do not introduce Dry / wet Element weighting technique
Mobile boundary technique
Partial Wet Technique Element Removal Technique
Partial Wet Technique Groundwater level simulation

As shown in Table 4, the RAM2 model can reflect the physical preservation characteristics of variables by applying p (uh), q (vh) and h rather than u, v, and h of existing models in the variables used. It was configured to be. In the case of the finite element analysis introduced, the RMA-2 model is the classical Bubnov-Galerkin method, whereas the RAM2 uses the SU / PG method that considers the physical propagation characteristics of waves. In the construction of the element network, the existing model considers only the secondary element, while the RAM2 can considerably calculate both the primary element and the secondary element as necessary. The RAM2 and River2D models can be easily computed in chute & pool for ecological river analysis by easily calculating the upstream-stream and transition streams, but the RMA-2 model cannot calculate the quadrature because Will diverge. The RAM2 model can analyze the rapid flows such as dam collapse and bank collapse, so that the wave calculation can be performed stably, while other models cannot calculate such rapid currents.

The RMA-2 model, which has been widely used up to now, introduces an artificial viscous term to ensure the convergence of the calculations, and then contradicts the contradiction of interpreting the delivery-dominated flow as the diffusion-dominated flow. The problem of accuracy of the solution implies that the RAM2 model does not introduce additional artificial viscous terms itself. In the dry / wet analysis process, the RAM2 model can be analyzed using various techniques such as element weighting, moving boundary conditions, and partial wet conditions, but the RMA-2 model can use only partial wet conditions. The state is simulated by considering the subsurface water level using the groundwater equation.

4. Vegetation  Transition prediction model

4.2 Configuration and Features

4.2.1 Model Construction

Egger's model is a GIS-based spatial analysis model. The structure is composed of a start module, a dynamic module, and an output view module (FIG. 11). The starter module creates a potential natural vegetation community (PNV), which generates initial vegetation maps based on predetermined rules using water level, topography, and spatial (vegetable settlement) information. The dynamic module determines the transition and degeneration of each vegetation by inputting the shear stress and submerged duration in the vegetation chart created in the start module. The visualization module outputs the results as color-coded vegetation.

The rule is important in this model. First, vegetation transition stages should be categorized and range-specific ranges should be specified. Next, the minimum and maximum durations of the transition stages are determined, and the shear stress and submersion duration tolerance of each transition stage should be determined. These data are collected and processed by field and literature studies.

In this study, we propose a basic model for predicting vegetation transition considering flow and transition stages in the current year. That is, it only simulates the change of the transition stage according to the shear stress. Variables for this are shown in Table 5.

Parameters of the model. Transition stage Water level Shear stress (N / m ² ) Duration at least maximum Fuck <0.0 One One One exploitation <0.1 5 2 3 Reed 0.1-0.3 10 3 5 shrub 0.3 0.7 15 3 15 conduct 0.7-1.2 15 11 100

4.2.2 Model Features

Spatial data analysis using GIS does not require complex algorithms and can easily process and extract the required spatial information. Also, it shortens the program development time and can convert and use the DB file produced by other software. For example, numerical simulation results can be easily converted into a raster file, and attribute information can be obtained from data such as digital maps and aerial photographs to extract DEM or vegetation. There are also various tools to visualize the results so you can print them in the format you want.

In this study, ArcGIS (Ver. 9.3.1) developed by ESRI was used, and the model builder was used to easily modify each calculation step.

4.3 Model Application

4.3.1 Structure of Input Data and Model

end. Target section

The application model for the prediction model is Nakdong River, located in Gyepyeong-ri, Pungsan-eup, Andong-si, Gyeongsangbuk-do.

I. Repair, hydrologic characteristics

12 shows the results of analyzing the flow rate data of the Andong water table. After analyzing the average of daily average flow rates before Andong dam construction (1960-1975), before Imha dam construction (1976-1991) and after construction of Imha dam (1991-2007), Significant decreases and annual maximum floods were found. Shear stresses for each year were calculated from the maximum annual floods from 1988 to 2007 and converted into a raster file (FIG. 13).

All. Vegetation  With transition stage Shear stress

Vegetation transition stages were applied to the beginning, pioneer, reed, and arbor stage. Shear stress at each stage was selected to compare the actual vegetation stage to the most similar condition. The determined parameters are shown in Table 6.

Vegetation Transition Stages and Limit Shear Stresses. Transition stage Shear stress (N / m ² ) Fuck One exploitation 5 Reed 10 Pedestrian 15

la. modelling

The algorithm of the stream vegetation transition prediction basic model considering the stream flow and vegetation transition stage is shown in FIG. 14. The initial condition is open four weeks. The shear stress calculated by the maximum amount of flood in each year is input to simulate the transition and degeneration processes. The simulated results are repeated for entering the next year shear stress. The final result was visualized and compared to calculate the area.

The input file is shown in Table 7. Each input file was created in ArcGIS. The initial conditions were classified into topography, vegetation potential open sand, and arable land using the 2009 survey results. Vegetation transitions were ranged to occur only in a predetermined area (open key). Shear stress was converted into a raster file using interpolation of the numerical simulation results output in point coordinates. Each input file is a global geodetic system, coordinate system, grid size 5 m.

Input file. division File type Original material Grid size Coordinate system Initial condition Leicester Topographic Survey Results 5 m 5 m World Geodetic System Shear stress Leicester Simulation results based on annual maximum floods 5 m 5 m World Geodetic System

The calculation of each transition stage was based on the overlap analysis between the initial condition raster file consisting of open keys and the shear stress raster file in 1988. The transition stage for each critical shear stress is simulated using the property value of the region with the same coordinate value between the two layers. For example, if a shear stress value of 1 or less overlaps with a cell whose initial attribute is an open key of 1, the cell attribute value of the output is converted to 2 in the pioneering step. This step automates the transition process by using the model builder in ArcGIS and adds it to ArcToolbox to make the modification and modeling process easier (Figure 15).

The final result was produced and outputted with maps with the past aerial photographs by varying the colors of the simulation results in the transition stage in ArcGIS (Fig. 16).

4.3.2 Simulation Results

The simulation results from 1988 to 2007 from the initial conditions are shown in FIGS. 17 to 19. It can be seen that the initial conditions were prepared in the same environment compared to the aerial photograph in 1971. However, the terrain is somewhat different using the conditions at the time of the 2009 survey. The final simulation results are relatively accurate in predicting the inertia of the left and right eye banks in comparison to the 2005 aerial photo.

4.4 Model Validation

As a result of comparing the simulation results of vegetation streams based on the results of the past aerial photograph analysis, it was difficult to compare in the corresponding year, but the final results were determined to be relatively identical (FIGS. 20 and 21). Compared to the results of the 2009 field survey, the area of the initial stage was 22%, which was greatly predicted compared to the 7% of the field survey, and no reed phase was observed. Instructor forests were 29%, which was predicted to be close to 20% according to field surveys. It is necessary to adjust shear stress at the reed stage in the future, and it is going to verify the vegetation change along with the topographical change by adding similar movement module, vegetation settlement and growth module to be added in the composite model.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the present invention. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

Claims (13)

Recording media storing computer programs for predicting vegetation transitions for river management, including programs capable of executing the following processes:
(a) a start module for inputting a start condition;
(b) a dynamic module that derives potential vegetation through a succession and retrogression process; And
(c) a process for predicting vegetation transitions consisting of visualization modules representing riparian vegetation derived in process (b).
The recording medium of claim 1, wherein the starting condition of step (a) comprises region, average water level, and topographic map information.
2. The recording medium of claim 1, wherein the transition and degeneration processes of step (b) are analyzed through shear stress, area, average water level, topographic map, and flood duration.
4. The recording medium of claim 3, wherein the transition process is divided into a start step, a carve step, a reed step, a shrub step, and an arbor step.
5. The recording medium of claim 4, wherein the transition process is determined according to a smooth water level, a shear stress, and a sustained year.
6. The recording medium of claim 5, wherein the transition step is determined according to the shear stress.
4. The recording medium of claim 3, wherein the regression process occurs in a start-up or development stage.
(a) a start module for inputting a start condition; (b) a dynamic module that derives potential vegetation through a succession and retrogression process; And (c) an output view module representing a riparian vegetation.
9. The method of claim 8, wherein the starting condition comprises region, average water level, and topographic map information.
9. The method of claim 8, wherein the transition and degeneration processes are analyzed through shear stress, area, mean water level, topographical map, and flood duration.
The method of claim 10, wherein the transition process is divided into a start step, a carve step, a reed step, a shrub step, and an arbor step.
12. The method of claim 11, wherein the transition stage is determined according to a smooth water level, a shear stress, and a sustained year.
13. The method of claim 12, wherein the transition step is determined according to shear stress.

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